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In Veterinary and comparative oncology

We report a precision medicine platform that evaluates the probability of chemotherapy drug efficacy for canine lymphoma by combining ex vivo chemosensitivity and immunophenotyping assays with computational modeling. We isolated live cancer cells from fresh fine needle aspirates of affected lymph nodes and collected post-treatment clinical responses in 261 canine lymphoma patients scheduled to receive at least 1 of 5 common chemotherapy agents (doxorubicin, vincristine, cyclophosphamide, lomustine, and rabacfosadine). We used flow cytometry analysis for immunophenotyping and ex vivo chemosensitivity testing. For each drug, 70% of 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. Most models showed a test set ROC-AUC > 0.65, and all models had overall ROC-AUC > 0.95. Predicted response scores significantly distinguished (P < 0.001) positive responses from negative responses in B-cell and T-cell disease and newly diagnosed and relapsed patients. Patient groups with predicted response scores >50% showed a statistically significant reduction (log-rank P < 0.05) in time to complete response when compared to the groups with scores <50%. The computational models developed in this study enabled the conversion of ex vivo cell-based chemosensitivity assay results into a predicted probability of in vivo therapeutic efficacy, which may help improve treatment outcomes of individual canine lymphoma patients by providing predictive estimates of positive treatment response. This article is protected by copyright. All rights reserved.

Bohannan Zach, Pudupakam Raghavendra Sumanth, Koo Jamin, Horwitz Harrison, Tsang Josephine, Polley Amanda, Han Enyang James, Fernandez Elmer, Park Stanley, Swartzfager Deanna, Qi Nicholas Seah Xi, Tu Chantal, Rankin Wendi Velando, Thamm Douglas H, Lee Hye-Ryeon, Lim Sungwon


chemosensitivity, dog, lymphosarcoma, machine learning, precision medicine