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

Deciphering whether actionable driver mutations are found in all or a subset of tumor cells will likely be required to improve drug development and precision medicine strategies. We analyzed nine cancer types to determine the subclonal frequencies of driver events, to time mutational processes during cancer evolution, and to identify drivers of subclonal expansions. Although Table S1. Driver genes within each cancer type (provided as a separate Excel file). Table S2. Mutational spectra of cancer genes (provided as a separate Excel file). Table S3. Cancer genes identified through clonality and temporal dissection analysis (provided as a separate Excel file). Table S4. Genes linked with targeted therapeutics (provided as a separate Excel file). Competing interests: C.S. sits on the Roche/Genentech clinical trial steering committee. All other authors declare that they have no competing interests.Data and materials availability: Data and code are available online at https://bitbucket.org/nmcgranahan/pancancerclonality/ downloads/McGranahan_data.zip. Europe PMC Funders Group Europe PMC Funders Author ManuscriptsEurope PMC Funders Author Manuscripts mutations in known driver genes typically occurred early in cancer evolution, we also identified later subclonal "actionable" mutations, including BRAF(V600E), IDH1(R132H), PIK3CA(E545K), EGFR(L858R), and KRAS(G12D), which may compromise the efficacy of targeted therapy approaches. More than 20% of IDH1 mutations in glioblastomas, and 15% of mutations in genes in the PI3K(phosphatidylinositol 3-kinase)-AKT-mTOR (mammalian target of rapamycin) signaling axis across all tumor types were subclonal. Mutations in the RAS-MEK (mitogen-activated protein kinase kinase) signaling axis were less likely to be subclonal than mutations in genes associated with PI3K-AKT-mTORsignaling. Analysis of late mutations revealed a link between APOBEC-mediated mutagenesis and the acquisition of subclonal driver mutations and uncovered putative cancer genes involved in subclonal expansions, including CTNNA2 and ATXN1. Our results provide a pan-cancer census of driver events within the context of intratumor heterogeneity and reveal patterns of tumor evolution across cancers. The frequent presence of subclonal driver mutations suggests the need to stratify targeted therapy response according to the proportion of tumor cells in which the driver is identified.

We describe our algorithm and software for determining copy number profiles from tumor genome sequencing data, and find that it compares favorably to existing algorithms for the same purpose.

Highlights d Clock-like mutation process attributed to APOBEC3 mediates earliest mutations in PC d Identification of four molecular subgroups that stratifies intermediate-risk disease d Rearrangements at the ESRP1 locus associated with aggressive and proliferative cancer d Development of method to predict clinical trajectories of PC from DNA sequencing data