The Landscape of the Heritable Cancer Genome.

Genome-wide association studies (GWAS) have found hundreds of single-nucleotide polymorphisms (SNP) associated with increased risk of cancer. However, the amount of heritable risk explained by SNPs is limited, leaving most of the cancer heritability unexplained. Tumor sequencing projects have shown that causal mutations are enriched in genic regions. We hypothesized that SNPs located in protein coding genes and nearby regulatory regions could explain a significant proportion of the heritable risk of cancer. To perform gene-level heritability analysis, we developed a new method, called Bayesian Gene Heritability Analysis (BAGHERA), to estimate the heritability explained by all genotyped SNPs and by those located in genic regions using GWAS summary statistics. BAGHERA was specifically designed for low heritability traits such as cancer and provides robust heritability estimates under different genetic architectures. BAGHERA-based analysis of 38 cancers reported in the UK Biobank showed that SNPs explain at least 10% of the heritable risk for 14 of them, including late onset malignancies. We then identified 1,146 genes, called cancer heritability genes (CHG), explaining a significant proportion of cancer heritability. CHGs were involved in hallmark processes controlling the transformation from normal to cancerous cells. Importantly, 60 of them also harbored somatic driver mutations, and 27 are tumor suppressors. Our results suggest that germline and somatic mutation information could be exploited to identify subgroups of individuals at higher risk of cancer in the broader population and could prove useful to establish strategies for early detection and cancer surveillance. SIGNIFICANCE: This study describes a new statistical method to identify genes associated with cancer heritability in the broader population, creating a map of the heritable cancer genome with gene-level resolution.See related commentary by Bader, p. 2586.

Germline and Somatic Genetic Variants in the p53 Pathway Interact to Affect Cancer Risk, Progression, and Drug Response.

Insights into oncogenesis derived from cancer susceptibility loci (SNP) hold the potential to facilitate better cancer management and treatment through precision oncology. However, therapeutic insights have thus far been limited by our current lack of understanding regarding both interactions of these loci with somatic cancer driver mutations and their influence on tumorigenesis. For example, although both germline and somatic genetic variation to the p53 tumor suppressor pathway are known to promote tumorigenesis, little is known about the extent to which such variants cooperate to alter pathway activity. Here we hypothesize that cancer risk-associated germline variants interact with somatic TP53 mutational status to modify cancer risk, progression, and response to therapy. Focusing on a cancer risk SNP (rs78378222) with a well-documented ability to directly influence p53 activity as well as integration of germline datasets relating to cancer susceptibility with tumor data capturing somatically-acquired genetic variation provided supportive evidence for this hypothesis. Integration of germline and somatic genetic data enabled identification of a novel entry point for therapeutic manipulation of p53 activities. A cluster of cancer risk SNPs resulted in increased expression of prosurvival p53 target gene KITLG and attenuation of p53-mediated responses to genotoxic therapies, which were reversed by pharmacologic inhibition of the prosurvival c-KIT signal. Together, our results offer evidence of how cancer susceptibility SNPs can interact with cancer driver genes to affect cancer progression and identify novel combinatorial therapies. SIGNIFICANCE: These results offer evidence of how cancer susceptibility SNPs can interact with cancer driver genes to affect cancer progression and present novel therapeutic targets.

Heritable genetic variants in key cancer genes link cancer risk with anthropometric traits.

BACKGROUND: Height and other anthropometric measures are consistently found to associate with differential cancer risk. However, both genetic and mechanistic insights into these epidemiological associations are notably lacking. Conversely, inherited genetic variants in tumour suppressors and oncogenes increase cancer risk, but little is known about their influence on anthropometric traits. METHODS: By integrating inherited and somatic cancer genetic data from the Genome-Wide Association Study Catalog, expression Quantitative Trait Loci databases and the Cancer Gene Census, we identify SNPs that associate with different cancer types and differential gene expression in at least one tissue type, and explore the potential pleiotropic associations of these SNPs with anthropometric traits through SNP-wise association in a cohort of 500,000 individuals. RESULTS: We identify three regulatory SNPs for three important cancer genes, FANCA, MAP3K1 and TP53 that associate with both anthropometric traits and cancer risk. Of particular interest, we identify a previously unrecognised strong association between the rs78378222[C] SNP in the 3' untranslated region (3'-UTR) of TP53 and both increased risk for developing non-melanomatous skin cancer (OR=1.36 (95% 1.31 to 1.41), adjusted p=7.62E-63), brain malignancy (OR=3.12 (2.22 to 4.37), adjusted p=1.43E-12) and increased standing height (adjusted p=2.18E-24, beta=0.073±0.007), lean body mass (adjusted p=8.34E-37, beta=0.073±0.005) and basal metabolic rate (adjusted p=1.13E-31, beta=0.076±0.006), thus offering a novel genetic link between these anthropometric traits and cancer risk. CONCLUSION: Our results clearly demonstrate that heritable variants in key cancer genes can associate with both differential cancer risk and anthropometric traits in the general population, thereby lending support for a genetic basis for linking these human phenotypes.

Dissecting the heritable risk of breast cancer: From statistical methods to susceptibility genes.

Decades of research have shown that rare highly penetrant mutations can promote tumorigenesis, but it is still unclear whether variants observed at high-frequency in the broader population could modulate the risk of developing cancer. Genome-wide Association Studies (GWAS) have generated a wealth of data linking single nucleotide polymorphisms (SNPs) to increased cancer risk, but the effect of these mutations are usually subtle, leaving most of cancer heritability unexplained. Understanding the role of high-frequency mutations in cancer can provide new intervention points for early diagnostics, patient stratification and treatment in malignancies with high prevalence, such as breast cancer. Here we review state-of-the-art methods to study cancer heritability using GWAS data and provide an updated map of breast cancer susceptibility loci at the SNP and gene level.

CD44 SNPrs187115: A Novel Biomarker Signature that Predicts Survival in Resectable Pancreatic Ductal Adenocarcinoma.

PURPOSE: Although pancreatic ductal adenocarcinoma (PDAC) is an aggressive tumor, like other common cancers, it displays a wide range of biology. However, at present, there are no reliable tests to predict patients' cancer-specific outcomes and guide personalized treatment decisions. In this study, we aim to identify such biomarkers in resectable PDAC by studying SNPs in the CD44 gene, which drives the progression of pancreatic cancer. EXPERIMENTAL DESIGN: A total of 348 PDAC patients from three independent cohorts [Switzerland, Germany, The Cancer Genome Atlas (TCGA)] who underwent pancreatic resection are included in the study. Information on the haplotype structure of the CD44 gene is obtained using 1000 Genomes Project data, and the genotypes of the respective tagging SNPs are determined. Cox proportional hazards models are utilized to analyze the impact of SNP genotype on patients' survival. RESULTS: We identify an SNP in the CD44 gene (SNPrs187115) that independently associates with allelic differences in prognosis in all study cohorts. Specifically, in 121 Swiss patients, we observe an up to 2.38-fold (P = 0.020) difference in tumor-related death between the genotypes of SNPrs187115 We validate those results in both the German (HR = 2.32, P = 0.044, 101 patients) and the TCGA cohort (HR = 2.36, P = 0.044, 126 patients). CONCLUSIONS: CD44 SNPrs187115 can serve as a novel biomarker readily available at the time of PDAC diagnosis that identifies patients at risk for faster tumor progression and guide personalized treatment decisions. It has the potential to significantly expand the pool of patients that would benefit from tumor resection. Clin Cancer Res; 22(24); 6069-77. ©2016 AACR.

The importance of p53 pathway genetics in inherited and somatic cancer genomes.

Decades of research have shown that mutations in the p53 stress response pathway affect the incidence of diverse cancers more than mutations in other pathways. However, most evidence is limited to somatic mutations and rare inherited mutations. Using newly abundant genomic data, we demonstrate that commonly inherited genetic variants in the p53 pathway also affect the incidence of a broad range of cancers more than variants in other pathways. The cancer-associated single nucleotide polymorphisms (SNPs) of the p53 pathway have strikingly similar genetic characteristics to well-studied p53 pathway cancer-causing somatic mutations. Our results enable insights into p53-mediated tumour suppression in humans and into p53 pathway-based cancer surveillance and treatment strategies.

Generalized pattern search algorithm for Peptide structure prediction.

Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small molecules, called peptides. In this research work, we propose a new ab initio algorithm, the generalized pattern search algorithm, based on the well-known class of Search-and-Poll algorithms. We performed an extensive set of simulations over a well-known set of 44 peptides to investigate the robustness and reliability of the proposed algorithm, and we compared the peptide conformation with a state-of-the-art algorithm for peptide structure prediction known as PEPstr. In particular, we tested the algorithm on the instances proposed by the originators of PEPstr, to validate the proposed algorithm; the experimental results confirm that the generalized pattern search algorithm outperforms PEPstr by 21.17% in terms of average root mean-square deviation, RMSD C(alpha).