Identification of subsets of actionable genetic alterations in KRAS-mutant lung cancers using association rule mining

Lung cancer is the leading cause of cancer-related death in both men and women. KRAS mutations occur in ~ 25% of patients with lung cancer, and the presence of these mutations is associated with a poor prognosis. Unfortunately, efforts to directly target KRAS or its associated downstream MAPK or PI3K/AKT/mTOR pathways have seen little or no benefits. Here, I hypothesize that KRAS-mutant tumors do not respond to KRAS pathway therapies due to the co-occurrence of other activated cell survival pathways and/or mechanisms.

To identify other potentially activated cell survival pathways in KRAS-mutant tumors, I performed association rule mining on somatic mutations in 725 metastatic lung cancer patient samples. I identified 67 additional genes that were mutated in at least 10% of the samples with KRAS mutations. This gene list was enriched with genes involved in the MAPK, AKT and STAT3 pathways, as well as in cell-cell adhesion, DNA repair, chromatin remodeling and the Wnt/β-catenin pathway. I also identified 160 overlapping subsets of three or more genes that code for oncogenic or tumor suppressive proteins that were mutated in at least 10% of the KRAS-mutant tumors.

I identified several genes that are co-mutated in primary KRAS-mutant lung cancer samples. I also identified subpopulations of KRAS-mutant lung cancers based on sets of genes that were co-mutated. Pre-clinical models that capture these subsets of KRAS-mutant tumors may enhance our understanding of lung cancer development and, in addition, facilitate the design of personalized treatment strategies for lung cancer patients carrying KRAS mutations.