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

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Highlights d Protein interaction screening identifies 336 MYC-interacting partner proteins d MB0 interacts with TFIIF to modulate transcription and accelerates tumor growth d MBII interacts with TRRAP-HAT complexes and is essential for tumor initiation d Co-expression of dysfunctional DMBII and DMB0 MYC proteins restores MYC activity

Cancer differs significantly between men and women; even after adjusting for known epidemiologic risk factors, the sexes differ in incidence, outcome, and response to therapy. These differences occur in many but not all tumor types, and their origins remain largely unknown. Here, we compare somatic mutation profiles between tumors arising in men and in women. We discovered large differences in mutation density and sex biases in the frequency of mutation of specific genes; these differences may be associated with sex biases in DNA mismatch repair genes or microsatellite instability. Sex-biased genes include well-known drivers of cancer such as b-catenin and BAP1. Sex influenced biomarkers of patient outcome, where different genes were associated with tumor aggression in each sex. These data call for increased study and consideration of the molecular role of sex in cancer etiology, progression, treatment, and personalized therapy.Significance: This study provides a comprehensive catalog of sex differences in somatic alterations, including in cancer driver genes, which influence prognostic biomarkers that predict patient outcome after definitive local therapy.