update figure commits

content/03.results.md

@@ -25,7 +25,7 @@ To ensure that new code executed in the development environment, we used the con
 
 We followed a similar process in our Manubot-powered [@doi:10.1371/journal.pcbi.1007128] repository for proposed manuscript additions (**Figure {@fig:Fig1}C**); peer reviewers ensured clarity and scientific accuracy, and Manubot performed spell-checking.
 
-![**Overview of the OpenPBTA Project.** A, CBTN and PNOC collected tumors from 943 patients. 22 tumor cell lines were created, and over 2000 specimens were sequenced (N = 1035 RNA-Seq, N = 940 WGS, and N = 32 WXS or targeted panel). Data was harmonized by the Kids First Data Resource Center using Amazon S3 through CAVATICA. B, Stacked bar plot of the number of biospecimens per phase of therapy. Each panel denotes a broad histology and each bar denotes a cancer group. (Abbreviations: GNG = ganglioglioma, Other LGG = other low-grade glioma, PA = pilocytic astrocytoma, PXA = pleomorphic xanthoastrocytoma, SEGA = subependymal giant cell astrocytoma, DIPG = diffuse intrinsic pontine glioma, DMG = diffuse midline glioma, Other HGG = other high-grade glioma, ATRT = atypical teratoid rhabdoid tumor, MB = medulloblastoma, Other ET = other embryonal tumor, EPN = ependymoma, PNF = plexiform neurofibroma, DNET = dysembryoplastic neuroepithelial tumor, CRANIO = craniopharyngioma, EWS = Ewing sarcoma, CPP = choroid plexus papilloma). C, Overview of the open analysis and manuscript contribution model. A contributor proposed an analysis, implemented it in their fork, and filed a pull request (PR) to add changes to the analysis repository. PRs underwent review for scientific rigor and implementation correctness. Using container and continuous integration technologies, PRs were checked to ensure all software dependencies were included and code was not sensitive to underlying data changes. Finally, a contributor filed a PR documenting their methods and results to the Manubot-powered manuscript repository for review. D, A potential path for an analytical PR. Arrows indicate revisions. Panel A created with BioRender.com.](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/2eb889d2d495a7327fb51e377df2f9d780c47117/figures/pngs/figure1.png?sanitize=true){#fig:Fig1 width="7in"}
+![**Overview of the OpenPBTA Project.** A, CBTN and PNOC collected tumors from 943 patients. 22 tumor cell lines were created, and over 2000 specimens were sequenced (N = 1035 RNA-Seq, N = 940 WGS, and N = 32 WXS or targeted panel). Data was harmonized by the Kids First Data Resource Center using Amazon S3 through CAVATICA. B, Stacked bar plot of the number of biospecimens per phase of therapy. Each panel denotes a broad histology and each bar denotes a cancer group. (Abbreviations: GNG = ganglioglioma, Other LGG = other low-grade glioma, PA = pilocytic astrocytoma, PXA = pleomorphic xanthoastrocytoma, SEGA = subependymal giant cell astrocytoma, DIPG = diffuse intrinsic pontine glioma, DMG = diffuse midline glioma, Other HGG = other high-grade glioma, ATRT = atypical teratoid rhabdoid tumor, MB = medulloblastoma, Other ET = other embryonal tumor, EPN = ependymoma, PNF = plexiform neurofibroma, DNET = dysembryoplastic neuroepithelial tumor, CRANIO = craniopharyngioma, EWS = Ewing sarcoma, CPP = choroid plexus papilloma). C, Overview of the open analysis and manuscript contribution model. A contributor proposed an analysis, implemented it in their fork, and filed a pull request (PR) to add changes to the analysis repository. PRs underwent review for scientific rigor and implementation correctness. Using container and continuous integration technologies, PRs were checked to ensure all software dependencies were included and code was not sensitive to underlying data changes. Finally, a contributor filed a PR documenting their methods and results to the Manubot-powered manuscript repository for review. D, A potential path for an analytical PR. Arrows indicate revisions. Panel A created with BioRender.com.](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/37ec62fdc2fd9ff157f2f2c10b69e9bb36673363/figures/pngs/figure1.png?sanitize=true){#fig:Fig1 width="7in"}
 
 ### Molecular Subtyping of OpenPBTA CNS Tumors
 
@@ -147,7 +147,7 @@ We observed somatic mutations or fusions in _NF2_ in 41% (7/17) of meningiomas,
 DNETs harbored alterations in MAPK/PI3K pathway genes, as was previously reported [@doi:10.1093/jnen/nlz101], including _FGFR1_ (21%, 4/19), _PDGFRA_ (10%, 2/19), and _BRAF_ (5%, 1/19).
 **Figure {@fig:S3}A** depicts frequent mutations in additional rare brain tumor histologies.
 
-![**Mutational landscape of PBTA tumors.** Frequencies of canonical somatic gene mutations, CNVs, fusions, and TMB (top bar plot) for the top mutated genes across primary tumors within the OpenPBTA dataset. A, LGGs (N = 226): pilocytic astrocytoma (N = 104), other LGG (N = 68), ganglioglioma (N = 35), pleomorphic xanthoastrocytoma (N = 9), subependymal giant cell astrocytoma (N = 10). B, Embryonal tumors (N = 129): medulloblastoma (N = 95), atypical teratoid rhabdoid tumor (N = 24), other embryonal tumor (N = 10). C, HGGs (N = 63): diffuse midline glioma (N = 36) and other HGG (N = 27). D, Other CNS tumors (N = 153): ependymoma (N = 60), craniopharyngioma (N = 31), meningioma (N = 17), dysembryoplastic neuroepithelial tumor (N = 19), Ewing sarcoma (N = 7), schwannoma (N = 12), and neurofibroma plexiform (N = 7). Rare CNS tumors are displayed in **Figure {@fig:S3}B**. Histology (`Cancer Group`) and sex (`Germline sex estimate`) annotations are displayed under each plot. Only tumors with mutations in the listed genes are shown. Multiple CNVs are denoted as a complex event. N denotes the number of unique tumors (one tumor per patient).](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/2eb889d2d495a7327fb51e377df2f9d780c47117/figures/pngs/figure2.png?sanitize=true){#fig:Fig2 width="9in"}
+![**Mutational landscape of PBTA tumors.** Frequencies of canonical somatic gene mutations, CNVs, fusions, and TMB (top bar plot) for the top mutated genes across primary tumors within the OpenPBTA dataset. A, LGGs (N = 226): pilocytic astrocytoma (N = 104), other LGG (N = 68), ganglioglioma (N = 35), pleomorphic xanthoastrocytoma (N = 9), subependymal giant cell astrocytoma (N = 10). B, Embryonal tumors (N = 129): medulloblastoma (N = 95), atypical teratoid rhabdoid tumor (N = 24), other embryonal tumor (N = 10). C, HGGs (N = 63): diffuse midline glioma (N = 36) and other HGG (N = 27). D, Other CNS tumors (N = 153): ependymoma (N = 60), craniopharyngioma (N = 31), meningioma (N = 17), dysembryoplastic neuroepithelial tumor (N = 19), Ewing sarcoma (N = 7), schwannoma (N = 12), and neurofibroma plexiform (N = 7). Rare CNS tumors are displayed in **Figure {@fig:S3}B**. Histology (`Cancer Group`) and sex (`Germline sex estimate`) annotations are displayed under each plot. Only tumors with mutations in the listed genes are shown. Multiple CNVs are denoted as a complex event. N denotes the number of unique tumors (one tumor per patient).](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/37ec62fdc2fd9ff157f2f2c10b69e9bb36673363/figures/pngs/figure2.png?sanitize=true){#fig:Fig2 width="9in"}
 
 <!--SAMPLECOUNT-->
 
@@ -177,7 +177,7 @@ This trend may have resulted from therapy-induced mutations that produced additi
 
 <!-- To do: possibly explore gene mutations driving signature 8, specifically for meningiomas, and describe -->
 
-![**Mutational co-occurrence and signatures highlight key oncogenic drivers.** A, Bar plot of nonsynonymous mutations for 50 most commonly-mutated genes across all histologies. "Other" denotes a histology with <10 tumors. B, Co-occurrence and mutual exclusivity of mutated genes. The co-occurrence score is defined as $I(-\log_{10}(P))$ where $P$ is Fisher's exact test and $I$ is 1 when mutations co-occur more often than expected or -1 when exclusivity is more common. C, Number of SV breaks significantly correlate with CNV breaks (Adjusted R = 0.443, p = 1.05e-38). D, Chromothripsis frequency across cancer groups with N >= 3 tumors. E, Sina plots of RefSig signature weights for signatures 1, 11, 18, 19, 3, 8, N6, MMR2, and Other across cancer groups. Box plot represents 5% (lower whisker), 25% (lower box), 50% (median), 75% (upper box), and 95% (upper whisker) quantiles.](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/2eb889d2d495a7327fb51e377df2f9d780c47117/figures/pngs/figure3.png?sanitize=true){#fig:Fig3 width="7in"}
+![**Mutational co-occurrence and signatures highlight key oncogenic drivers.** A, Bar plot of nonsynonymous mutations for 50 most commonly-mutated genes across all histologies. "Other" denotes a histology with <10 tumors. B, Co-occurrence and mutual exclusivity of mutated genes. The co-occurrence score is defined as $I(-\log_{10}(P))$ where $P$ is Fisher's exact test and $I$ is 1 when mutations co-occur more often than expected or -1 when exclusivity is more common. C, Number of SV breaks significantly correlate with CNV breaks (Adjusted R = 0.443, p = 1.05e-38). D, Chromothripsis frequency across cancer groups with N >= 3 tumors. E, Sina plots of RefSig signature weights for signatures 1, 11, 18, 19, 3, 8, N6, MMR2, and Other across cancer groups. Box plot represents 5% (lower whisker), 25% (lower box), 50% (median), 75% (upper box), and 95% (upper whisker) quantiles.](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/37ec62fdc2fd9ff157f2f2c10b69e9bb36673363/figures/pngs/figure3.png?sanitize=true){#fig:Fig3 width="7in"}
 
 ### Transcriptomic Landscape of Pediatric Brain Tumors
 
@@ -255,7 +255,7 @@ Since we observed the negative prognostic effect of _TP53_ scores for HGGs, we a
 We found that DMG H3 K28 tumors with _TP53_ loss had significantly worse prognosis (HR = 2.8, CI = 1.4-5.6, p = 0.003) than did DMG H3 K28 tumors with wildtype _TP53_ (**Figure {@fig:Fig4}G** and **Figure {@fig:Fig4}H**).
 This finding was also recently reported in two recent restrospective analyses of DIPG tumors [@doi:10.1158/1078-0432.CCR-22-0803; @doi:10.1007/s11060-021-03890-9].
 
-![**_TP53_ and telomerase activity** A, Receiver Operating Characteristic for _TP53_ classifier run on stranded FPKM RNA-Seq. B, Violin and strip plots of _TP53_ scores plotted by _TP53_ alteration type (N<sub>activated</sub> = 11, N<sub>lost</sub> = 100, N<sub>other</sub> = 866). C, Violin and strip plots of _TP53_ RNA expression plotted by _TP53_ activation status (N<sub>activated</sub> = 11, N<sub>lost</sub> = 100, N<sub>other</sub> = 866). D, Box plots of _TP53_ and telomerase (EXTEND) scores across cancer groups. TMB status is highlighted in orange (hypermutant) or red (ultra-hypermutant). E, Heatmap of RefSig mutational signatures for patients with at least one hypermutant tumor or cell line. F, Forest plot depicting prognostic effects of _TP53_ and telomerase scores on overall survival (OS), controlling for extent of tumor resection, LGG group, and HGG group. G, Forest plot depicting the effect of molecular subtype on HGG OS. For F and G, hazard ratios (HR) with 95% confidence intervals and p-values (multivariate Cox) are listed. Significant p-values are denoted with black diamonds. Reference groups are denoted by grey diamonds. H, Kaplan-Meier curve of HGGs by molecular subtype. Box plot represents 5% (lower whisker), 25% (lower box), 50% (median), 75% (upper box), and 95% (upper whisker) quantiles.](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/2eb889d2d495a7327fb51e377df2f9d780c47117/figures/pngs/figure4.png?sanitize=true){#fig:Fig4 width="7in"}
+![**_TP53_ and telomerase activity** A, Receiver Operating Characteristic for _TP53_ classifier run on stranded FPKM RNA-Seq. B, Violin and strip plots of _TP53_ scores plotted by _TP53_ alteration type (N<sub>activated</sub> = 11, N<sub>lost</sub> = 100, N<sub>other</sub> = 866). C, Violin and strip plots of _TP53_ RNA expression plotted by _TP53_ activation status (N<sub>activated</sub> = 11, N<sub>lost</sub> = 100, N<sub>other</sub> = 866). D, Box plots of _TP53_ and telomerase (EXTEND) scores across cancer groups. TMB status is highlighted in orange (hypermutant) or red (ultra-hypermutant). E, Heatmap of RefSig mutational signatures for patients with at least one hypermutant tumor or cell line. F, Forest plot depicting prognostic effects of _TP53_ and telomerase scores on overall survival (OS), controlling for extent of tumor resection, LGG group, and HGG group. G, Forest plot depicting the effect of molecular subtype on HGG OS. For F and G, hazard ratios (HR) with 95% confidence intervals and p-values (multivariate Cox) are listed. Significant p-values are denoted with black diamonds. Reference groups are denoted by grey diamonds. H, Kaplan-Meier curve of HGGs by molecular subtype. Box plot represents 5% (lower whisker), 25% (lower box), 50% (median), 75% (upper box), and 95% (upper whisker) quantiles.](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/37ec62fdc2fd9ff157f2f2c10b69e9bb36673363/figures/pngs/figure4.png?sanitize=true){#fig:Fig4 width="7in"}
 
 #### Histologic and oncogenic pathway clustering
 
@@ -291,4 +291,4 @@ While adamantinomatous craniopharyngiomas and Group 3 and Group 4 medulloblastom
 To explore the potential influence of tumor purity, selected transcriptomic analyses were repeated using samples with tumor purities at or above the median tumor purity of their cancer group (see **STAR Methods**).
 The analyses using all stranded samples were broadly consistent (**Figure {@fig:S7}D-I**) with those using samples with high tumor purity. 
 
-![**Transcriptomic and immune landscape of pediatric brain tumors** A, First two dimensions from UMAP of transcriptome data. Points colored by broad histology. B, Heatmap of with significant GSVA scores for Hallmark gene sets with tumors ordered by cancer group. C, Box plots of quanTIseq estimates of immune cell proportions in select cancer groups with N > 15 tumors. Note: other HGGs and other LGGs have immune cell proportions similar to DMG and pilocytic astrocytoma, respectively, and are not shown. D, Forest plot depicting additive effects of _CD274_ expression, immune cell proportion, and extent of tumor resection on OS of medulloblastoma patients. HRs with 95% confidence intervals and p-values (multivariate Cox) are listed. Significant p-values are denoted with black diamonds. Reference groups are denoted by grey diamonds. Note: the Macrophage M1 HR was 0 (coefficient = -9.90e+4) with infinite upper and lower CIs, and thus was not included in the figure. E, Box plot of _CD274_ expression (log<sub>2</sub> FPKM) for medulloblastomas grouped by subtype. Bonferroni-corrected p-values from Wilcoxon tests are shown. Box plot represents 5% (lower whisker), 25% (lower box), 50% (median), 75% (upper box), and 95% (upper whisker) quantiles. Only stranded RNA-Seq data is plotted.](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/2eb889d2d495a7327fb51e377df2f9d780c47117/figures/pngs/figure5.png?sanitize=true){#fig:Fig5 width="7in"}
+![**Transcriptomic and immune landscape of pediatric brain tumors** A, First two dimensions from UMAP of transcriptome data. Points colored by broad histology. B, Heatmap of with significant GSVA scores for Hallmark gene sets with tumors ordered by cancer group. C, Box plots of quanTIseq estimates of immune cell proportions in select cancer groups with N > 15 tumors. Note: other HGGs and other LGGs have immune cell proportions similar to DMG and pilocytic astrocytoma, respectively, and are not shown. D, Forest plot depicting additive effects of _CD274_ expression, immune cell proportion, and extent of tumor resection on OS of medulloblastoma patients. HRs with 95% confidence intervals and p-values (multivariate Cox) are listed. Significant p-values are denoted with black diamonds. Reference groups are denoted by grey diamonds. Note: the Macrophage M1 HR was 0 (coefficient = -9.90e+4) with infinite upper and lower CIs, and thus was not included in the figure. E, Box plot of _CD274_ expression (log<sub>2</sub> FPKM) for medulloblastomas grouped by subtype. Bonferroni-corrected p-values from Wilcoxon tests are shown. Box plot represents 5% (lower whisker), 25% (lower box), 50% (median), 75% (upper box), and 95% (upper whisker) quantiles. Only stranded RNA-Seq data is plotted.](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/37ec62fdc2fd9ff157f2f2c10b69e9bb36673363/figures/pngs/figure5.png?sanitize=true){#fig:Fig5 width="7in"}