An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects

A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on \u201cchromatin-state\u201d to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer 3c140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers. Causal enhancer-target-gene relationships are inferred from a systematic analysis of 33 cancer types

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field

A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories. By performing molecular analyses of 2,579 TCGA gynecological (OV, UCEC, CESC, and UCS) and breast tumors, Berger et al. identify five prognostic subtypes using 16 key molecular features and propose a decision tree based on six clinically assessable features that classifies patients into the subtypes

Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas

DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in 3c20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy. Knijnenburg et al. present The Cancer Genome Atlas (TCGA) Pan-Cancer analysis of DNA damage repair (DDR) deficiency in cancer. They use integrative genomic and molecular analyses to identify frequent DDR alterations across 33 cancer types, correlate gene- and pathway-level alterations with genome-wide measures of genome instability and impaired function, and demonstrate the prognostic utility of DDR deficiency scores

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic features associated with MYC and the PMN across the 33 cancers of The Cancer Genome Atlas. Pan-cancer, 28% of all samples had at least one of the MYC paralogs amplified. In contrast, the MYC antagonists MGA and MNT were the most frequently mutated or deleted members, proposing a role as tumor suppressors. MYC alterations were mutually exclusive with PIK3CA, PTEN, APC, or BRAF alterations, suggesting that MYC is a distinct oncogenic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such as immune response and growth factor signaling; chromatin, translation, and DNA replication/repair were conserved pan-cancer. This analysis reveals insights into MYC biology and is a reference for biomarkers and therapeutics for cancers with alterations of MYC or the PMN. We present a computational study determining the frequency and extent of alterations of the MYC network across the 33 human cancers of TCGA. These data, together with MYC, positively correlated pathways as well as mutually exclusive cancer genes, will be a resource for understanding MYC-driven cancers and designing of therapeutics

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs) whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor) and TUG1 and WT1-AS (inferred onco-lncRNAs) dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts. Chiu et al. present a pan-cancer analysis of lncRNA regulatory interactions. They suggest that the dysregulation of hundreds of lncRNAs target and alter the expression of cancer genes and pathways in each tumor context. This implies that hundreds of lncRNAs can alter tumor phenotypes in each tumor context