diff --git a/build/assets/custom-dictionary.txt b/build/assets/custom-dictionary.txt index 60ac333a..b0d5bd37 100644 --- a/build/assets/custom-dictionary.txt +++ b/build/assets/custom-dictionary.txt @@ -86,6 +86,8 @@ Bo Bobick Bonferroni Bornhorst +Boxplot +Boxplots Boyajian bp BRAF @@ -539,6 +541,7 @@ oncogenicity oncoprint oncoprints OpenPBTA +OpenPBTA's openpbta OpenPedCan orcid @@ -556,6 +559,7 @@ Partap pathogenicity Payal PBTA +PBTA's pbta PCPG Pearson @@ -742,6 +746,7 @@ tbl TCGA Tectum telomerase +telomere telomeres telomeric temozolomide diff --git a/content/03.results.md b/content/03.results.md index 1a1c7623..4797af43 100644 --- a/content/03.results.md +++ b/content/03.results.md @@ -129,7 +129,7 @@ We detected canonical _SMARCB1/SMARCA4_ deletions or inactivating mutations in a Across HGGs, _TP53_ (57%, 36/63) and _H3F3A_ (54%, 34/63) were both most mutated and co-occurring genes (**Figure {@fig:Fig2}A and C**), followed by frequent mutations in _ATRX_ (29%, 18/63) which is commonly mutated in gliomas [@doi:10.1080/14728222.2018.1487953]. We observed recurrent amplifications and fusions in _EGFR_, _MET_, _PDGFRA_, and _KIT_, highlighting that these tumors leverage multiple oncogenic mechanisms to activate tyrosine kinases, as previously reported [@doi:10.1002/ijc.32258; @doi:10.1016/j.ccell.2017.08.017; @doi:10.1186/s40478-020-00905-w]. GSVA showed upregulation (ANOVA Bonferroni-corrected p < 0.01) of DNA repair, G2M checkpoint, and MYC pathways as well as downregulation of the TP53 pathway (**Figure {@fig:Fig5}B**). -The two ultra-hypoermutated tumors (> 100 Mutations/Mb) were from patients with mismatch repair deficiency syndrome [@doi:10.1093/neuonc/noz192]. +The two ultra-hypermutated tumors (> 100 Mutations/Mb) were from patients with mismatch repair deficiency syndrome [@doi:10.1093/neuonc/noz192]. Considering embryonal tumors, 25% (15/60) of ependymomas were _C11orf95::RELA_ (now, _ZFTA::RELA_) fusion-positive [@doi:10.1038/nature13109], and 68% (21/31) of craniopharyngiomas contained _CTNNB1_ mutations (**Figure {@fig:Fig2}D**). @@ -259,7 +259,7 @@ For example, HGG, DMG, MB, and ATRT tumors are known to upregulate _MYC_ [@doi:1 Indeed, we detected significant (Bonferroni-corrected p < 0.05) upregulation of _MYC_ and _E2F_ targets, as well as G2M (cell cycle phase following S phase) in MBs, ATRTs, and HGGs compared to several other cancer groups. In contrast, LGGs showed significant downregulation (Bonferroni-corrected p < 0.05, multiple cancer group comparisons) of these pathways. Schwannomas and neurofibromas, which have an inflammatory immune microenvironment of T and B lymphocytes and tumor-associated macrophages (TAMs), are driven by upregulation of cytokines such as IFN$\gamma$, IL-1, and IL-6, and TNF$\alpha$ [@doi:10.1093/noajnl/vdaa023]. -GSVA releaved significant upregulation of these cytokines in hallmark pathways (Bonferroni-corrected p < 0.05, multiple cancer group comparisons) (**Figure {@fig:Fig5}B**), and monocytes dominated these tumors' immune cell repertoire (**Figure {@fig:Fig5}C**). +GSVA revealed significant upregulation of these cytokines in hallmark pathways (Bonferroni-corrected p < 0.05, multiple cancer group comparisons) (**Figure {@fig:Fig5}B**), and monocytes dominated these tumors' immune cell repertoire (**Figure {@fig:Fig5}C**). We also observed significant upregulation of pro-inflammatory cytokines IFN$\alpha$ and IFN$\gamma$ in both LGGs and craniopharyngiomas when compared to either medulloblastoma or ependymomas (Bonferroni-corrected p < 0.05) (**Figure {@fig:Fig5}B**). Together, these results support previous proteogenomic findings that aggressive medulloblastomas and ependymomas have lower immune infiltration compared to _BRAF_-driven LGGs and craniopharyngiomas [@doi:10.1016/j.cell.2020.10.044]. diff --git a/content/04.discussion.md b/content/04.discussion.md index f6fefc69..b176457f 100644 --- a/content/04.discussion.md +++ b/content/04.discussion.md @@ -17,7 +17,7 @@ For example, we subtyped medulloblastoma tumors, of which only 35% (43/122) had We advanced the integrative analyses and cross-cohort comparison via a number of validated modules. We used an expression classifier to determine whether tumors have dysfunctional _TP53_ [@doi:10.1016/j.celrep.2018.03.076] and the EXTEND algorithm to determine their degree of telomerase activity using a 13-gene signature [@doi:10.1038/s41467-020-20474-9]. -Interestingly, we found that hypermutant HGGs universally displayed _TP53_ disregulation, in contrast to adult cancers like colorectal cancer and gastric adenocarcinoma in whose hypermutated tumors _TP53_ disregulation is less common [@doi:10.18632/oncotarget.22783; @https://doi.org/10.1038/NATURE13480]. +Interestingly, we found that hypermutant HGGs universally displayed _TP53_ dysregulation, in contrast to adult cancers like colorectal cancer and gastric adenocarcinoma in whose hypermutated tumors _TP53_ dysregulation is less common [@doi:10.18632/oncotarget.22783; @https://doi.org/10.1038/NATURE13480]. Furthermore, high _TP53_ scores were a significant prognostic marker for poor overall survival for patients with tumor types including H3 K28-mutant DMGs and ependymomas. We also show that EXTEND scores are a robust surrogate measure for telomerase activity in pediatric brain tumors. By assessing _TP53_ and telomerase activity prospectively from expression data, information usually only attainable with DNA sequencing and/or qPCR, we incorporated oncogenic biomarker and prognostic knowledge thereby expanding our biological understanding of these tumors. @@ -30,7 +30,7 @@ Finally, we reproduced the overall known poor infiltration of CD8+ T cells and g While large-scale collaborative efforts may take a longer time to complete, adoption an open science framework substantially mitigated this concern. By maintaining all data, analytical code, and results in public repositories, we ensured that such logistics did not hinder progress in pediatric cancer research. -Indeed, OpenPBTA is already a foundational data analysis and processing layer for several discovery research and translational projects which will continue to add other genomic modalities and analyses, inclyding germline, epigenomic, single-cell, splicing, imaging, and model drug response data. +Indeed, OpenPBTA is already a foundational data analysis and processing layer for several discovery research and translational projects which will continue to add other genomic modalities and analyses, including germline, epigenomic, single-cell, splicing, imaging, and model drug response data. For example, the OpenPBTA RNA fusion filtering module led to the development of the R package _annoFuse_ [@doi:10.1186/s12859-020-03922-7] and an R Shiny application [_shinyFuse_](http://shiny.imbei.uni-mainz.de:3838/shinyFuse/). Leveraging OpenPBTA's medulloblastoma subtyping and immune deconvolution analyses, Dang and colleagues showed that SHH tumors are enriched with monocyte and microglia-derived macrophages, which may accumulate following radiation therapy [@doi:10.1016/j.celrep.2021.108917]. Expression and CNV analyses demonstrated that _GPC2_ is a highly expressed and copy-number gained immunotherapeutic target in ETMRs, medulloblastomas, choroid plexus carcinomas, H3 wildtype high-grade gliomas, and DMGs. diff --git a/content/05.limitations.md b/content/05.limitations.md index ef2b71cc..9f6c09e3 100644 --- a/content/05.limitations.md +++ b/content/05.limitations.md @@ -1,6 +1,6 @@ ## Limitations of Study -Notablt, PBTA brain tumor samples were collected over decades, and RNA samples were prepared using two distinct library preparations (stranded or poly-A, **Figure {@fig:S7}A**) by multiple sequencing centers. +Notably, PBTA brain tumor samples were collected over decades, and RNA samples were prepared using two distinct library preparations (stranded or poly-A, **Figure {@fig:S7}A**) by multiple sequencing centers. While we noted a strong library preparation batch effect (**Figure {@fig:S7}B**) and a possible sequencing center batch effect (**Figure {@fig:S7}C**), cancer groups are highly unbalanced across library preparations (**Figure {@fig:S7}A**). We did not perform batch correction because removing batch effects across unbalanced groups may induce false differences among groups [@doi:10.1093/biostatistics/kxv027; @doi:10.1016/j.tibtech.2017.02.012]. Instead, we circumvent batch effects by analyzing only stranded RNA-Seq expression data, which comprises the vast majority of the PBTA cohort, for transcriptomic analyses presented in **Figure {@fig:4}** and **Figure {@fig:5}** . diff --git a/content/test.docx b/content/test.docx new file mode 100644 index 00000000..10ac168c Binary files /dev/null and b/content/test.docx differ diff --git a/content/~$test.docx b/content/~$test.docx new file mode 100644 index 00000000..f1c48323 Binary files /dev/null and b/content/~$test.docx differ