Presentation Detail
- Speaker: Sungwon Lim, PhD, CEO & Co-Founder of ImpriMed, Inc.
- Date: October 15, 2020
Research Abstract
Introduction
For a life-threatening disease like cancer, optimal treatment may need to be personalized for each patient. We developed a precision medicine platform that evaluates the probability of chemotherapeutic efficacy for canine lymphoma by combining ex vivo chemosensitivity and immunophenotyping assays with computational modeling. This study evaluated the correlation between our ex vivo data-driven prediction and reported responses in a series of canine lymphoma patients.
Methods
We collected live cancer cells from fresh fine needle aspirates taken from affected lymph nodes and collected post-treatment clinical responses in 261 canine lymphoma patients who were scheduled to be treated with at least 1 of 5 common chemotherapy agents(doxorubicin, vincristine, cyclophosphamide, lomustine, and rabacfosadine). Samples were subject to flow cytometry analysis for immunophenotyping and ex vivo chemosensitivity testing. For each drug, 70% of the treated patients were randomly selected to train a random forest model to predict the probability of positive Veterinary Cooperative Oncology Group (VCOG) clinical response based on input variables including antigen expression profiles and treatment sensitivity readouts for each patient’s cancer cells. The remaining 30% of patients were used to test model performance.
Results
Most models showed good performance by ROC-AUC and Brier score. Predictive efficacy scores significantly distinguished (P < 0.0001) positive responses from negative responses, both when comparing B- vs. T-cell patients and naïve vs. relapse patients. In addition, the patient group with predictive efficacy scores > 50% showed a statistically significant reduction (log-rank P < 0.05) in time to complete response when compared to the group with scores < 50%.
Conclusion
The computational model developed in this study enabled conversion of ex vivo cell-based assay results into probability of in vivo therapeutic efficacy, which may help improve treatment outcomes in individual canine lymphoma patients by providing predictive estimates of positive treatment response.
