BACKGROUNDRecent studies have provided a detailed census of genes that are mutated in acute myeloid leukemia (AML). Our next challenge is to understand how this genetic diversity defines the pathophysiology of AML and informs clinical practice. METHODSWe enrolled a total of 1540 patients in three prospective trials of intensive therapy. Combining driver mutations in 111 cancer genes with cytogenetic and clinical data, we defined AML genomic subgroups and their relevance to clinical outcomes. RESULTSWe identified 5234 driver mutations across 76 genes or genomic regions, with 2 or more drivers identified in 86% of the patients. Patterns of co-mutation compartmentalized the cohort into 11 classes, each with distinct diagnostic features and clinical outcomes. In addition to currently defined AML subgroups, three heterogeneous genomic categories emerged: AML with mutations in genes encoding chromatin, RNAsplicing regulators, or both (in 18% of patients); AML with TP53 mutations, chromosomal aneuploidies, or both (in 13%); and, provisionally, AML with IDH2 R172 mutations (in 1%). Patients with chromatin-spliceosome and TP53-aneuploidy AML had poor outcomes, with the various class-defining mutations contributing independently and additively to the outcome. In addition to class-defining lesions, other co-occurring driver mutations also had a substantial effect on overall survival. The prognostic effects of individual mutations were often significantly altered by the presence or absence of other driver mutations. Such gene-gene interactions were especially pronounced for NPM1-mutated AML, in which patterns of co-mutation identified groups with a favorable or adverse prognosis. These predictions require validation in prospective clinical trials. CONCLUSIONSThe driver landscape in AML reveals distinct molecular subgroups that reflect discrete paths in the evolution of AML, informing disease classification and prognostic stratification. (Funded by the Wellcome Trust and others; ClinicalTrials.gov number, NCT00146120.) a bs tr ac t

We analysed whole genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. 93 protein-coding cancer genes carried likely driver mutations. Some non-coding regions exhibited high mutation frequencies but most have distinctive structural features probably causing elevated mutation rates and do not harbour driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed 12 base substitution and six rearrangement signatures. Three rearrangement signatures, characterised by tandem duplications or deletions, appear associated with defective homologous recombination based DNA repair: one with deficient BRCA1 function; another with deficient BRCA1 or BRCA2 function; the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operative, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

Key Points• MDS is characterized by mutations in .40 genes, a complex structure of gene-gene interactions and extensive subclonal diversification.• The total number of oncogenic mutations and early detection of subclonal mutations are significant prognostic variables in MDS.Myelodysplastic syndromes (MDS) are a heterogeneous group of chronic hematological malignancies characterized by dysplasia, ineffective hematopoiesis and a variable risk of progression to acute myeloid leukemia. Sequencing of MDS genomes has identified mutations in genes implicated in RNA splicing, DNA modification, chromatin regulation, and cell signaling. We sequenced 111 genes across 738 patients with MDS or closely related neoplasms (including chronic myelomonocytic leukemia and MDS-myeloproliferative neoplasms) to explore the role of acquired mutations in MDS biology and clinical phenotype. Seventy-eight percent of patients had 1 or more oncogenic mutations. We identify complex patterns of pairwise association between genes, indicative of epistatic interactions involving components of the spliceosome machinery and epigenetic modifiers. Coupled with inferences on subclonal mutations, these data suggest a hypothesis of genetic "predestination," in which early driver mutations, typically affecting genes involved in RNA splicing, dictate future trajectories of disease evolution with distinct clinical phenotypes. Driver mutations had equivalent prognostic significance, whether clonal or subclonal, and leukemia-free survival deteriorated steadily as numbers of driver mutations increased. Thus, analysis of oncogenic mutations in large, well-characterized cohorts of patients illustrates the interconnections between the cancer genome and disease biology, with considerable potential for clinical application. (Blood. 2013;122(22):3616-3627) Continuing Medical

How somatic mutations accumulate in normal cells is central to understanding cancer development, but is poorly understood. We performed ultra-deep sequencing of 74 cancer genes in small (0.8-4.7mm 2 ) biopsies of normal skin. Across 234 biopsies of sun-exposed eyelid epidermis from four individuals, the burden of somatic mutations averaged 2-6 mutations/megabase/cell, similar to many cancers, and exhibited characteristic signatures of ultraviolet light exposure. Remarkably, multiple cancer genes are under strong positive selection even in physiologically normal skin, including most of the key drivers of cutaneous squamous cell carcinomas. Positively selected 'driver' mutations were found in 18-32% of normal skin cells at a density of ~140/cm 2 . We observed variability in the driver landscape among individuals and variability in sizes of clonal expansions across genes. Thus, aged, sun-exposed skin is a patchwork of thousands of evolving clones, with over a quarter of cells carrying cancer-causing mutations while maintaining the physiological functions of epidermis.The standard narrative of tumor evolution depicts accumulation of driver mutations in cancer genes, causing waves of expansion of progressively more disordered clones (1, 2). Central to this model is the presumption that randomly distributed somatic mutations must accumulate in normal cells before transformation (3), but directly observing them has proved challenging due to the polyclonal composition of normal tissue. Retrospective reconstructions of clonal evolution from sequencing of tumors give only partial insights, leaving us with fundamental gaps in our understanding of the earliest stages of cancer development. Critical, but largely unanswered, questions include the burden of somatic mutations in normal cells, which mutational processes are operative in normal tissues, the extent of positive selection among competing clones within a organ, and the patterns of * Correspondence to: phj20@mrc-cu.cam.ac.uk; pc8@sanger.ac.uk. Europe PMC Funders Group Europe PMC Funders Author ManuscriptsEurope PMC Funders Author Manuscripts clonal expansion induced by the very first driver mutations (4, 5). These questions have been partially addressed in blood cells, where somatic mutations, including some driver mutations, have been found to accumulate at a low rate with increasing age (6-10).To study the burden, mutational processes and clonal architecture of somatic mutations in normal non-hematological tissue, we focused on sun-exposed skin. Previous studies have reported the existence of clonal patches of skin cells carrying TP53 mutations (11)(12)(13)(14)(15). Motivated by this, we designed a sequencing strategy capable of detecting such clones by performing ultra-deep sequencing of small biopsies and adapting algorithms to detect mutations in a small fraction of cells. We used eyelid epidermis because of its relatively high levels of sun exposure and being one of the few body sites to have normal skin excised (blepharoplasty). This procedure is perfo...

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