eTumorRisk, an algorithm predicts cancer risk based on comutated gene networks in an individual’s germline genome

Early cancer detection has potentials to reduce cancer burden. A prior identification of the high-risk population of cancer will facilitate cancer early detection. Traditionally, cancer predisposition genes such as BRCA1/2 have been used for identifying high-risk population of developing breast and ovarian cancers. However, such high-risk genes have only a few. Moreover, the complexity of cancer hints multiple genes involved but also prevents from identifying such predictors for predicting high-risk subpopulation. Therefore, we asked if the germline genomes could be used to identify high-risk cancer population. So far, none of such predictive models has been developed. Here, by analyzing of the germline genomes of 3,090 cancer patients representing 12 common cancer types and 25,701 non-cancer individuals, we discovered significantly differential co-mutated gene pairs between cancer and non-cancer groups, and even between cancer types. Based on these findings, we developed a network-based algorithm, eTumorRisk, which enables to predict individuals cancer risk of six genetic-dominant cancers including breast, colon, brain, leukemia, ovarian and endometrial cancers with the prediction accuracies of 74.1-91.7% and have 1-3 false-negatives out of the validating samples (n=14,701). The eTumorRisk which has a very low false-negative rate might be useful in screening of general population for identifying high-risk cancer population.