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Epistasis in genomic and survival data of cancer patients.

ABSTRACT: Cancer aggressiveness and its effect on patient survival depends on mutations in the tumor genome. Epistatic interactions between the mutated genes may guide the choice of anticancer therapy and set predictive factors of its success. Inhibitors targeting synthetic lethal partners of genes mutated in tumors are already utilized for efficient and specific treatment in the clinic. The space of possible epistatic interactions, however, is overwhelming, and computational methods are needed to limit the experimental effort of validating the interactions for therapy and characterizing their biomarkers. Here, we introduce SurvLRT, a statistical likelihood ratio test for identifying epistatic gene pairs and triplets from cancer patient genomic and survival data. Compared to established approaches, SurvLRT performed favorable in predicting known, experimentally verified synthetic lethal partners of PARP1 from TCGA data. Our approach is the first to test for epistasis between triplets of genes to identify biomarkers of synthetic lethality-based therapy. SurvLRT proved successful in identifying the known gene TP53BP1 as the biomarker of success of the therapy targeting PARP in BRCA1 deficient tumors. Search for other biomarkers for the same interaction revealed a region whose deletion was a more significant biomarker than deletion of TP53BP1. With the ability to detect not only pairwise but twelve different types of triple epistasis, applicability of SurvLRT goes beyond cancer therapy, to the level of characterization of shapes of fitness landscapes.

SUBMITTER: Matlak D

PROVIDER: S-EPMC5517071 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Identifying relevant signatures for clinical patient outcome is a fundamental task in high-throughput studies. Signatures, composed of features such as mRNAs, miRNAs, SNPs or other molecular variables, are often non-overlapping, even though they have been identified from similar experiments considering samples with the same type of disease. The lack of a consensus is mostly due to the fact that sample sizes are far smaller than the numbers of candidate features to be considered, and therefore signature selection suffers from large variation. We propose a robust signature selection method that enhances the selection stability of penalized regression algorithms for predicting survival risk. Our method is based on an aggregation of multiple, possibly unstable, signatures obtained with the preconditioned lasso algorithm applied to random (internal) subsamples of a given cohort data, where the aggregated signature is shrunken by a simple thresholding strategy. The resulting method, RS-PL, is conceptually simple and easy to apply, relying on parameters automatically tuned by cross validation. Robust signature selection using RS-PL operates within an (external) subsampling framework to estimate the selection probabilities of features in multiple trials of RS-PL. These probabilities are used for identifying reliable features to be included in a signature. Our method was evaluated on microarray data sets from neuroblastoma, lung adenocarcinoma, and breast cancer patients, extracting robust and relevant signatures for predicting survival risk. Signatures obtained by our method achieved high prediction performance and robustness, consistently over the three data sets. Genes with high selection probability in our robust signatures have been reported as cancer-relevant. The ordering of predictor coefficients associated with signatures was well-preserved across multiple trials of RS-PL, demonstrating the capability of our method for identifying a transferable consensus signature. The software is available as an R package rsig at CRAN (http://cran.r-project.org).

Project description:OBJECTIVES:To evaluate the role of cardiovascular disease (CVD) comorbidity in survival of patients with non-small cell lung cancer (NSCLC). MATERIALS AND METHODS:The impact of seven CVDs (at the time of NSCLC diagnosis and during subsequent follow-up) on overall survival was studied for NSCLC patients aged 65+ years using the Surveillance, Epidemiology, and End Results data linked to the U.S. Medicare data, cancer stage- and treatment-specific. Cox regression was applied to evaluate death hazard ratios of CVDs in univariable and multivariable analyses (controlling by age, TNM statuses, and 78 non-CVD comorbidities) and to investigate the effects of 128 different combinations of CVDs on patients' survival. RESULTS:Overall, 95,167 patients with stage I (n=29,836, 31.4%), II (n=5133, 5.4%), IIIA (n=11,884, 12.5%), IIIB (n=18,020, 18.9%), and IV (n=30,294, 31.8%) NSCLC were selected. Most CVDs increased the risk of death for stages I-IIIB patients, but did not significantly impact survival of stage IV patients. The worse survival of patients was associated with comorbid heart failure, myocardial infarction, and cardiac arrhythmias that occurred during a period of follow-up: HRs up to 1.85 (p<0.001), 1.96 (p<0.05), and 1.67 (p<0.001), respectively, varying by stage and treatment. The presence of hyperlipidemia at baseline (HR down to 0.71, p<0.05) was associated with better prognosis. Having multiple co-existing CVDs significantly increased mortality for all treatments, especially for stages I and II patients treated with surgery (HRs up to 2.89, p<0.05) and stages I-IIIB patients treated with chemotherapy (HRs up to 2.59, p<0.001) and chemotherapy and radiotherapy (HRs up to 2.20, p<0.001). CONCLUSION:CVDs impact the survival of NSCLC patients, particularly when multiple co-existing CVDs are present; the impacts vary by stage and treatment. This data should be considered in improving cancer treatment selection process for such potentially challenging patients as the elderly NSCLC patients with CVD comorbidities.

Project description:Epistasis is defined as a statistical interaction between two or more genomic loci in terms of their association with a phenotype of interest. Epistatic loci that are identified using data from Genome-Wide Association Studies (GWAS) provide insights into the interplay among multiple genetic factors, with applications including assessment of susceptibility to complex diseases, decision making in precision medicine, and gaining insights into disease mechanisms. Since the number of genomic loci assayed by GWAS is extremely large (usually in the order of millions), identification of epistatic loci is a statistically difficult and computationally intensive problem. Even when only pairwise interactions are considered, the size of the search space ranges from hundreds of millions to billions of locus pairs. The large number of statistical tests performed also makes sufficient type one error correction imperative. Consequently, efficient algorithms are required to filter the tests that are performed and evaluate large GWAS data sets in a reasonable amount of computation time. It has been observed that many pairwise tests are redundant due to correlations in their genotype values across samples, known as linkage disequilibrium. However, algorithms that have been developed for efficient identification of epistatic loci do not systematically exploit linkage disequilibrium. Here, we propose a new algorithm for fast epistasis detection based on hierarchical representation of linkage disequilibrium (LinDen). We utilize redundancies in genotype patterns between neighboring loci to generate a hierarchical structure and execute a branch-and-bound search to prioritize loci testing based on approximations of a test statistic for pairs of locus groups. The hierarchical organization of tests performed by LinDen allows for efficient scaling based on the screened loci. We test LinDen comprehensively on three data sets obtained from the Wellcome Trust Case Control Consortium: type two diabetes, psoriasis, and hypertension. Our results show that, as compared other state-of-the-art tools for fast epistasis detection, LinDen drastically reduces the number of tests performed while discovering statistically significant locus pairs. LinDen is implemented in C++ and is available as open source at http://compbio.Caseedu/linden/.

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Epistasis in genomic and survival data of cancer patients.

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