diff --git a/.prettierignore b/.prettierignore index 2df91949..7b36ebe1 100644 --- a/.prettierignore +++ b/.prettierignore @@ -9,6 +9,8 @@ *min.css *sh *yml +*.jpg +*.jpeg *json *md *bak @@ -27,3 +29,4 @@ yarn.lock *.seg *.screenshottest node_modules/** +*webp diff --git a/components/DataStandard.tsx b/components/DataStandard.tsx index b004126d..8a85f4eb 100644 --- a/components/DataStandard.tsx +++ b/components/DataStandard.tsx @@ -1,5 +1,4 @@ import React from 'react'; -import Breadcrumb from 'react-bootstrap/Breadcrumb'; import Container from 'react-bootstrap/Container'; import Row from 'react-bootstrap/Row'; @@ -7,7 +6,11 @@ import { DataSchemaData } from '../lib/dataSchemaHelpers'; import { CmsData } from '../types'; import DataSchema from './DataSchema'; import Footer from './Footer'; -import HtanNavbar from './HtanNavbar'; +import { HtanNavbar } from './HtanNavbar'; +import { Col } from 'react-bootstrap'; +import Link from 'next/link'; +import { FontAwesomeIcon } from '@fortawesome/react-fontawesome'; +import { faArrowLeft } from '@fortawesome/free-solid-svg-icons/faArrowLeft'; export interface DataStandardProps { title: string; @@ -21,21 +24,15 @@ const DataStandard: React.FunctionComponent = (props) => { <> - - - Home - - Data Standards - - {props.title} - + + + +   + Back to Data Standards + - + {props.children} {props.schemaDataById && props.dataSchemaData && ( diff --git a/components/HomePage.tsx b/components/HomePage.tsx index 5f691566..6573d42f 100644 --- a/components/HomePage.tsx +++ b/components/HomePage.tsx @@ -1,20 +1,30 @@ -import React, { useEffect, useState } from 'react'; +import _ from 'lodash'; +import React from 'react'; import Row from 'react-bootstrap/Row'; import Col from 'react-bootstrap/Col'; import ButtonToolbar from 'react-bootstrap/ButtonToolbar'; import Button from 'react-bootstrap/Button'; import Container from 'react-bootstrap/Container'; import Jumbotron from 'react-bootstrap/Jumbotron'; -import { WPAtlas } from '../types'; -import styles from './homeStyles.module.scss'; -import { EntityReport, getAtlasPageURL } from '../lib/helpers'; import { Helmet } from 'react-helmet'; +import { ScalePropType } from 'victory-core'; +import { WPAtlas } from '../types'; +import { EntityReport } from '../lib/helpers'; +import { + EntityReportByAttribute, + computeUniqueAttributeValueCount, +} from '../lib/entityReportHelpers'; +import SummaryChart from './SummaryChart'; +import Image from 'next/image'; +import htanMarkerPaper from '../public/HTAN-Marker-Paper-Table.png'; export interface IHomePropsProps { hero_blurb: string; cards: any[]; atlases: WPAtlas[]; synapseCounts: EntityReport[]; + organSummary: EntityReportByAttribute[]; + assaySummary: EntityReportByAttribute[]; } function dashboardIcon(text: string, description: string) { @@ -30,35 +40,65 @@ function dashboardIcon(text: string, description: string) { ); } +const chartScale: { x: ScalePropType; y: ScalePropType } = { + x: 'linear', + y: 'log', +}; + +// starting from y=1 doesn't work when case count=1. +// so we start from a slightly smaller value for a better bar chart visualization +const minDomain = { y: 0.95 }; + +function dependentAxisTickFormat(t: number) { + // only show tick labels for the integer powers of 10 + return _.isInteger(Math.log10(t)) ? t : ''; +} + const HomePage: React.FunctionComponent = ({ hero_blurb, cards, synapseCounts, atlases, + organSummary, + assaySummary, }) => { return ( <> - - - - - -

Human Tumor Atlas Network Data Portal

+ + + +

+ Human Tumor Atlas Network +


- + +

+ HTAN is a National Cancer Institute (NCI)-funded + Cancer MoonshotSM initiative to construct + 3-dimensional atlases of the dynamic cellular, + morphological, and molecular features of human + cancers as they evolve from precancerous lesions to + advanced disease. ( + Cell April 2020) +

+
@@ -67,22 +107,29 @@ const HomePage: React.FunctionComponent = ({ href="/explore" variant="primary" className="mr-4" + size="lg" + > + Explore latest Data + +
- - @@ -92,6 +139,70 @@ const HomePage: React.FunctionComponent = ({ )} + {/* + + About this Release: + + */} + + +

+ The latest HTAN data release includes tumors originating + from{' '} + + {computeUniqueAttributeValueCount(organSummary)} + {' '} + primary tumor sites: +

+ + + + + +

+ The tumors were profiled with{' '} + + {computeUniqueAttributeValueCount(assaySummary)} + {' '} + different types of assays: +

+ + + + + +

+ Many more profiled tumors will be available in the future. Stay tuned! +

+ + {/*
*/} {/* {atlases.map((atlas) => {*/} diff --git a/components/HtanNavbar.tsx b/components/HtanNavbar.tsx index 43f6998e..970433bf 100644 --- a/components/HtanNavbar.tsx +++ b/components/HtanNavbar.tsx @@ -1,6 +1,8 @@ import Navbar from 'react-bootstrap/Navbar'; import Nav from 'react-bootstrap/Nav'; -import React from 'react'; +import React, { useState } from 'react'; +import { Dropdown, NavDropdown } from 'react-bootstrap'; +import Link from 'next/link'; function togglePreview(on: any) { if (process.browser) { @@ -17,36 +19,105 @@ function togglePreview(on: any) { } } } +const NavSection: React.FunctionComponent<{ + text: string; + landingPage?: string; +}> = ({ text, children }) => { + const [open, setOpen] = useState(false); -const HtanNavbar = () => ( - - - HTAN Data Portal - - - - - - - -); + , + + + + htan@googlegroups.com + + , + + + Data Updates + + + Twitter + + , + ]; + + return ( + + + HTAN Data Portal + + + + + + + ); +}; export default HtanNavbar; diff --git a/components/PageWrapper.tsx b/components/PageWrapper.tsx index e4ad0490..b4ad9546 100644 --- a/components/PageWrapper.tsx +++ b/components/PageWrapper.tsx @@ -1,14 +1,20 @@ import * as React from 'react'; -import HtanNavbar from './HtanNavbar'; import Footer from './Footer'; +import HtanNavbar from './HtanNavbar'; + +export interface IPageWrapperProps {} -const PageWrapper = (props: any) => { +const PageWrapper: React.FunctionComponent = ({ + children, +}) => { return ( -
- - {props.children} + <> +
+ + {children} +
-
+ ); }; diff --git a/components/SummaryChart.tsx b/components/SummaryChart.tsx new file mode 100644 index 00000000..76c9d154 --- /dev/null +++ b/components/SummaryChart.tsx @@ -0,0 +1,584 @@ +import _ from 'lodash'; +import { action, computed, makeObservable, observable } from 'mobx'; +import { observer } from 'mobx-react'; +import pluralize from 'pluralize'; +import React from 'react'; +import { Popover, PopoverContent } from 'react-bootstrap'; +import DataTable from 'react-data-table-component'; +import * as ReactDOM from 'react-dom'; +import { VictoryBar, VictoryBarTTargetType } from 'victory-bar'; +import { VictoryChart } from 'victory-chart'; +import { VictoryAxis } from 'victory-axis'; +import { + D3Scale, + ScalePropType, + VictoryLabel, + VictoryTheme, +} from 'victory-core'; +import { VictoryStack } from 'victory-stack'; + +import { urlEncodeSelectedFilters } from '../lib/helpers'; +import { + DistributionByAttribute, + EntityReportByCenter, + EntityReportByAttribute, + entityReportByAttributeToByCenter, + dataWithoutUnknownValues, +} from '../lib/entityReportHelpers'; +import { getDefaultDataTableStyle } from '../lib/dataTableHelpers'; +import { + AttributeMap, + AttributeNames, + ExploreSelectedFilter, +} from '../lib/types'; + +export interface SummaryChartProps { + data: EntityReportByAttribute[]; + stackedByCenter?: boolean; // each bar will be stacked to visualize an additional distribution by center + dependentAxisEntityName?: string; // case, file, biospecimen, etc. + dependentAxisTickFormat?: + | any[] + | ((tick: any, index: number, ticks: any[]) => string | number); + entityColorMap?: { [attribute: string]: string }; + defaultBarColor?: string; + categoricalAxisSortValues?: ((r: EntityReportByAttribute) => any)[]; // sort criteria for the categorical axis labels + categoricalAxisSortDirections?: ('asc' | 'desc')[]; // sort direction for the categorical axis labels + scale?: // log, linear, etc. + | ScalePropType + | D3Scale + | { + x?: ScalePropType | D3Scale; + y?: ScalePropType | D3Scale; + }; + minDomain?: number | { x?: number; y?: number }; // useful when using a non linear scale +} + +export const VERTICAL_OFFSET = 17; +export const HORIZONTAL_OFFSET = 8; + +interface TooltipDatum { + attributeName: string; + attributeValue?: string; + totalCount: number; + countColumnEntityName: string; + attributeFilterValues: string[]; + center?: string; + distributionByAttribute?: DistributionByAttribute[]; +} + +export const DEFAULT_BAR_COLOR = 'rgb(36, 202, 213)'; + +// TODO these colors are mostly random +const CENTER_COLORS: { [center: string]: string } = { + 'HTAN BU': '#e6194B', + 'HTAN HTAPP': '#f58231', + 'HTAN CHOP': '#ffe119', + 'HTAN Duke': '#bfef45', + 'HTAN HMS': '#3cb44b', + 'HTAN MSK': '#42d4f4', + 'HTAN OHSU': '#4363d8', + 'HTAN Stanford': '#ffa600', + 'HTAN Vanderbilt': '#911eb4', + 'HTAN WUSTL': '#f032e6', +}; + +// TODO these colors are mostly random +export const assayColorMap: { [center: string]: string } = { + 'Bulk DNA': '#e6194B', + 'Bulk RNA-seq': '#f58231', + CyCIF: '#ffe119', + 'H&E': '#666add', + IMC: '#bfef45', + 'LC-MS/MS': '#3cb44b', + 'LC-MS3': '#42d4f4', + 'Shotgun MS (lipidomics)': '#4363d8', + mIHC: '#ffa600', + 'scATAC-seq': '#911eb4', + 'scRNA-seq': '#f032e6', + 't-CyCIF': '#de5601', +}; + +// TODO these should not be random! +export const organColorMap: { [organ: string]: string } = { + 'Bone Marrow': '#e6194B', + Breast: '#f58231', + Colorectal: '#ffe119', + Lung: '#bfef45', + Pancreas: '#3cb44b', +}; + +function generateBaseExploreUrl( + attributeId: string, + attributeFilterValues: string[], + center?: string +) { + const filters: ExploreSelectedFilter[] = []; + + // filter by original attribute values + filters.push( + ...attributeFilterValues.map((value) => ({ + group: attributeId, + value, + })) + ); + + if (center) { + // filter by atlas name + filters.push({ + group: 'AtlasName', + value: center, + }); + } + + const urlParams = urlEncodeSelectedFilters(filters); + + return `/explore?selectedFilters=${urlParams}`; +} + +const TooltipContent: React.FunctionComponent = (props) => { + const attributeName = props.distributionByAttribute?.length + ? props.distributionByAttribute[0].attributeName + : 'unknown'; + + const columns = [ + { + name: + AttributeMap[attributeName as AttributeNames]?.displayName || + attributeName, + selector: (distribution: DistributionByAttribute) => + distribution.attributeValue, + sortable: true, + grow: 3, + }, + { + name: `# ${props.countColumnEntityName}s`, + selector: (distribution: DistributionByAttribute) => + distribution.caseCount, + sortable: true, + }, + ]; + + const tableStyle = getDefaultDataTableStyle(); + tableStyle.headCells.style.fontSize = 14; + tableStyle.cells.style.fontSize = 12; + + const baseUrl = generateBaseExploreUrl( + props.attributeName, + props.attributeFilterValues, + props.center + ); + const caseHref = `${baseUrl}&tab=cases`; + const biospecimenHref = `${baseUrl}&tab=biospecimen`; + const fileHref = `${baseUrl}&tab=file`; + + const dataTable = props.distributionByAttribute?.length ? ( + + ) : null; + + const centerInfo = props.center ? ( + <> + {' '} + from {props.center} + + ) : null; + + return ( + +
+ {props.totalCount}{' '} + {pluralize( + props.countColumnEntityName.toLowerCase(), + props.totalCount + )} + {centerInfo}. Explore cases,{' '} + biospecimens, or{' '} + files. +
+ {dataTable} + + ); +}; + +@observer +export default class SummaryChart extends React.Component { + constructor(props: SummaryChartProps) { + super(props); + makeObservable(this); + } + + @observable private toolTipCounter = 0; + @observable private isTooltipHovered = false; + @observable private shouldUpdatePosition = false; // prevents chasing tooltip + @observable private tooltipDatum: TooltipDatum | null = null; + + @observable mousePosition = { x: 0, y: 0 }; + + get rightPadding() { + return 0; + } + + get leftPadding() { + return 150; + } + + get topPadding() { + return -40; + } + + get barWidth() { + return 15; + } + + get barSeparation() { + return 20; + } + + get chartHeight() { + return ( + this.categoryLabels.length * (this.barWidth + this.barSeparation) + + 100 + ); + } + + get chartWidth() { + // TODO responsive? + return 600; + } + + private get svgHeight() { + return this.chartHeight; + } + + get svgWidth() { + return this.chartWidth + this.rightPadding + this.leftPadding; + } + + get domainPadding() { + return 20; + } + + get categoryAxisDomainPadding() { + return this.domainPadding; + } + + get numericalAxisDomainPadding() { + return this.domainPadding; + } + + get chartDomainPadding() { + return { + y: this.numericalAxisDomainPadding, + x: this.categoryAxisDomainPadding, + }; + } + + get dependentAxisEntityName() { + return this.props.dependentAxisEntityName + ? _.capitalize(this.props.dependentAxisEntityName) + : 'Case'; + } + get dependentAxisLabel() { + return `# of ${this.dependentAxisEntityName}s`; + } + + get categoryLabels(): string[] { + return this.data.map((d) => d.attributeValue!); + } + + /* + * returns events configuration for Victory chart + */ + get barPlotEvents() { + const self = this; + return [ + { + target: 'data' as VictoryBarTTargetType, + eventHandlers: { + onMouseEnter: () => { + return [ + { + target: 'data', + mutation: (props: any) => { + self.shouldUpdatePosition = true; + self.tooltipDatum = props.datum; + self.toolTipCounter++; + }, + }, + ]; + }, + onMouseLeave: () => { + return [ + { + target: 'data', + mutation: () => { + // Freeze tool tip position and give user a moment to mouse over it + self.shouldUpdatePosition = false; + + setTimeout(() => { + // If they don't, get rid of it + if ( + !self.isTooltipHovered && + self.toolTipCounter === 1 + ) { + self.tooltipDatum = null; + } + self.toolTipCounter--; + }, 100); + }, + }, + ]; + }, + }, + }, + ]; + } + + get data() { + return _.orderBy( + dataWithoutUnknownValues(this.props.data), + this.props.categoricalAxisSortValues || [ + // first sort by count + (r) => r.totalCount, + // then alphabetically in case of a tie + (r) => r.attributeValue, + ], + this.props.categoricalAxisSortDirections || ['asc', 'desc'] + ); + } + + get diagnosisReportByCenter() { + return entityReportByAttributeToByCenter(this.data); + } + + get simpleBars() { + const data: TooltipDatum[] = this.data.map((r) => ({ + attributeName: r.attributeName, + attributeValue: r.attributeValue, + totalCount: r.totalCount, + countColumnEntityName: this.dependentAxisEntityName, + attributeFilterValues: r.attributeFilterValues, + distributionByAttribute: r.distributionByCenter.map((d) => ({ + attributeName: 'Atlas', + attributeValue: d.center, + caseCount: d.totalCount, + })), + })); + + return ( + { + const color = this.props.entityColorMap + ? this.props.entityColorMap[ + d.datum.attributeValue + ] + : undefined; + return ( + color || + this.props.defaultBarColor || + DEFAULT_BAR_COLOR + ); + }, + }, + }} + events={this.barPlotEvents} + data={data} + x="attributeValue" + y="totalCount" + /> + ); + } + + get bars() { + return this.props.stackedByCenter ? ( + {this.stackedBars} + ) : ( + this.simpleBars + ); + } + + get stackedBars() { + return this.diagnosisReportByCenter.map( + (r: EntityReportByCenter, i: number) => { + const data: TooltipDatum[] = r.distributionByAttribute + .filter((d) => d.attributeValue?.length) + .map((d) => ({ + attributeName: d.attributeName, + attributeValue: d.attributeValue, + totalCount: d.totalCount, + countColumnEntityName: this.dependentAxisEntityName, + center: r.center, + distributionByAttribute: + d.distributionByAdditionalAttribute, + attributeFilterValues: d.attributeFilterValues, + })); + + const dataByAttribute = _.keyBy(data, (d) => d.attributeValue!); + const attributeName = data[0]?.attributeName || 'N/A'; + + // add zero counts to properly sort data + const barData: TooltipDatum[] = this.categoryLabels.map( + (value) => + dataByAttribute[value] + ? dataByAttribute[value] + : { + attributeName: attributeName, + attributeValue: value, + totalCount: 0, + countColumnEntityName: this + .dependentAxisEntityName, + center: r.center, + attributeFilterValues: [], + distributionByAdditionalAttribute: [], + } + ); + + return ( + CENTER_COLORS[d.datum.center], + }, + }} + events={this.barPlotEvents} + data={barData} + key={i} + x="attributeValue" + y="totalCount" + /> + ); + } + ); + } + + private get barChart() { + return ( + + + + + + + } + style={{ + ticks: { size: 0 }, + tickLabels: { fontSize: 16 }, + }} + /> + + {this.bars} + + + + ); + } + + @computed + private get tooltip() { + if (!this.tooltipDatum) { + return null; + } else { + const datum = this.tooltipDatum; + const minWidth = 450; + + return (ReactDOM as any).createPortal( + + + , + document.body + ); + } + } + + public render() { + return ( +
+ {this.barChart} + {this.tooltip} +
+ ); + } + + @action.bound + private tooltipMouseEnter(): void { + this.isTooltipHovered = true; + } + + @action.bound + private tooltipMouseLeave(): void { + this.isTooltipHovered = false; + this.tooltipDatum = null; + } + + @action.bound + private onMouseMove(e: React.MouseEvent) { + if (this.shouldUpdatePosition) { + this.mousePosition.x = e.pageX; + this.mousePosition.y = e.pageY; + } + } +} diff --git a/components/WPAtlasTable.tsx b/components/WPAtlasTable.tsx index 89cef345..ef69db2c 100644 --- a/components/WPAtlasTable.tsx +++ b/components/WPAtlasTable.tsx @@ -113,49 +113,91 @@ const AtlasMetadataLinkModal: React.FunctionComponent ( +const MinervaStoryViewerLink = (props: { url: string; count: number }) => ( - {props.count < 100 && '\u00A0'}{props.count < 10 && '\u00A0'}{props.count} + {props.count < 100 && '\u00A0'} + {props.count < 10 && '\u00A0'} + {props.count}{' '} + ); -const AutoMinervaViewerLink = (props: { url: string, count: number }) => ( +const AutoMinervaViewerLink = (props: { url: string; count: number }) => ( - {props.count < 100 && '\u00A0'}{props.count < 10 && '\u00A0'}{props.count} + {props.count < 100 && '\u00A0'} + {props.count < 10 && '\u00A0'} + {props.count}{' '} + ); -const CBioPortalViewerLink = (props: { url: string, count: number }) => ( +const CBioPortalViewerLink = (props: { url: string; count: number }) => ( - {props.count < 100 && '\u00A0'}{props.count < 10 && '\u00A0'}{props.count} + {props.count < 100 && '\u00A0'} + {props.count < 10 && '\u00A0'} + {props.count}{' '} + ); -const CellxgeneViewerLink = (props: { url: string, count: number }) => ( +const CellxgeneViewerLink = (props: { url: string; count: number }) => ( - {props.count < 100 && '\u00A0'}{props.count < 10 && '\u00A0'}{props.count} + {props.count < 100 && '\u00A0'} + {props.count < 10 && '\u00A0'} + {props.count}{' '} + ); @@ -234,8 +276,9 @@ export default class WPAtlasTable extends React.Component { atlas.WPAtlas ? ( {atlas.WPAtlas.title.rendered} @@ -314,28 +357,58 @@ export default class WPAtlasTable extends React.Component { cell: (atlas: Atlas) => { if (atlas.htan_name === 'HTAN MSK') { return ( - + ); } else if (atlas.htan_name === 'HTAN Duke') { return ( <> - + ); } else if (atlas.htan_name === 'HTAN OHSU') { return ( <> - - + + ); } else if (atlas.htan_name === 'HTAN HMS') { return ( - + ); } else if (atlas.htan_name === 'HTAN BU') { return ( - + ); } else if (atlas.htan_name === 'HTAN Vanderbilt') { return ( @@ -344,23 +417,52 @@ export default class WPAtlasTable extends React.Component { {/**/} - {'\u00A0'}{'\u00A0'}{1} + {'\u00A0'} + {'\u00A0'} + {1}{' '} + - + ); } else if (atlas.htan_name === 'HTAN WUSTL') { return ( <> - + ); } else if (atlas.htan_name === 'HTAN CHOP') { return ( - + ); } else { return null; diff --git a/data/human-organ-mappings.json b/data/human-organ-mappings.json new file mode 100644 index 00000000..ed48f3a5 --- /dev/null +++ b/data/human-organ-mappings.json @@ -0,0 +1,519 @@ +{ + "Adrenal Gland": { + "byPrimarySite": ["Adrenal gland"], + "byTissueOrOrganOfOrigin": [ + "Adrenal gland, NOS", + "Cortex of adrenal gland", + "Medulla of adrenal gland" + ] + }, + "Bile Duct": { + "byPrimarySite": ["Other and unspecified parts of biliary tract"], + "byTissueOrOrganOfOrigin": [ + "Ampulla of Vater", + "Biliary tract, NOS", + "Extrahepatic bile duct", + "Overlapping lesion of biliary tract" + ] + }, + "Bladder": { + "byPrimarySite": ["Bladder"], + "byTissueOrOrganOfOrigin": [ + "Anterior wall of bladder", + "Bladder neck", + "Bladder, NOS", + "Dome of bladder", + "Lateral wall of bladder", + "Overlapping lesion of bladder", + "Posterior wall of bladder", + "Trigone of bladder", + "Urachus", + "Ureteric orifice" + ] + }, + "Bone": { + "byPrimarySite": [ + "Bones, joints and articular cartilage of limbs", + "Bones, joints and articular cartilage of other and unspecified sites", + "Other and ill-defined sites" + ], + "byTissueOrOrganOfOrigin": [ + "Bone of limb, NOS", + "Bone, NOS", + "Bones of skull and face and associated joints", + "Long bones of lower limb and associated joints", + "Long bones of upper limb, scapula and associated joints", + "Mandible", + "Overlapping lesion of bones, joints and articular cartilage of limbs", + "Overlapping lesion of bones, joints and articular cartilage", + "Pelvic bones, sacrum, coccyx and associated joints", + "Pelvis, NOS", + "Rib, sternum, clavicle and associated joints", + "Short bones of lower limb and associated joints", + "Short bones of upper limb and associated joints", + "Vertebral column" + ] + }, + "Bone Marrow": { + "byPrimarySite": ["Hematopoietic and reticuloendothelial systems"], + "byTissueOrOrganOfOrigin": [ + "Blood", + "Bone marrow", + "Hematopoietic system, NOS", + "Reticuloendothelial system, NOS", + "Spleen" + ] + }, + "Brain": { + "byPrimarySite": ["Brain"], + "byTissueOrOrganOfOrigin": [ + "Brain stem", + "Brain, NOS", + "Cerebellum, NOS", + "Cerebrum", + "Frontal lobe", + "Occipital lobe", + "Overlapping lesion of brain", + "Parietal lobe", + "Temporal lobe", + "Ventricle, NOS" + ] + }, + "Breast": { + "byPrimarySite": ["Breast"], + "byTissueOrOrganOfOrigin": [ + "Axillary tail of breast", + "Breast, NOS", + "Central portion of breast", + "Lower-inner quadrant of breast", + "Lower-outer quadrant of breast", + "Nipple", + "Overlapping lesion of breast", + "Upper-inner quadrant of breast", + "Upper-outer quadrant of breast" + ] + }, + "Cervix": { + "byPrimarySite": ["Cervix uteri"], + "byTissueOrOrganOfOrigin": [ + "Cervix uteri", + "Endocervix", + "Exocervix", + "Overlapping lesion of cervix uteri" + ] + }, + "Colorectal": { + "byPrimarySite": [ + "Colon", + "Rectosigmoid junction", + "Rectum" + ], + "byTissueOrOrganOfOrigin": [ + "Appendix", + "Ascending colon", + "Cecum", + "Colon, NOS", + "Descending colon", + "Hepatic flexure of colon", + "Overlapping lesion of colon", + "Rectosigmoid junction", + "Rectum, NOS", + "Sigmoid colon", + "Splenic flexure of colon", + "Transverse colon" + ] + }, + "Esophagus": { + "byPrimarySite": ["Esophagus"], + "byTissueOrOrganOfOrigin": [ + "Abdominal esophagus", + "Cervical esophagus", + "Esophagus, NOS", + "Lower third of esophagus", + "Middle third of esophagus", + "Overlapping lesion of esophagus", + "Thoracic esophagus", + "Upper third of esophagus" + ] + }, + "Eye": { + "byPrimarySite": ["Eye and adnexa"], + "byTissueOrOrganOfOrigin": [ + "Choroid", + "Ciliary body", + "Conjunctiva", + "Cornea, NOS", + "Eye, NOS", + "Lacrimal gland", + "Orbit, NOS", + "Overlapping lesion of eye and adnexa", + "Retina" + ] + }, + "Head and Neck": { + "byPrimarySite": [ + "Accessory sinuses", + "Base of tongue", + "Floor of mouth", + "Gum", + "Hypopharynx", + "Larynx", + "Lip", + "Nasal cavity and middle ear", + "Nasopharynx", + "Oropharynx", + "Other and ill-defined sites in lip, oral cavity and pharynx", + "Other and ill-defined sites", + "Other and unspecified major salivary glands", + "Other and unspecified parts of mouth", + "Other and unspecified parts of tongue", + "Palate", + "Parotid gland", + "Pyriform sinus", + "Tonsil", + "Trachea" + ], + "byTissueOrOrganOfOrigin": [ + "Accessory sinus, NOS", + "Anterior 2/3 of tongue, NOS", + "Anterior floor of mouth", + "Anterior surface of epiglottis", + "Anterior wall of nasopharynx", + "Base of tongue, NOS", + "Border of tongue", + "Branchial cleft", + "Cheek mucosa", + "Commissure of lip", + "Dorsal surface of tongue, NOS", + "Ethmoid sinus", + "External lip, NOS", + "External lower lip", + "External upper lip", + "Floor of mouth, NOS", + "Frontal sinus", + "Glottis", + "Gum, NOS", + "Hard palate", + "Head, face or neck, NOS", + "Hypopharyngeal aspect of aryepiglottic fold", + "Hypopharynx, NOS", + "Laryngeal cartilage", + "Larynx, NOS", + "Lateral floor of mouth", + "Lateral wall of nasopharynx", + "Lateral wall of oropharynx", + "Lingual tonsil", + "Lip, NOS", + "Lower gum", + "Major salivary gland, NOS", + "Maxillary sinus", + "Middle ear", + "Mouth, NOS", + "Mucosa of lip, NOS", + "Mucosa of lower lip", + "Mucosa of upper lip", + "Nasal cavity", + "Nasopharynx, NOS", + "Oropharynx, NOS", + "Overlapping lesion of accessory sinuses", + "Overlapping lesion of floor of mouth", + "Overlapping lesion of hypopharynx", + "Overlapping lesion of larynx", + "Overlapping lesion of lip, oral cavity and pharynx", + "Overlapping lesion of lip", + "Overlapping lesion of major salivary glands", + "Overlapping lesion of nasopharynx", + "Overlapping lesion of other and unspecified parts of mouth", + "Overlapping lesion of palate", + "Overlapping lesion of tongue", + "Overlapping lesion of tonsil", + "Overlapping lesions of oropharynx", + "Palate, NOS", + "Parotid gland", + "Pharynx, NOS", + "Postcricoid region", + "Posterior wall of hypopharynx", + "Posterior wall of nasopharynx", + "Posterior wall of oropharynx", + "Pyriform sinus", + "Retromolar area", + "Soft palate, NOS", + "Sphenoid sinus", + "Subglottis", + "Sublingual gland", + "Submandibular gland", + "Superior wall of nasopharynx", + "Supraglottis", + "Tongue, NOS", + "Tonsil, NOS", + "Tonsillar fossa", + "Tonsillar pillar", + "Trachea", + "Upper Gum", + "Uvula", + "Vallecula", + "Ventral surface of tongue, NOS", + "Vestibule of mouth", + "Waldeyer ring" + ] + }, + "Kidney": { + "byPrimarySite": ["Kidney"], + "byTissueOrOrganOfOrigin": ["Kidney, NOS"] + }, + "Liver": { + "byPrimarySite": ["Liver and intrahepatic bile ducts"], + "byTissueOrOrganOfOrigin": ["intrahepatic bile duct", "Liver"] + }, + "Lung": { + "byPrimarySite": ["Bronchus and lung"], + "byTissueOrOrganOfOrigin": [ + "Lower lobe, lung", + "Lung, NOS", + "Main bronchus", + "Middle lobe, lung", + "Overlapping lesion of lung", + "Upper lobe, lung" + ] + }, + "Lymph Nodes": { + "byPrimarySite": ["Lymph nodes"], + "byTissueOrOrganOfOrigin": [ + "Intra-abdominal lymph nodes", + "Intrathoracic lymph nodes", + "Lymph node, NOS", + "Lymph nodes of axilla or arm", + "Lymph nodes of head, face and neck", + "Lymph nodes of inguinal region or leg", + "Lymph nodes of multiple regions", + "Pelvic lymph nodes" + ] + }, + "Nervous System": { + "byPrimarySite": [ + "Meninges", + "Peripheral nerves and autonomic nervous system", + "Spinal cord, cranial nerves, and other parts of central nervous system" + ], + "byTissueOrOrganOfOrigin": [ + "Acoustic nerve", + "Autonomic nervous system, NOS", + "Cauda equina", + "Cerebral meninges", + "Cranial nerve, NOS", + "Meninges, NOS", + "Nervous system, NOS", + "Olfactory nerve", + "Optic nerve", + "Overlapping lesion of brain and central nervous system", + "Overlapping lesion of peripheral nerves and autonomic nervous system", + "Peripheral nerves and autonomic nervous system of abdomen", + "Peripheral nerves and autonomic nervous system of head, face, and neck", + "Peripheral nerves and autonomic nervous system of lower limb and hip", + "Peripheral nerves and autonomic nervous system of pelvis", + "Peripheral nerves and autonomic nervous system of thorax", + "Peripheral nerves and autonomic nervous system of trunk, NOS", + "Peripheral nerves and autonomic nervous system of upper limb and shoulder", + "Spinal cord", + "Spinal meninges" + ] + }, + "Not Reported": { + "byPrimarySite": ["Not Reported", "unknown"], + "byTissueOrOrganOfOrigin": ["Not Reported", "unknown"] + }, + "Other and Ill-defined Sites": { + "byPrimarySite": [ + "Anus and anal canal", + "Gallbladder", + "Other and ill-defined digestive organs", + "Other and ill-defined sites within respiratory system and intrathoracic organs", + "Other and ill-defined sites", + "Other and unspecified female genital organs", + "Other and unspecified male genital organs", + "Other and unspecified urinary organs", + "Other endocrine glands and related structures", + "Penis", + "Placenta", + "Renal pelvis", + "Retroperitoneum and peritoneum", + "Unknown primary site", + "Ureter", + "Vagina", + "Vulva" + ], + "byTissueOrOrganOfOrigin": [ + "Abdomen, NOS", + "Anal canal", + "Anus, NOS", + "Aortic body and other paraganglia", + "Body of penis", + "Broad ligament", + "Carotid body", + "Clitoris", + "Cloacogenic zone", + "Craniopharyngeal duct", + "Endocrine gland, NOS", + "Epididymis", + "Fallopian tube", + "Female genital tract, NOS", + "Gallbladder", + "Gastrointestinal tract, NOS", + "Glans penis", + "Ill-defined sites within respiratory system", + "Intestinal tract, NOS", + "Labium majus", + "Labium minus", + "Lower limb, NOS", + "Male genital organs, NOS", + "Other ill-defined sites", + "Other specified parts of female genital organs", + "Other specified parts of male genital organs", + "Overlapping lesion of digestive system", + "Overlapping lesion of endocrine glands and related structures", + "Overlapping lesion of female genital organs", + "Overlapping lesion of ill-defined sites", + "Overlapping lesion of male genital organs", + "Overlapping lesion of penis", + "Overlapping lesion of rectum, anus and anal canal", + "Overlapping lesion of respiratory system and intrathoracic organs", + "Overlapping lesion of retroperitoneum and peritoneum", + "Overlapping lesion of urinary organs", + "Overlapping lesion of vulva", + "Parametrium", + "Parathyroid gland", + "Paraurethral gland", + "Penis, NOS", + "Peritoneum, NOS", + "Pineal gland", + "Pituitary gland", + "Placenta", + "Prepuce", + "Renal pelvis", + "Retroperitoneum", + "Round ligament", + "Scrotum, NOS", + "Specified parts of peritoneum", + "Spermatic cord", + "Thorax, NOS", + "Unknown primary site", + "Upper limb, NOS", + "Upper respiratory tract, NOS", + "Ureter", + "Urethra", + "Urinary system, NOS", + "Uterine adnexa", + "Vagina, NOS", + "Vulva, NOS" + ] + }, + "Ovary": { + "byPrimarySite": ["Ovary"], + "byTissueOrOrganOfOrigin": ["Ovary"] + }, + "Pancreas": { + "byPrimarySite": ["Pancreas"], + "byTissueOrOrganOfOrigin": [ + "Body of pancreas", + "Head of pancreas", + "Islets of Langerhans", + "Other specified parts of pancreas", + "Overlapping lesion of pancreas", + "Pancreas, NOS", + "Pancreatic duct", + "Tail of pancreas" + ] + }, + "Pleura": { + "byPrimarySite": ["Heart, mediastinum, and pleura"], + "byTissueOrOrganOfOrigin": [ + "Anterior mediastinum", + "Heart", + "Mediastinum, NOS", + "Overlapping lesion of heart, mediastinum and pleura", + "Pleura, NOS", + "Posterior mediastinum" + ] + }, + "Prostate": { + "byPrimarySite": ["Prostate gland"], + "byTissueOrOrganOfOrigin": ["Prostate gland"] + }, + "Skin": { + "byPrimarySite": ["Skin"], + "byTissueOrOrganOfOrigin": [ + "External ear", + "Eyelid", + "Overlapping lesion of skin", + "Skin of lip, NOS", + "Skin of lower limb and hip", + "Skin of other and unspecified parts of face", + "Skin of scalp and neck", + "Skin of trunk", + "Skin of upper limb and shoulder", + "Skin, NOS" + ] + }, + "Soft Tissue": { + "byPrimarySite": ["Connective, subcutaneous and other soft tissues"], + "byTissueOrOrganOfOrigin": [ + "Connective, Subcutaneous and other soft tissues of abdomen", + "Connective, Subcutaneous and other soft tissues of head, face, and neck", + "Connective, Subcutaneous and other soft tissues of lower limb and hip", + "Connective, Subcutaneous and other soft tissues of pelvis", + "Connective, Subcutaneous and other soft tissues of thorax", + "Connective, Subcutaneous and other soft tissues of trunk, NOS", + "Connective, Subcutaneous and other soft tissues of upper limb and shoulder", + "Connective, Subcutaneous and other soft tissues, NOS", + "Overlapping lesion of connective, subcutaneous and other soft tissues" + ] + }, + "Stomach": { + "byPrimarySite": ["Small intestine", "Stomach"], + "byTissueOrOrganOfOrigin": [ + "Body of stomach", + "Cardia, NOS", + "Duodenum", + "Fundus of stomach", + "Gastric antrum", + "Greater curvature of stomach, NOS", + "Ileum", + "Jejunum", + "Lesser curvature of stomach, NOS", + "Meckel diverticulum", + "Overlapping lesion of small intestine", + "Overlapping lesion of stomach", + "Pylorus", + "Small intestine, NOS", + "Stomach, NOS" + ] + }, + "Testis": { + "byPrimarySite": ["Testis"], + "byTissueOrOrganOfOrigin": [ + "Descended testis", + "Testis, NOS", + "Undescended testis" + ] + }, + "Thymus": { + "byPrimarySite": ["Thymus"], + "byTissueOrOrganOfOrigin": ["Thymus"] + }, + "Thyroid": { + "byPrimarySite": ["Thyroid gland"], + "byTissueOrOrganOfOrigin": ["Thyroid gland"] + }, + "Uterus": { + "byPrimarySite": ["Corpus uteri", "Uterus, NOS"], + "byTissueOrOrganOfOrigin": [ + "Corpus uteri", + "Endometrium", + "Fundus uteri", + "Isthmus uteri", + "Myometrium", + "Overlapping lesion of corpus uteri", + "Uterus, NOS" + ] + } +} \ No newline at end of file diff --git a/e2e/screenshots/reference/render-initial-unfiltered-state-of-explore-page_element_Chrome_v94_1300x768.png b/e2e/screenshots/reference/render-initial-unfiltered-state-of-explore-page_element_Chrome_v94_1300x768.png new file mode 100644 index 00000000..f4ad193c Binary files /dev/null and b/e2e/screenshots/reference/render-initial-unfiltered-state-of-explore-page_element_Chrome_v94_1300x768.png differ diff --git a/lib/entityReportHelpers.ts b/lib/entityReportHelpers.ts new file mode 100644 index 00000000..1d484612 --- /dev/null +++ b/lib/entityReportHelpers.ts @@ -0,0 +1,311 @@ +import _ from 'lodash'; +import { Entity } from './helpers'; +import { AttributeNames } from './types'; + +const organMapping: OrganMapping = require('../data/human-organ-mappings.json'); + +export type OrganMapping = { + [organ: string]: { + byPrimarySite: string[]; + byTissueOrOrganOfOrigin: string[]; + }; +}; + +export type DistributionByAttribute = { + attributeName: string; + attributeValue: string; + caseCount: number; + // TODO adultCaseCount, pediatricCaseCount, etc. +}; + +export type EntityReportByAttribute = { + attributeName: string; + attributeValue?: string; + totalCount: number; + attributeFilterValues: string[]; // useful when attributeValue is normalized + distributionByCenter: { + attributeFilterValues: string[]; + center: string; + totalCount: number; + distributionByAdditionalAttribute?: DistributionByAttribute[]; + }[]; +}; + +export type EntityReportByCenter = { + center: string; + distributionByAttribute: { + attributeName: string; + attributeValue?: string; + attributeFilterValues: string[]; // useful when attributeValue is normalized + totalCount: number; + distributionByAdditionalAttribute?: DistributionByAttribute[]; + }[]; +}; + +function normalizeTissueOrOrganOrSite(value: string) { + return value.toLowerCase().replace(/,/g, ''); +} + +function initTissueOrOrganOrSiteToOrganMap( + organMapping: OrganMapping +): { [tissueOrOrganOrSite: string]: string } { + const map: { [tissueOrOrganOrSite: string]: string } = {}; + + _.forEach( + organMapping, + ( + value: { + byPrimarySite: string[]; + byTissueOrOrganOfOrigin: string[]; + }, + organ: string + ) => { + value.byPrimarySite.forEach( + (site) => (map[normalizeTissueOrOrganOrSite(site)] = organ) + ); + value.byTissueOrOrganOfOrigin.forEach( + (tissueOrOrgan) => + (map[normalizeTissueOrOrganOrSite(tissueOrOrgan)] = organ) + ); + } + ); + + return map; +} + +const tissueOrOriginToOrganMap = initTissueOrOrganOrSiteToOrganMap( + organMapping +); + +export function computeEntityReportByAttribute( + entities: Entity[], + getAttributeValue: (entity: Entity) => string | undefined, + getUniqByAttribute: (entity: Entity) => string, + computeDistributionByCenter: ( + entities: Entity[] + ) => EntityReportByAttribute | undefined +): EntityReportByAttribute[] { + const entitiesByAttributeValue: { [value: string]: Entity[] } = {}; + + for (const entity of entities) { + const value = getAttributeValue(entity); + if (value) { + entitiesByAttributeValue[value] = + entitiesByAttributeValue[value] || []; + entitiesByAttributeValue[value].push(entity); + } + } + + // remove duplicates if any + for (const value in entitiesByAttributeValue) { + entitiesByAttributeValue[value] = _.uniqBy( + entitiesByAttributeValue[value], + getUniqByAttribute + ); + } + + // convert to EntityReportByAttribute[] + return _.compact( + _.values(entitiesByAttributeValue).map(computeDistributionByCenter) + ); +} + +export function getNormalizedOrgan(entity: Entity) { + return ( + tissueOrOriginToOrganMap[ + normalizeTissueOrOrganOrSite(entity.TissueorOrganofOrigin) + ] || entity.TissueorOrganofOrigin + ); +} + +export function getNormalizedAssay(entity: Entity) { + return entity.assayName; +} + +export function computeEntityReportByAssay( + files: Entity[] +): EntityReportByAttribute[] { + return computeEntityReportByAttribute( + files, + getNormalizedAssay, + (d) => d.HTANDataFileID, + computeAssayDistributionByCenter + ); +} + +export function computeEntityReportByOrgan( + files: Entity[] +): EntityReportByAttribute[] { + return computeEntityReportByAttribute( + _.flatten(files.map((file) => file.diagnosis)), + getNormalizedOrgan, + (d) => d.HTANParticipantID, + computeOrganDistributionByCenter + ); +} + +export function entityReportByAttributeToByCenter( + report: EntityReportByAttribute[] +): EntityReportByCenter[] { + const distributionByAttribute: { + [center: string]: { + attributeName: string; + attributeValue?: string; + totalCount: number; + attributeFilterValues: string[]; + distributionByAdditionalAttribute?: DistributionByAttribute[]; + }[]; + } = {}; + + report.forEach((r) => { + r.distributionByCenter.forEach((d) => { + distributionByAttribute[d.center] = + distributionByAttribute[d.center] || []; + distributionByAttribute[d.center].push({ + attributeName: r.attributeName, + attributeValue: r.attributeValue, + attributeFilterValues: d.attributeFilterValues, + totalCount: d.totalCount, + distributionByAdditionalAttribute: + d.distributionByAdditionalAttribute, + }); + }); + }); + + return _.sortBy( + _.entries(distributionByAttribute).map( + ([center, distributionByAttribute]) => ({ + center, + distributionByAttribute, + }) + ), + (d) => d.center + ); +} + +export function computeDistributionByDisease( + entities: Entity[] +): DistributionByAttribute[] { + // assuming entities are diagnoses + return _.entries(_.countBy(entities, (d) => d.PrimaryDiagnosis)).map( + ([disease, caseCount]) => ({ + attributeName: AttributeNames.PrimaryDiagnosis, + attributeValue: disease, + caseCount, + }) + ); +} + +export function computeAttributeValueDistributionByCenter( + entitiesByAttributeValue: Entity[], + getAttributeFilterValues: (entities: Entity[]) => string[], + countEntities?: (entities: Entity[]) => number, + computeDistributionByAdditionalAttribute?: ( + entities: Entity[] + ) => DistributionByAttribute[] +) { + const entitiesByAtlas = _.groupBy( + entitiesByAttributeValue, + (d) => d.atlas_name + ); + + return _.entries(entitiesByAtlas).map(([center, entities]) => { + return { + center, + totalCount: countEntities + ? countEntities(entities) + : entities.length, + attributeFilterValues: getAttributeFilterValues(entities), + distributionByAdditionalAttribute: computeDistributionByAdditionalAttribute + ? computeDistributionByAdditionalAttribute(entities) + : undefined, + }; + }); +} + +export function getOrganFilterValues(entities: Entity[]) { + return _.uniq(entities.map((d) => d.TissueorOrganofOrigin)); +} + +export function getAssayFilterValues(entities: Entity[]) { + return _.compact(_.uniq(entities.map((d) => d.assayName))); +} + +export function computeAttributeDistributionByCenter( + entities: Entity[], + attributeName: string, + getAttributeValue: (diagnosis: Entity) => string | undefined, + getAttributeFilterValues: (diagnoses: Entity[]) => string[], + countEntities?: (entities: Entity[]) => number, + computeDistributionByAdditionalAttribute: ( + diagnoses: Entity[] + ) => DistributionByAttribute[] = computeDistributionByDisease +) { + if (entities.length > 0) { + const distributionByCenter = computeAttributeValueDistributionByCenter( + entities, + getAttributeFilterValues, + countEntities, + computeDistributionByAdditionalAttribute + ); + + return { + // assuming that attribute value is the same for all entities in the input list + attributeValue: getAttributeValue(entities[0]), + attributeName: attributeName, + attributeFilterValues: _.uniq( + _.flatten( + distributionByCenter.map((d) => d.attributeFilterValues) + ) + ), + totalCount: _.sumBy(distributionByCenter, (d) => d.totalCount), + distributionByCenter, + }; + } else { + return undefined; + } +} + +export function computeOrganDistributionByCenter( + diagnosesByOrgan: Entity[] +): EntityReportByAttribute | undefined { + return computeAttributeDistributionByCenter( + diagnosesByOrgan, + AttributeNames.TissueorOrganofOrigin, + getNormalizedOrgan, + getOrganFilterValues, + (entities) => entities.length, + computeDistributionByDisease + ); +} + +export function computeAssayDistributionByCenter( + entitiesByAssays: Entity[] +): EntityReportByAttribute | undefined { + return computeAttributeDistributionByCenter( + entitiesByAssays, + AttributeNames.assayName, + getNormalizedAssay, + getAssayFilterValues, + // entities are files but we want to show total number of unique cases, not files + (entities) => + _.uniq(_.flatten(entities.map((e) => e.diagnosisIds))).length, + // TODO assay distribution by disease does not work since entities are files not diagnoses, + // we need to modify the function to support a list of files as well, or show another attribute for assays + // this distribution is only displayed when we show a stacked bar chart which is currently a hidden feature + computeDistributionByDisease + ); +} + +export function dataWithoutUnknownValues(data: EntityReportByAttribute[]) { + return data.filter( + (r) => r.attributeValue && r.attributeValue !== 'Not Reported' + ); +} + +export function computeUniqueAttributeValueCount( + data: EntityReportByAttribute[] +) { + return _.uniq(dataWithoutUnknownValues(data).map((r) => r.attributeValue)) + .length; +} diff --git a/lib/pageUtils.ts b/lib/pageUtils.ts new file mode 100644 index 00000000..cf7658ed --- /dev/null +++ b/lib/pageUtils.ts @@ -0,0 +1,24 @@ +import matter from 'gray-matter'; +import fs from 'fs'; +const path = require('path'); + +const docsDirectory = path.join(process.cwd(), 'pages/static'); + +export function getPageByPageName(name: string) { + const realSlug = name.replace(/\.md$/, ''); + const fullPath = path.join(docsDirectory, `${realSlug}.html`); + const fileContents = fs.readFileSync(fullPath, 'utf8'); + return fileContents; +} + +export function getAllPages() { + const contents = fs.readdirSync(docsDirectory); + + const metadata = contents.map((filename) => { + const fullPath = path.join(docsDirectory, filename); + const fileContents = fs.readFileSync(fullPath, 'utf8'); + return matter(fileContents); + }); + + return metadata; +} diff --git a/missing.d.ts b/missing.d.ts index a50b8eb4..8bf475a2 100644 --- a/missing.d.ts +++ b/missing.d.ts @@ -1,2 +1,3 @@ declare module 'better-react-spinkit'; -declare module 'pluralize'; \ No newline at end of file +declare module 'pluralize'; +declare module '@mdx-js/mdx'; diff --git a/next.config.js b/next.config.js index b0800219..c5845790 100644 --- a/next.config.js +++ b/next.config.js @@ -1,3 +1,17 @@ -module.exports = { - swcMinify: true -} +const withMDX = require('@next/mdx')({ + extension: /\.mdx?$/, +}); +module.exports = withMDX({ + swcMinify: true, + pageExtensions: ['js', 'jsx', 'mdx', 'tsx'], + redirects: async () => { + // Note: don't put trailing slash in the redirect URLs + return [ + { + source: '/htan-authors', + destination: '/authors', + permanent: true, + }, + ] + }, +}); diff --git a/package.json b/package.json index 2c09c7d1..bf5fe165 100644 --- a/package.json +++ b/package.json @@ -18,6 +18,10 @@ "@fortawesome/fontawesome-svg-core": "^1.2.34", "@fortawesome/free-solid-svg-icons": "^5.15.2", "@fortawesome/react-fontawesome": "^0.1.14", + "@mdx-js/loader": "^1.6.22", + "@mdx-js/mdx": "^1.6.22", + "@mdx-js/react": "^1.6.22", + "@next/mdx": "^12.0.4", "@types/jmespath": "^0.15.0", "@types/jquery": "^3.5.4", "@types/micro-cors": "^0.1.0", @@ -30,6 +34,7 @@ "better-react-spinkit": "^2.0.4", "bootstrap": "^4.4.1", "global": "^4.4.0", + "gray-matter": "^4.0.3", "jmespath": "^0.15.0", "jquery": "^3.5.1", "lodash": "^4.17.21", @@ -37,6 +42,7 @@ "mobx": "^6.0.4", "mobx-react": "^7.0.0", "mobx-utils": "^5.6.2", + "next-mdx-remote": "^3.0.8", "next": "12.1", "node-fetch": "^2.6.0", "node-sass": "6.0.1", @@ -56,11 +62,13 @@ "react-spinkit": "^3.0.0", "react-spinners": "^0.10.4", "react-truncate-markup": "^5.1.0", + "remark-frontmatter": "^4.0.1", "roman-numerals": "^0.3.2", "styled-components": "^5.1.0", "swr": "^0.1.18", "unfetch": "^4.1.0", - "vercel": "^21.1.0" + "vercel": "^21.1.0", + "victory": "^36.2.0" }, "devDependencies": { "@types/lodash": "^4.14.149", diff --git a/pages/[page].tsx b/pages/[page].tsx new file mode 100644 index 00000000..36554530 --- /dev/null +++ b/pages/[page].tsx @@ -0,0 +1,61 @@ +import { getAllPages, getPageByPageName } from '../lib/pageUtils'; +import matter from 'gray-matter'; +import PreReleaseBanner from '../components/PreReleaseBanner'; +import React from 'react'; +import PageWrapper from '../components/PageWrapper'; +import Container from 'react-bootstrap/Container'; +import Row from 'react-bootstrap/Row'; + +import { GetStaticProps } from 'next'; + +export const getStaticProps: GetStaticProps = async function getStaticProps( + context +) { + // @ts-ignore + const page = getPageByPageName(context.params.page); + + const { data: frontMatter, content } = matter(page); + + return { + props: { + html: content, + }, + }; +}; + +function Page({ html }: any) { + return ( + <> + + + + +
+ + + + + ); +} + +export default Page; + +export async function getStaticPaths() { + const pages = getAllPages(); + + const paths = pages.map((p) => { + return { + params: { + page: p.data.page, + }, + }; + }); + + return { + paths, + fallback: false, + }; +} diff --git a/pages/_document.js b/pages/_document.js index 9dfe24fe..7b4252a8 100644 --- a/pages/_document.js +++ b/pages/_document.js @@ -25,6 +25,7 @@ export default class MyDocument extends Document { `, }} /> +
diff --git a/pages/explore.tsx b/pages/explore.tsx index b733a68d..9da81a82 100644 --- a/pages/explore.tsx +++ b/pages/explore.tsx @@ -331,7 +331,7 @@ class Search extends React.Component< if (this.filteredFiles) { return ( -
+
{ return ( @@ -56,6 +60,8 @@ export const getStaticProps: GetStaticProps = async (context) => { cards: cards, atlases, synapseCounts: computeDashboardData(files), + organSummary: computeEntityReportByOrgan(files), + assaySummary: computeEntityReportByAssay(files), }, }; }; diff --git a/pages/standard/biospecimen.tsx b/pages/standard/biospecimen.tsx index 8dadabff..8b96d548 100644 --- a/pages/standard/biospecimen.tsx +++ b/pages/standard/biospecimen.tsx @@ -6,7 +6,145 @@ import { getStaticContent } from '../../ApiUtil'; import { getDataSchema, SchemaDataId } from '../../lib/dataSchemaHelpers'; const Biospecimen: React.FunctionComponent = (props) => { - return ; + return ( + +

HTAN Biospecimen Data Standard

+

Overview

+

+ This page describes the data levels and data collection for the + HTAN biospecimen data standard. +

+

Description of Model

+

+ The HTAN biospecimen data model is designed to capture essential + biospecimen data elements, including: +

+
    +
  • + Acquisition method, e.g. autopsy, biopsy, fine needle + aspirate, etc. +
    • +
  • + Topography Code, indicating site within the body, e.g. based + on ICD-O-3. +
    • +
  • + Collection information e.g. time, duration of ischemia, + temperature, etc.   +
    • +
  • + Processing of parent biospecimen information e.g. fresh, + frozen, etc.  +
    • +
  • + Biospecimen and derivative clinical metadata ie Histologic + Morphology Code, e.g. based on ICD-O-3. +
    • +
  • + Coordinates for derivative biospecimen from their parent + biospecimen. +
    • +
  • + Processing of derivative biospecimen for downstream analysis + e.g. dissociation, sectioning, analyte isolation, etc. +
    • +
+

The model consists of two tiers:

+ + + + + + + + + + + + + + + + + +
Data LevelDescription
Tier 1 + Base biospecimen data common to most assays and HTAN + Research Network atlases +
Tier 2 + Assay-specific or atlas-specific extensions to the + base model +
+

+ Biospecimen Tier 1 +

+

+ Baseline HTAN biospecimen data leverages existing common data + elements from four sources: +

+ +

+ Additionally, if a comparable CDE could not be found in these + sources for a specific attribute, an HTAN-specific attribute was + created. +

+

+ Biospecimen Tier 2 +

+

+ Attributes identified for inclusion in Tier 2 include those + described in the{' '} + + caDSR system + {' '} + and, similarly to Tier 1, HTAN-specific elements: +

+
    +
  • + Atlas-specific – Attributes that are atlas specific, + but may used by more than 1 atlas. +
    • +
  • + Histological Assessment – Biospecimen attributes + specific to histological assessment. +
    • +
  • + Multiplex Image Staining – Biospecimen attributes + specific to multiplex image staining. +
    • +
  • + Single Cell RNA / Single Nucleus RNA Seq – + Biospecimen attributes specific to Single Cell RNA / Single + Nucleus RNA Seq +
    • +
  • + Bulk RNA & DNA Seq – Biospecimen attributes + specific to Bulk RNA & DNA Seq +
    • +
+ + ); }; export const getStaticProps: GetStaticProps = async (context) => { diff --git a/pages/standard/bulkdnaseq.tsx b/pages/standard/bulkdnaseq.tsx index 1fce295b..c62ca6a7 100644 --- a/pages/standard/bulkdnaseq.tsx +++ b/pages/standard/bulkdnaseq.tsx @@ -6,7 +6,68 @@ import { getStaticContent } from '../../ApiUtil'; import { getDataSchema, SchemaDataId } from '../../lib/dataSchemaHelpers'; const BulkDNASeq: React.FunctionComponent = (props) => { - return ; + return ( + +

HTAN Bulk DNA Sequencing Data Standard

+

Overview

+ +

+ This page describes the data levels, metadata attributes, and + file structure for bulk DNA sequencing. +

+ +

Description of Assay

+ +

+ Bulk DNA sequencing produces the DNA sequence of a biological + sample. The sequence is summarized into a list of variants in + comparison to a given reference genome. This data model should + be applicable to assays including bulk tumor Whole Genome + Sequencing (WGS), bulk tumor Whole Exome Sequencing (WES), bulk + cfDNA WES (cell free), bulk tumor targeted DNA sequencing, and + bulk ctDNA targeted DNA sequencing. +

+ +

Metadata Levels

+ +

+ The defined metadata leverages existing common data elements + from the{' '} + + Genomic Data Commons (GDC) + + . The HTAN data model currently supports Level 1, 2 and 3 DNA + sequencing data: +

+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Level NumberDefinitionExample Data
1Raw unaligned read dataFASTQ
2Genome aligned readsBAM
3Sample level summaryVCF/ MAF
+ + ); }; export const getStaticProps: GetStaticProps = async (context) => { diff --git a/pages/standard/bulkrnaseq.tsx b/pages/standard/bulkrnaseq.tsx index 4d193aba..806dcf42 100644 --- a/pages/standard/bulkrnaseq.tsx +++ b/pages/standard/bulkrnaseq.tsx @@ -6,7 +6,64 @@ import { getStaticContent } from '../../ApiUtil'; import { getDataSchema, SchemaDataId } from '../../lib/dataSchemaHelpers'; const BulkRNASeq: React.FunctionComponent = (props) => { - return ; + return ( + +

HTAN Bulk RNA Sequencing Data Standard

+ +

Overview

+ +

+ This page describes the data levels, metadata attributes, and + file structure for bulk RNA sequencing. +

+ +

Description of Assay

+ +

+ Bulk RNA sequencing identifies the average gene expression + profile of a biological sample. +

+ +

Metadata Levels

+ +

+ The defined metadata leverages existing common data elements + from the{' '} + + Genomic Data Commons (GDC) + + . The HTAN data model currently supports Level 1, 2 and 3 RNA + sequencing data: +

+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Level NumberDefinitionExample Data
1Unaligned readsFASTQ
2Aligned readsBAM
3Gene level expression, unnormalizedGene & isoform expression-level data (.csv)
+ + ); }; export const getStaticProps: GetStaticProps = async (context) => { diff --git a/pages/standard/clinical.tsx b/pages/standard/clinical.tsx index b6bebf8e..9f98c184 100644 --- a/pages/standard/clinical.tsx +++ b/pages/standard/clinical.tsx @@ -6,11 +6,150 @@ import { getStaticContent } from '../../ApiUtil'; import { getDataSchema, SchemaDataId } from '../../lib/dataSchemaHelpers'; const Cds: React.FunctionComponent = (props) => { - return ; + return ( + +

Clinical Data

+ +

Overview

+ +

+ This page describes the data tiers and data collection for the + HTAN clinical data standard. +

+ +

Description of Model

+ +

+ HTAN clinical data consists of four tiers. Tier 1 is in + alignment with the Genomic Data Commons (GDC) guidelines for + clinical data, while Tier 2, 3 and 4 are extensions to this + model. +

+ + + + + + + + + + + + + + + + + + + + + + + + + + +
TierDescription
1 + Demographics, Diagnosis, Exposure, Treatment, Follow-up, + Molecular Test and Family History +
2Disease-agnostic extensions to the GDC
3Disease-specific extensions to the GDC
4 + HTAN Research Network Atlas-specific clinical data + elements not covered in a previous tier and that are + recognized as a requirement by the atlas. These are + attributes specific to an atlas +
+ +

+ Tier 1 Clinical Data +

+ +

+ To establish consistency across HTAN atlases and tumor types, + the HTAN Tier 1 clinical data consists of GDC clinical data + elements defined as Required, Preferred and Optional. The HTAN + CDE Dictionary contains a comprehensive list of elements + attributed as Required, Preferred and Optional. +

+ +
    +
  • + Required CDE: Data submitters must provide a value for this + attribute; “Unknown”, “Not Reported” or “null” are valid + values for many of these attributes if the required + information is missing for a subset of patients +
    • +
  • + Preferred CDE: Data submitters are strongly recommended to + provide values for this attribute +
    • +
  • + Optional CDE: Data submitters can populate this attribute + based on availability of the data requested +
    • +
+ +

+ Tier 2 Clinical Data +

+ +

+ Describes disease-agnostic extensions to the GDC not included in + Tier 1 Clinical Data that were suggested for inclusion by the + HTAN Research Network. These suggested CDEs were compared to NCI + standards described in the caDSR system and the most similar CDE + was selected for inclusion in the HTAN Clinical Data model. If + no comparable CDE was found for a particular element, an + HTAN-specific CDE was developed. +

+ +

+ Tier 3 Clinical Data +

+ +

+ Describes disease-specific extensions to the GDC not included in + Tier 1 Clinical Data that were suggested for inclusion by the + HTAN Research Network. These suggested CDEs were compared to NCI + standards described in the caDSR system and the most similar CDE + was selected for inclusion in the HTAN Clinical Data model. If + no comparable CDE was found for a particular element, an + HTAN-specific CDE was developed. +

+ +

Attributes are divided by tumor type:

+ +
    +
  • Melanoma-specific
    • +
  • + Lung pre-cancer and cancer-specific Colorectal pre-cancer + and cancer-specific +
    • +
  • Breast pre-cancer and cancer-specific
    • +
  • Neuroblastoma-specific
    • +
  • Glioma-specific
    • +
  • Pancreatic pre-cancer and cancer-specific
    • +
  • Acute lymphoblastic leukemia-specific
    • +
  • Sarcoma-specific
    • +
  • Ovarian pre-cancer and cancer-specific
    • +
  • Prostate pre-cancer and cancer-specific
    • +
+ +

+ Tier 3 Clinical Data +

+

+ Describes clinical data elements identified as required to be + included in the HTAN data model and specific to an atlas, and + not covered in a previous tier. +

+ + ); }; export const getStaticProps: GetStaticProps = async (context) => { - const data = await getStaticContent(['data-standards-cds-blurb']); + //const data = await getStaticContent(['data-standards-cds-blurb']); // TODO this may not be the complete list of clinical data const { dataSchemaData, schemaDataById } = await getDataSchema([ @@ -34,7 +173,7 @@ export const getStaticProps: GetStaticProps = async (context) => { SchemaDataId.SarcomaTier3, ]); - return { props: { data, dataSchemaData, schemaDataById } }; + return { props: { dataSchemaData, schemaDataById } }; }; export default Cds; diff --git a/pages/standard/design.tsx b/pages/standard/design.tsx index 2ff56302..f01fa8f4 100644 --- a/pages/standard/design.tsx +++ b/pages/standard/design.tsx @@ -5,7 +5,99 @@ import DataStandard, { DataStandardProps } from '../../components/DataStandard'; import { getStaticContent } from '../../ApiUtil'; const Design: React.FunctionComponent = (props) => { - return ; + return ( + +

Design Principles

+ +

Introduction

+ +

+ In order to maximize the utility of the data HTAN generates, we + have defined a structured schema of all data, associated + metadata and their relationships. HTAN has been fortunate enough + to start at a time with ample of great data modeling examples + from other projects including The Cancer Genome Atlas (TCGA), + the International Cancer Genome Consortium (ICGC) and the NCI + Genomic Data Commons (GDC). HTAN tries to follow in those + footsteps and strives to provide compatibility with other + relevant platforms, such as the Human Cell Atlas (HCA) Data + Coordination Platform and the Human Biomolecular Atlas Program + (HuBMAP). +

+ +

HTAN Atlases

+ + + +

+ The HTAN data is generated by 12 different atlases. An atlas is + a group of people from one our more institutes that study a + specific cancer type. All data is associated to an atlas. +

+ +

Tiered Data Organization for Clinical Data and BioSpecimens

+ +

+ The clinical metadata on Research Participants and BioSpecimens + follows a tiered approach, where the first tier contain the most + common metadata and the higher tiers are more specific dependent + on particular use cases. See e.g. the organization of clinical + data: +

+ + + +

Levels for other types of data (Imaging, Sequencing)

+ +

+ The other types of data follow a leveled approach, similar to + TCGA, where the raw data is level 1 and higher levels are + further processed data. E.g. for single cell RNASeq: +

+ + + + + + + + + + + + + + + + + + + + + + +
Level 1Raw primary data, e.g. FASTQs and unaligned BAMs
Level 2Aligned primary data, e.g. aligned BAMs
Level 3 + Derived biomolecular data, i.e. gene expression matrix + file +
Level 4Sample level summary, i.e. t-SNE plot coordinates
Level 5 + Cohort level summary, i.e. significantly mutated genes +
+ +

Data Model Implementation

+ +

+ HTAN uses bioschemas to define the data model. Bioschema extends + schema.org, a community effort used by many search engines that + provides a way to define information with properties. Bioschemas + define profiles over types that state which properties must be + used (minimum), should be used (recommended), and could be used + (optional). HTAN and other consortiums, including the Human Cell + Atlas and HuBMAP are working together to provide common shared + schemas. One can find more info on bioschemas.org. +

+ + + ); }; export const getStaticProps: GetStaticProps = async (context) => { diff --git a/pages/standard/imaging.tsx b/pages/standard/imaging.tsx index 8619eb27..30d2d18e 100644 --- a/pages/standard/imaging.tsx +++ b/pages/standard/imaging.tsx @@ -6,7 +6,94 @@ import { getStaticContent } from '../../ApiUtil'; import { getDataSchema, SchemaDataId } from '../../lib/dataSchemaHelpers'; const Imaging: React.FunctionComponent = (props) => { - return ; + return ( + +

Overview

+ +

+ This page describes the assays and data levels for the HTAN + imaging data model. +

+ +

Description of Standard

+ +

+ The HTAN imaging data model captures attributes from all HTAN + experiments for which imaging data is generated, including: +

+ +
    +
  • H&E
    • +
  • t-CyCif
    • +
  • MxIF
    • +
  • Clinical and Multiplex IHC
    • +
  • SABER
    • +
  • IMC
    • +
  • MIBI
    • +
  • CODEX
    • +
  • GeoMx-DSP
    • +
  • MERFISH
    • +
  • Metadata Levels
    • +
+ +

The HTAN data model currently supports Level 2 image data:

+ + + + + + + + + + + + + + +
Level Number DefinitionAllowed file types
2Pre-processed image data OME-TIFF*
+ +

+ * OME-TIFF files are required to contain image pyramids (unless + the full image is relatively small, or a pyramid representation + is not appropriate, for example for a segmentation mask), + conforming to BioFormats 6.0.0 (or later), with compression + strongly suggested. +

+ +

+ Metadata elements have been collected from the OME-XML metadata + standard, as well as the extension proposed by the 4D Nucleosome + Imaging Standards Working Group, which divides fluorescence + microscopy data provenance metadata into categories: +

+ +
    +
  • + biospecimen preparation — eg fixation, staining, and + mounting conditions +
    • +
  • + experimental — eg tissue culture conditions, number of + conditions and/or replicates +
    • +
  • + image acquisition — eg microscope specification, imaging + settings +
    • +
  • + image data structure — eg number of focal planes, targets + (aka channels), and/or time points, dimensions (including + order), resolution/pixel size +
    • +
  • + data analysis — ie details regarding algorithms (including + versions and parameters) used for any processing steps used + to generate an image file +
    • +
+ + ); }; export const getStaticProps: GetStaticProps = async (context) => { diff --git a/pages/standard/rnaseq.tsx b/pages/standard/rnaseq.tsx index a47e5752..1095f514 100644 --- a/pages/standard/rnaseq.tsx +++ b/pages/standard/rnaseq.tsx @@ -7,10 +7,79 @@ import { getDataSchema, SchemaDataId } from '../../lib/dataSchemaHelpers'; const RnaSeq: React.FunctionComponent = (props) => { return ( - + +

+ HTAN Single Cell/Single Nucleus RNA Sequencing Data Standard +

+ +

Overview

+ +

+ This page describes the data levels, metadata attributes, and + file structure for single cell and single nucleus RNA sequencing + assays. +

+ +

Description of Assay

+ +

+ Single cell RNA sequencing is an emerging technology used to + investigate the expression profiles of individual cells and/or + nuclei. This technique is becoming increasingly useful for + investigating the tumor microenvironment, which is composed of a + heterogeneous population of cancer cells and tumor-adjacent + stromal cells. In these experiments, tissues are enzymatically + dissociated, and individual cells are isolated via microfluidics + using oil droplet emulsion. Similarly to bulk RNA sequencing, + individual transcriptomes are then uniquely tagged, reversed + transcribed, amplified and sequenced. While sc-RNA sequencing + captures both cytoplasmic and nuclear transcripts, single + nucleus RNA sequencing measures the transcriptome of individual + nuclei. Advantages of sn-RNA sequencing include differentiating + cell states and identifying rare or novel cell types in + heterogeneous populations. +

+ +

+ In alignment with{' '} + + The Cancer Genome Atlas & NCI Genomic Data Commons + + , data are divided into levels: +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Level NumberDefinitionExample Data
1Raw dataFASTQs, unaligned BAMs
2Aligned primary dataAligned BAMs
3Derived biomolecular dataGene expression matrix files, VCFs
4Sample level summary datat-SNE plot coordinates
+ ); }; diff --git a/pages/standard/scatacseq.tsx b/pages/standard/scatacseq.tsx index 9760325d..6a27fa7e 100644 --- a/pages/standard/scatacseq.tsx +++ b/pages/standard/scatacseq.tsx @@ -6,7 +6,81 @@ import { getStaticContent } from '../../ApiUtil'; import { getDataSchema, SchemaDataId } from '../../lib/dataSchemaHelpers'; const ScatacSeq: React.FunctionComponent = (props) => { - return ; + return ( + +

+ HTAN Single Cell/Single Nucleus RNA Sequencing Data Standard +

+ +

Overview

+ +

+ This page describes the data levels, metadata attributes, and + file structure for single cell and single nucleus RNA sequencing + assays. +

+ +

Description of Assay

+ +

+ Single cell RNA sequencing is an emerging technology used to + investigate the expression profiles of individual cells and/or + nuclei. This technique is becoming increasingly useful for + investigating the tumor microenvironment, which is composed of a + heterogeneous population of cancer cells and tumor-adjacent + stromal cells. In these experiments, tissues are enzymatically + dissociated, and individual cells are isolated via microfluidics + using oil droplet emulsion. Similarly to bulk RNA sequencing, + individual transcriptomes are then uniquely tagged, reversed + transcribed, amplified and sequenced. While sc-RNA sequencing + captures both cytoplasmic and nuclear transcripts, single + nucleus RNA sequencing measures the transcriptome of individual + nuclei. Advantages of sn-RNA sequencing include differentiating + cell states and identifying rare or novel cell types in + heterogeneous populations. +

+ +

Metadata Levels

+ +

+ In alignment with{' '} + + The Cancer Genome Atlas & NCI Genomic Data Commons + + , data are divided into levels: +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Level NumberDefinitionExample Data
1Raw dataFASTQs, unaligned BAMs
2Aligned primary dataAligned BAMs
3Derived biomolecular dataGene expression matrix files, VCFs
4Sample level summary datat-SNE plot coordinates
+ + ); }; export const getStaticProps: GetStaticProps = async (context) => { diff --git a/pages/standards.tsx b/pages/standards.tsx index c61146ce..f3018262 100644 --- a/pages/standards.tsx +++ b/pages/standards.tsx @@ -21,30 +21,18 @@ const Standards = (data: StandardsProps) => { - - - Home - - Data Standards - - - -

Data Standards

- - - - - -

Browse Standards

- - +

+ The HTAN Network has adopted a set of data standards + covering clinical, biospecimen, and assay metadata. + As standards are finalized, we will publish their + specifications below. +

+ +

Browser Standards

+
  • diff --git a/pages/static/aboutus.html b/pages/static/aboutus.html new file mode 100644 index 00000000..a02ec7e1 --- /dev/null +++ b/pages/static/aboutus.html @@ -0,0 +1,109 @@ +--- +page: aboutus +navText: About Us +--- + +

    Data Coordinating Center

    + +

    + The HTAN Data Coordinating Center (DCC) proudly supports each of the HTAN + atlas teams and pilot programs as well as the broader Network by + coordinating Network activities; providing centralized resources for data + and resource storage and access within HTAN as well as dissemination to the + wider scientific community; developing powerful data analysis and + visualization tools to enable researchers to make novel discoveries using + HTAN data; and conducting outreach to the community. Within HTAN, the DCC + also leads various efforts to develop an extensible data model as well as + clinical data and metadata standards that will ensure the accessibility and + interoperability of HTAN data with the wider cancer research data ecosystem. +

    + +

    + Comprised of individuals from four institutions, the DCC team brings to HTAN + extensive experience participating in major research consortia, including + TCGA, AACR Project GENIE, and the Cancer Systems Biology Consortium, as well + as leading open-source software development projects such as cBioPortal for + Cancer Genomics, Synapse, and the ISB Cancer Genomics Cloud (ISB-CGC) and + community engagement projects such as DREAM Challenges. The DCC team draws + upon this collective experience and deep technical expertise in biomedical + science and data science to support the goals of HTAN and to promote a + collaborative research and discovery environment within HTAN and the wider + cancer research community. +

    + +

    Principal Investigators

    + +

    ETHAN CERAMI, Ph.D.

    + +

    + Dr. Ethan Cerami is Director of the Knowledge Systems Group and Principal + Scientist in the Department of Data Sciences at Dana-Farber Cancer Institute + (DFCI). He has an M.S. in Computer Science from New York University and a + Ph.D. in Computational Biology from Cornell University. Prior to joining + Dana-Farber, Dr. Cerami was Director of Computational Biology at Blueprint + Medicines and Director of Cancer Informatics Development at Memorial Sloan + Kettering Cancer Center (MSKCC). While at MSKCC, Dr. Cerami co-founded the + cBioPortal for Cancer Genomics, and his current group at DFCI remains active + in its continued development while also being central contributors to other + major consortium efforts such as the NCI-funded Cancer Immunologic Data + Commons and AACR Project GENIE. +

    + +

    JUSTIN GUINNEY, Ph.D.

    + +

    + Dr. Justin Guinney is Director of Computational Oncology at Sage + Bionetworks. As an expert in large-scale analysis of genomic data, he has + worked extensively with clinicians to link models to complex cancer + phenotypes. His research is focused on the development of integrative + modeling approaches in cancer using large-scale biomedical and + high-throughput genomic data, with an emphasis on translational cancer + research and the utilization of molecular data to optimize the prediction of + patient outcomes and response to treatment. Dr. Guinney has played key + leadership roles in several large cancer consortia, including the Colorectal + Cancer Molecular Subtyping Consortium, AACR Project GENIE, and the + Neo-epitope Prediction Consortium. He is currently also PI for the Data and + Resource Coordinating Center for the NCI Cancer Systems Biology Consortium + and co-PI on the CRI iAtlas project, a platform supporting immune oncology + research. In addition, Dr. Guinney is co-director of the DREAM Challenges + and has led several challenges related to cancer prognosis, combination + therapy, and image analysis of digital mammograms. +

    + +

    NIKOLAUS SCHULTZ, Ph.D.

    + +

    + Dr. Niki Schultz is Associate Attending in the Computational Oncology + Service of the Department of Epidemiology and Biostatistics and Affiliate + Member of the Human Oncology and Pathogenesis Program at Memorial Sloan + Kettering Cancer Center (MSKCC). As head of the Knowledge Systems Group in + the Marie-Josée and Henry R. Kravis Center for Molecular Oncology, he leads + development of the cBioPortal for Cancer Genomics, a web-based resource for + analysis of complex cancer genomics data, and of OncoKB, a precision + oncology knowledge base. His research focuses on identifying the genomic + alterations that underlie cancer, their mechanisms of action, and novel + therapeutic approaches. Dr. Schultz has made significant contributions to + several projects of TCGA and AACR Project GENIE, and he is an investigator + in the Stand Up to Cancer Prostate Cancer Dream Team. He has a particular + interest in enabling discoveries by developing novel computational methods + and databases that help bridge the divide between computer scientists on one + side and clinicians and researchers on the other. +

    + +

    VÉSTEINN THORSSON, Ph.D.

    + +

    + Dr. Vésteinn Thorsson is a Senior Research Scientist at the Institute for + Systems Biology. His research encompasses cancer genomics and + immuno-oncology, and he has extensive experience working with data analysis + and data coordination in collaborative cancer genomics projects. As part of + The Cancer Genome Atlas (TCGA) Research Network, Dr. Thorsson contributed + substantially to published studies on gastrointestinal tumors, including + serving as both Data and Analysis Coordinator and playing a key role in + determining gastric molecular subtypes. Dr. Thorsson also served as Co-Chair + of a working group that recently completed a comprehensive analysis of all + TCGA gastrointestinal tumor samples and of a working group dedicated to + characterizing immune response in the more than 10,000 TCGA tumor samples. + In addition, he serves as a project lead for the CRI iAtlas project + (cri-iatlas.org), an interactive web resource for immuno-oncology research. +

    diff --git a/pages/static/authors.html b/pages/static/authors.html new file mode 100644 index 00000000..a4b750db --- /dev/null +++ b/pages/static/authors.html @@ -0,0 +1,17 @@ +--- +page: authors +navText: Authors +--- + +

    HTAN Publication Authors

    + + diff --git a/pages/static/consortium.html b/pages/static/consortium.html new file mode 100644 index 00000000..2425be21 --- /dev/null +++ b/pages/static/consortium.html @@ -0,0 +1,17 @@ +--- +page: consortium +navText: Consortium +--- + +

    Consortium

    + + diff --git a/pages/static/dashboard.html b/pages/static/dashboard.html new file mode 100644 index 00000000..e6b2a563 --- /dev/null +++ b/pages/static/dashboard.html @@ -0,0 +1,56 @@ +--- +page: dashboard +navText: Dashboard +--- + +

    Dashboard

    + +

    + An alpha version of the Data Portal can be found at + https://data.humantumoratlas.org +

    +

    + +

    + Atlas datasets generated by the HTAN Research Centers and pilot projects + will be made available to the public via scheduled releases that we expect + to begin in late 2019 or 2020. Detailed information about upcoming data + releases will be provided here once it is available. +

    + +

    + In anticipation of our first data release, the tables below summarize the + types of experimental data being generated by each of the atlas teams across + the specified tissue and tumor types and patient populations. +

    + + + +

    + Figure 4. Key Tumors Studied by the Consortium HTAN centers will generate 3D + atlases of human tumors spanning different tissue types across adult and + pediatric tumors from patients with pre-cancer, primary tumors, and + metastases, as well as resistant tumors before and after treatment. + Projected ranges for the number of samples to be profiled over the 5-year + HTAN period are depicted for each center and tumor type. In some cases, + multiple samples will be profiled from the same patient. HTAN includes the + following centers: Children’s Hospital of Philadelphia (CHOP), Dana-Farber + Cancer Institute (DFCI), Oregon Health & Science University (OHSU), + Washington University in St. Louis (WUSTL), Duke University School of + Medicine, Pre-Cancer Atlas Pilot Project (PCAPP), Human Tumor Pilot Project + (HTAPP), Vanderbilt University Medical Center (VUMC), Stanford University, + Boston University (BU), Memorial Sloan Kettering Cancer Center (MSCKK), and + Harvard Medical School (HMS). +

    + + + + diff --git a/pages/static/data-updates.html b/pages/static/data-updates.html new file mode 100644 index 00000000..3ab5782c --- /dev/null +++ b/pages/static/data-updates.html @@ -0,0 +1,45 @@ +--- +page: data-updates +navText: Data Updates +--- + +

    Data Updates

    +

    March 30th, 2022

    +

    + Additional HTAN data have been released (Release 2.1). +

    +

    March 25th, 2022

    +

    + Disabled Vanderbilt's cellxgene instance pending new data updates. +

    +

    March 5th, 2022

    +

    + The second data release of HTAN public data is now available on the HTAN + Data Portal. This v2.0 release includes new data from several HTAN atlases: + +

      +
    • 192 cases with new imaging data of which 86 have MIBI data, 105 + t-CyCIF, and 1 both CyCIF and mIHC. This means people can now browse + and view over 500 images in Minerva.
      • + +
    • 100 cases with new scRNASeq data of which 62 have level 3 and higher + available for download, allowing for download and analysis of scRNASeq + data from over 150 cases and 250 biospecimens.
      • + +
    • 1 case with both bulk DNASeq and RNASeq, which can now be explored + in cBioPortal as well as downloaded from the data portal.
      • +
    + + + + + +

    + + +

    May 24th, 2021

    +

    + The first release of HTAN public data is now available on the HTAN Data + Portal. This release includes Level 3 and 4 scRNAseq data from several HTAN + atlases. +

    \ No newline at end of file diff --git a/pages/static/hta1.html b/pages/static/hta1.html new file mode 100644 index 00000000..680c8080 --- /dev/null +++ b/pages/static/hta1.html @@ -0,0 +1,134 @@ +--- +page: hta1 +navText: Human Tumor Atlas Pilot Project (HTAPP) +--- + +

    Human Tumor Atlas Pilot

    +
    + + + +
    +
    +View the HTAPP Webinar Series +

    Overview

    + +

    Human Tumor Atlas Pilot Project (HTAPP)

    + +

    + To diagnose, study, monitor, and treat human cancer, there is an enormous + need to define and characterize the cells that comprise tumors, chart their + spatial organization, and decipher their functional connections. The + generation of tumor atlases poses substantial challenges, including + development and dissemination of robust SOPs for tissue collection, + development of experimental design strategies, establishment of strategies + to integrate across cellular and spatial data, and development of effective + data sharing approaches. +

    + +

    + To address these challenges and lay a foundation for HTAN, HTAPP was + initiated in partnership with the National Cancer Institute (NCI) and the + Frederick National Laboratory for Cancer Research (FNLCR). HTAPP aims to: + (1) gain an understanding of the number of cells, tumors, and spatial + methods required to describe tumor heterogeneity; (2) develop, validate, and + transfer knowledge of SOPs for tissue procurement, sampling parameters, and + data generation and analysis from seven tumor types; (3) generate + pilot-scale, spatially resolved tumor atlases of metastatic breast cancer + and pediatric neuroblastoma; (4) provide data and resources generated to the + HTAN and broader community; and (5) assess the feasibility of applying + single-cell genomics to FFPE tissues. Our team is composed of clinical, + single-cell genomics, spatial genomics and proteomics, and computation + experts, who have pioneered the development and application of these + approaches to tumors. +

    +
    + +
    + +

    Principal Investigators

    + +

    AVIV REGEV, Ph.D.

    + +

    + + Dr. Aviv Regev’s research centers on understanding how complex molecular + circuits function in cells and between cells in tissues. She is a professor + in the Department of Biology at MIT, Chair of the Faculty and founding + director of the Klarman Cell Observatory and Cell Circuits Program at the + Broad Institute of MIT and Harvard, where she is a core member, and an + Investigator at the Howard Hughes Medical Institute. Her lab has been a + single-cell genomics pioneer – inventing key experimental methods and + computational algorithms in the field, demonstrating their application, and + inferring the molecular and cellular circuits that control cellular and + tissue function in health and disease. She is Founding Co-Chair of the + international initiative to build a Human Cell Atlas (HCA), whose mission is + to create comprehensive reference maps of all human cells – the fundamental + units of life – as a basis for understanding human health and diagnosing, + monitoring, and treating disease. +

    + +

    ORIT ROZENBLATT-ROSEN, Ph.D.

    + +

    + + Dr. Orit Rozenblatt-Rosen is Scientific Director of the Klarman Cell + Observatory at the Broad Institute of MIT and Harvard and the lead scientist + at the Broad for the international Human Cell Atlas Initiative. Dr. + Rozenblatt-Rosen has a background in cancer research, epigenetics, systems + biology, genomics, and single-cell genomics. She helped develop and + implement systematic pipelines for genomic profiling and analysis of single + cells from freshly dissected tumors. Previously, Dr. Rozenblatt-Rosen was a + research scientist at the Dana-Farber Cancer Institute. She led a team that + performed systematic analyses of host network perturbations induced by DNA + tumor viruses to help interpret cancer genomes. As a postdoctoral fellow at + the Dana-Farber Cancer Institute, she focused on understanding the links + between tumor suppression and epigenetic mechanisms. Dr. Rozenblatt-Rosen + earned a B.S. in Biology from Tel Aviv University and a Ph.D. from the + Weizmann Institute of Science. +

    + +

    BRUCE JOHNSON, M.D.

    + +

    + + Dr. Bruce Johnson is the Chief Clinical Research Officer at the Dana-Farber + Cancer Institute and has organized and run clinical trials for more than 30 + years. His translational research is devoted to testing novel therapeutic + agents for their efficacy against lung cancer and other malignancies with + specific genomic changes and the effects of the tumor microenvironment on + efficacy of immunotherapy. Dr. Johnson was one of the scientists who + discovered the association between epidermal growth factor receptor + mutations and response to epidermal growth factor receptor-tyrosine kinase + inhibitors. As the Director of the Center for Precision Medicine at the + Dana-Farber Cancer Institute, he oversees the characterization of tumor + specimens from patients both before and after therapy with chemotherapeutic + agents, targeted agents, and immunotherapy. Dr. Johnson is a Professor of + Medicine at Harvard Medical School, an Institute Physician at the + Dana-Farber and Brigham and Women’s Hospital, and Past President of the + American Society of Clinical Oncology. +

    + +

    MARIO SUVÀ, M.D., Ph.D.

    + +

    + + Dr. Mario Suvà is a physician-scientist in the Department of Pathology at + Massachusetts General Hospital (MGH) and an institute member at the Broad + Institute. Dr. Suvà’s expertise is in clinical neuropathology, single-cell + sequencing technology, and cancer research. Dr. Suvà co-directed landmark + studies characterizing glioblastoma, oligodendroglioma, astrocytoma, and + pediatric gliomas with single-cell genomic technologies, shedding light on + tumor heterogeneity, tumor classification, glioma cell lineages, cancer stem + cell programs, tumor evolution, and the composition of the tumor + microenvironment. His laboratory focuses on dissecting the heterogeneity of + patient tumors and relating transcriptional and genetic programs of + individual cancer cells. Dr. Suvà earned his M.D. and Ph.D. from the + University of Lausanne, Switzerland. His doctoral research identified cancer + stem cells in Ewing sarcoma and highlighted mechanisms underlying their + emergence. He conducted postdoctoral research at MGH and the Broad + Institute, applying chromatin analysis and functional approaches to identify + master regulators of glioma stem cell programs. +

    diff --git a/pages/static/hta10.html b/pages/static/hta10.html new file mode 100644 index 00000000..f03cca0a --- /dev/null +++ b/pages/static/hta10.html @@ -0,0 +1,109 @@ +--- +page: hta10 +navText: PRE-CANCER ATLAS FAMILIAL ADENOMATOUS POLYPOSIS +--- + +

    PRE-CANCER ATLAS: FAMILIAL ADENOMATOUS POLYPOSIS

    +
    + +
    +

    Overview

    + +

    + Multi-omic Characterization of Transformation of Familial Adenomatous + Polyposis +

    +

    + We have established a state-of-the-art PreCancer Altas (PCA) for colorectal + cancer (CRC) using Familial Adenomatous Polyposis (FAP) as a model system. + CRC is one of the leading causes of death in the United States with 140,000 + new cases arising each year. Mutations in the adenomatous polyposis coli + (APC) gene are thought to be one of the early events in sporadic colorectal + cancer. FAP families have inherited mutations in APC, and affected + individuals classically develop hundreds of polyps, which often progress to + invasive colon cancer by the third decade of life. Due to the + extraordinarily high number of polyps that can be obtained from a single + individual, FAP is an ideal disease system to study the host factors driving + molecular heterogeneity in precancerous lesions. We expect these studies to + inform diagnostic, preventative, and therapeutic measures for sporadic CRC. + In order to obtain a comprehensive understanding of the early events that + lead to and drive CRC, we will construct a PreCancer Atlas of the + pre-cancerous polyps, early adenocarcinoma, and nearby normal tissue + isolated from FAP patients. To build this atlas and execute this project, we + have assembled an expert team of investigators with extensive experience + working in large consortia. Together, we have already developed pipelines + for multi-omics and imaging characterization, biospecimen collection, and + analysis that we have leveraged for FAP samples. +

    + +

    Principal Investigators

    + +

    MICHAEL SNYDER, Ph.D.

    + +

    + + Dr. Michael Snyder is the Stanford Ascherman Professor and Chair of Genetics + and the Director of the Center of Genomics and Personalized Medicine. Dr. + Snyder received his Ph.D. training at the California Institute of Technology + and did his postdoc at Stanford University. He is a leader in the field of + functional genomics and proteomics and one of the leaders of ENCODE. His + laboratory was the first to perform a large-scale functional genomics + project in any organism and has developed many technologies in genomics and + proteomics including high-resolution tiling arrays for the entire human + genome, methods for global mapping of transcription factor binding sites, + paired-end sequencing for mapping of structural variation in eukaryotes, de + novo genome sequencing of genomes using high-throughput technologies, and + RNA-Seq. These technologies have been used for characterizing genomes, + proteomes, and regulatory networks. +

    + +

    JAMES FORD, M.D.

    + +

    + + Dr. James Ford is a medical oncologist and geneticist at Stanford and is + devoted to studying the genetic basis of breast and GI cancer development, + treatment, and prevention. Dr. Ford graduated from Yale University, where he + later received his M.D. He was an internal medicine resident, Clinical + Fellow in Medical Oncology, and Research Fellow of Biological Sciences at + Stanford, and he joined the faculty in 1998. He is currently Professor of + Medicine and Genetics and Director of the Stanford Cancer Genetics Clinic + and the Cancer Genomics Program. Dr. Ford’s research goals are to understand + the role of genetic changes in cancer genes in the development of cancer. He + studies the role of the p53 and BRCA1 tumor suppressor genes in DNA repair + and uses high-throughput genomic analyses of cancer to identify molecular + signatures for targeted therapies. Dr. Ford’s clinical interests include the + diagnosis and treatment of patients with a hereditary predisposition to + cancer. +

    + +

    CHRISTINA CURTIS, Ph.D.

    + +

    + + Trained in molecular and computational biology and jointly appointed in + Medicine and Genetics, Dr. Christina Curtis pursues systems biology and + computational approaches to establish a quantitative and mechanistic + understanding of cancer progression. Her research is focused on the + development and application of innovative experimental and computational + approaches to improve the diagnosis, treatment, and earlier detection of + cancer by leveraging genome-scale data derived from clinical samples coupled + with computational modeling and iterative experimentation. To this end, Dr. + Curtis and her team have developed an integrated experimental and + computational framework to measure clinically relevant patient-specific + parameters and to measure clonal dynamics during tumor progression and + through therapy. This led to their description of a Big Bang model of + effectively neutral tumor evolution, thereby advancing a quantitative + understanding of tumor progression and refining the de facto clonal + evolution model. +

    + +

    WILLIAM GREENLEAF, Ph.D.

    + +

    + + Dr. William Greenleaf studies the physical genome both by investigating the + relationship between the folding of DNA in the nucleus and gene expression + regulation, as well as the relationship between DNA sequence and the + structure and function of encoded biomolecules. +

    diff --git a/pages/static/hta11.html b/pages/static/hta11.html new file mode 100644 index 00000000..be048ab0 --- /dev/null +++ b/pages/static/hta11.html @@ -0,0 +1,112 @@ +--- +page: hta11 +navText: INTEGRATIVE SINGLE-CELL ATLAS OF HOST AND MICROENVIRONMENT IN COLORECTAL NEOPLASTIC TRANSFORMATION +--- + +

    + INTEGRATIVE SINGLE-CELL ATLAS OF HOST AND MICROENVIRONMENT IN COLORECTAL + NEOPLASTIC TRANSFORMATION +

    +
    + + +
    +

    Overview

    + +

    COLON MAP: Colon Molecular Atlas Project

    +

    + Colorectal cancer (CRC) is among the top three most prevalent cancers in + global incidence and mortality. Most of these cancers develop from + pre-cancerous adenomas. There is an unmet need to develop new preventive + strategies and risk stratification models to decrease incidence, improve + early detection, and prevent deaths from CRC. +

    +

    + We believe that the ability to provide the most effective precision + diagnostics and preventive strategies can only be achieved with single-cell + analysis. As such, we will map spatial relationships across the spectrum of + normal colon, early polyps, and late adenomas, including their unique + stromal and microbial microenvironments to identify unique molecular + phenotypes. +

    +

    + Our goal will be accomplished through prospective, standardized collection + and analysis of colorectal tissue, associated biospecimens, and related + clinical and epidemiological data from participants undergoing colonoscopy + or surgical resection. The biospecimens from these participants will be used + for single-cell RNA sequencing, whole exome sequencing, multiplex + immunofluorescence, species-specific bacterial fluorescence in situ + hybridization, and other approaches. Finally, the information from these + approaches will be integrated to develop a single-cell pre-cancer atlas with + defined molecular phenotypes for dissemination to the broader scientific + community. +

    + +

    Principal Investigators

    + +

    ROBERT COFFEY, M.D.

    + +

    + + Dr. Robert Coffey is John B. Wallace Professor of Medicine, Professor in the + Department of Cell and Developmental Biology, and an Ingram Professor of + Cancer Research at Vanderbilt University Medical Center. He directs the + Vanderbilt Epithelial Biology Center and is principal investigator of + Vanderbilt’s NCI-funded GI Specialized Programs of Research Excellence + (SPORE), which focuses on colorectal cancer. He recently received an NCI + Outstanding Investigator Award. His basic research focuses on the spatial + compartmentalization of the EGF receptor (EGFR), its cognate ligands, and + relevant signaling molecules in the context of polarized epithelial cells + and how their dysregulation contributes to cancer. His lab identified a new + mode of EGFR ligand signaling via exosomes, and he was a principal + investigator within the U19 Common Fund initiative to understand the + biogenesis and function of exosome-associated secreted RNAs. Other prior + studies led by Dr. Coffey found that a pan-ERBB negative regulator, LRIG1, + marks colonic stem cells and acts as a tumor suppressor in vivo. As part of + a Common Fund NCI partnership grant with GE Healthcare, his lab also helped + develop the optimized multiplex immunofluorescence (MxIF) platform used in + this project. +

    + +

    KEN LAU, Ph.D.

    + +

    + + Dr. Ken Lau earned his B.S. degree and his Ph.D. in Proteomics and + Bioinformatics from the University of Toronto. After a joint postdoctoral + fellowship at MIT and Massachusetts General Hospital, he joined the faculty + at Vanderbilt as an Assistant Professor of Cell and Developmental Biology in + 2013. Dr. Lau is an active member of the Vanderbilt Epithelial Biology + Center and is currently an Associate Professor of Cell and Developmental + Biology. Dr. Lau’s research revolves around how different cell populations + in the gut integrate into tissue function with a specific focus on the gut’s + role in microbial sensing and stem cells in colorectal cancer. His lab + applies systems biology tools to understand tissue ecosystems as cell + networks, spanning the realms of both experimental and computational + biology. Dr. Lau has made various contributions to single-cell technologies, + including multiplex microscopy, mass cytometry, and single-cell + transcriptomics. +

    + +

    MARTHA J. SHRUBSOLE, Ph.D.

    + +

    + + Dr. Martha Shrubsole is Research Professor of Medicine at Vanderbilt + University Medical Center where she leads a research portfolio of molecular, + nutritional, and interventional epidemiology. A major focus of Dr. + Shrubsole’s research is to understand the etiology of gastrointestinal + neoplasia. She seeks to identify and evaluate modifiable factors, + biomarkers, and molecular mechanisms for the prevention, early detection, + and precision-based interception of cancer and its precursor lesions with + emphasis on sessile serrated polyps and conventional colorectal adenomas. + Some of these areas of study include nutrients such as one-carbon + metabolism, inflammatory markers, gut microbiome, molecular landscape of + colorectal polyps, health disparities, and predictors of metachronous + adenomas. In addition, Dr. Shrubsole has had leading roles in multiple + randomized trials and large-scale epidemiologic studies based in the United + States and globally. +

    diff --git a/pages/static/hta12.html b/pages/static/hta12.html new file mode 100644 index 00000000..3d5527fd --- /dev/null +++ b/pages/static/hta12.html @@ -0,0 +1,72 @@ +--- +page: hta12 +navText: WASHINGTON UNIVERSITY HUMAN TUMOR ATLAS RESEARCH CENTER +--- + +

    WASHINGTON UNIVERSITY HUMAN TUMOR ATLAS RESEARCH CENTER

    +
    + +
    +

    Principal Investigators

    + +

    LI DING, Ph.D.

    + +

    + + Dr. Li Ding’s current research focuses on the discovery of genetic changes + (somatic and germline) contributing to human diseases by integrating various + data types, including DNA, RNA, and proteomics data. Her research team has + developed a collection of widely-used computational tools, including + VarScan, SomaticSniper, SciClone, BreakDancer, BreakFusion, MSIsensor, + Pindel-C, GenomeVIP, HotSpot3D, PathScan, and MuSiC. Dr. Ding plays + significant roles in The Cancer Genome Atlas (TCGA), the International + Cancer Genome Consortium (ICGC), and the Clinical Proteomic Tumor Analysis + Consortium (CPTAC). She co-chairs the TCGA PanCanAtlas Oncogenic Process + Group, the TCGA Sarcoma Analysis Working Group, and the ICGC Mutation + Calling Group. Dr. Ding also serves on the Steering Committees of the + Genomic Data Commons (GDC), CPTAC, and TCGA. Dr. Ding has successfully led + many large-scale, multi-institute studies on the genomics of lung + adenocarcinomas, AML, and breast cancer. Building on this foundation, her + team has produced a series of seminal publications in the fields of cancer + genomics research and cancer biology. +

    + +

    RYAN FIELDS, M.D.

    + +

    + + Dr. Ryan Fields’ clinical expertise includes pancreatic, liver, bile duct, + stomach and small intestine cancer; melanoma and other skin cancers; and + sarcoma. His research interests include care of patients with tumors of the + pancreas, liver (primary and metastatic), bile ducts, stomach, and small + intestine, as well as patients with melanoma and other skin cancers and + sarcomas. Dr. Fields’ research focuses on the identification and treatment + of patients at high risk for recurrence of their cancer and the use of novel + treatment modalities to improve survival. +

    + +

    WILLIAM GILLANDERS, M.D.

    + +

    + + Dr. William Gillanders’ clinical expertise includes breast cancer and + surgical endocrinology, including minimally invasive endocrine surgery. His + research interests include the molecular detection of breast cancer in + sentinel lymph nodes, peripheral blood, and bone marrow as well as + antigen-specific T cell responses to cancer vaccines. +

    + +

    SAMUEL ACHILEFU, Ph.D.

    + +

    + + Our research interests are in the design, development, and biochemical + evaluation of molecular imaging agents and drugs in cells and living + organisms. Through highly multidisciplinary research, we also develop new + imaging systems for clinical applications. Our most recent accomplishments + include the discovery of a broad spectrum cancer imaging agent, a portable + goggle device for image-guided surgery, a depth-independent photo + therapeutic platform for treating diseases and a reporter molecular system + for reporting kinase activities. The ultimate goal is to validate new + research concepts in the lab and translate them to humans. +

    diff --git a/pages/static/hta3.html b/pages/static/hta3.html new file mode 100644 index 00000000..9c4438cf --- /dev/null +++ b/pages/static/hta3.html @@ -0,0 +1,90 @@ +--- +page: hta3 +navText: LUNG PRE CANCER ATLAS +--- + +

    LUNG PRE CANCER ATLAS

    +
    + + + +
    +

    Overview

    +

    The Lung PCA: A Multi-Dimensional Atlas of Pulmonary Premalignancy

    + +

    + The Lung PCA: A Multi-Dimensional Atlas of Pulmonary Premalignancy is one of + five research centers developing Pre-Cancer Atlases focused on unraveling + the molecular underpinnings enabling the progression of pre-cancerous + lesions to cancer. The Lung PCA is led by Dr. Avrum Spira (Boston + University) and co-led by Dr. Steven Dubinett (University of California, Los + Angeles), and Dr. Sarah Mazzilli (Boston University) in addition to + collaborators across 10 institutions including: University of California Los + Angeles; Stanford University; University of Colorado, Denver; MD Anderson + Cancer Center; University College London; DFCI/Harvard University; Broad + Institute; Roswell Park Comprehensive Cancer Center; University of + Pennsylvania; and Vanderbilt University. The Lung PCA aims to build a + high-resolution atlas characterizing the earliest cellular and molecular + alterations in pre-cancerous lesions of the airway and lung, providing new + opportunities for early cancer detection and novel targets to intercept the + development of lung cancer. The atlas leverages unique retrospective and + prospective cohorts assembled at multiple medical centers via our + Biospecimen Unit, led by Daniel Merrick of the University of Colorado, to + collect, annotate, and process premalignant lesion biospecimens of the lung + and airway. Our Characterization Unit, led by Steven Dubinett of UCLA, will + apply existing and emerging molecular profiling tools to characterize the + genomic, transcriptomic, and proteomic landscape of these lesions. Our Data + Analysis Unit, led by Marc Lenburg of Boston University, will develop + pipelines for standardized multidimensional data processing, quality + control, analysis and integration, leading to creation of a web-based portal + for data dissemination and online integrative analysis to benefit the + greater research community. +

    + +
    + +
    + +

    Principal Investigators

    + +

    AVRUM SPIRA, M.D.

    + +

    + + Dr. Avrum Spira is a Professor of Medicine, Pathology, and Bioinformatics, + and the Alexander Graham Bell Professor in Health Care Entrepreneurship at + Boston University. He is founding Chief of the Division of Computational + Biomedicine in the Department of Medicine, attends in the Medical Intensive + Care Unit at Boston Medical Center, and directs the Translational + Bioinformatics Program in the Clinical and Translational Science Institute + at Boston University. He has recently been named global head of the new Lung + Cancer Initiative within Johnson & Johnson and Director of the Johnson & + Johnson Innovation Lung Cancer Center at Boston University. Dr. Spira has + built a translational research program that focuses on genomic alterations + associated with smoking-related lung disease, leading to a molecular test + for the early detection of lung cancer (PerceptaTM) that has successfully + translated into the clinic as well as a novel therapeutic for COPD that is + in preclinical development. +

    + +

    STEVEN DUBINETT, M.D.

    + +

    + + Dr. Steven M. Dubinett is Director of the University of California, Los + Angeles (UCLA) Clinical & Translational Science Institute (CTSI), Associate + Vice Chancellor, and Senior Associate Dean for Translational Research. He + oversees the translation of UCLA biomedical discoveries into medical + products and health interventions and is responsible for the efficient + integration of the research infrastructure through the CTSI. He is currently + Chair of the Executive Committee of UC Biomedical Research Acceleration, + Integration, and Development (UC BRAID), which integrates clinical and + translational research across the University of California. Dr. Dubinett has + extensive experience in translational investigation, academic + administration, mentorship, and peer review. He previously served as + Director for Biomarker Development for the American College of Surgeons + Oncology Group, directing biospecimen utilization in the context of clinical + trials. Dr. Dubinett currently serves as the Chair of the Research + Evaluation Panel for biospecimen utilization for the American College of + Radiology Imaging Network/ National Lung Screening Trial. +

    diff --git a/pages/static/hta4.html b/pages/static/hta4.html new file mode 100644 index 00000000..4273bf89 --- /dev/null +++ b/pages/static/hta4.html @@ -0,0 +1,91 @@ +--- +page: hta4 +navText: CENTER FOR PEDIATRIC TUMOR CELL ATLAS +--- + +

    CENTER FOR PEDIATRIC TUMOR CELL ATLAS

    +
    + + +
    + +

    Overview

    +

    Center for Pediatric Tumor Cell Atlas

    +

    + Because cancers in children are highly distinct from those in adults, the + causes, mechanisms, and therapeutic approaches cannot be extrapolated from + the study of adult malignancies. To impact the burden of pediatric cancers + requires the specific study of pediatric cancers. This project will + characterize three specific subtypes of pediatric malignancies, which + together account for over 50% of all pediatric cancer deaths: high grade + glioma (pHGG), high risk neuroblastoma (NB), and very high risk acute + lymphoblastic B-cell precursor leukemia (VHR-ALL). All three tumor types + share the characteristic of typically exhibiting an initial response to + therapy followed by the emergence of resistance and refractory disease. We + will map molecular and cellular changes in tumor cells, the + microenvironment, and the immune system using comprehensive + multi-dimensional single-cell and in situ technologies associated with two + critical and high-priority transitions: initial response and emergence of + resistant disease. Treatment modalities will include standard chemotherapy, + molecularly-targeted therapies, and chimeric antigen receptor-T (CAR-T) cell + therapy, capturing the cutting edge of current cancer therapy. The final + products of this project will include publically available atlases of + comprehensive multi-omic, single-cell analysis, critical computational tools + and pipelines, and banked biospecimens available to the research community + for follow-up studies. +

    + +
    + +
    + +

    Principal Investigators

    + +

    KAI TAN, Ph.D.

    + +

    + + Dr. Kai Tan is a systems biologist and Associate Professor in the Department + of Pediatrics, The Children’s Hospital of Philadelphia and University of + Pennsylvania. He has extensive experience studying transcriptional and + epigenetic regulation in normal development and oncogenesis through a + combination of experimental genomics and computational models. Dr. Tan’s + laboratory has developed a number of approaches for modeling transcriptional + regulatory networks, which have been used to dissect the gene regulatory + networks controlling embryonic hematopoiesis, T cell differentiation, and + leukemogenesis. In addition, his group has pioneered a number of popular + computational algorithms for constructing models of transcriptional + regulatory networks by integrating multi-dimensional genomic, epigenomic, + and transcriptomic datasets. The ultimate goal of his work is to understand + dynamic molecular networks, the role of combinatorial epigenetic + modifications and transcriptional regulation, and the role of 3-D genomic + organization in gene regulation to improve the diagnosis and treatment of + cancer patients. +

    + +

    STEPHEN P. HUNGER, M.D

    + +

    + + Dr. Stephen P. Hunger is a pediatric oncologist and Chief of the Division of + Oncology, Director of the Center for Childhood Cancer Research at The + Children’s Hospital of Philadelphia and University of Pennsylvania. His + research is focused on improving understanding of the molecular genetics of + childhood acute lymphoblastic leukemia (ALL) and translating new discoveries + into improved outcomes via clinical trials and linked translational studies. + As Vice Chair (2002-2007) and Chair of the Children’s Oncology Group (COG) + ALL Committee (2008-2015), he was responsible for overseeing the design and + conduct of clinical trials and translational research studies that enroll + over 70% of children in the United States with ALL. Dr. Hunger was Principal + Investigator of the COG High Risk ALL TARGET Project, which has revealed new + sentinel genetic lesions in childhood ALL, identified the Ph-like ALL high + risk ALL subtype, and defined its genomic landscape, all of which are + discoveries that have led to additional trials testing molecularly targeted + therapies. +

    diff --git a/pages/static/hta5.html b/pages/static/hta5.html new file mode 100644 index 00000000..abacc7c4 --- /dev/null +++ b/pages/static/hta5.html @@ -0,0 +1,79 @@ +--- +page: hta5 +navText: THE CELLULAR GEOGRAPHY OF THERAPEUTIC RESISTANCE IN CANCER +--- + +

    THE CELLULAR GEOGRAPHY OF THERAPEUTIC RESISTANCE IN CANCER

    +
    + + +
    +

    Overview

    +

    The Cellular Geography of Therapeutic Resistance in Cancer

    +

    + The majority of patients die from cancer because their tumors either do not + respond to therapy or eventually develop resistance to the treatments. The + Boston Human Tumor Atlas Network Research Center includes research + institutions in Boston, Stanford, and Princeton that are mapping the cell + types in breast cancer, melanoma, and colon cancer. The resulting tumor + atlases will generate spatial and single-cell resolution information to + provide an unprecedented understanding of the malignant, immune, and stromal + cellular and tissue compartments that drive intrinsic and acquired + resistance to existing and emerging therapeutics. These compartments then + can be used to inform patient stratification and new treatment strategies + and to make precision cancer medicine a reality. This detailed information + is essential for the targeted therapies (e.g., CDK4/6 inhibition) and + immunotherapies (e.g., anti-PD-1 therapies) we are examining, because these + therapies typically benefit only a subset of patients treated with them for + a limited period of time, and mechanisms of resistance to them are thus far + poorly understood. Our studies will help develop both new insights to + identify patients at risk of resistance to their cancer therapies and new + treatment strategies to overcome both de novo and acquired resistance, + ultimately improving the outcome for patients with cancer. +

    + +

    Principal Investigators

    + +

    BRUCE JOHNSON, M.D.

    + +

    + + Dr. Bruce Johnson is the Chief Clinical Research Officer at the Dana-Farber + Cancer Institute and has organized and run clinical trials for more than 30 + years. His translational research is devoted to testing novel therapeutic + agents for their efficacy against lung cancer and other malignancies with + specific genomic changes and the effects of the tumor microenvironment on + efficacy of immunotherapy. Dr. Johnson was one of the scientists who + discovered the association between epidermal growth factor receptor + mutations and response to epidermal growth factor receptor-tyrosine kinase + inhibitors. As the Director of the Center for Precision Medicine at the + Dana-Farber Cancer Institute, he oversees the characterization of tumor + specimens from patients both before and after therapy with chemotherapeutic + agents, targeted agents, and immunotherapy. Dr. Johnson is a Professor of + Medicine at Harvard Medical School, an Institute Physician at the + Dana-Farber Cancer Institute and Brigham and Women’s Hospital, and Past + President of the American Society of Clinical Oncology. +

    + +

    AVIV REGEV, Ph.D.

    + +

    + + Dr. Aviv Regev’s research centers on understanding how complex molecular + circuits function in cells and between cells in tissues. She is a professor + in the Department of Biology at MIT, Chair of the Faculty and founding + director of the Klarman Cell Observatory and Cell Circuits Program at the + Broad Institute of MIT and Harvard, where she is a core member, and an + Investigator at the Howard Hughes Medical Institute. Her lab has been a + single-cell genomics pioneer – inventing key experimental methods and + computational algorithms in the field, demonstrating their application, and + inferring the molecular and cellular circuits that control cellular and + tissue function in health and disease. She also is Founding Co-Chair of the + international initiative to build a Human Cell Atlas (HCA), whose mission is + to create comprehensive reference maps of all human cells – the fundamental + units of life – as a basis for understanding human health and diagnosing, + monitoring, and treating disease. +

    diff --git a/pages/static/hta6.html b/pages/static/hta6.html new file mode 100644 index 00000000..4fd9752e --- /dev/null +++ b/pages/static/hta6.html @@ -0,0 +1,102 @@ +--- +page: hta6 +navText: BREAST PRE-CANCER ATLAS +--- + +

    BREAST PRE-CANCER ATLAS

    +
    + + + +
    +

    Overview

    +

    Breast Pre-Cancer Atlas

    +

    + The Breast Pre-Cancer Atlas Center is designed to construct an atlas that + can be used to better understand ductal carcinoma in situ (DCIS), a + preinvasive breast cancer precursor. DCIS is an extremely common clinical + diagnosis that is essentially a disease of screening triggered by the + detection of abnormal breast calcifications on mammography. Before the + advent of mammography, DCIS was an incidental and relatively uncommon + finding. Over 60,000 women in the United States will be presented with this + diagnosis each year with relatively weak evidence-based guidance for disease + management, which ranges from active surveillance to bilateral mastectomy. + We propose to compile multi-dimensional and multi-scale information on DCIS + to construct a Pre-Cancer Atlas that can be used to better understand the + disease but also to better stratify risk of progression, a useful + translational endpoint. +

    +

    + To do this, we have assembled a team of investigators with deep and + complementary clinical, experimental, and quantitative expertise and + experience with DCIS and breast cancer in general. Further, we conduct these + studies with full consideration of tumor evolution and ecology as it + pertains to precancer development and progression. Specific aspects of the + proposed atlas construction include: 1) several types of DCIS cohorts that + will capture spatial and longitudinal information including a prospective + clinical trial cohort undergoing active surveillance; 2) analyses designed + to maintain relevant spatial organization of the disease for evolutionary + and atlas building considerations based on 3) + radiologic-histologic-cellular-molecular registration approaches; 4) + characterization at multiple scales including whole-tumor, single-duct, and + single-cell levels; 5) characterization of relevant parameters including + mutations, copy number changes, methylation, gene expression, and + microenvironmental elements including inflammatory cell profiles; 6) + incorporation of the breast cancer-intrinsic subtype paradigm into the + analytic phase; and 7) layered, spatial, and longitudinal data + visualization. Overall, this work will provide a comprehensive platform to + guide the next generation of studies on DCIS and other precancers. +

    + +

    Principal Investigators

    + +

    E. SHELLEY HWANG, M.D., M.P.H.

    + +

    + + Dr. E. Shelley Hwang is the Mary and Deryl Hart Professor of Surgery at Duke + University School of Medicine and Chief of Breast Surgery for the Duke + Cancer Institute, an NIH-designated Comprehensive Cancer Center. Her + research interests have spanned a broad range of projects in translational + research that seek to elaborate biomarkers in the tumor, the + microenvironment, and blood. Dr. Hwang is a surgeon-scientist and + experienced clinical trial investigator with an over 15-year interest in + both the biology and treatment of early-stage breast cancer. She also is the + PI of a national cooperative group study through the ALLIANCE, known as the + COMET study, which evaluates the role of active surveillance compared to + usual care for DCIS. +

    + +

    ROB WEST, M.D., Ph.D.

    + +

    + + Dr. Rob West is a Professor of Pathology at Stanford University Medical + Center. He is a clinician scientist with experience in translational + genomics research to identify new prognostic and therapeutic markers in + cancer. His research focus is on the progression of neoplasia to invasive + carcinoma. His lab has developed spatially oriented in situ methods to study + archival specimens. In addition to running a research laboratory, Dr. West + also serves as a surgical pathologist specializing in breast pathology at + Stanford University Hospital. +

    + +

    CARLO MALEY, Ph.D., M.Sc.

    + +

    + + Dr. Carlo Maley is Associate Professor of the Biodesign Institute at the + Arizona State University School of Life Sciences. He is a cancer biologist, + evolutionary biologist, and computational biologist working at the + intersection of those fields. His team applies evolutionary and ecological + theory to three problems in cancer: (1) neoplastic progression: the + evolutionary dynamics among cells of a tumor that drive progression from + normal tissue to malignant cancers; (2) acquired therapeutic resistance: the + evolutionary dynamics by which our therapies select for resistance and we + fail to cure cancer; and (3) the evolution of cancer suppression mechanisms + in large, long-lived animals like elephants and whales (a problem called + Peto’s Paradox). Dr. Maley’s lab uses genomic data mining, phylogenetics, + computational modeling, as well as wet lab techniques to solve these + problems. In all of this work, their goals are to develop better methods to + prevent cancer and improve cancer management. +

    diff --git a/pages/static/hta7.html b/pages/static/hta7.html new file mode 100644 index 00000000..d6aeb126 --- /dev/null +++ b/pages/static/hta7.html @@ -0,0 +1,100 @@ +--- +page: hta7 +navText: PRE-CANCER ATLASES OF CUTANEOUS & HEMATOLOGIC ORIGIN (PATCH) CENTER +--- + +

    PRE-CANCER ATLASES OF CUTANEOUS & HEMATOLOGIC ORIGIN (PATCH) CENTER

    +
    + + +
    +

    Overview

    + +

    + An Atlas for Melanoma Precursors and a Pilot Atlas for Clonal Hematopoiesis +

    +

    + We are constructing a multi-dimensional atlas of pre-melanoma focused on + understanding genetic and epigenetic events that transform melanocytes into + invasive tumors. Melanoma is a cancer of increasing prevalence that is + curable with minor surgery if detected early but life-threatening when it + metastasizes. Melanomas metastasize when still small, making early detection + essential but challenging. Our atlas will delineate the precise sequence of + events leading up to pre-melanoma through detailed spatial analysis of + cell-autonomous events such as oncogene mutation and non-autonomous events + such as escape from immune surveillance. The atlas is based on + highly-multiplexed tissue imaging and single cell sequencing and focused on + samples in which the full sequence of events from atypia to invasive + melanoma can be visualized in a single specimen. The atlas will serve as a + publicly accessible resource for research scientists, physicians, and + patients and improve our ability to (i) highlight lesions likely to progress + to cancer, (ii) identify high-risk patients to inform decisions on surgery, + (iii) identify low-risk patients to reduce unnecessary procedures, (iv) + design improved procedures for routine screening of all individuals, and (v) + inform treatment options when surgery is insufficient. Complementary studies + with similar goals (but not supported by HTAN) are studying later stage + melanomas. +

    + + + +

    Principal Investigators

    + +

    PETER SORGER, Ph.D.

    + +

    + + Dr. Peter Sorger, Otto Krayer Professor of Systems Pharmacology at Harvard + Medical School, leads the PATCH Center and is director of a shared research + laboratory (the Laboratory of Systems Pharmacology; LSP) in which much of + the PCA work is based. The LSP is a multi-investigator lab focused on + improving the fundamental science used to develop therapeutic drugs, + evaluate them in clinical trials, and identify patients most likely to + benefit. Research in the Sorger lab focuses on mammalian signal transduction + and oncogenesis in several types of cancer, including melanoma, using a mix + of experimental and computational approaches. The laboratory developed the + cyclic-immunofluorescence (CyCIF) method being used to construct the PCA + pre-melanoma atlas as well as the software tools being used to analyze the + resulting images, and the Open Microscopy Environment (OME) that is being + used to manage PCA image data also was started in the lab. This pre-melanoma + atlas is part of a larger digital histology effort, the Harvard Tissue + Atlas, also led by Dr. Sorger. +

    + +

    SANDRO SANTAGATA, M.D., Ph.D.

    + +

    + + Dr. Sandro Santagata is an Associate Professor in Pathology at Harvard + Medical School and a Neuropathologist in the Department of Pathology at + Brigham and Women’s Hospital and the Department of Oncologic Pathology at + Dana-Farber Cancer Institute. He is a member of the Ludwig Center at + Harvard, the Harvard Program in Therapeutic Science (HiTS), and the HMS + Laboratory of Systems Pharmacology (LSP). Dr. Santagata applies novel tissue + imaging methods, including tissue-based cyclic immunofluorescence (t-CyCIF), + to understand the spatial and molecular phenotypes of human cancer. Using + clinical trial specimens, he measures and models therapeutic responses in + tumors and their microenvironment. Drs. Santagata and Sorger co-direct all + aspects of the Harvard Tissue Atlas, of which the pre-melanoma atlas is an + essential component. +

    + +

    JOHN ASTER, M.D., Ph.D.

    + +

    + + Dr. Jon Aster is the Chief of Hematopathology at the Brigham and Women’s + Hospital and the Dana-Farber Cancer Institute and co-leads the Lymphoma and + Myeloma Program of the pan-Harvard Dana-Farber/Harvard Cancer Center + (DF/HCC). He also serves as Director of the DF/HCC Specialized + Histopathology Core Laboratory and is a member of the Harvard Ludwig Cancer + Center. His research is focused on Notch signaling in T-cell acute + lymphoblastic leukemia (T-ALL) and other cancers. Notch receptors + participate in a signaling pathway that controls differentiation and other + fundamental cellular processes in a context-specific fashion. The Aster lab + has demonstrated that Notch signals can induce T-cell development from bone + marrow progenitors and has shown that T-ALL cells depend on continued Notch + signaling for growth. Dr. Aster is leading a pilot project in the PATCH + Center to develop atlases of hematological pre-cancers and an effort to use + multiplex tissue imaging data to improve medical education in pathology. +

    diff --git a/pages/static/hta8.html b/pages/static/hta8.html new file mode 100644 index 00000000..244a31b7 --- /dev/null +++ b/pages/static/hta8.html @@ -0,0 +1,81 @@ +--- +page: hta8 +navText: TRANSITION TO METASTATIC STATE LUNG CANCER, PANCREATIC CANCER, AND BRAIN METASTASIS +--- + +

    + TRANSITION TO METASTATIC STATE: LUNG CANCER, PANCREATIC CANCER, AND BRAIN + METASTASIS +

    +
    + +
    + +

    Overview

    + +

    + Transition to Metastatic State: Lung Cancer, Pancreatic Cancer and Brain + Metastasis +

    +

    + Metastasis embodies the whole-organism pathophysiology of cancer. The spread + of cancer cells beyond the primary tumor site is responsible for the + majority of cancer deaths and is the most overt expression of cancer’s + complex evolutionary dynamics. Intimately related to the intricate processes + of development and immunity, the transition from locally invasive to + metastatic cancer also poses a major scientific hurdle. Recent technological + and computational advancements enable dynamic, multidimensional, multiplanar + analysis of multiple tissues. We are applying these advances to clinical + samples with the aim of generating a high-resolution spatiotemporal tissue + atlas of the most lethal cancers in the United States: lung cancer, + pancreatic cancer, and metastases of the central nervous system. Our + approach is to obtain high-quality human biospecimens from surgical + resections, biopsy, or autopsy of primary and disseminated tumors. Samples + are interrogated using single-cell and single-nucleus RNA sequencing as well + as spatially informative multiplexed molecular profiling using protein and + RNA in situ hybridization–based technologies. Integration, analysis, and + presentation of these datasets will be undertaken with the goal of + generating human tumor atlases of value to the entire cancer research + community. +

    + +

    Principal Investigators

    + +

    DANA PE’ER, Ph.D.

    + +

    + + Dr. Dana Pe’er is Chair of the Computational and Systems Biology Program and + Scientific Director of The Alan and Sandra Gerry Metastasis and Tumor + Ecosystems Center at Memorial Sloan Kettering Cancer Center. She develops + novel computational methods to characterize regulatory circuit dynamics at + the single-cell level in the context of complex tissues such as the tumor + microenvironment. She received a Ph.D. in Computer Science at The Hebrew + University of Jerusalem and trained as a postdoctoral fellow with Dr. George + Church at Harvard University. Dr. Pe’er has been recognized with a number of + honors including a Burroughs Wellcome Fund Career Award, an NIH Director’s + New Innovator Award, an NIH Director’s Pioneer Award, and the Packard + Fellowship in Science and Engineering. She currently serves on the editorial + board of the journal Cell and the organizing committee of the Human Cell + Atlas project, co-leading computational analysis for this project. +

    + +

    CHRISTINE IACOBUZIO-DONAHUE, M.D., Ph.D.

    + +

    + + Dr. Christine Iacobuzio-Donahue is a board-certified Anatomic Pathologist + with specialty training in gastrointestinal pathology and cancer genetics. + She currently serves as an Attending Physician in Pathology, Director of the + David M. Rubenstein Center for Pancreatic Cancer Research, and Director of + the Last Wish Program at Memorial Sloan Kettering Cancer Center. Her lab + employs a variety of models and methods, including a strong emphasis on + genomic and bioinformatics analyses of human primary and metastatic + pancreatic cancer tissues obtained from rapid autopsies, mouse models of + pancreatic cancer, and, most recently, the development of a long-term + evolutionary model system for functional analyses of clonal evolution and + adaptive mechanisms. Dr. Iacobuzio-Donahue is the recipient of an R35 + Outstanding Investigator Award and maintains a collaboration with the + American Museum of Natural History funded by the Kleberg Foundation to + develop novel tools and analytics for the evolutionary biology of cancer. +

    diff --git a/pages/static/hta9.html b/pages/static/hta9.html new file mode 100644 index 00000000..08fdb6a1 --- /dev/null +++ b/pages/static/hta9.html @@ -0,0 +1,108 @@ +--- +page: hta9 +navText: OMIC AND MULTIDIMENSIONAL SPATIAL ATLAS OF METASTATIC BREAST CANCERS +--- + +

    OMIC AND MULTIDIMENSIONAL SPATIAL ATLAS OF METASTATIC BREAST CANCERS

    +
    + +
    + +

    Overview

    + +

    Omic and Multidimensional Spatial (OMS) Atlas

    +

    + The overall goal of the HTAN OMS Atlas Center is to elucidate mechanisms by + which metastatic breast cancers become resistant to current generation + pathway- and immune checkpoint-targeted treatments. The OMS Atlas is + motivated by the appreciation that these treatments are often effective in + primary tumors but only transiently effective in the metastatic setting. + Possible resistance mechanisms include tumor-intrinsic genomic instability + and epigenomic plasticity, as well as events extrinsic to the cancer cells, + including chemical and mechanical signals from the microenvironments, + production of mechanical extracellular matrix barriers and/or changes in + vasculature that reduce drug and/or immune cell access, nanoscale cancer + cell-microenvironment interactions that reduce drug efficacy, and a plethora + of immune resistance mechanisms, such as loss of HLA expression and antigen + presentation, and immune exhaustion. These mechanisms likely vary between + patients and within individual patients and change with time as tumors + respond to therapeutic attack. The OMS Atlas will focus on elucidating + resistance mechanisms in two specific current generation clinical trial + scenarios: (a) hormone receptor-positive breast cancer (HRBC) undergoing + treatment with a CDK4/6 inhibitor in combination with endocrine therapy and + (b) triple negative breast cancer (TNBC) undergoing treatment with a PARP + inhibitor and an immunomodulatory agent. +

    + +

    Principal Investigators

    + +

    JOE GRAY, Ph.D.

    + +

    + Dr. Joe W. Gray, a physicist and engineer by training, is the Director of + the Center for Spatial Systems Biomedicine and Associate Director for + Biophysical Oncology at the Knight Cancer Institute, both at Oregon Health & + Science University. Dr. Gray’s research program uses integrated omic + analysis and multiscale imaging to identify drug resistance mechanisms that + are intrinsic to the tumor or that derive from tumor-microenvironment + interactions. He has contributed to development of a number of important + analytical tools including high-speed chromosome sorting, BrdUrd/DNA + analysis of cell proliferation, Fluorescence In Situ Hybridization, + Comparative Genomic Hybridization and End Sequence Profiling. Currently, he + is applying omic and multiscale image analysis approaches to identify + mechanisms of therapeutic resistance and vulnerability in metastatic breast + cancer. He is author of more 500 publications and holds 80 US patents. +

    + +

    GORDON B. MILLS, M.D., Ph.D.

    + +

    + Dr. Gordon B. Mills earned his M.D. and Ph.D. in biochemistry and completed + his training in Obstetrics and Gynecology at the University of Alberta. + Prior to moving to the Knight Cancer Institute, Dr. Mills was at the MD + Anderson Cancer Center, the number one ranked cancer center in the United + States. At the Knight Cancer Institute at the Oregon Health & Science + University, Dr. Mills is Director of Precision Oncology and SMMART trials. + He is responsible for the implementation of an integrated program of tumor + analysis, decision-making, and implementation of novel precision oncology + trials. Dr. Mills is recognized as one of the most highly quoted scientists + in the world with over 1,000 publications, and he also holds more than 20 + patents. +

    + +

    GEORGE V. THOMAS, M.D.

    + +

    + + Dr. George V. Thomas is Professor of Pathology at Oregon Health & Science + University. He is a member of the Knight Cancer Institute, Director of the + Histopathology Shared Resource, and Associate Medical Director of the Knight + Diagnostic Laboratories. Dr. Thomas completed his pathology residency and + fellowships in the Harvard and UCLA medical systems and was on the faculty + at UCLA prior to joining OHSU. As a specialist in genitourinary cancers, he + has served as an invited member of the Mouse Models of Human Cancer + Consortium, Stand Up 2 Cancer Prostate Dream Team, Mechanisms of Cancer + Therapeutics study section, TGCA Papillary Kidney Cancer Analysis Working + Group, and the TCGA Pan-Cancer Metabolism Working Group. Clinically, he has + leveraged his molecular insights to lead the molecular diagnostics efforts + that guide the precision oncology program at the Knight Cancer Institute. +

    + +

    JEREMY GOECKS, Ph.D.

    + +

    + + Dr. Jeremy Goecks is an Associate Professor of Biomedical Engineering and + Computational Biology at Oregon Health & Science University (OHSU). His + research program develops computational tools and infrastructure for + analysis of large biomedical datasets. At the OHSU Knight Cancer Institute, + Dr. Goecks leads the development of computational methods for precision + oncology that integrate clinical, imaging, and omics data to (1) predict + tumor response to targeted therapies and (2) identify tumor cellular + pathways associated with adaptation and susceptibility to therapies. He has + leadership positions in several national and international computational + biomedical infrastructure projects funded by NCI, NHGRI, and NSF. He also is + a lead investigator for the Galaxy Project (http://galaxyproject.org), a + web-based computational workbench used by thousands of scientists throughout + the world. +

    diff --git a/pages/static/htan-dcc.html b/pages/static/htan-dcc.html new file mode 100644 index 00000000..9b05c46a --- /dev/null +++ b/pages/static/htan-dcc.html @@ -0,0 +1,121 @@ +--- +page: htan-dcc +navText: HTAN Data Coordinating Center (DCC) +--- + +

    HTAN Data Coordinating Center

    +
    + + + + +
    +

    Overview

    + +

    Data Coordinating Center

    + +

    + The HTAN Data Coordinating Center (DCC) proudly supports each of the HTAN + atlas teams and pilot programs as well as the broader Network by + coordinating Network activities; providing centralized resources for data + and resource storage and access within HTAN as well as dissemination to the + wider scientific community; developing powerful data analysis and + visualization tools to enable researchers to make novel discoveries using + HTAN data; and conducting outreach to the community. Within HTAN, the DCC + also leads various efforts to develop an extensible data model as well as + clinical data and metadata standards that will ensure the accessibility and + interoperability of HTAN data with the wider cancer research data ecosystem. +

    + +

    + Comprised of individuals from four institutions, the DCC team brings to HTAN + extensive experience participating in major research consortia, including + TCGA, AACR Project GENIE, and the Cancer Systems Biology Consortium, as well + as leading open-source software development projects such as cBioPortal for + Cancer Genomics, Synapse, and the ISB Cancer Genomics Cloud (ISB-CGC) and + community engagement projects such as DREAM Challenges. The DCC team draws + upon this collective experience and deep technical expertise in biomedical + science and data science to support the goals of HTAN and to promote a + collaborative research and discovery environment within HTAN and the wider + cancer research community. +

    +

    Principal Investigators

    + +

    ETHAN CERAMI, Ph.D.

    + +

    + + Dr. Ethan Cerami is Director of the Knowledge Systems Group and Principal + Scientist in the Department of Data Sciences at Dana-Farber Cancer Institute + (DFCI). He has an M.S. in Computer Science from New York University and a + Ph.D. in Computational Biology from Cornell University. Prior to joining + Dana-Farber, Dr. Cerami was Director of Computational Biology at Blueprint + Medicines and Director of Cancer Informatics Development at Memorial Sloan + Kettering Cancer Center (MSKCC). While at MSKCC, Dr. Cerami co-founded the + cBioPortal for Cancer Genomics, and his current group at DFCI remains active + in its continued development while also being central contributors to other + major consortium efforts such as the NCI-funded Cancer Immunologic Data + Commons and AACR Project GENIE. +

    + +

    JUSTIN GUINNEY, Ph.D.

    + +

    + + Dr. Justin Guinney is Director of Computational Oncology at Sage + Bionetworks. As an expert in large-scale analysis of genomic data, he has + worked extensively with clinicians to link models to complex cancer + phenotypes. His research is focused on the development of integrative + modeling approaches in cancer using large-scale biomedical and + high-throughput genomic data, with an emphasis on translational cancer + research and the utilization of molecular data to optimize the prediction of + patient outcomes and response to treatment. Dr. Guinney has played key + leadership roles in several large cancer consortia, including the Colorectal + Cancer Molecular Subtyping Consortium, AACR Project GENIE, and the + Neo-epitope Prediction Consortium. He is currently also PI for the Data and + Resource Coordinating Center for the NCI Cancer Systems Biology Consortium + and co-PI on the CRI iAtlas project, a platform supporting immune oncology + research. In addition, Dr. Guinney is co-director of the DREAM Challenges + and has led several challenges related to cancer prognosis, combination + therapy, and image analysis of digital mammograms. +

    + +

    NIKOLAUS SCHULTZ, Ph.D.

    + +

    + + Dr. Niki Schultz is Associate Attending in the Computational Oncology + Service of the Department of Epidemiology and Biostatistics and Affiliate + Member of the Human Oncology and Pathogenesis Program at Memorial Sloan + Kettering Cancer Center (MSKCC). As head of the Knowledge Systems Group in + the Marie-Josée and Henry R. Kravis Center for Molecular Oncology, he leads + development of the cBioPortal for Cancer Genomics, a web-based resource for + analysis of complex cancer genomics data, and of OncoKB, a precision + oncology knowledge base. His research focuses on identifying the genomic + alterations that underlie cancer, their mechanisms of action, and novel + therapeutic approaches. Dr. Schultz has made significant contributions to + several projects of TCGA and AACR Project GENIE, and he is an investigator + in the Stand Up to Cancer Prostate Cancer Dream Team. He has a particular + interest in enabling discoveries by developing novel computational methods + and databases that help bridge the divide between computer scientists on one + side and clinicians and researchers on the other. +

    + +

    VÉSTEINN THORSSON, Ph.D.

    + +

    + + Dr. Vésteinn Thorsson is a Senior Research Scientist at the Institute for + Systems Biology. His research encompasses cancer genomics and + immuno-oncology, and he has extensive experience working with data analysis + and data coordination in collaborative cancer genomics projects. As part of + The Cancer Genome Atlas (TCGA) Research Network, Dr. Thorsson contributed + substantially to published studies on gastrointestinal tumors, including + serving as both Data and Analysis Coordinator and playing a key role in + determining gastric molecular subtypes. Dr. Thorsson also served as Co-Chair + of a working group that recently completed a comprehensive analysis of all + TCGA gastrointestinal tumor samples and of a working group dedicated to + characterizing immune response in the more than 10,000 TCGA tumor samples. + In addition, he serves as a project lead for the CRI iAtlas project + (cri-iatlas.org), an interactive web resource for immuno-oncology research. +

    diff --git a/pages/static/htapp-webinar-series.html b/pages/static/htapp-webinar-series.html new file mode 100644 index 00000000..bd8261cd --- /dev/null +++ b/pages/static/htapp-webinar-series.html @@ -0,0 +1,171 @@ +--- +page: htapp-webinar-series +navText: HTAPP Webinar Series +--- + +

    Human Tumor Atlas Pilot Project (HTAPP) Webinar Series

    + + + +

    LINKS

    + +

    Toolbox paper

    +

    + Slyper et al + Nature Medicine volume 26, pp 792–802(2020): + A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen + human tumors. +

    +

    Abstract

    +

    + Single-cell genomics is essential to chart tumor ecosystems. Although + single-cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from + fresh tumors, single-nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen + or hard-to-dissociate tumors. Each requires customization to different + tissue and tumor types, posing a barrier to adoption. Here, we have + developed a systematic toolbox for profiling fresh and frozen clinical tumor + samples using scRNA-Seq and snRNA-Seq, respectively. We analyzed 216,490 + cells and nuclei from 40 samples across 23 specimens spanning eight tumor + types of varying tissue and sample characteristics. We evaluated protocols + by cell and nucleus quality, recovery rate and cellular composition. + scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types, + but at different proportions. Our work provides guidance for studies in a + broad range of tumors, including criteria for testing and selecting methods + from the toolbox for other tumors, thus paving the way for charting tumor + atlases. +

    + +

    Protocols

    + +

    + Protocols.io links to protocols discussed in the webinar and other protocols + included in + + Slyper et al + are provided below. +

    + +

    Nuclei isolation from frozen tissue for single-nucleus RNA-Seq

    + +
      +
    1. + HTAPP_NST- Nuclei isolation from frozen tissue + V.2 +
      2. +
    2. + HTAPP_TST- Nuclei isolation from frozen tissue + V.2 +
      3. +
    3. + HTAPP_CST- Nuclei isolation from frozen tissue + V.2 +
      4. +
    + +

    Fresh tissue processing protocols for single-cell RNA-Seq

    + +
      +
    1. + HTAPP_Dissociation of human metastatic breast cancer core needle + biopsy to a single-cell suspension for single-cell RNA-seq + V.2 +
      2. +
    2. + HTAPP_Dissociation of human primary lung cancer resection to a + single-cell suspension for single-cell RNA-seq + V.2 +
      3. +
    3. + HTAPP_Depletion of CD45+ cells from single-cell suspension for + single-cell RNA-seq +
      4. +
    4. + HTAPP_Dissociation of human ovarian cancer resection to a + single-cell suspension for single-cell RNA-seq + V.2 +
      5. +
    5. + HTAPP_Processing human ovarian cancer ascites to a single-cell + suspension for single-cell RNA-seq + V.2 +
      6. +
    6. + HTAPP_Depletion of CD45+ cells from ovarian cancer ascites single + cell suspensions for single-cell RNA-Seq + V.2 +
      7. +
    7. + HTAPP_Dissociation of human neuroblastoma tumors to a single-cell + suspension for single-cell RNA-seq using Papain +
      8. +
    + +

    + WEBINAR 1: Tissue acquisition workflows and sc/snRNA-Seq processing + protocols +

    +

    December 3rd, 2020

    + +

    AGENDA

    +
      +
    1. HTAPP overview
      2. +
    2. Fresh tissue acquisition workflow
      3. +
    3. Key components of a tissue acquisition and allocation workflow
      4. +
    4. Key aspects to keep in mind for each component
      5. +
    5. Fresh tissue processing for sc-RNAseq
      6. +
    6. Description of the processing workflow
      7. +
    7. Guidelines for testing and selecting protocols
      8. +
    8. Frozen tissue processing for snRNA-Seq
      9. +
    9. Description of the processing workflow
      10. +
    10. Guidelines for testing and selecting protocols
      11. +
    11. Single-cell vs single nucleus RNA-Seq processing
      12. +
    12. Lessons learned & challenges
      13. +
    13. Q&A
      14. +
    + +

    PRESENTERS

    +

    + Judit Jané-Valbuena, Ph.D, Scientific Manager, Klarman Cell + Observatory, Broad Institute +

    +

    + Sébastien Vigneau, Ph.D. Head of the Innovation Lab, Center + for Cancer Genomics, Dana-Farber Cancer Institute +

    +

    + Michal Slyper, Ph.D. Research Scientist, Klarman Cell + Observatory, Broad Institute +

    diff --git a/pages/static/overview.html b/pages/static/overview.html new file mode 100644 index 00000000..4d820fa8 --- /dev/null +++ b/pages/static/overview.html @@ -0,0 +1,37 @@ +--- +page: overview +--- + +

    Overview

    + +
    +

    + Launched in September of 2018, the Human Tumor Atlas Network (HTAN) is a + National Cancer Institute (NCI)-funded Cancer MoonshotSM + initiative through which a collaborative network of Research Centers and a + central Data Coordinating Center are constructing 3-dimensional atlases of + the cellular, morphological, and molecular features of human cancers as they + evolve from precancerous lesions to advanced disease. Across a diverse set + of cancer types, these atlases aim to define critical processes and events + throughout the life cycle of human cancers, such as the transition of + pre-malignant lesions to malignant tumors, the progression of malignant + tumors to metastatic cancer, tumor response to therapeutics, and the + development of therapeutic resistance. The diverse set of cancer types under + investigation include tumors that affect minority and underserved + populations, tumors with a hereditary component, and highly aggressive + pediatric cancers. +

    + +

    + Within HTAN, ten Research Centers are working to identify the molecular and + cellular conditions that cause healthy cells to become cancerous and that + driver critical transitions in advanced cancers. Two atlas pilot projects – + one Pre-Cancer Atlas Pilot Project (PCAPP) and one Human Tumor Atlas Pilot + Project (HTAPP) – were funded previously by NCI and also are contributing + data and resources to the Network. A single HTAN Data Coordinating Center + (DCC) supports each of the atlas teams and the broader Network by + coordinating Network activities, providing centralized resources for data + and resource storage, access, and sharing, and conducting outreach to the + community. +

    +
    \ No newline at end of file diff --git a/pages/static/publications.html b/pages/static/publications.html new file mode 100644 index 00000000..2dc76f11 --- /dev/null +++ b/pages/static/publications.html @@ -0,0 +1,1881 @@ +--- +page: publications +navText: Publications +--- + +

    Publications

    + +

    + 2021 | + Single-cell multiomics reveals increased plasticity, resistant + populations and stem-cell-like blasts in KMT2A-rearranged + leukemia. + Chen C, Yu W, Alikarami F, Qiu Q, Chen CH, Flournoy J, Gao P, Uzun Y, + Fang L, Davenport JW, Hu Y, Zhu Q, Wang K, Libbrecht C, Felmeister A, + Rozich I, Ding YY, Hunger SP, Felix CA, Wu H, Brown PA, Guest EM, + Barrett DM, Bernt KM, Tan K. Blood. 2021 Dec 5:blood.2021013442. doi: 10.1182/blood.20210134420.PMID: 34864916. +

    +

    + 2021 | Signatures of plasticity, metastasis, and immunosuppression in an atlas + of human small cell lung cancer. Chan, JM, Quintanal-Villalonga, Á, Gao, VR, Xie, Y, + Allaj, V, Chaudhary, O, Masilionis, I, Egger, J, Chow, A, Walle, T, Mattar, + M, Yarlagadda, DVK, Wang, JL, Uddin, F, Offin, M, Ciampricotti, M, Qeriqi, + B, Bahr, A, de Stanchina, E, Bhanot, UK, Lai, WV, Bott, MJ, Jones, DR, Ruiz, + A, Baine, MK, Li, Y, Rekhtman, N, Poirier, JT, Nawy, T, Sen, T, Mazutis, L, + Hollmann, TJ, Pe’er, D, Rudin, CM. Cancer Cell. 39 + (11): 1479-1496.e18. doi: 10.1016/j.ccell.2021.09.008. PubMed PMID:34653364 PubMed Central PMC8628860 +

    +

    + 2021 | Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment + in Multivariate Image Data. Jessup, J, Krueger, R, Warchol, S, Hoffer, J, Muhlich, + J, Ritch, CC, Gaglia, G, Coy, S, Chen, YA, Lin, JR, Santagata, S, Sorger, + PK, Pfister, H. IEEE Trans Vis Comput Graph. PP : . + doi: 10.1109/TVCG.2021.3114786. PubMed PMID:34606456 +

    +

    + 2021 | Narrative online guides for the interpretation of digital-pathology + images and tissue-atlas data. Rashid, R, Chen, YA, Hoffer, J, Muhlich, JL, Lin, JR, + Krueger, R, Pfister, H, Mitchell, R, Santagata, S, Sorger, PK. Nat Biomed Eng. : . doi: 10.1038/s41551-021-00789-8. PubMed PMID:34750536 +

    +

    + 2021 | MCMICRO: a scalable, modular image-processing pipeline for multiplexed + tissue imaging. Schapiro, D, Sokolov, A, Yapp, C, Chen, YA, Muhlich, JL, + Hess, J, Creason, AL, Nirmal, AJ, Baker, GJ, Nariya, MK, Lin, JR, Maliga, Z, + Jacobson, CA, Hodgman, MW, Ruokonen, J, Farhi, SL, Abbondanza, D, McKinley, + ET, Persson, D, Betts, C, Sivagnanam, S, Regev, A, Goecks, J, Coffey, RJ, + Coussens, LM, Santagata, S, Sorger, PK. Nat Methods. : . doi: 10.1038/s41592-021-01308-y. PubMed PMID:34824477 +

    +

    + 2021 | Highly Multiplexed Phenotyping of Immunoregulatory Proteins in the + Tumor Microenvironment by CODEX Tissue Imaging. Phillips, D, Schürch, CM, Khodadoust, MS, Kim, YH, + Nolan, GP, Jiang, S. Front Immunol. 12 : 687673. + doi: 10.3389/fimmu.2021.687673. PubMed PMID:34093591 PubMed Central PMC8170307 +

    +

    + 2021 | Strategies for Accurate Cell Type Identification in CODEX Multiplexed + Imaging Data. Hickey, JW, Tan, Y, Nolan, GP, Goltsev, Y. Front Immunol. 12 : 727626. doi: 10.3389/fimmu.2021.727626. PubMed PMID:34484237 PubMed Central PMC8415085 +

    +

    + 2021 | Smoking Modulates Different Secretory Subpopulations Expressing + SARS-CoV-2 Entry Genes in the Nasal and Bronchial Airways. Xu, K, Shi, X, Husted, C, Hong, R, Wang, Y, Ning, B, + Sullivan, T, Rieger-Christ, K, Duan, F, Marques, H, Gower, A, Xiao, X, Liu, + H, Liu, G, Duclos, G, Platt, M, Spira, A, Mazzilli, S, Billatos, E, Lenburg, + M, Campbell, J, Beane, J. Res Sq. : . doi: 10.21203/rs.3.rs-887718/v1. PubMed PMID:34729557 PubMed Central PMC8562543 +

    +

    + 2021 | scMRMA: single cell multiresolution marker-based annotation. Li, J, Sheng, Q, Shyr, Y, Liu, Q. Nucleic Acids Res. : . doi: 10.1093/nar/gkab931. + PubMed PMID:34648021 +

    +

    + 2021 | Chromatin accessibility associates with protein-RNA correlation in + human cancer. Sanghi, A, Gruber, JJ, Metwally, A, Jiang, L, Reynolds, + W, Sunwoo, J, Orloff, L, Chang, HY, Kasowski, M, Snyder, MP. Nat Commun. 12 (1): 5732. doi: 10.1038/s41467-021-25872-1. PubMed PMID:34593797 PubMed Central PMC8484618 +

    +

    + 2021 | In-depth triacylglycerol profiling using MS3 Q-Trap mass spectrometry. Cabruja, M, Priotti, J, Domizi, P, Papsdorf, K, Kroetz, + DL, Brunet, A, Contrepois, K, Snyder, MP. Anal Chim Acta. 1184 : 339023. doi: 10.1016/j.aca.2021.339023. PubMed PMID:34625255 PubMed Central PMC8502232 +

    +

    + 2021 | Multiomics analysis of serial PARP inhibitor treated metastatic TNBC + inform on rational combination therapies. Labrie, M, Li, A, Creason, A, Betts, C, Keck, J, + Johnson, B, Sivagnanam, S, Boniface, C, Ma, H, Blucher, A, Chang, YH, Chin, + K, Vuky, J, Guimaraes, AR, Downey, M, Lim, JY, Gao, L, Siex, K, Parmar, S, + Kolodzie, A, Spellman, PT, Goecks, J, Coussens, LM, Corless, CL, Bergan, R, + Gray, JW, Mills, GB, Mitri, ZI. NPJ Precis Oncol. 5 + (1): 92. doi: 10.1038/s41698-021-00232-w. PubMed PMID:34667258 PubMed Central PMC8526613 +

    +

    + 2021 | Mass spectrometry-based metabolomics: a guide for annotation, + quantification and best reporting practices. Alseekh, S, Aharoni, A, Brotman, Y, Contrepois, K, + D’Auria, J, Ewald, J, C Ewald, J, Fraser, PD, Giavalisco, P, Hall, RD, + Heinemann, M, Link, H, Luo, J, Neumann, S, Nielsen, J, Perez de Souza, L, + Saito, K, Sauer, U, Schroeder, FC, Schuster, S, Siuzdak, G, Skirycz, A, + Sumner, LW, Snyder, MP, Tang, H, Tohge, T, Wang, Y, Wen, W, Wu, S, Xu, G, + Zamboni, N, Fernie, AR. Nat Methods. 18 (7): + 747-756. doi: 10.1038/s41592-021-01197-1. PubMed PMID:34239102 +

    +

    + 2021 | Characterization of the tumor immune microenvironment of sinonasal + squamous-cell carcinoma. Gu, JT, Claudio, N, Betts, C, Sivagnanam, S, Geltzeiler, + M, Pucci, F. Int Forum Allergy Rhinol. : . + doi: 10.1002/alr.22867. + PubMed PMID:34510766 +

    +

    + 2021 | Adjacent Cell Marker Lateral Spillover Compensation and Reinforcement + for Multiplexed Images. Bai, Y, Zhu, B, Rovira-Clave, X, Chen, H, Markovic, M, + Chan, CN, Su, TH, McIlwain, DR, Estes, JD, Keren, L, Nolan, GP, Jiang, + S. Front Immunol. 12 : 652631. doi: 10.3389/fimmu.2021.652631. PubMed PMID:34295327 PubMed Central PMC8289709 +

    +

    + 2021 | + MITI Minimum Information guidelines for highly multiplexed tissue + images. Denis Schapiro, Clarence Yapp, Artem Sokolov, Sheila M. Reynolds, Yu-An + Chen, Damir Sudar, Yubin Xie, Jeremy Muhlich, Raquel Arias-Camison, Milen + Nikolov, Madison Tyler, Jia-Ren Lin, Erik A. Burlingame, Sarah Arena, Human + Tumor Atlas Network, Young H. Chang, Samouil L Farhi, Vésteinn Thorsson, + Nithya Venkatamohan, Julia L. Drewes, Dana Pe’er, David A. Gutman, Markus D. + Herrmann, Nils Gehlenborg, Peter Bankhead, Joseph T. Roland, John M. + Herndon, Michael P. Snyder, Michael Angelo, Garry Nolan, Jason Swedlow, + Nikolaus Schultz, Daniel T. Merrick, Sarah A. Mazzilli, Ethan Cerami, Scott + J. Rodig, Sandro Santagata, Peter K. Sorger. + arXiv:2108.09499 +

    +

    + 2021 | Co-clinical FDG-PET radiomic signature in predicting response to + neoadjuvant chemotherapy in triple-negative breast cancer. Roy, S, Whitehead, TD, Li, S, Ademuyiwa, FO, Wahl, RL, + Dehdashti, F, Shoghi, KI. Eur J Nucl Med Mol Imaging. : . doi: 10.1007/s00259-021-05489-8. PubMed PMID:34328530 +

    +

    + 2021 | Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) + Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic + Pipeline to Tumor Probability Boundary. Dutta, K, Roy, S, Whitehead, TD, Luo, J, Jha, AK, Li, S, + Quirk, JD, Shoghi, KI. Cancers (Basel). 13 (15): . + doi: 10.3390/cancers13153795. PubMed PMID:34359696 PubMed Central PMC8345151 +

    +

    + 2021 | A domain damage index to prioritizing the pathogenicity of missense + variants. Chen, HC, Wang, J, Liu, Q, Shyr, Y. Hum Mutat. : . doi: 10.1002/humu.24269. + PubMed PMID:34350656 +

    + +

    + 2021 | + DCIS genomic signatures define biology and correlate with clinical + outcome: a Human Tumor Atlas Network (HTAN) analysis of TBCRC 038 and + RAHBT cohorts.  Siri H Strand, Belén Rivero-Gutiérrez, Kathleen E Houlahan, Jose A Seoane, + Lorraine King, Tyler Risom, Lunden A Simpson, Sujay Vennam, Aziz Khan, Luis + Cisneros, Timothy Hardman, Bryan Harmon, Fergus Couch, Kristalyn Gallagher, + Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla F McAuliffe, + Julie Nangia, Joanna Lee, Jennifer Tseng, Anna Maria Storniolo, Alastair + Thompson, Gaorav Gupta, Robyn Burns, Deborah J Veis, Katherine DeSchryver, + Chunfang Zhu, Magdalena Matusiak, Jason Wang, Shirley X Zhu, Jen Tappenden, + Daisy Yi Ding, Dadong Zhang, Jingqin Luo, Shu Jiang, Sushama Varma, Lauren + Anderson, Cody Straub, Sucheta Srivastava, Christina Curtis, Rob Tibshirani, + Robert Michael Angelo, Allison Hall, Kouros Owzar, Kornelia Polyak, Carlo + Maley, Jeffrey R Marks, Graham A Colditz, E Shelley Hwang, Robert B West. + bioRxiv 2021.06.16.448585; doi: + https://doi.org/10.1101/2021.06.16.448585 +

    +

    + 2021 | Immu-Mela: An open resource for exploring immunotherapy-related + multidimensional genomic profiles in melanoma. Yang, J, Zhao, S, Wang, J, Sheng, Q, Liu, Q, Shyr, + Y. J Genet Genomics. : . doi: 10.1016/j.jgg.2021.03.016. PubMed PMID:34127402 +

    +

    + 2021 | Ex Vivo Analysis of Primary Tumor Specimens + for Evaluation of Cancer Therapeutics. Tognon, CE, Sears, RC, Mills, GB, Gray, JW, Tyner, + JW. Annu Rev Cancer Biol. 5 : 39-57. doi: 10.1146/annurev-cancerbio-043020-125955. PubMed PMID:34222745 PubMed Central PMC8248658 +

    +

    + 2021 | A new method to accurately identify single nucleotide variants using + small FFPE breast samples. Fortunato, A, Mallo, D, Rupp, SM, King, LM, Hardman, T, + Lo, JY, Hall, A, Marks, JR, Hwang, ES, Maley, CC. Brief Bioinform. : . doi: 10.1093/bib/bbab221. + PubMed PMID:34117742 +

    +

    + 2021 | Resolving the spatial and cellular architecture of lung adenocarcinoma + by multiregion single-cell sequencing. Sinjab, A, Han, G, Treekitkarnmongkol, W, Hara, K, + Brennan, PM, Dang, M, Hao, D, Wang, R, Dai, E, Dejima, H, Zhang, J, + Bogatenkova, E, Sanchez-Espiridion, B, Chang, K, Little, DR, Bazzi, S, Tran, + LM, Krysan, K, Behrens, C, Duose, DY, Parra, ER, Raso, MG, Solis, LM, + Fukuoka, J, Zhang, J, Sepesi, B, Cascone, T, Byers, LA, Gibbons, DL, Chen, + J, Moghaddam, SJ, Ostrin, EJ, Rosen, D, Heymach, JV, Scheet, P, Dubinett, + SM, Fujimoto, J, Wistuba, II, Stevenson, CS, Spira, A, Wang, L, Kadara, + H. Cancer Discov. : . doi: 10.1158/2159-8290.CD-20-1285. PubMed PMID:33972311 +

    +

    + 2021 | CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Black, S, Phillips, D, Hickey, JW, Kennedy-Darling, J, + Venkataraaman, VG, Samusik, N, Goltsev, Y, Schürch, CM, Nolan, GP. Nat Protoc. : . doi: 10.1038/s41596-021-00556-8. PubMed PMID:34215862 +

    +

    + 2021 | + The Human Tumor Atlas Network (HTAN) Breast PreCancer Atlas: A + multi-omic integrative analysis of ductal carcinoma in situ with + clinical outcomes. Siri H Strand, Belen Rivero-Gutierrez, Kathleen E Houlahan, Jose A Seoane, + Lorraine King, Tyler Risom, Lunden A Simpson, Sujay Vennam, Aziz Khan, Luis + Cisneros, Timothy Hardman, Bryan Harmon, Fergus Couch, Kristalyn Gallagher, + Mark Kilgore, Gabrielle B Rocque, Angela DeMichele, Tari King, Priscilla + McAuliffe, Julie Nangia, Joanna Lee, Jennifer Tseng, Ana Maria Storniolo, + Alastair Thompson, Gaorav Gupta, Robyn Burns, Deborah J Veis, Katherine + DeSchryver, Chunfang Zhu, Magdalena Matusiak, Jason Wang, Shirley X Zhu, Jen + Tappenden, Daisy Yi Ding, Dadong Zhang, Jingqin Luo, Shu Jiang, Sushama + Varma, Lauren Anderson, Cody Straub, Sucheta Srivastava, Christina Curtis, + Rob Tibshirani, Robert Michael Angelo, Allison Hall, Kouros Owzar, Kornelia + Polyak, Carlo Maley, Jeffrey R Marks, Graham A Colditz, E Shelley Hwang, + Robert B West
    + bioRxiv 2021.06.16.448585; doi: + https://doi.org/10.1101/2021.06.16.448585 +

    +

    + 2021 | Systematic interrogation of mutation groupings reveals divergent + downstream expression programs within key cancer genes. Grzadkowski, MR, Holly, HD, Somers, J, Demir, E. BMC Bioinformatics. 22 (1): 233. doi: 10.1186/s12859-021-04147-y. PubMed PMID:33957863 PubMed Central PMC8101181 +

    +

    + 2021 | Galaxy-ML: An accessible, reproducible, and scalable machine learning + toolkit for biomedicine. Gu, Q, Kumar, A, Bray, S, Creason, A, Khanteymoori, A, + Jalili, V, Grüning, B, Goecks, J. PLoS Comput Biol. + 17 (6): e1009014. doi: 10.1371/journal.pcbi.1009014. PubMed PMID:34061826 PubMed Central PMC8213174 +

    +

    + 2021 | Neoadjuvant selicrelumab, an agonist CD40 antibody, induces changes in + the tumor microenvironment in patients with resectable pancreatic + cancer. Byrne, KT, Betts, CB, Mick, R, Sivagnanam, S, Bajor, DL, + Laheru, DA, Chiorean, EG, O’Hara, MH, Liudahl, SM, Newcomb, CW, Alanio, C, + Ferreira, AP, Park, BS, Ohtani, T, Huffman, AP, Väyrynen, SA, Dias Costa, A, + Kaiser, JC, Lacroix, AM, Redlinger, C, Stern, M, Nowak, JA, Wherry, EJ, + Cheever, MA, Wolpin, BM, Furth, EE, Jaffee, EM, Coussens, LM, Vonderheide, + RH. Clin Cancer Res. : . doi: 10.1158/1078-0432.CCR-21-1047. PubMed PMID:34112709 +

    +

    + 2021 | + The breast pre-cancer atlas illustrates the molecular and + micro-environmental diversity of ductal carcinoma in situ. + Daniela Nachmanson, Adam Officer, Hidetoshi Mori, Jonathan Gordon, Mark F. + Evans, Joseph Steward, Huazhen Yao, Thomas O’Keefe, Farnaz Hasteh, Gary S. + Stein, Kristen Jepsen, Donald L. Weaver, Gillian L. Hirst, Brian L. Sprague, + Laura J. Esserman, Alexander D. Borowsky, Janet L. Stein, Olivier + Harismendy
    + bioRxiv 2021.05.11.443641; doi: + https://doi.org/10.1101/2021.05.11.443641 +

    +

    + 2021 | + The spatial landscape of progression and immunoediting in primary + melanoma at single cell resolution. + Ajit J. Nirmal, Zoltan Maliga, Tuulia Vallius, Brian Quattrochi, Alyce A. + Chen, Connor A. Jacobson, Roxanne J. Pelletier, Clarence Yapp, Raquel + Arias-Camison, Yu-An Chen, Christine G. Lian, George F. Murphy, Sandro + Santagata, Peter K. Sorger
    + bioRxiv 2021.05.23.445310; doi: + https://doi.org/10.1101/2021.05.23.445310 +

    +

    + 2021 | + Stitching and registering highly multiplexed whole slide images of + tissues and tumors using ASHLAR software. Jeremy Muhlich, Yu-An Chen, Douglas Russell, Peter K Sorger
    + bioRxiv 2021.04.20.440625; doi: + https://doi.org/10.1101/2021.04.20.440625 +

    +

    + 2021 | + UnMICST: Deep learning with real augmentation for robust segmentation + of highly multiplexed images of human tissues. Clarence Yapp, Edward Novikov, Won-Dong Jang, Yu-An Chen, Marcelo Cicconet, + Zoltan Maliga, Connor A. Jacobson, Donglai Wei, Sandro Santagata, Hanspeter + Pfister, Peter K. Sorger
    + bioRxiv 2021.04.02.438285; doi: + https://doi.org/10.1101/2021.04.02.438285 +

    +

    + 2021 | Co-evolution of tumor and immune cells during progression of multiple + myeloma. Liu, R, Gao, Q, Foltz, SM, Fowles, JS, Yao, L, Wang, JT, + Cao, S, Sun, H, Wendl, MC, Sethuraman, S, Weerasinghe, A, Rettig, MP, + Storrs, EP, Yoon, CJ, Wyczalkowski, MA, McMichael, JF, Kohnen, DR, King, J, + Goldsmith, SR, O’Neal, J, Fulton, RS, Fronick, CC, Ley, TJ, Jayasinghe, RG, + Fiala, MA, Oh, ST, DiPersio, JF, Vij, R, Ding, L. Nat Commun. 12 (1): 2559. doi: 10.1038/s41467-021-22804-x. PubMed PMID:33963182 PubMed Central PMC8105337 +

    +

    + 2021 | Targeting Treg cells with GITR activation alleviates resistance to + immunotherapy in murine glioblastomas. Amoozgar, Z, Kloepper, J, Ren, J, Tay, RE, Kazer, SW, + Kiner, E, Krishnan, S, Posada, JM, Ghosh, M, Mamessier, E, Wong, C, Ferraro, + GB, Batista, A, Wang, N, Badeaux, M, Roberge, S, Xu, L, Huang, P, Shalek, + AK, Fukumura, D, Kim, HJ, Jain, RK. Nat Commun. 12 + (1): 2582. doi: 10.1038/s41467-021-22885-8. PubMed PMID:33976133 PubMed Central PMC8113440 +

    +

    + 2021 | Tumor and immune reprogramming during immunotherapy in advanced renal + cell carcinoma. Bi, K, He, MX, Bakouny, Z, Kanodia, A, Napolitano, S, + Wu, J, Grimaldi, G, Braun, DA, Cuoco, MS, Mayorga, A, DelloStritto, L, + Bouchard, G, Steinharter, J, Tewari, AK, Vokes, NI, Shannon, E, Sun, M, + Park, J, Chang, SL, McGregor, BA, Haq, R, Denize, T, Signoretti, S, + Guerriero, JL, Vigneau, S, Rozenblatt-Rosen, O, Rotem, A, Regev, A, + Choueiri, TK, Van Allen, EM. Cancer Cell. 39 (5): + 649-661.e5. doi: 10.1016/j.ccell.2021.02.015. PubMed PMID:33711272 PubMed Central PMC8115394 +

    +

    + 2021 | Integrative single-cell analysis of allele-specific copy number + alterations and chromatin accessibility in cancer. Wu, CY, Lau, BT, Kim, HS, Sathe, A, Grimes, SM, Ji, HP, + Zhang, NR. Nat Biotechnol. : . doi: 10.1038/s41587-021-00911-w. PubMed PMID:34017141 +

    +

    + 2021 | + Multiplexed 3D atlas of state transitions and immune interactions in + colorectal cancer. Jia-Ren Lin, Shu Wang, Shannon Coy, Madison Tyler, Clarence Yapp, Yu-An + Chen, Cody N. Heiser, Ken S. Lau, Sandro Santagata, Peter K. Sorger.
    + bioRxiv 2021.03.31.437984; doi: + https://doi.org/10.1101/2021.03.31.437984 +

    +

    + 2021 | + Single-cell analyses reveal a continuum of cell state and composition + changes in the malignant transformation of polyps to colorectal cancer. Winston R. Becker, Stephanie A. Nevins, Derek C. Chen, Roxanne Chiu, Aaron + Horning, Rozelle Laquindanum, Meredith Mills, Hassan Chaib, Uri Ladabaum, + Teri Longacre, Jeanne Shen, Edward D. Esplin, Anshul Kundaje, James M. Ford, + Christina Curtis, Michael P. Snyder, William J. Greenleaf.
    + bioRxiv 2021.03.24.436532; doi: + https://doi.org/10.1101/2021.03.24.436532 +

    +

    + 2021 | Automated quality control and cell identification of droplet-based + single-cell data using dropkick. Heiser, CN, Wang, VM, Chen, B, Hughey, JJ, Lau, KS. Genome Res. : . doi: 10.1101/gr.271908.120. PubMed PMID:33837131 +

    +

    + 2021 | Beyond Programmed Death-Ligand 1: B7-H6 Emerges as a Potential + Immunotherapy Target in SCLC. Thomas, PL, Groves, SM, Zhang, YK, Li, J, + Gonzalez-Ericsson, P, Sivagnanam, S, Betts, CB, Chen, HC, Liu, Q, Lowe, C, + Chen, H, Boyd, KL, Kopparapu, PR, Yan, Y, Coussens, LM, Quaranta, V, Tyson, + DR, Iams, W, Lovly, CM. J Thorac Oncol. : . + doi: 10.1016/j.jtho.2021.03.011. PubMed PMID:33839362 +

    +

    + 2021 | + CytoTalk: De novo construction of signal transduction networks + using single-cell transcriptomic data. Hu Y, Peng T, Gao L, Tan K. Sci Adv. 2021 Apr + 14;7(16):eabf1356. doi: + 10.1126/sciadv.abf1356. PubMed + PMID: 33853780 +

    +

    + 2021 | Integrative bulk and single-cell profiling of pre-manufacture T-cell + populations reveals factors mediating long-term persistence of CAR + T-cell therapy. Chen, GM, Chen, C, Das, RK, Gao, P, Chen, CH, + Bandyopadhyay, S, Ding, YY, Uzun, Y, Yu, W, Zhu, Q, Myers, RM, Grupp, SA, + Barrett, DM, Tan, K. Cancer Discov. : . doi: 10.1158/2159-8290.CD-20-1677. PubMed PMID:33820778 +

    +

    + 2021 | ArchR is a scalable software package for integrative single-cell + chromatin accessibility analysis. Granja, JM, Corces, MR, Pierce, SE, Bagdatli, ST, + Choudhry, H, Chang, HY, Greenleaf, WJ. Nat Genet. + 53 (3): 403-411. doi: 10.1038/s41588-021-00790-6. PubMed PMID:33633365 +

    +

    + 2021 | Unmasking the immune microecology of ductal carcinoma in situ with deep + learning. Narayanan, PL, Raza, SEA, Hall, AH, Marks, JR, King, L, + West, RB, Hernandez, L, Guppy, N, Dowsett, M, Gusterson, B, Maley, C, Hwang, + ES, Yuan, Y. NPJ Breast Cancer. 7 (1): 19. + doi: 10.1038/s41523-020-00205-5. PubMed PMID:33649333 PubMed Central PMC7921670 +

    +

    + 2021 | Processing single-cell RNA-seq data for dimension reduction-based + analyses using open-source tools. Chen, B, Ramirez-Solano, MA, Heiser, CN, Liu, Q, Lau, + KS. STAR Protoc. 2 (2): 100450. doi: 10.1016/j.xpro.2021.100450. PubMed PMID:33982010 PubMed Central PMC8082116 +

    +

    + 2021 | Highly multiplexed tissue imaging using repeated oligonucleotide + exchange reaction. Kennedy-Darling, J, Bhate, SS, Hickey, JW, Black, S, + Barlow, GL, Vazquez, G, Venkataraaman, VG, Samusik, N, Goltsev, Y, Schürch, + CM, Nolan, GP. Eur J Immunol. : . doi: 10.1002/eji.202048891. PubMed PMID:33548142 +

    +

    + 2021 | + Comprehensive generation, visualization, and reporting of quality + control metrics for single-cell RNA sequencing data. Hong, R., Koga, Y., Bandyadka, S., Leshchyk, A., Wang, Z., Alabdullatif, + S., Wang, Y., Akavoor, V., Cao, X., Sarfraz, I., Jansen, F., Johnson, W. E., + Yajima, M., Campbell, J. D.bioRxiv 2020.11.16.385328; doi: + https://doi.org/10.1101/2020.11.16.385328 +

    +

    + 2021 | + Spatial drivers and pre-cancer populations collaborate with the + microenvironment in untreated and chemo-resistant pancreatic cancer. Daniel Cui Zhou, Reyka G. Jayasinghe, John M. Herndon, Erik Storrs, + Chia-Kuei Mo, Yige Wu, Robert S. Fulton, Matthew A. Wyczalkowski, Catrina C. + Fronick, Lucinda A. Fulton, Lisa Thammavong, Kazuhito Sato, Houxiang Zhu, + Hua Sun, Liang-Bo Wang, Yize Li, Chong Zuo, Joshua F. McMichael, Sherri R. + Davies, Elizabeth L. Appelbaum, Keenan J. Robbins, Sara E. Chasnoff, Xiaolu + Yang, Ruiyang Liu, Ashley N. Reeb, Michael C. Wendl, Clara Oh, Mamatha + Serasanambati, Preet Lal, Rajees Varghese, R. Jay Mashl, Jennifer Ponce, + Nadezhda V. Terekhanova, Nataly Naser Al Deen, Lijun Yao, Fang Wang, Lijun + Chen, Michael Schnaubelt, Sidharth V. Puram, Albert H. Kim, Sheng-Kwei Song, + Kooresh I. Shoghi, Tao Ju, William G. Hawkins, Ken Chen, Deyali Chatterjee, + Hui Zhang, Milan G. Chheda, Samuel Achilefu, David G. DeNardo, Stephen T. + Oh, Feng Chen, William E. Gillanders, Ryan C. Fields, Li Ding.bioRxiv + 2021.01.13.426413; doi: + https://doi.org/10.1101/2021.01.13.426413 +

    +

    + 2021 | + Transition to invasive breast cancer is associated with progressive + changes in the structure and composition of tumor stroma. Risom, T., Glass, D. R., Liu, C. C., Rivero-Gutierrez, B., Baranski, A., + McCaffrey, E. F., Greenwald, N. F., Kagel, A. C., Strand, S. H., Varma, S., + Kong, A., Keren, L., Srivastava, S., Zhu, C., Khair, Z., Veis, D. J., + Deschryver, K., Vennam, S., Maley, C., Hwang, E. S., Marks, J. R., Bendall, + S. C., Colditz, G. A., West, R. B., Angelo, M.bioRxiv 2021.01.05.425362; + doi: + https://doi.org/10.1101/2021.01.05.425362 +

    +

    + 2021 | + Multicellular immune hubs and their organization in MMRd and MMRp + colorectal cancer. Karin Pelka, Matan Hofree, Jonathan Chen, Siranush Sarkizova, Joshua D. + Pirl, Vjola Jorgji, Alborz Bejnood, Danielle Dionne, William H. Ge, + Katherine H. Xu, Sherry X. Chao, Daniel R. Zollinger, David J. Lieb, Jason + W. Reeves, Christopher A. Fuhrman, Margaret L. Hoang, Toni Delorey, Lan T. + Nguyen, Julia Waldman, Max Klapholz, Isaac Wakiro, Ofir Cohen, Christopher + S. Smillie, Michael S. Cuoco, Jingyi Wu, Mei-ju Su, Jason Yeung, Brinda + Vijaykumar, Angela M. Magnuson, Natasha Asinovski, Tabea Moll, Max N. + Goder-Reiser, Anise S. Applebaum, Lauren K. Brais, Laura K. DelloStritto, + Sarah L. Denning, Susannah T. Phillips, Emma K. Hill, Julia K. Meehan, + Dennie T. Frederick, Tatyana Sharova, Abhay Kanodia, Ellen Z. Todres, Judit + Jané-Valbuena, Moshe Biton, Benjamin Izar, Conner D. Lambden, Thomas E. + Clancy, Ronald Bleday, Nelya Melnitchouk, Jennifer Irani, Hiroko Kunitake, + David L. Berger, Amitabh Srivastava, Jason L. Hornick, Shuji Ogino, Asaf + Rotem, Sébastien Vigneau, Bruce E. Johnson, Ryan Corcoran, Arlene H. Sharpe, + Vijay K. Kuchroo, Kimmie Ng, Marios Giannakis, Linda T. Nieman, Genevieve M. + Boland, Andrew J. Aguirre, Ana C. Anderson, Orit Rozenblatt-Rosen, Aviv + Regev, Nir Hacohen. bioRxiv 2021.01.30.426796; doi: https://doi.org/10.1101/2021.01.30.426796 +

    +

    + 2021 | + Expansion Sequencing: Spatially Precise In Situ Transcriptomics in + Intact Biological Systems. Alon S, Goodwin DR, Sinha A, Wassie AT, Chen F, Daugharthy ER, Bando Y, + Kajita A, Xue AG, Marrett K, Prior R, Cui Y, Payne AC, Yao CC, Suk HJ, Wang + R, Yu CJ, Tillberg P, Reginato P, Pak N, Liu S, Punthambaker S, Iyer EPR, + Kohman RE, Miller JA, Lein ES, Lako A, Cullen N, Rodig S, Helvie K, + Abravanel DL, Wagle N, Johnson BE, Klughammer J, Slyper M, Waldman J, + Jané-Valbuena J, Rozenblatt-Rosen O, Regev A; IMAXT Consortium, Church GM, + Marblestone AH, Boyden ES. Expansion sequencing: Spatially precise in situ + transcriptomics in intact biological systems. Science. 2021 Jan + 29;371(6528):eaax2656. doi: + 10.1126/science.aax2656. PubMed + PMID: 33509999; + PubMed Central + PMC7900882 +

    +

    + 2021 | + GLUER: integrative analysis of single-cell omics and imaging data by + deep neural network. Peng, T, Chen, GM, Tan, K. bioRxiv 2021.01.25.427845; doi: + https://doi.org/10.1101/2021.01.25.427845 +

    +

    + 2021 | + Human colorectal pre-cancer atlas identifies distinct molecular + programs underlying two major subclasses of pre-malignant tumors. Chen, B, McKinley, ET, Simmons, AJ, Ramirez, MA, Zhu, X, Southard-Smith, + AN, Markham, NO, Sheng, Q, Drewes, J, Xu, Y, Heiser, CN, Zhou, Y, Revetta, + F, Berry, LD, Zheng, W, Washington, MK, Cai, Q, Sears, CL, Goldenring, JR, + Franklin, JL, Vandekar, S, Roland, JT, Su, T, Huh, WJ, Liu, Q, Coffey, RJ, + Shrubsole, M J, Lau, K. bioRxiv 2021.01.11.426044; doi: + https://doi.org/10.1101/2021.01.11.426044 +

    +

    + 2021 | Dynamic transcriptional and epigenetic changes drive cellular + plasticity in the liver. Merrell, AJ, Peng, T, Li, J, Sun, K, Li, B, Katsuda, T, + Grompe, M, Tan, K, Stanger, BZ. Hepatology. : . + doi: 10.1002/hep.31704. + PubMed PMID:33423324 +

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    + 2021 | + Predictive modeling of single-cell DNA methylome data enhances + integration with transcriptome data. + Uzun, Y, Wu, H, Tan, K. Genome Res. 2021 Jan; 31 (1): 101-109. doi: + 10.1101/gr.267047.120. + PubMed PMID: 33219054  +

    +

    + 2020 | + Single-cell characterization of subsolid and solid lesions in the lung + adenocarcinoma spectrum. Yanagawa, J., Tran, L. M., Fung, E., Wallace, W. D., Prosper, A. E., + Fishbein, G. A., Shea, C., Hong, R., Liu, B., Salehi-Rad, R., Deng, J. Z., + Gower, A., Campbell, J. D., Mazzilli, S. A., Beane, J., Kadara, H., Lenburg, + M. E., Spira, A., Aberle, D. R., Krysan, K., Dubinett, S. M. bioRxiv 2020.12.25.424416; doi: https://doi.org/10.1101/2020.12.25.424416 +

    +

    + 2020 | + An Integrated Clinical, Omic, and Image Atlas of an Evolving Metastatic + Breast Cancer. Johnson, B. E., Creason, A. L., Stommel, J. M., Keck, J., Parmar, S., + Betts, C. B., Blucher, A., Boniface, C., Bucher, E., Burlingame, E. A., + Chin, K., Eng, J., Feiler, H. S., Kolodzie, A., Kong, B., Labrie, M., + Leyshock, P., Mitri, S., Patterson, J., Riesterer, J. L., Sivagnanam, S., + Sudar, D., Thibault, G., Zheng, C., Nan, X., Heiser, L. M., Spellman, P. T., + Thomas, G. V., Demir, E., Chang, Y. H., Coussens, L. M., Guimaraes, A. R., + Corless, C., Goecks, J., Bergan, R., Mitri, Z., Mills, G. B., Gray, J. + W. bioRxiv 2020.12.03.408500; doi: https://doi.org/10.1101/2020.12.03.408500 +

    +

    + 2020 | + Single-cell multi-omics reveals elevated plasticity and stem-cell-like + blasts relevant to the poor prognosis of KMT2A-rearranged + leukemia. Chen, C., Yu, W., Alikarami, F., Qiu, Q., Chen, C.-h., Flournoy, J., Gao, + P., Uzun, Y., Fang, L., Yu, Y., Zhu, Q., Wang, K., Libbrecht, C., + Felmeister, A., Rozich, I., Ding, Y.-y., Hunger, S. P., Wu, H., Brown, P. + A., Guest, E. M., Barrett, D. M., Bernt, K. M., Tan, K. bioRxiv 2020.12.06.413930; doi: https://doi.org/10.1101/2020.12.06.413930 +

    +

    + 2020 | + Succinate Produced by Intestinal Microbes Promotes Specification of + Tuft Cells to Suppress Ileal Inflammation. Banerjee A, Herring CA, Chen B, Kim H, Simmons AJ, Southard-Smith AN, + Allaman MM, White JR, Macedonia MC, Mckinley ET, Ramirez-Solano MA, Scoville + EA, Liu Q, Wilson KT, Coffey RJ, Washington MK, Goettel JA, Lau KS. + Gastroenterology. 2020 Dec;159(6):2101-2115.e5. doi: + 10.1053/j.gastro.2020.08.029. PubMed + PMID: 32828819; + PubMed Central PMC7725941. +

    +

    + 2020 | + Diverse noncoding mutations contribute to deregulation of + cis-regulatory landscape in pediatric cancers. He, B, Gao, P, Ding, Y.-y, Chen, C.-h, Chen, G, Chen, C, Kim, H, Tasian, + SK, Hunger, SP, Tan, K. Sci Adv. 2020 Jul 24;6(30):eaba3064. doi: + 10.1126/sciadv.aba3064. + PubMed + PMID:32832663; + PubMed Central + PMC7439310. +

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    + 2020 | SHIFT: speedy histological-to-immunofluorescent translation of a tumor + signature enabled by deep learning. Burlingame, EA, McDonnell, M, Schau, GF, Thibault, G, + Lanciault, C, Morgan, T, Johnson, BE, Corless, C, Gray, JW, Chang, YH. Sci Rep. 10 (1): 17507. doi: 10.1038/s41598-020-74500-3. PubMed PMID:33060677 PubMed Central PMC7566625 +

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    + 2020 | VISTA: VIsual Semantic Tissue Analysis for pancreatic disease + quantification in murine cohorts. Ternes, L, Huang, G, Lanciault, C, Thibault, G, Riggers, + R, Gray, JW, Muschler, J, Chang, YH. Sci Rep. 10 + (1): 20904. doi: 10.1038/s41598-020-78061-3. PubMed PMID:33262400 PubMed Central PMC7708430 +

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    + 2020 | Lifetime cancer prevalence and life history traits in mammals. Boddy, AM, Abegglen, LM, Pessier, AP, Aktipis, A, + Schiffman, JD, Maley, CC, Witte, C. Evol Med Public Health. 2020 (1): 187-195. doi: 10.1093/emph/eoaa015. + PubMed PMID:33209304 PubMed Central PMC7652303 +

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    + 2020 | Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at + the Colorectal Cancer Invasive Front. Schürch, CM, Bhate, SS, Barlow, GL, Phillips, DJ, Noti, + L, Zlobec, I, Chu, P, Black, S, Demeter, J, McIlwain, DR, Kinoshita, S, + Samusik, N, Goltsev, Y, Nolan, GP. Cell. 182 (5): + 1341-1359.e19. doi: 10.1016/j.cell.2020.07.005. PubMed PMID:32763154 PubMed Central PMC7479520 +

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    + 2020 | Landscape of coordinated immune responses to H1N1 challenge in + humans. Rahil, Z, Leylek, R, Schürch, CM, Chen, H, + Bjornson-Hooper, Z, Christensen, SR, Gherardini, PF, Bhate, SS, Spitzer, MH, + Fragiadakis, GK, Mukherjee, N, Kim, N, Jiang, S, Yo, J, Gaudilliere, B, + Affrime, M, Bock, B, Hensley, SE, Idoyaga, J, Aghaeepour, N, Kim, K, Nolan, + GP, McIlwain, DR. J Clin Invest. 130 (11): + 5800-5816. doi: 10.1172/JCI137265. + PubMed PMID:33044226 PubMed Central PMC7598057 +

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    + 2020 | Biomarkers for Lung Cancer Screening and Detection. Ostrin, EJ, Sidransky, D, Spira, A, Hanash, SM. Cancer Epidemiol Biomarkers Prev. 29 (12): 2411-2415. doi: 10.1158/1055-9965.EPI-20-0865. PubMed PMID:33093160 PubMed Central PMC7710615 +

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    + 2020 | Massively parallel and time-resolved RNA sequencing in single cells + with scNT-seq. Qiu, Q, Hu, P, Qiu, X, Govek, KW, Cámara, PG, Wu, + H. Nat Methods. 17 (10): 991-1001. doi: 10.1038/s41592-020-0935-4. PubMed PMID:32868927 +

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    + 2020 | The Evolution of Human Cancer Gene Duplications across Mammals. Tollis, M, Schneider-Utaka, AK, Maley, CC. Mol Biol Evol. 37 (10): 2875-2886. doi: 10.1093/molbev/msaa125. PubMed PMID:32421773 PubMed Central PMC7530603 +

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    + 2020 | The Pathobiology of Skin Aging: New Insights into an Old Dilemma. Russell-Goldman, E, Murphy, GF. Am J Pathol. 190 (7): 1356-1369. doi: 10.1016/j.ajpath.2020.03.007. PubMed PMID:32246919 PubMed Central PMC7481755 +

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    + 2020 | Accelerated single cell seeding in relapsed multiple myeloma. Landau, HJ, Yellapantula, V, Diamond, BT, Rustad, EH, + Maclachlan, KH, Gundem, G, Medina-Martinez, J, Ossa, JA, Levine, MF, Zhou, + Y, Kappagantula, R, Baez, P, Attiye, M, Makohon-Moore, A, Zhang, L, Boyle, + EM, Ashby, C, Blaney, P, Patel, M, Zhang, Y, Dogan, A, Chung, DJ, Giralt, S, + Lahoud, OB, Peled, JU, Scordo, M, Shah, G, Hassoun, H, Korde, NS, Lesokhin, + AM, Lu, S, Mailankody, S, Shah, U, Smith, E, Hultcrantz, ML, Ulaner, GA, van + Rhee, F, Morgan, GJ, Landgren, O, Papaemmanuil, E, Iacobuzio-Donahue, C, + Maura, F. Nat Commun. 11 (1): 3617. doi: 10.1038/s41467-020-17459-z. PubMed PMID:32680998 PubMed Central PMC7368016 +

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    + 2020 | Age-dependent regulation of SARS-CoV-2 cell entry genes and cell death + programs correlates with COVID-19 disease severity. Inde, Z, Yapp, C, Joshi, GN, Spetz, J, Fraser, C, + Deskin, B, Ghelfi, E, Sodhi, C, Hackam, D, Kobzik, L, Croker, B, Brownfield, + D, Jia, H, Sarosiek, KA. bioRxiv. : . doi: 10.1101/2020.09.13.276923. PubMed PMID:32935109 PubMed Central PMC7491524 +

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    + 2020 | Clinically adaptable polymer enables simultaneous spatial analysis of + colonic tissues and biofilms. Macedonia, MC, Drewes, JL, Markham, NO, Simmons, AJ, + Roland, JT, Vega, PN, Scurrah, CR, Coffey, RJ, Shrubsole, MJ, Sears, CL, + Lau, KS. NPJ Biofilms Microbiomes. 6 (1): 33. + doi: 10.1038/s41522-020-00143-x. PubMed PMID:32973205 PubMed Central PMC7518420 +

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    + 2020 | Use of Single-Cell -Omic Technologies to Study the Gastrointestinal + Tract and Diseases, From Single Cell Identities to Patient Features. Islam, M, Chen, B, Spraggins, JM, Kelly, RT, Lau, + KS. Gastroenterology. 159 (2): 453-466.e1. + doi: 10.1053/j.gastro.2020.04.073. PubMed PMID:32417404 PubMed Central PMC7484006 +

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    + 2020 | A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen + human tumors. Slyper, M, Porter, CBM, Ashenberg, O, Waldman, J, Drokhlyansky, + E, Wakiro, I, Smillie, C, Smith-Rosario, G, Wu, J, Dionne, D, Vigneau, + S, Jané-Valbuena, J, Tickle, TL, Napolitano, S, Su, MJ, Patel, AG, + Karlstrom, A, Gritsch, S, Nomura, M, Waghray, A, Gohil, SH, Tsankov, AM, + Jerby-Arnon, L, Cohen, O, Klughammer, J, Rosen, Y, Gould, J, Nguyen, L, + Hofree, M, Tramontozzi, PJ, Li, B, Wu, CJ, Izar, B, Haq, R, Hodi, FS, + Yoon, CH, Hata, AN, Baker, SJ, Suvà, ML, Bueno, R, Stover, EH, Clay, MR, + Dyer, MA, Collins, NB, Matulonis, UA, Wagle, N, Johnson, BE, Rotem, A, + Rozenblatt-Rosen, O, Regev, A. Nat. Med.. 26 (5): 792-802. doi: 10.1038/s41591-020-0844-1. PubMed PMID:32405060 PubMed Central PMC7220853 +

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    + 2020 | Systematic comparison of single-cell and single-nucleus RNA-sequencing + methods. Ding, J, Adiconis, X, Simmons, SK, Kowalczyk, MS, Hession, CC, + Marjanovic, ND, Hughes, TK, Wadsworth, MH, Burks, T, Nguyen, LT, Kwon, + JYH, Barak, B, Ge, W, Kedaigle, AJ, Carroll, S, Li, S, Hacohen, N, + Rozenblatt-Rosen, O, Shalek, AK, Villani, AC, Regev, A, Levin, + JZ. Nat. Biotechnol.. 38 (6): 737-746. doi: 10.1038/s41587-020-0465-8. PubMed PMID:32341560 PubMed Central PMC7289686 +

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    + 2020 | Minimal barriers to invasion during human colorectal tumor growth. Ryser, MD, Mallo, D, Hall, A, Hardman, T, King, LM, Tatishchev, + S, Sorribes, IC, Maley, CC, Marks, JR, Hwang, ES, Shibata, + D. Nat Commun. 11 (1): 1280. doi: 10.1038/s41467-020-14908-7. PubMed PMID:32152322 PubMed Central PMC7062901 +

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    + 2020 | Cancer cells deploy lipocalin-2 to collect limiting iron in + leptomeningeal metastasis. Chi, Y, Remsik, J, Kiseliovas, V, Derderian, C, Sener, U, + Alghader, M, Saadeh, F, Nikishina, K, Bale, T, Iacobuzio-Donahue, C, + Thomas, T, Pe’er, D, Mazutis, L, Boire, A. Science. 369 (6501): 276-282. doi: 10.1126/science.aaz2193. PubMed PMID:32675368 +

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    + 2020 | A workflow for visualizing human cancer biopsies using large-format + electron microscopy. Riesterer, JL, López, CS, Stempinski, ES, Williams, M, Loftis, K, + Stoltz, K, Thibault, G, Lanicault, C, Williams, T, Gray, JW. Methods Cell Biol.. 158 : 163-181. doi: 10.1016/bs.mcb.2020.01.005. PubMed PMID:32423648 +

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    + 2020 | RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed + imaging. Chang, YH, Chin, K, Thibault, G, Eng, J, Burlingame, E, Gray, + JW. Commun Biol. 3 (1): 111. doi: 10.1038/s42003-020-0828-1. PubMed PMID:32152447 PubMed Central PMC7062831 +

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    + 2020 | Dual indexed library design enables compatibility of in-Drop + single-cell RNA-sequencing with exAMP chemistry sequencing platforms. Southard-Smith, AN, Simmons, AJ, Chen, B, Jones, AL, Ramirez + Solano, MA, Vega, PN, Scurrah, CR, Zhao, Y, Brenan, MJ, Xuan, J, + Shrubsole, MJ, Porter, EB, Chen, X, Brenan, CJH, Liu, Q, Quigley, LNM, + Lau, KS. BMC Genomics. 21 (1): 456. doi: 10.1186/s12864-020-06843-0. PubMed PMID:32616006 PubMed Central PMC7331155 +

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    + 2020 | A Quantitative Framework for Evaluating Single-Cell Data Structure + Preservation by Dimensionality Reduction Techniques. Heiser, CN, Lau, KS. Cell Rep. 31 (5): 107576. doi: 10.1016/j.celrep.2020.107576. PubMed PMID:32375029 PubMed Central PMC7305633 +

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    + 2020 | Host responses to mucosal biofilms in the lung and gut. Domingue, JC, Drewes, JL, Merlo, CA, Housseau, F, Sears, + CL. Mucosal Immunol. 13 (3): 413-422. doi: 10.1038/s41385-020-0270-1. PubMed PMID:32112046 +

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    + 2020 | Evolution and structure of clinically relevant gene fusions in multiple + myeloma. Foltz, SM, Gao, Q, Yoon, CJ, Sun, H, Yao, L, Li, Y, Jayasinghe, + RG, Cao, S, King, J, Kohnen, DR, Fiala, MA, Ding, L, Vij, R. Nat Commun. 11 (1): 2666. doi: 10.1038/s41467-020-16434-y. PubMed PMID:32471990 PubMed Central PMC7260243 +

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    + 2020 | scATAC-pro: a comprehensive workbench for single-cell chromatin + accessibility sequencing data. Yu, W, Uzun, Y, Zhu, Q, Chen, C, Tan, K. Genome Biol.. 21 + (1): 94. doi: 10.1186/s13059-020-02008-0. PubMed PMID:32312293 PubMed Central PMC7169039 +

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    + 2020 | Dissecting the tumor-immune landscape in chimeric antigen receptor + T-cell therapy: key challenges and opportunities for a systems + immunology approach. Chen, GM, Azzam, A, Ding, YY, Barrett, DM, Grupp, SA, Tan, K. Clin. Cancer Res.. : . doi: 10.1158/1078-0432.CCR-19-3888. PubMed PMID:32127393 +

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    + 2020 | Surface protein imputation from single cell transcriptomes by deep + neural networks. Zhou, Z, Ye, C, Wang, J, Zhang, NR. Nat Commun. 11 (1): + 651. doi: 10.1038/s41467-020-14391-0. PubMed PMID:32005835 PubMed Central PMC6994606 +

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    + 2020 | DENDRO: genetic heterogeneity profiling and subclone detection by + single-cell RNA sequencing. Zhou, Z, Xu, B, Minn, A, Zhang, NR. Genome Biol.. 21 (1): + 10. doi: 10.1186/s13059-019-1922-x. PubMed PMID:31937348 PubMed Central PMC6961311 +

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    + 2020 | Pediatric high-grade glioma resources from the Children’s Brain Tumor + Tissue Consortium. Ijaz, H, Koptyra, M, Gaonkar, KS, Rokita, JL, Baubet, VP, Tauhid, L, + Zhu, Y, Brown, M, Lopez, G, Zhang, B, Diskin, SJ, Vaksman, Z, Children’s + Brain Tumor Tissue Consortium, Mason, JL, Appert, E, Lilly, J, Lulla, R, De + Raedt, T, Heath, AP, Felmeister, A, Raman, P, Nazarian, J, Santi, MR, Storm, + PB, Resnick, A, Waanders, AJ, Cole, KA. Neuro-oncology. 22 (1): + 163-165. doi: 10.1093/neuonc/noz192. PubMed PMID:31613963 PubMed Central PMC6954395 +

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    + 2020 | An update on the CNS manifestations of neurofibromatosis type 2. Coy, S, Rashid, R, Stemmer-Rachamimov, A, Santagata, S. Acta Neuropathol.. 139 (4): 643-665. doi: 10.1007/s00401-019-02029-5. PubMed PMID:31161239 PubMed Central PMC7038792 +

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    + 2020 | Regenerative lineages and immune-mediated pruning in lung cancer + metastasis. Laughney, AM, Hu, J, Campbell, NR, Bakhoum, SF, Setty, M, Lavallée, + VP, Xie, Y, Masilionis, I, Carr, AJ, Kottapalli, S, Allaj, V, Mattar, M, + Rekhtman, N, Xavier, JB, Mazutis, L, Poirier, JT, Rudin, CM, Pe’er, D, + Massagué, J. Nat. Med.. 26 (2): 259-269. doi: 10.1038/s41591-019-0750-6. PubMed PMID:32042191 PubMed Central PMC7021003 +

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    + 2020 | The Human Tumor Atlas Network: Charting Tumor Transitions across Space + and Time at Single-Cell Resolution. Rozenblatt-Rosen, O, Regev, A, Oberdoerffer, P, Nawy, T, + Hupalowska, A, Rood, JE, Ashenberg, O, Cerami, E, Coffey, RJ, Demir, E, + Ding, L, Esplin, ED, Ford, JM, Goecks, J, Ghosh, S, Gray, JW, Guinney, + J, Hanlon, SE, Hughes, SK, Hwang, ES, Iacobuzio-Donahue, CA, + Jané-Valbuena, J, Johnson, BE, Lau, KS, Lively, T, Mazzilli, SA, Pe’er, + D, Santagata, S, Shalek, AK, Schapiro, D, Snyder, MP, Sorger, PK, Spira, + AE, Srivastava, S, Tan, K, West, RB, Williams, EH, Human Tumor Atlas + Network. Cell. 181 (2): 236-249. doi: 10.1016/j.cell.2020.03.053. PubMed PMID:32302568 PubMed Central PMC7376497 +

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    + 2020 | A harmonized meta-knowledgebase of clinical interpretations of somatic + genomic variants in cancer. Wagner, AH, Walsh, B, Mayfield, G, Tamborero, D, Sonkin, D, Krysiak, + K, Deu-Pons, J, Duren, RP, Gao, J, McMurry, J, Patterson, S, Del Vecchio + Fitz, C, Pitel, BA, Sezerman, OU, Ellrott, K, Warner, JL, Rieke, DT, + Aittokallio, T, Cerami, E, Ritter, DI, Schriml, LM, Freimuth, RR, Haendel, + M, Raca, G, Madhavan, S, Baudis, M, Beckmann, JS, Dienstmann, R, + Chakravarty, D, Li, XS, Mockus, S, Elemento, O, Schultz, N, Lopez-Bigas, N, + Lawler, M, Goecks, J, Griffith, M, Griffith, OL, Margolin, AA, Variant + Interpretation for Cancer Consortium. Nat. Genet.. 52 (4): + 448-457. doi: 10.1038/s41588-020-0603-8. PubMed PMID:32246132 PubMed Central PMC7127986 +

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    + 2020 | + Predicting primary site of secondary liver cancer with a neural + estimator of metastatic origin. Schau, GF, Burlingame, EA, Thibault, G, Anekpuritanang, T, Wang, Y, Gray, + JW, Corless, C, Chang, YH. J. Med. Imag. 7(1) 012706 (18 February + 2020). doi: + 10.1117/1.JMI.7.1.012706. +

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    + 2020 | How Machine Learning Will Transform Biomedicine. Goecks, J, Jalili, V, Heiser, LM, Gray, JW. Cell. 181 + (1): 92-101. doi: 10.1016/j.cell.2020.03.022. PubMed PMID:32243801 PubMed Central PMC7141410 +

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    + 2020 | High-dimensional multiplexed immunohistochemical characterization of + immune contexture in human cancers. Banik, G, Betts, CB, Liudahl, SM, Sivagnanam, S, Kawashima, R, + Cotechini, T, Larson, W, Goecks, J, Pai, SI, Clayburgh, DR, Tsujikawa, T, + Coussens, LM. Meth. Enzymol.. 635 : 1-20. doi: 10.1016/bs.mie.2019.05.039. PubMed PMID:32122539 +

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    + 2020 | Yogurt consumption and colorectal polyps. Rifkin, + SB, Giardiello, FM, Zhu, X, Hylind, LM, Ness, RM, Drewes, JL, Murff, HJ, + Spence, EH, Smalley, WE, Gills, JJ, Mullin, GE, Kafonek, D, La Luna, L, + Zheng, W, Sears, CL, Shrubsole, MJ, Biofilm Study Consortium. Br. J. Nutr.. : 1-12. doi: 10.1017/S0007114520000550. PubMed PMID:32077397 +

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    + 2020 | Cyclic Multiplexed-Immunofluorescence (cmIF), a Highly Multiplexed + Method for Single-Cell Analysis. Eng, J, Thibault, G, Luoh, SW, Gray, JW, Chang, YH, Chin, K. Methods Mol. Biol.. 2055 : 521-562. doi: 10.1007/978-1-4939-9773-2_24. PubMed PMID:31502168 +

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    + 2020 | Comprehensive 3D phenotyping reveals continuous morphological variation + across genetically diverse sorghum inflorescences. Li, M, Shao, MR, Zeng, D, Ju, T, Kellogg, EA, Topp, CN. New Phytol.. : . doi: 10.1111/nph.16533. PubMed PMID:32162345 +

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    + 2019 | A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Packer, JS, Zhu, Q, Huynh, C, Sivaramakrishnan, P, Preston, E, Dueck, + H, Stefanik, D, Tan, K, Trapnell, C, Kim, J, Waterston, RH, Murray, + JI. Science. 365 (6459): . doi: 10.1126/science.aax1971. PubMed PMID:31488706 +

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    + 2019 | Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease + Progression and Immunity. van Galen, P, Hovestadt, V, Wadsworth Ii, MH, Hughes, + TK, Griffin, GK, Battaglia, S, Verga, JA, Stephansky, J, Pastika, TJ, + Lombardi Story, J, Pinkus, GS, Pozdnyakova, O, Galinsky, I, Stone, RM, + Graubert, TA, Shalek, AK, Aster, JC, Lane, AA, Bernstein, BE. Cell. 176 (6): 1265-1281.e24. doi: 10.1016/j.cell.2019.01.031. PubMed PMID:30827681 PubMed Central PMC6515904 +

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    + 2019 | Highly multiplexed immunofluorescence images and single-cell data of + immune markers in tonsil and lung cancer. Rashid, R, Gaglia, G, Chen, YA, Lin, JR, Du, Z, Maliga, Z, Schapiro, + D, Yapp, C, Muhlich, J, Sokolov, A, Sorger, P, Santagata, S. Sci Data. 6 (1): 323. doi: 10.1038/s41597-019-0332-y. PubMed PMID:31848351 PubMed Central PMC6917801 +

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    + 2019 | Qualifying antibodies for image-based immune profiling and multiplexed + tissue imaging. Du, Z, Lin, JR, Rashid, R, Maliga, Z, Wang, S, Aster, JC, Izar, B, + Sorger, PK, Santagata, S. Nat Protoc. 14 (10): 2900-2930. + doi: 10.1038/s41596-019-0206-y. PubMed PMID:31534232 PubMed Central PMC6959005 +

    +

    + 2019 | Innate αβ T Cells Mediate Antitumor Immunity by Orchestrating + Immunogenic Macrophage Programming. Hundeyin, M, Kurz, E, Mishra, A, Rossi, JAK, Liudahl, SM, Leis, KR, + Mehrotra, H, Kim, M, Torres, LE, Ogunsakin, A, Link, J, Sears, RC, + Sivagnanam, S, Goecks, J, Islam, KMS, Dolgalev, I, Savadkar, S, Wang, W, + Aykut, B, Leinwand, J, Diskin, B, Adam, S, Israr, M, Gelas, M, Lish, J, + Chin, K, Farooq, MS, Wadowski, B, Wu, J, Shah, S, Adeegbe, DO, Pushalkar, S, + Vasudevaraja, V, Saxena, D, Wong, KK, Coussens, LM, Miller, G. Cancer Discov. 9 (9): 1288-1305. doi: 10.1158/2159-8290.CD-19-0161. PubMed PMID:31266770 PubMed Central PMC6726581 +

    +

    + 2019 | Cancer cell evolution through the ages. Maley, CC, Shibata, D. Science. 365 (6452): 440-441. doi: + 10.1126/science.aay2859. PubMed PMID: 31371596. +

    +

    + 2019 | scRNABatchQC: Multi-samples quality control for single cell RNA-seq + data. Liu, Q, Sheng, Q, Ping, J, Ramirez, MA, Lau, KS, Coffey, + R, Shyr, Y. Bioinformatics. 2019 Aug 2; pii: + btz601. doi: 10.1093/bioinformatics/btz601. PubMed PMID: 31373345. +

    +

    + 2019 | An update on the CNS manifestations of neurofibromatosis type + 2. Coy, S, Rashid, R, Stemmer-Rachamimov, A, Santagata, + S. Acta Neuropathol. 2019 Jun + 4. doi: 10.1007/s00401-019-02029-5. PubMed PMID: 31161239. +

    +

    + 2019 | The immune contexture associates with the genomic landscape in lung + adenomatous premalignancy. Krysan K, Tran LM, Grimes BS, Fishbein GA, Seki A, + Gardner BK, Walser TC, Salehi-Rad R, Yanagawa J, Lee JM, Sharma S, Aberle D, + Spira AE, Elashoff DA, Wallace WD, Fishbein MC, Dubinett SM. Cancer Res. Epub 2019 May 29. 2019 Oct 1;79(19):5022-5033. + doi: 10.1158/0008-5472.CAN-19-0153. PubMed PMID: 31142513. +

    +

    + 2019 | SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq + data. Peng T, Zhu Q, Yin P, Tan K. Genome Biol. 2019 May 6; 20(1):88. doi: 10.1186/s13059-019-1681-8. PubMed PMID: 31060596. PubMed Central PMCID: PMC6501316. +

    +

    + 2019 | Transforming Growth Factor-β Signaling in Immunity and Cancer. Batlle, E, Massagué, J. Immunity. 50 (4): 924-940. doi: 10.1016/j.immuni.2019.03.024. PubMed PMID: 30995507. +

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    + 2019 | The Pediatric Cell Atlas: Defining the Growth Phase of Human + Development at Single-Cell Resolution. Taylor DM, Aronow BJ, Tan K, Bernt K, Salomonis N, + Greene CS, Frolova A, Henrickson SE, Wells A, Pei L, Jaiswal JK, Whitsett J, + Hamilton KE, MacParland SA, Kelsen J, Heuckeroth RO, Potter SS, Vella LA, + Terry NA, Ghanem LR, Kennedy BC, Helbig I, Sullivan KE, Castelo-Soccio L, + Kreigstein A, Herse F, Nawijn MC, Koppelman GH, Haendel M, Harris NL, Rokita + JL, Zhang Y, Regev A, Rozenblatt-Rosen O, Rood JE, Tickle TL, Vento-Tormo R, + Alimohamed S, Lek M, Mar JC, Loomes KM, Barrett DM, Uapinyoying P, Beggs AH, + Agrawal PB, Chen YW, Muir AB, Garmire LX, Snapper SB, Nazarian J, Seeholzer + SH, Fazelinia H, Singh LN, Faryabi RB, Raman P, Dawany N, Xie HM, Devkota B, + Diskin SJ, Anderson SA, Rappaport EF, Peranteau W, Wikenheiser-Brokamp KA, + Teichmann S, Wallace D, Peng T, Ding YY, Kim MS, Xing Y, Kong SW, Bönnemann + CG, Mandl KD, White PS. Dev Cell. 2019 Apr 8; + 49(1):10-29. doi: 10.1016/j.devcel.2019.03.001. PubMed PMID: 30930166. PubMed Central PMCID: PMC6616346. +

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    + 2019 | Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 + Cell Heterogeneity. Tsujikawa T, Thibault G, Azimi V, Sivagnanam S, Banik G, + Means C, Kawashima R, Clayburgh DR, Gray JW, Coussens LM, Chang YH. Cytometry A. 2019 Apr; 95(4):389-398. doi: 10.1002/cyto.a.23726. + PubMed PMID: 30714674. PubMed Central PMCID: PMC6461524. +

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    + 2019 | Therapeutic Clues from an Integrated Omic Assessment of East Asian + Triple Negative Breast Cancers. Heiser LM, Mills GB, Gray JW. Cancer Cell. 2019 Mar 18; 35(3):341-343. doi: 10.1016/j.ccell.2019.02.012. PubMed PMID: 30889376. PubMed Central PMCID: PMC6499473. +

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    + 2018 | pyNVR: Investigating factors affecting feature selection from scRNA-seq + data for lineage reconstruction. Chen B, Herring CA, Lau KS. Bioinformatics. 2018 Nov 16; pii: 5184958. doi: 10.1093/bioinformatics/bty950. PubMed PMID: 30445607. PubMed Central PMCID: PMC6596893. +

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    + 2018 | A Cancer Cell Program Promotes T Cell Exclusion and Resistance to + Checkpoint Blockade. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms + JC, Leeson R, Kanodia A, Mei S, Lin JR, Wang S, Rabasha B, Liu D, Zhang G, + Margolais C, Ashenberg O, Ott PA, Buchbinder EI, Haq R, Hodi FS, Boland GM, + Sullivan RJ, Frederick DT, Miao B, Moll T, Flaherty KT, Herlyn M, Jenkins + RW, Thummalapalli R, Kowalczyk MS, Cañadas I, Schilling B, Cartwright ANR, + Luoma AM, Malu S, Hwu P, Bernatchez C, Forget MA, Barbie DA, Shalek AK, + Tirosh I, Sorger PK, Wucherpfennig K, Van Allen EM, Schadendorf D, Johnson + BE, Rotem A, Rozenblatt-Rosen O, Garraway LA, Yoon CH, Izar B, Regev A. Cell. 2018 Nov 1; 175(4):984-997.e24. doi: 10.1016/j.cell.2018.09.006. PubMed PMID: 30388455. PubMed Central PMCID: PMC6410377. +

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    + 2018 | Quantitative assessment of cell population diversity in single-cell + landscapes. Liu Q, Herring CA, Sheng Q, Ping J, Simmons AJ, Chen B, Banerjee A, Li W, + Gu G, Coffey RJ, Shyr Y, Lau KS. PLoS Biol. 2018 + Oct 22; 16(10):e2006687. doi: 10.1371/journal.pbio.2006687. PubMed PMID: 30346945. PubMed Central PMCID: PMC6211764. +

    +

    + 2018 | The PreCancer Atlas (PCA). Srivastava S, Ghosh S, Kagan J, Mazurchuk R. Trends Cancer. 2018 Aug; 4(8):513-514. doi: 10.1016/j.trecan.2018.06.003. PubMed PMID: 30064657. +

    +

    + 2018 | The Making of a PreCancer Atlas: Promises, Challenges, and + Opportunities. Srivastava S, Ghosh S, Kagan J, Mazurchuk R. Trends Cancer. 2018 Aug;4(8):523-536. doi: 10.1016/j.trecan.2018.06.007. PubMed PMID: 30064661. +

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