1. Predicting likelihood of in vivo chemotherapy response in canine lymphoma using ex vivo drug sensitivity and immunophenotyping data in a machine learning model
- Author
-
Wendi Velando Rankin, Sung Won Lim, Stanley Park, Harrison Horwitz, Elmer Fernandez, Nicholas Seah Xi Qi, Amanda Polley, Zach Bohannan, Josephine Tsang, Deanna Swartzfager, Chantal Tu, Douglas H. Thamm, Enyang James Han, Koo Jamin, Hye-Ryeon Lee, and R. S. Pudupakam
- Subjects
Oncology ,Male ,medicine.medical_specialty ,Vincristine ,Cyclophosphamide ,Lymphoma ,precision medicine ,Antineoplastic Agents ,Models, Biological ,Immunophenotyping ,Machine Learning ,Dogs ,In vivo ,Predictive Value of Tests ,Internal medicine ,medicine ,Animals ,Dog Diseases ,Canine Lymphoma ,General Veterinary ,business.industry ,Lomustine ,Original Articles ,chemosensitivity ,lymphosarcoma ,Drug Resistance, Neoplasm ,dog ,Original Article ,Female ,Lymph Nodes ,business ,Chemosensitivity assay ,Ex vivo ,medicine.drug - Abstract
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 modelling. 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 50% showed a statistically significant reduction (log‐rank P
- Published
- 2020