Allele-specific miRNA-binding analysis identifies candidate target genes for breast cancer risk

Most breast cancer (BC) risk-associated single-nucleotide polymorphisms (raSNPs) identified in genome-wide association studies (GWAS) are believed to cis-regulate the expression of genes. We hypothesise that cis-regulatory variants contributing to disease risk may be affecting microRNA (miRNA) genes and/or miRNA binding. To test this, we adapted two miRNA-binding prediction algorithms-TargetScan and miRanda-to perform allele-specific queries, and integrated differential allelic expression (DAE) and expression quantitative trait loci (eQTL) data, to query 150 genome-wide significant ( P≤5×10-8 ) raSNPs, plus proxies. We found that no raSNP mapped to a miRNA gene, suggesting that altered miRNA targeting is an unlikely mechanism involved in BC risk. Also, 11.5% (6 out of 52) raSNPs located in 3'-untranslated regions of putative miRNA target genes were predicted to alter miRNA::mRNA (messenger RNA) pair binding stability in five candidate target genes. Of these, we propose RNF115, at locus 1q21.1, as a strong novel target gene associated with BC risk, and reinforce the role of miRNA-mediated cis-regulation at locus 19p13.11. We believe that integrating allele-specific querying in miRNA-binding prediction, and data supporting cis-regulation of expression, improves the identification of candidate target genes in BC risk, as well as in other common cancers and complex diseases.Funding Agency Portuguese Foundation for Science and Technology CRESC ALGARVE 2020 European Union (EU) 303745 Maratona da Saude Award DL 57/2016/CP1361/CT0042 SFRH/BPD/99502/2014 CBMR-UID/BIM/04773/2013 POCI-01-0145-FEDER-022184info:eu-repo/semantics/publishedVersio

Fine mapping of chromosome 5p15.33 based on a targeted deep sequencing and high density genotyping identifies novel lung cancer susceptibility loci

Chromosome 5p15.33 has been identified as a lung cancer susceptibility locus, however the underlying causal mechanisms were not fully elucidated. Previous fine-mapping studies of this locus have relied on imputation or investigated a small number of known, common variants. This study represents a significant advance over previous research by investigating a large number of novel, rare variants, as well as their underlying mechanisms through telomere length. Variants for this fine-mapping study were identified through a targeted deep sequencing (average depth of coverage greater than 4000×) of 576 individuals. Subsequently, 4652 SNPs, including 1108 novel SNPs, were genotyped in 5164 cases and 5716 controls of European ancestry. After adjusting for known risk loci, rs2736100 and rs401681, we identified a new, independent lung cancer susceptibility variant in LPCAT1: rs139852726 (OR = 0.46, P = 4.73×10(–9)), and three new adenocarcinoma risk variants in TERT: rs61748181 (OR = 0.53, P = 2.64×10(–6)), rs112290073 (OR = 1.85, P = 1.27×10(–5)), rs138895564 (OR = 2.16, P = 2.06×10(–5); among young cases, OR = 3.77, P = 8.41×10(–4)). In addition, we found that rs139852726 (P = 1.44×10(–3)) was associated with telomere length in a sample of 922 healthy individuals. The gene-based SKAT-O analysis implicated TERT as the most relevant gene in the 5p15.33 region for adenocarcinoma (P = 7.84×10(–7)) and lung cancer (P = 2.37×10(–5)) risk. In this largest fine-mapping study to investigate a large number of rare and novel variants within 5p15.33, we identified novel lung and adenocarcinoma susceptibility loci with large effects and provided support for the role of telomere length as the potential underlying mechanism

Alcohol consumption and lung cancer risk: A pooled analysis from the International Lung Cancer Consortium and the SYNERGY study

Background: There is inadequate evidence to determine whether there is an effect of alcohol consumption on lung cancer risk. We conducted a pooled analysis of data from the International Lung Cancer Consortium and the SYNERGY study to investigate this possible association by type of beverage with adjustment for other potential confounders. Methods: Twenty one case-control studies and one cohort study with alcohol-intake data obtained from questionnaires were included in this pooled analysis (19,149 cases and 362,340 controls). Adjusted odds ratios (OR) or hazard ratios (HR) with corresponding 95% confidence intervals (CI) were estimated for each measure of alcohol consumption. Effect estimates were combined using random or fixed-effects models where appropriate. Associations were examined for overall lung cancer and by histological type. Results: We observed an inverse association between overall risk of lung cancer and consumption of alcoholic beverages compared to non-drinkers, but the association was not monotonic. The lowest risk was observed for persons who consumed 10-19.9 g/day ethanol (OR vs. non-drinkers = 0.78; 95% CI: 0.67, 0.91), where 1 drink is approximately 12-15 g. This J-shaped association was most prominent for squamous cell carcinoma (SCC). The association with all lung cancer varied little by type of alcoholic beverage, but there were notable differences for SCC. We observed an association with beer intake (OR for >= 20 g/day vs nondrinker = 1.42; 95% CI: 1.06, 1.90). Conclusions: Whether the non-monotonic associations we observed or the positive association between beer drinking and squamous cell carcinoma reflect real effects await future analyses and insights about possible biological mechanisms

Alcohol and lung cancer risk among never smokers: A pooled analysis from the international lung cancer consortium and the SYNERGY study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136447/1/ijc30618-sup-0001-supptables.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136447/2/ijc30618_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136447/3/ijc30618.pd

Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.

Although several lung cancer susceptibility loci have been identified, much of the heritability for lung cancer remains unexplained. Here 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated genome-wide association study (GWAS) analysis of lung cancer in 29,266 cases and 56,450 controls. We identified 18 susceptibility loci achieving genome-wide significance, including 10 new loci. The new loci highlight the striking heterogeneity in genetic susceptibility across the histological subtypes of lung cancer, with four loci associated with lung cancer overall and six loci associated with lung adenocarcinoma. Gene expression quantitative trait locus (eQTL) analysis in 1,425 normal lung tissue samples highlights RNASET2, SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2, and the telomere-related genes OFBC1 and RTEL1. Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer

Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations

Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103-14. ©2016 AACR

A Two-Dimensional Pooling Strategy for Rare Variant Detection on Next-Generation Sequencing Platforms

<div><p>We describe a method for pooling and sequencing DNA from a large number of individual samples while preserving information regarding sample identity. DNA from 576 individuals was arranged into four 12 row by 12 column matrices and then pooled by row and by column resulting in 96 total pools with 12 individuals in each pool. Pooling of DNA was carried out in a two-dimensional fashion, such that DNA from each individual is present in exactly one row pool and exactly one column pool. By considering the variants observed in the rows and columns of a matrix we are able to trace rare variants back to the specific individuals that carry them. The pooled DNA samples were enriched over a 250 kb region previously identified by GWAS to significantly predispose individuals to lung cancer. All 96 pools (12 row and 12 column pools from 4 matrices) were barcoded and sequenced on an Illumina HiSeq 2000 instrument with an average depth of coverage greater than 4,000×. Verification based on Ion PGM sequencing confirmed the presence of 91.4% of confidently classified SNVs assayed. In this way, each individual sample is sequenced in multiple pools providing more accurate variant calling than a single pool or a multiplexed approach. This provides a powerful method for rare variant detection in regions of interest at a reduced cost to the researcher.</p></div

Bar graph of variant classes by frequency of observation.

<p>A breakdown of the classifications of variants that were observed in exactly 1, 2, 3 or 4 of the 4 matrices. Variants that were rare within a matrix (and thus labeled <i>Pinnable</i> or <i>Singleton</i>) were predominantly seen in only 1 of the 4 matrices. Similarly, variants that were common within a matrix (<i>Multiples</i>) were also common between the 4 matrices.</p

Definition of variant classes.

<p>Variant calls are classified based on their relationship to the pooled individuals. The three possible classes are <i>Pinnable</i>, <i>Multiple</i> and <i>Singleton</i>. (A) <i>Pinnable</i> variants were those where the carrying individuals may be identified because there is exactly one row or exactly one column containing a variant and at least one intersecting row or column pool. (B) <i>Multiple</i> variants were those where the variant is observed in more than one row and more than one column and it is not possible to determine precisely which individuals possess the variant. (C) <i>Singletons</i> were calls that are only observed in either a row or a column but not both.</p