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. Somatic mutations in cancer genomes are caused by mutational processes of both exogenous and endogenous origin that operate during the cell lineage between the fertilized egg and the cancer cell 16. Each mutational process may involve components of DNA damage or modification, DNA repair and DNA replication (which may be normal or abnormal), and generates a characteristic mutational signature that potentially includes base substitutions, small insertions and deletions (indels), genome rearrangements and chromosome copy-number changes 1. The mutations in an individual cancer genome may have been generated by multiple mutational processes, and thus incorporate multiple superimposed mutational signatures. Therefore, to systematically characterize the mutational processes that contribute to cancer, mathematical methods have previously been used to decipher mutational signatures from somatic mutation catalogues, estimate the number of mutations that are attributable to each signature in individual samples and annotate each mutation class in each tumour with the probability that it arose from each signature 6,9,17-27. Previous studies of multiple types of cancer have identified more than 30 single-base substitution (SBS) signatures, some of knownbut many of unknown-aetiologies, some ubiquitous and others rare, some part of normal cell biology and others associated with abnormal exposures or neoplastic progression 3-5,7-15. Genome rearrangement signatures have also previously been described 11,25,28-30. However, the analysis of other classes of mutation has been relatively limited 3,11,31-33 .
Summary Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Here, we describe the genomic landscape of 496 PTCs. We observed a low frequency of somatic alterations (relative to other carcinomas) and extended the set of known PTC driver alterations to include EIF1AX, PPM1D and CHEK2 and diverse gene fusions. These discoveries reduced the fraction of PTC cases with unknown oncogenic driver from 25% to 3.5%. Combined analyses of genomic variants, gene expression, and methylation demonstrated that different driver groups lead to different pathologies with distinct signaling and differentiation characteristics. Similarly, we identified distinct molecular subgroups of BRAF-mutant tumors and multidimensional analyses highlighted a potential involvement of oncomiRs in less-differentiated subgroups. Our results propose a reclassification of thyroid cancers into molecular subtypes that better reflect their underlying signaling and differentiation properties, which has the potential to improve their pathological classification and better inform the management of the disease.
We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.
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SUMMARY Liver cancer has the second highest worldwide cancer mortality rate and has limited therapeutic options. We analyzed 363 hepatocellular carcinoma (HCC) cases by whole exome sequencing and DNA copy number analyses, and 196 HCC also by DNA methylation, RNA, miRNA, and proteomic expression. DNA sequencing and mutation analysis identified significantly mutated genes including LZTR1, EEF1A1, SF3B1, and SMARCA4. Significant alterations by mutation or down-regulation by hypermethylation in genes likely to result in HCC metabolic reprogramming (ALB, APOB, and CPS1) were observed. Integrative molecular HCC subtyping incorporating unsupervised clustering of five data platforms identified three subtypes, one of which was associated with poorer prognosis in three HCC cohorts. Integrated analyses enabled development of a p53 target gene expression signature correlating with poor survival. Potential therapeutic targets for which inhibitors exist include WNT signaling, MDM4, MET, VEGFA, MCL1, IDH1, TERT, and immune checkpoint proteins CTLA-4, PD-1, and PD-L1.
The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis
Purpose Genomic profiling studies suggest 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. Patients and Methods RNA and DNA profiling analyses were conducted on 198 TNBC tumors (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. An external data set of 7 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: (1) Luminal-AR (LAR); 2) Mesenchymal (MES); 3) Basal-Like Immune-Suppressed (BLIS), and 4) Basal-Like Immune-Activated (BLIA). Of these, prognosis is worst for BLIS tumors and best for BLIA tumors for both DFS (logrank test p=0.042 and 0.041, respectively) and DSS (logrank test p=0.039 and 0.029, respectively). DNA copy number analysis produced two major groups (LAR and MES/BLIS/BLIA), and suggested gene amplification drives gene expression in some cases (FGFR2 (BLIS)). Putative subtype-specific targets were identified: 1) LAR: androgen receptor and the cell surface mucin MUC1; 2) MES: growth factor receptors (PDGF receptor A; c-Kit); 3) BLIS: an immune suppressing molecule (VTCN1); and 4) 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 effective treatment of TNBCs.
40 Somatic mutations in cancer genomes are caused by multiple mutational processes each of 41 which generates a characteristic mutational signature. Using 84,729,690 somatic mutations 42 from 4,645 whole cancer genome and 19,184 exome sequences encompassing most cancer 43 types we characterised 49 single base substitution, 11 doublet base substitution, four 44 clustered base substitution, and 17 small insertion and deletion mutational signatures. The 45 substantial dataset size compared to previous analyses enabled discovery of new signatures, 46 separation of overlapping signatures and decomposition of signatures into components that 47 may represent associated, but distinct, DNA damage, repair and/or replication mechanisms. 48 Estimation of the contribution of each signature to the mutational catalogues of individual 49 cancer genomes revealed associations with exogenous and endogenous exposures and 50 defective DNA maintenance processes. However, many signatures are of unknown cause. 51 This analysis provides a systematic perspective on the repertoire of mutational processes 52 contributing to the development of human cancer including a comprehensive reference set 53 of mutational signatures in human cancer. 54 55 56
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