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

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

Ionizing radiation is a potent carcinogen, inducing cancer through DNA damage. The signatures of mutations arising in human tissues following in vivo exposure to ionizing radiation have not been documented. Here, we searched for signatures of ionizing radiation in 12 radiation-associated second malignancies of different tumour types. Two signatures of somatic mutation characterize ionizing radiation exposure irrespective of tumour type. Compared with 319 radiation-naive tumours, radiation-associated tumours carry a median extra 201 deletions genome-wide, sized 1–100 base pairs often with microhomology at the junction. Unlike deletions of radiation-naive tumours, these show no variation in density across the genome or correlation with sequence context, replication timing or chromatin structure. Furthermore, we observe a significant increase in balanced inversions in radiation-associated tumours. Both small deletions and inversions generate driver mutations. Thus, ionizing radiation generates distinctive mutational signatures that explain its carcinogenic potential.

In chronic myeloid leukemia (CML) patients, a breakpoint cluster region-Abelson (BCR-ABL1) value >10% at 3 months of therapy is statistically associated with poorer outcome, yet many of these patients still achieve satisfactory outcomes. We investigated 528 first-line imatinib-treated patients to determine whether patients with the poorest outcome can be better discriminated at 3 months. All outcomes were significantly superior for the 410 patients with BCR-ABL1 ≤10% at 3 months (P < .001). However, the poorest outcomes among the 95 evaluable patients with BCR-ABL1 >10% at 3 months were identified by the rate of BCR-ABL1 decline from baseline, assessed by estimating the number of days over which BCR-ABL1 halved. Patients with BCR-ABL1 halving time <76 days (n = 74) had significantly superior outcomes compared with patients whose BCR-ABL1 values did not halve by 76 days (n = 21; 4-year overall survival, 95% vs 58%, P = .0002; progression-free survival, 92% vs 63%, P = .008; failure-free survival, 59% vs 6%, P < .0001; and major molecular response, 54% vs 5%, P = .008). By multivariate analysis, the halving time was an independent predictor of outcome in this poor risk group. Our study highlighted that the rate of BCR-ABL1 decline may be a critical prognostic discriminator of the patients with very poor outcome among those >10% at 3 months. The International Randomized IFN vs STI571 (IRIS) trial was registered at http://www.clinicaltrials.gov as #NCT00006343. The Tyrosine Kinase Inhibitor Optimization and Selectivity (TOPS) trial was registered at http://www.clinicaltrials.gov as #NCT00124748. The Therapeutic Intensification in DE-novo Leukaemia (TIDEL) I trial was registered at http://www.ANZCTR.org.au as #ACTRN12607000614493. The TIDEL II trial was registered at http://www.ANZCTR.org.au as #ACTRN12607000325404.