Presentation Detail
- Speaker: Sungwon Lim, PhD, CEO & Co-Founder of ImpriMed, Inc.
- Date: March 21, 2024 (5pm JST)
Research Abstract
Introduction
There is no standard of care for dogs with high-grade T-cell lymphoma (hTCL) or relapsed B-cell lymphoma(rBCL), and clinical outcomes are often poor.
Methods
We performed our ex vivo drug sensitivity assay on live cancer cells from fresh fine needle aspirates taken from affected lymph nodes of canine lymphoma patients. The drug panel included the seven most common chemotherapy drugs for canine lymphoma treatment: doxorubicin, cyclophosphamide, vincristine, prednisone, lomustine, TANOVEA-CA1, and L-asparaginase. 180 correlations between drug response prediction and clinical outcome were evaluated retrospectively in 80 canine lymphoma patients provided by 14 clinical sites, by collecting the patient’ s treatment history and the post-treatment clinical outcomes 60-90 days after our prediction report.
Results
For both hTCL and rBCL patients, complete remission rates were dramatically higher in the high matching groups (hTCL: 9.1% low, 59 % high, P=0.006; rBCL: 13% low, 53%high, P=0.002) and patient survival times were significantly longer in the high matching groups (hTCL: P=0.004, rBLC: P<0.001). Strikingly, rBCL patients in the high matching group experienced better clinical outcomes for every metric we analyzed (overall response rate, complete response rate, patient survival, response duration) and relative to both the low matching group and historical controls.
Conclusion
AI algorithms, when used in conjunction with drug sensitivity profiling and multicolor flow cytometry, can effectively identify drug treatments with real-word clinical efficacy.
Clinical significance of the results
This study strongly supports the efficacy of combining clinical expertise with AI decision support to optimize health outcomes for dogs with hTCL and rBCL.
