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

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.

In metastatic melanoma, it is vital to identify and validate biomarkers of prognosis. Previous studies have systematically evaluated protein biomarkers or mRNA-based expression signatures. No such analyses have been applied to microRNA (miRNA)-based prognostic signatures. As a first step, we identified two prognostic miRNA signatures from publicly available data sets (Gene Expression Omnibus/The Cancer Genome Atlas) of global miRNA expression profiling information. A 12-miRNA signature predicted longer survival after surgery for resection of American Joint Committee on Cancer stage III disease (>4 years, no sign of relapse) and outperformed American Joint Committee on Cancer standard-of-care prognostic markers in leave-one-out cross-validation analysis (error rates 34% and 38%, respectively). A similar 15-miRNA biomarker derived from The Cancer Genome Atlas miRNA-seq data performed slightly worse (39%) than these current biomarkers. Both signatures were then assessed for replication in two independent data sets and subjected to systematic cross-validation together with the three other miRNA-based prognostic signatures proposed in the literature to date. Five miRNAs (miR-142-5p, miR-150-5p, miR-342-3p, miR-155-5p, and miR-146b-5p) were reproducibly associated with patient outcome and have the greatest potential for application in the clinic. Our extensive validation approach highlighted among multiple independent cohorts the translational potential and limitations of miRNA signatures, and pointed to future directions in the analysis of this emerging class of markers.