Case Study of Single-cell Protein Activity Based Drug Prediction for Precision Treatment of Cholangiocarcinoma

Abstract

Cholangiocarcinoma is a rare, aggressive malignancy with limited treatment options, due to a paucity of actionable mutations and low response to immune checkpoint inhibitors. Furthermore, its extreme heterogeneity prevents identification of actionable dependencies from bulk-tissue profiles. To address these challenges, we introduce a highly generalizable, single-cell framework for the mechanism-based prioritization of drugs to treat rare, highly heterogeneous tumors. Analysis of transformed cells, accounting for only 10% of a cholangiocarcinoma patient biopsy revealed three molecularly-distinct subpopulations, predicted to be sensitive to four drugs by regulatory network-based analysis. Validation in a low-passage, patient-derived xenograft (PDX) from the same patient confirmed tumor growth rate control by two of these drugs (plicamycin and dacinostat) and further validated predicted subpopulation-specific effects, suggesting they may represent promising candidates for follow-up clinical trials, either alone or in combination with current standard-of-care chemotherapies. The proposed approach can be generalized to elucidate complementary dependencies of rare, heterogeneous tumors, at the single cell level.