The repertoire of mutational signatures in human cancerAbstract Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature 1 . Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium 2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses 3–15 , enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.
Integrated Genomic Characterization of Papillary Thyroid CarcinomaComprehensive and Integrative Genomic Characterization of Hepatocellular CarcinomaComprehensive Genomic Analysis Identifies Novel Subtypes and Targets of Triple-Negative Breast CancerPURPOSE: Genomic profiling studies suggest that triple-negative breast cancer (TNBC) is a heterogeneous disease. In this study, we sought to define TNBC subtypes and identify subtype-specific markers and targets. EXPERIMENTAL DESIGN: RNA and DNA profiling analyses were conducted on 198 TNBC tumors [estrogen receptor (ER) negativity defined as Allred scale value ≤ 2] with >50% cellularity (discovery set: n = 84; validation set: n = 114) collected at Baylor College of Medicine (Houston, TX). An external dataset of seven publically accessible TNBC studies was used to confirm results. DNA copy number, disease-free survival (DFS), and disease-specific survival (DSS) were analyzed independently using these datasets. RESULTS: We identified and confirmed four distinct TNBC subtypes: (i) luminal androgen receptor (AR; LAR), (ii) mesenchymal (MES), (iii) basal-like immunosuppressed (BLIS), and (iv) basal-like immune-activated (BLIA). Of these, prognosis is worst for BLIS tumors and best for BLIA tumors for both DFS (log-rank test: P = 0.042 and 0.041, respectively) and DSS (log-rank test: P = 0.039 and 0.029, respectively). DNA copy number analysis produced two major groups (LAR and MES/BLIS/BLIA) and suggested that gene amplification drives gene expression in some cases [FGFR2 (BLIS)]. Putative subtype-specific targets were identified: (i) LAR: androgen receptor and the cell surface mucin MUC1, (ii) MES: growth factor receptors [platelet-derived growth factor (PDGF) receptor A; c-Kit], (iii) BLIS: an immunosuppressing molecule (VTCN1), and (iv) BLIA: Stat signal transduction molecules and cytokines. CONCLUSION: There are four stable TNBC subtypes characterized by the expression of distinct molecular profiles that have distinct prognoses. These studies identify novel subtype-specific targets that can be targeted in the future for the effective treatment of TNBCs.
Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic PipelinesThe 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.