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

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.

Highlights d This large analysis identified ancestry correlates in cancer d Ancestry-associated artifacts and confounders were identified d Ancestry effects are profoundly tissue specific d Rates of FBXW7, VHL, and PBRM1 mutations and immune activity vary by ancestry

Local concentrations of mutations are well-known in human cancers. However, their 3-dimensional (3D) spatial relationships have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and inter-molecular clusters, some of which showed tumor/tissue specificity. In addition, we identified 369 rare mutations from genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium recurrence mutations from genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all residing within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high throughput phosphorylation data and cell-line based experimental evaluation. Finally, drug-mutation cluster/network analysis predicted over 800 promising candidates of druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

Previous studies of rare germline variants in cancer has largely been limited to the coding regions of known predisposition genes. The TCGA PanCanAtlas Germline Working Group is analyzing germline predisposing variants of 10,389 cases in 33 cancer types. We deployed more than 121,000 virtual machines running for over 600,000 hours on the ISB Cancer Genome Cloud to conduct massively parallel variant calling and analyses, and the resulting data are shared with scientists across institutions worldwide. Carriers of the functional regulatory variants add on to the 8.9% of cases carrying known pathogenic variants. Burden analyses reveal enrichment of rare variants in the 3'UTR region of NHP2 and POLH. Further, we observed variants aggregating in conserved regions of selected microRNA families that are also affected by somatic mutations, including mir-17 and mir-29. We nominate regulatory variants by using GWAVA and FunSeq2 corroborated with their enrichment in cancer. The prioritized variants are then further evaluated by further co-occurrence of two-hit events and expression changes in their respective tumor samples. Finally, we examine ancestries, familial history and age at onset for carriers of these variants. Overall, we aim to discover and establish the role of regulatory germline variants in oncogenesis. Citation Format: Kuan-lin Huang, Amila Weerasinghe, Yige Wu, Wen-wei Liang, R. Jay Mashl, Sheila Reynolds, Kathleen E. Houlahan, Ninad Oak, The Cancer Genome Atlas, Alexander J. Lazar, Michael C. Wendel, Ekta Khurana, Sharon Plon, Feng Chen, Mark Gerstein, Ilya Shmulevich, Li Ding. Regulatory germline variants in 10,389 adult cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5359.

Cancers require telomere maintenance mechanisms for unlimited replicative potential. They achieve this through TERT activation or alternative telomere lengthening associated with ATRX or DAXX loss. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we dissect whole-genome sequencing data of over 2500 matched tumor-control samples from 36 different tumor types aggregated within the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium to characterize the genomic footprints of these mechanisms. While the telomere content of tumors with ATRX or DAXX mutations (ATRX/DAXX trunc) is increased, tumors with TERT modifications show a moderate decrease of telomere content. One quarter of all tumor samples contain somatic integrations of telomeric sequences into non-telomeric DNA. This fraction is increased to 80% prevalence in ATRX/DAXX trunc tumors, which carry an aberrant telomere variant repeat (TVR) distribution as another genomic marker. The latter feature includes enrichment or depletion of the previously undescribed singleton TVRs TTCGGG and TTTGGG, respectively. Our systematic analysis provides new insight into the recurrent genomic alterations associated with telomere maintenance mechanisms in cancer.

Many primary tumours have low levels of molecular oxygen (hypoxia), and hypoxic tumours respond poorly to therapy. Pan-cancer molecular hallmarks of tumour hypoxia remain poorly understood, with limited comprehension of its associations with specific mutational processes, non-coding driver genes and evolutionary features. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we quantify hypoxia in 1188 tumours spanning 27 cancer types. Elevated hypoxia associates with increased mutational load across cancer types, irrespective of underlying mutational class. The proportion of mutations attributed to several mutational signatures of unknown aetiology directly associates with the level of hypoxia, suggesting underlying mutational processes for these signatures. At the gene level, driver mutations in TP53, MYC and PTEN are enriched in hypoxic tumours, and mutations in PTEN interact with hypoxia to direct tumour evolutionary trajectories. Overall, hypoxia plays a critical role in shaping the genomic and evolutionary landscapes of cancer.