The clinical approvals of KRAS G12C inhibitors have been a revolutionary advance in precision oncology, but response rates are often modest. To improve patient selection, we developed an integrated model to predict KRAS dependency. By integrating molecular profiles of a large panel of cell lines from the DEMETER2 dataset, we built a binary classifier to predict a tumor's KRAS dependency. Monte Carlo cross validation via ElasticNet within the training set was used to compare model performance and to tune parameters alpha and lambda. The final model was then applied to the validation set. We validated the model with genetic depletion assays and an external dataset of lung cancer cells treated with a G12C inhibitor. We then applied the model to several Cancer Genome Atlas (TCGA) datasets. The final "K20" model contains 20 features, including expression of 19 genes and KRAS mutation status. In the validation cohort, K20 had an AUC of 0.94 and accurately predicted KRAS dependency in both mutant and KRAS wild-type cell lines following genetic depletion. It was also highly predictive across an external dataset of lung cancer lines treated with KRAS G12C inhibition. When applied to TCGA datasets, specific subpopulations such as the invasive subtype in colorectal cancer and copy number high pancreatic adenocarcinoma were predicted to have higher KRAS dependency. The K20 model has simple yet robust predictive capabilities that may provide a useful tool to select patients with KRAS mutant tumors that are most likely to respond to direct KRAS inhibitors. Author summaryMutant KRAS drives approximately 25 of all cancers and has traditionally been considered "undruggable". However, the recent clinical approvals of inhibitors targeting KRAS with the specific G12C mutation in lung cancer has shepherded in a new era in precision medicine. Although promising, the responses are often modest and short-lived. Therefore, the ability to predict which tumors are dependent on KRAS will help select patients most likely to derive clinical benefit, and those who will not. We have developed an integrated "K20" model based on features that can improve prediction of KRAS-dependency beyond the presence of an activating KRAS mutation. When applied to lung adenocarcinoma, pancreatic adenocarcinoma, and colorectal cancer patient datasets, the K20 model identified specific subpopulations that correlate with greater dependency on KRAS. These findings present a novel approach for identifying biomarkers that can aid in the selection of patients who are most likely to benefit from KRAS inhibitors.
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