All cancers carry somatic mutations in their genomes. A subset, known as driver mutations, confer clonal selective advantage on cancer cells and are causally implicated in oncogenesis1, and the remainder are passenger mutations. The driver mutations and mutational processes operative in breast cancer have not yet been comprehensively explored. Here we examine the genomes of 100 tumours for somatic copy number changes and mutations in the coding exons of protein-coding genes. The number of somatic mutations varied markedly between individual tumours. We found strong correlations between mutation number, age at which cancer was diagnosed and cancer histological grade, and observed multiple mutational signatures, including one present in about ten per cent of tumours characterized by numerous mutations of cytosine at TpC dinucleotides. Driver mutations were identified in several new cancer genes including AKT2, ARID1B, CASP8, CDKN1B, MAP3K1, MAP3K13, NCOR1, SMARCD1 and TBX3. Among the 100 tumours, we found driver mutations in at least 40 cancer genes and 73 different combinations of mutated cancer genes. The results highlight the substantial genetic diversity underlying this common disease.

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

Warfarin-dosing algorithms incorporating CYP2C9 and VKORC1 ؊1639G>A improve dose prediction compared with algorithms based solely on clinical and demographic factors. However, these algorithms better capture dose variability among whites than Asians or blacks. Herein, we evaluate whether other VKORC1 polymorphisms and haplotypes explain additional variation in warfarin dose beyond that explained by VKORC1 ؊1639G>A among Asians (n ‫؍‬ 1103), blacks (n ‫؍‬ 670), and whites (n ‫؍‬ 3113).Participants were recruited from 11 countries as part of the International Warfarin Pharmacogenetics Consortium effort. Evaluation of the effects of individual VKORC1 single nucleotide polymorphisms (SNPs) and haplotypes on warfarin dose used both univariate and multivariable linear regression. VKORC1 ؊1639G>A and 1173C>T individually explained the greatest variance in dose in all 3 racial groups. Incorporation of additional VKORC1 SNPs or haplotypes did not further improve dose prediction.VKORC1 explained greater variability in dose among whites than blacks and Asians. Differences in the percentage of variance in dose explained by VKORC1 across race were largely accounted for by the frequency of the ؊1639A (or 1173T) allele. Thus, clinicians should recognize that, although at a population level, the contribution of VKORC1 toward dose requirements is higher in whites than in nonwhites; genotype predicts similar dose requirements across racial groups. (Blood. 2010;115(18):3827-3834) IntroductionWarfarin, the most commonly prescribed anticoagulant, exhibits large interpatient variability in dose requirements. Patient-specific factors (eg, age, body size, race, concurrent diseases, and medications) explain some of the variability in warfarin dose, but genetic factors influencing warfarin response explain a significantly higher proportion of the variability in dose. 1 Candidate-gene association studies 2-22 have identified 2 genes responsible for the main proportion of the genetic effect: CYP2C9, which codes for the enzyme cytochrome P450 2C9 that metabolizes S-warfarin, 23,24 and VKORC1, which codes for warfarin's target, vitamin K epoxide reductase. 25,26 The influence of CYP2C9 and VKORC1 has also been confirmed by genome-wide association studies among whites. 27,28 These studies suggest that identification of common variants in other genes exhibiting influence of magnitude similar to that of CYP2C9 and VKORC1 is unlikely in whites. The most influential CYP2C9 polymorphisms are nonsynonymous coding variants resulting in reduced enzyme activity and decreased metabolic capacity. [29][30][31] In contrast, common VKORC1 variants associated with warfarin dose are noncoding polymorphisms, the effects of which are thought to be mediated through differential expression of the VKOR protein. 32 These polymorphisms are within a region of strong linkage disequilibrium (LD) among patients of European ancestry; thus, they may all point to the same common causal polymorphism. 10,14 However, neither the causative VKORC1 polymorphism nor the molecula...

The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes genotype-based drug guidelines to help clinicians understand how available genetic test results could be used to optimize drug therapy. CPIC has focused initially on well-known examples of pharmacogenomic associations that have been implemented in selected clinical settings, publishing nine to date. Each CPIC guideline adheres to a standardized format and includes a standard system for grading levels of evidence linking genotypes to phenotypes and assigning a level of strength to each prescribing recommendation. CPIC guidelines contain the necessary information to help clinicians translate patient-specific diplotypes for each gene into clinical phenotypes or drug dosing groups. This paper reviews the development process of the CPIC guidelines and compares this process to the Institute of Medicine’s Standards for Developing Trustworthy Clinical Practice Guidelines.