Case Study of Single-cell Protein Activity Based Drug Prediction for Precision Treatment of Cholangiocarcinoma
Video
Team Information
Team Members
Lorenzo Tomassoni, Postdoctoral Researcher Scientist, Department of Systems Biology, CUIMC
Aleksandar Obradovic, MD-PhD Student, Columbia Center for Translational Immunology, Irving Medical Center; and Department of Systems Biology, Columbia University Irving Medical Center
Faculty Advisor: Andrea Califano, Clyde '56 and Helen Wu Professor of Chemical Biology (in Systems Biology) and Professor of Biomedical Informatics and Biochemistry and Molecular Biophysics and Professor of Medicine in the Institute for Cancer Genetics, Vagelos College of Physicians and Surgeons
Abstract
Cholangiocarcinoma (CCA) is an aggressive biliary adenocarcinoma, with a median survival of only 12-37.4 months and no effective treatment options. Studies of the CCA tumor micro-environment (TME) and intratumoral heterogeneity have been limited, despite clinically significant interaction between tumor and stromal or immune components across other tumor types. .Single-cell RNA Sequencing (scRNASeq) has recently emerged as a valuable technique to characterize the TME. Here we present a case study profiling CCA TME at the resolution of scRNASeq, and the first application of a unique OncoTarget (OTar) and OncoTreat (OTr) approach to predict and identify actionable drug targets at the single-cell level. These are both CLIA-certified algorithms for personalized drug prediction, now adapted for the first time to drug sensitivity prediction at the level of individual tumor cells.
scRNASeq from a human CCA sample revealed significant tumor immune infiltration, with T-cells comprising the largest population of all cells, and fewer than 10% of cells identified as tumor cells, such that transcriptional signal from traditional bulk-RNA-Seq would be dominated by non-tumor cells. VIPER-based protein activity inference of tumor cells identified three distinct sub-populations, not distinguished by gene expression alone. These were characterized by upregulation of KRAS signaling pathway, TNFa signaling with epithelial-mesenchymal transition, and upregulation of MYC targets, respectively.
Consensus OTar/OTr drug prediction analysis on both scRNASeq tumor cells and bulk RNA-Seq of a successfully engrafted PDX model from resected tumor tissue at time of biopsy, ranked Glasdegib, Plicamycin, Flavopiridol, AT9283, and Dacinostat as the top 5 drugs with best overall tumor cell coverage. Therefore, we administered these drugs to a cohort of 8 mice per treatment arm. Dacinostat and Plicamycin significantly reduced tumor growth rate (p=0.007 and p=0.03, respectively), with Dacinostat stabilizing tumor size over 28 days of treatment. Both of these drugs significantly extended survival time by Kaplan-Meier regression (p=0.001 and p=0.03, respectively). Furthermore, scRNASeq data of drug-treated PDXs showed that Dacinostat uniformly depleted all three tumor sub-populations compared to Vehicle control, whereas one of the tumor sub-clusters was resistant to Plicamycin, consistent with our single-cell-resolution drug sensitivity predicted by OTr.
Given the in vivo effect of these two drugs on inhibiting tumor growth, and particularly the effectiveness of Dacinostat across observed tumor cell phenotypes, as well as the high immune infiltration of this CCA sample, these drugs may be translated into pre-clinical and clinical trials alone and in combination with checkpoint immunotherapy.
Team Lead Contact
Lorenzo Tomassoni: lt2736@cumc.columbia.edu
