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. They present a fundamentally different approach for identifying the cause of individual instances of cancer: the search for combinations of genes with carcinogenic mutations (multi-hit combinations) instead of individual driver genes or mutations. You can also know more about cancerous tissue samples via http://www.geneticistinc.com.
They developed an algorithm that identified a set of multi-hit combinations that differentiate between tumor and normal tissue samples with 91% sensitivity and 93% specificity on average for seventeen cancer types. They then present an approach based on a 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.
By implementing a weighted set cover algorithm to identify 2-hit combinations of cancer-causing genes with mutations using a randomly selected training set of a tumor and normal tissue samples.
The set of combinations distinguish between tumor and normal tissue samples with over 90% sensitivity and specificity. This result is robust to different Training and Test set partitions of the available tumor and normal tissue samples. Although the identified combinations contain many genes previously implicated in cancer, our approach has also identified several potentially novel cancer genes. Our results suggest that some of the combinations identified are 2-hit subsets of 3+ hit combinations.