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Combinations of genetic data in a study of oral cancer.

ABSTRACT: In the single locus strategy a number of genetic variants are analyzed, in order to find variants that are distributed significantly different between controls and patients. A supplementary strategy is to analyze combinations of genetic variants. A combination that is the genetic basis for a polygenic disorder will not occur in in control persons genetically unrelated to patients, so the strategy is to analyze combinations of genetic variants present exclusively in patients. In a previous study of oral cancer and leukoplakia 325 SNPs were analyzed. This study has been supplemented with an analysis of combinations of two SNP genotypes from among the 325 SNPs. Two clusters of combinations containing 95 patient specific combinations were significantly associated with oral cancer or leukoplakia. Of 373 patients with oral cancer 205 patients had a number of these 95 combinations in their genome, whereas none of 535 control persons had any of these combinations in their genome.

SUBMITTER: Mellerup E

PROVIDER: S-EPMC4633169 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Combinations of genetic data in a study of oral cancer.

Mellerup Erling E Moeller Gert Lykke GL Mondal Pinaki P Roychoudhury Susanta S

Genes & cancer 20150901 9-10

In the single locus strategy a number of genetic variants are analyzed, in order to find variants that are distributed significantly different between controls and patients. A supplementary strategy is to analyze combinations of genetic variants. A combination that is the genetic basis for a polygenic disorder will not occur in in control persons genetically unrelated to patients, so the strategy is to analyze combinations of genetic variants present exclusively in patients. In a previous study ...[more]

PMID: 26622944

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Project description:Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

Dataset Information

Combinations of genetic data in a study of oral cancer.

Publications

Combinations of genetic data in a study of oral cancer.

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