Genome-wide association (GWA) studies have identified multiple new genomic loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D)1-11. Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to discover loci at which common alleles have modest effects, we performed meta-analysis of three T2D GWA scans encompassing 10,128 individuals of European-descent and ~2.2 million SNPs (directly genotyped and imputed). Replication testing was performed in an independent sample with an effective sample size of up to 53,975. At least six new loci with robust evidence for association were detected, including the JAZF1 (p=5.0×10 −14 ), CDC123/CAMK1D (p=1.2×10 −10 ), TSPAN8/ LGR5 (p=1.1×10 −9 ), THADA (p=1.1×10 −9 ), ADAMTS9 (p=1.2×10 −8 ), and NOTCH2 (p=4.1×10 −8 ) gene regions. The large number of loci with relatively small effects indicates the value of large discovery and follow-up samples in identifying additional clues about the inherited basis of T2D.Genome-wide association studies are unbiased by previous hypotheses concerning candidate genes and pathways, but challenged by the modest effect sizes of individual common susceptibility variants and the need for stringent statistical thresholds. For example, the largest allelic odds ratio of any established common variant for T2D is ~1.35 (TCF7L2), with the nine other validated associations to common variants (excluding FTO, which has its primary effect through obesity) having allelic odds ratios between 1.1 and 1. 21-6,11,12. To augment power to detect additional loci of similar and/or smaller effect, we increased sample size by combining three previously published GWA studies (Diabetes Genetics Initiative [DGI], Finland-United States Investigation of NIDDM Genetics [FUSION], and Wellcome Trust Case Control Consortium [WTCCC])1-4, and extended SNP coverage by imputing untyped SNPs based on patterns of haplotype variation from the HapMap dataset13 (Table 1).We started with a set of genotyped autosomal SNPs that passed quality control (QC) filters in each study: in WTCCC, 393,143 SNPs from the Affymetrix 500k chip (MAF>0.01; 1,924 cases and 2,938 population-based controls from the Wellcome Trust Case Control Consortium3,4); in DGI, 378,860 Using these directly measured and imputed genotypes, we tested for association of each SNP with T2D in each study separately, corrected each study for residual population stratification, cryptic relatedness or technical artifacts using genomic control, and then combined these results in a genome-wide meta-analysis across a total of 10,128 samples (4,549 cases, 5,579 controls) (Methods; Supplementary Methods). We calculated that this sample size provides reasonable power to detect additional variants with properties similar to those previously identified by less formal data combination efforts1,2,4 (Supplementary Table 2). Unless otherwise indicated, results presented are derived from...

Plasticity of the cell state has been proposed to drive resistance to multiple classes of cancer therapies, thereby limiting their effectiveness1–4. A high-mesenchymal cell state observed in human tumours and cancer cell lines has been associated with resistance to multiple treatment modalities across diverse cancer lineages, but the mechanistic underpinning for this state has remained incompletely understood1–6. Here we molecularly characterize this therapy-resistant high-mesenchymal cell state in human cancer cell lines and organoids and show that it depends on a druggable lipid-peroxidase pathway that protects against ferroptosis, a nonapoptotic form of cell death induced by the build-up of toxic lipid peroxides7,8. We show that this cell state is characterized by activity of enzymes that promote the synthesis of polyunsaturated lipids. These lipids are the substrates for lipid peroxidation by lipoxygenase enzymes8,9. This lipid metabolism creates a dependency on pathways converging on the phospholipid glutathione peroxidase (GPX4), a selenocysteine-containing enzyme that dissipates lipid peroxides and thereby prevents the iron-mediated reactions of peroxides that induce ferroptotic cell death8. Dependency on GPX4 was found to exist across diverse therapy-resistant states characterized by high expression of ZEB1, including epithelial-mesenchymal transition in epithelial-derived carcinomas, TGFβ-mediated therapy- resistance in melanoma, treatment-induced neuroendocrine transdifferentiation in prostate cancer, and sarcomas, which are fixed in a mesenchymal state owing to their cells of origin. We identify vulnerability to ferroptic cell death induced by inhibition of a lipid peroxidase pathway as a feature of therapy-resistant cancer cells across diverse mesenchymal cell-state contexts.

Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 × 10−8): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.

Changes in cellular gene expression in response to small-molecule or genetic perturbations have yielded signatures that can connect unknown mechanisms of action (MoA) to ones previously established. We hypothesized that differential basal gene expression could be correlated with patterns of small-molecule sensitivity across many cell lines to illuminate the actions of compounds whose MoA are unknown. To test this idea, we correlated the sensitivity patterns of 481 compounds with ~19,000 basal transcript levels across 823 different human cancer cell lines and identified selective outlier transcripts. This process yielded many novel mechanistic insights, including the identification of activation mechanisms, cellular transporters, and direct protein targets. We found that ML239, originally identified in a phenotypic screen for selective cytotoxicity in breast cancer stem-like cells, most likely acts through activation of fatty acid desaturase 2 (FADS2). These data and analytical tools are available to the research community through the Cancer Therapeutics Response Portal.

Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2).