Jhin Jieh Lim1 , Edward Kai-Hua Chow2 , Masturah Mohd Abdul Rashid1 .
1, KYAN Technologies Pte Ltd, Singapore, Singapore 2, Cancer Science Institute of Singapore, Singapore
PURPOSE:
KRAS is the most frequently mutated oncogene in almost all cancers, including pancreatic ductal carcinoma (PDAC) and colorectal cancer. Increased development efforts targeting KRAS mutations have yielded recent FDA approvals of two KRAS G12C inhibitors. However, this is only amenable for a small proportion of patients especially for indications like PDAC, where G12C mutations are rare (1%) while G12D mutations account for approximately 45%. Hence, there is a need to develop methods to target a broader range of KRAS mutations. Beyond direct gene mutation, activation of oncogenic KRAS and downstream or parallel signaling through other genomic alterations may benefit from KRAS-specific therapeutic strategies, especially combination therapy. In this study, by harnessing Optim.AI™ to identify optimal therapies and stratify efficacious drug combinations containing KRAS inhibitors in pancreatic cancer.
METHODS:
Optim.AI™ is a hybrid computational-experimental platform that uses small datasets to rationally converge upon optimal drug combinations within a defined drug search space. By mapping experimental data points to a second-order quadratic function, Optim.AI™ predicts cell killing efficacies for all possible combinations, independent of the background of the models utilized. In this study, pancreatic cancer cell lines, with different KRAS mutations, were tested with Optim.AI™’s 155 different combinations consisting of 12 drugs at varying concentrations. This consists of both FDA-approved KRAS G12C inhibitors, Sotorasib and Adagrasib, as well as other KRAS inhibitors in the drug development pipeline. Post-drug treatment viability was measured and used for Optim.AI™ analysis, to rank and compare the top therapies across the different cell lines.
RESULTS:
Through Optim.AI™ analysis, we observed a range of sensitivities towards KRAS inhibition, even for both Sotorasib and Adagrasib. Compared against HT-29 cell line, harboring wild type KRAS, we have identified distinct KRAS-based combinations on cell lines with G12D, G12V and G12C KRAS mutants. Subsequent validation of the top and bottom ranked combinations for the respective cell lines correspond to the predicted Optim.AI™ results.
CONCLUSIONS:
This study has demonstrated the differential sensitivities of the KRAS inhibitors across several pancreatic cancer cell lines, with different KRAS mutational profiles. Notably, we have identified KRAS-based combinations that are distinctly different from wild type KRAS cell line. These preliminary findings could be used to mine suitable biomarkers to potentially stratify pancreatic cancer patients and improve treatment outcomes for KRAS-based combinations. Future work includes evaluating the clinical significance of these combinations by validating them on patient-derived organoids.