Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations

Abstract

Cancer is known to result from a combination of a small number of genetic defects. However, the specific combinations of mutations responsible for the vast majority of cancers have not been identified. Current computational approaches focus on identifying driver genes and mutations. Although individually these mutations can increase the risk of cancer they do not result in cancer without additional mutations. We present a fundamentally different approach for identifying the cause of individual instances of cancer: we search for combinations of genes with carcinogenic mutations (multi-hit combinations) instead of individual driver genes or mutations. We developed an algorithm that identified a set of multi-hit combinations that differentiate between tumor and normal tissue samples with 91% sensitivity (95% Confidence Interval (CI) = 89–92%) and 93% specificity (95% CI = 91–94%) on average for seventeen cancer types. We then present an approach based on mutational profile that can be used to distinguish between driver and passenger mutations within these genes. These combinations, with experimental validation, can aid in better diagnosis, provide insights into the etiology of cancer, and provide a rational basis for designing targeted combination therapies.

Identifying Modules of Cooperating Cancer Drivers

“…Dash et al [97] proposed a method for identifying two-hit combinations that are likely responsible for carcinogenesis. The authors represented the problem as an instance of the weighted set cover problem [98] and showed that the identified set of combinations could discriminate normal and cancer samples with high accuracy.…”

“…However, there is a major difference in the optimization criteria in each of the methods. In [97] sets of combinations are prioritized based on how well they can discriminate normal and tumor samples in a training set. This choice of criteria assumes that combinations that occur only in tumors are likely to be cooperating drivers.…”

Identifying Combinations of Cancer Drivers in Individual Patients

“…Dash et al [28] recently proposed an innovative method for identifying two-hit combinations that are likely responsible for carcinogenesis. The authors represented the problem as an instance of the weighted set cover problem [29] and showed that the identified set of combinations could discriminate normal and cancer samples with high accuracy.…”

“…However, there is a major difference in the optimization criteria in each of the methods. In [28] sets of combinations are prioritized based on how well they can discriminate normal and tumor samples in a training set. This choice of criteria assumes that combinations that occur only in tumors are likely to be cooperating drivers.…”

“…Alternatively, CRSO prioritizes rule sets that maximize the likelihood of observing the data by minimizing the statistical penalty caused by passenger alterations. Additionally, the present work expands upon the approach presented in [28] by considering combinations that involve more than two events, and by exploring clinical applications of stratifying patients based on predicted driver combinations.…”