Many individuals with multiple or large colorectal adenomas, or early-onset colorectal cancer (CRC), have no detectable germline mutations in the known cancer predisposition genes. Using whole-genome sequencing, supplemented by linkage and association analysis, we identified specific heterozygous POLE or POLD1 germline variants in several multiple adenoma and/or CRC cases, but in no controls. The susceptibility variants appear to have high penetrance. POLD1 is also associated with endometrial cancer predisposition. The mutations map to equivalent sites in the proof-reading (exonuclease) domain of DNA polymerases ε and δ, and are predicted to impair correction of mispaired bases inserted during DNA replication. In agreement with this prediction, mutation carriers’ tumours were microsatellite-stable, but tended to acquire base substitution mutations, as confirmed by yeast functional assays. Further analysis of published data showed that the recently-described group of hypermutant, microsatellite-stable CRCs is likely to be caused by somatic POLE exonuclease domain mutations.

To identify risk variants for glioma, we conducted a meta-analysis of two genome-wide association studies by genotyping 550K tagging SNPs in a total of 1,878 cases and 3,670 controls, with validation in three additional independent series totaling 2,545 cases and 2,953 controls. We identified five risk loci for glioma at 5p15.33 (rs2736100, TERT; P = 1.50 × 10−17), 8q24.21 (rs4295627, CCDC26; P = 2.34 × 10−18), 9p21.3 (rs4977756, CDKN2A-CDKN2B; P = 7.24 × 10−15), 20q13.33 (rs6010620, RTEL1; P = 2.52 × 10−12) and 11q23.3 (rs498872, PHLDB1; P = 1.07 × 10−8). These data show that common low-penetrance susceptibility alleles contribute to the risk of developing glioma and provide insight into disease causation of this primary brain tumor.

Genome-wide association (GWA) studies have thus far identified 10 loci at which common variants influence the risk of developing colorectal cancer (CRC). To enhance power to identify additional loci, we conducted a meta-analysis of three GWA studies from the UK totalling 3,334 cases and 4,628 controls, followed by multiple validation analyses, involving a total of 18,095 CRC cases and 20,197 controls. We identified new associations at 4 CRC risk loci: 1q41 (rs6691170, OR=1.06, P=9.55x10-10; rs6687758, OR=1.09, P=2.27x10-9); 3q26.2 (rs10936599, OR=0.93, P=3.39x10-8); 12q13.13 (rs11169552, OR=0.92, P=1.89x10-10; rs7136702, OR=1.06, P=4.02=x10-8); and 20q13.33 (rs4925386, OR=0.93, P=1.89x10-10). As well as identifying multiple new CRC risk loci this analysis provides evidence that additional CRC-associated variants of similar effect size remain to be discovered.

Understanding protein interactions has broad implications for the mechanism of recognition, protein design, and assigning putative functions to uncharacterized proteins. Studying protein flexibility is a key component in the challenge of describing protein interactions. In this work, we characterize the observed conformational change for a set of 20 proteins that undergo large conformational change upon association (>2 Å C␣ RMSD) and ask what features of the motion are successfully reproduced by the normal modes of the system. We demonstrate that normal modes can be used to identify mobile regions and, in some proteins, to reproduce the direction of conformational change. In 35% of the proteins studied, a single low-frequency normal mode was found that describes well the direction of the observed conformational change. Finally, we find that for a set of 134 proteins from a docking benchmark that the characteristic frequencies of normal modes can be used to predict reliably the extent of observed conformational change. We discuss the implications of the results for the mechanics of protein recognition.conformational selection ͉ elastic network model ͉ induced fit ͉ protein interactions ͉ protein recognition P roteins are not static, and many undergo substantial rearrangements upon binding to other molecules (1). Such changes are central to protein function (2). The limitations of our understanding of conformational change impact markedly on our ability to model such changes. A successful approach to modeling protein flexibility, one of the key current challenges in developing protein-protein docking algorithms, would have far-reaching consequences for the fields of drug design and function prediction.The initial lock-and-key description of protein interaction, first introduced by Fischer in 1894 (3), did not account for conformational change and has since been modified, beginning with Koshland's induced fit hypothesis in 1958 (4). However, a range of studies including molecular dynamics (picosecondnanosecond time scales), NMR, and single-molecule FRET experiments (microsecond-millisecond time scales) (5-7), have questioned the extent to which conformational change can be considered induced by the binding partner.An alternative mechanism is conformational selection, where the native state of the protein exists in an ensemble of conformations, with the partner binding selectively to a specific conformation, thus shifting the equilibrium toward the binding conformation (8). An elaboration, proposed by Grunberg et al. (9), describes a three-stage process consisting of (i) independent diffusion of the receptor and ligand each subject to conformational fluctuations, (ii) an encounter between the receptor and ligand leading to a series of microcollisions that may result in the formation of a recognition complex, and (iii) either dissociation of the encounter complex or formation of the bound complex with possible further conformational changes resulting from induced fit. An important question to address is, therefore, ...

Using data from a genome-wide association study of 907 individuals with childhood acute lymphoblastic leukemia (cases) and 2,398 controls and with validation in samples totaling 2,386 cases and 2,419 controls, we have shown that common variation at 9p21.3 (rs3731217, intron 1 of CDKN2A) influences acute lymphoblastic leukemia risk (odds ratio = 0.71, P = 3.01 × 10−11), irrespective of cell lineage.