Functional Precision Medicine in T-Cell Lymphoma: Integrating Ex Vivo Drug Sensitivity Testing and Immunotherapy
Functional Precision Medicine (FPM) aims to improve treatment selection by providing insights by using patient-specific drug sensitivity data, beyond genomics. This approach is particularly valuable for T-cell lymphoma (TCL) patients who lack actionable biomarkers.
The Optim.AI™ Platform Methodology
The Optim.AI™ workflow begins with isolating tumor cells from patient biopsies or blood samples. These cells undergo drug sensitivity screening against a custom drug panel using a proprietary assay design. Researchers then use phenotypic quantification (cell death), such as fluorescence-based or bioluminescence-based viability assays, to measure drug effects on cell death. Finally, the Optim.AI™ analysis generates a report to guide clinical decisions. This process is efficient, with a median time-to-report of 4 (+/-2) working days for TCL samples.
Clinical Utility and Drug Sensitivity Testing
Data from prior studies in Non-Hodgkin lymphoma (NHL) demonstrate the platform’s predictive power. The platform achieved an accuracy of 74.5% and a sensitivity of 81.3% in predicting patient responses. Furthermore, patients receiving Optim.AI™-guided treatments showed significantly better outcomes than those receiving clinician-guided therapy.
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The 2-year survival rate was higher for the Optim.AI™-guided group (P = 0.0191)
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Retrospective analysis of TCL samples showed 81% concordance between predicted cell viability and prior-line treatment resistance.
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One TCL patient achieved a complete response lasting 2.2 times longer than their previous treatment.
Evaluating Immunotherapy Options
Optim.AI™ also evaluates sensitivities to combinatorial immunotherapy (IO). By using co-culture models, the platform can rank the effectiveness of 2-drug and 3-drug IO combinations. Parabolic response surface maps help visualize drug-drug interactions, identifying synergistic effects for specific patients. This capability is crucial for refining treatment selection when standard therapies fail.
“This study demonstrates Optim.AI™’s real-world clinical utility and feasibility for evaluating immunotherapy.”