The landscape of cancer treatment has shifted significantly with the rise of immunotherapies. However, response variability and a lack of reliable predictive biomarkers often limit their clinical impact. A recent study presented at AACR 2026 highlights a potential solution: Optim.AI™ 2.0. This co-culture, high-content functional precision platform is designed for predicting combinatorial immunotherapy response.
Evolution of the Optim.AI™ Platform
Previously, the functional precision medicine platform (known as QPOP/Optim.AI™) demonstrated clinical feasibility in identifying effective treatments for hematologic and solid tumors. These earlier studies focused on chemotherapy and targeted agents in Non-Hodgkin lymphoma (NHL), acute myeloid leukemia (AML), and soft tissue sarcoma.
The new 2.0 iteration addresses a critical gap by integrating immune components. By using tumor-immune co-cultures, the platform allows for the ex vivo immunotherapy screening of patient-specific responses.
Methodology: High-Content Imaging and Co-Culture
The Optim.AI™ 2.0 workflow begins with primary patient samples. The process involves several key steps:
-
Tumor cells are isolated and allowed to recover.
-
Prior to drug treatment, samples are stained with specific markers.
-
The platform applies a library of at least 121 combinations.
-
High-content imaging and analysis identify and count distinct cell populations.
-
The system measures cell viability using Live/Dead assays after 24 to 72 hours of treatment.
Finally, the platform utilizes a parabolic response surface to provide combinatorial drug sensitivity testing.
Clinical Utility in Non-Hodgkin Lymphoma (NHL)
The study emphasizes Non-Hodgkin lymphoma (NHL) treatment optimization. Clinical data from a prior feasibility study showed significant improvements in patient outcomes. Patients receiving Optim.AI-guided treatments had a 2-year survival advantage compared to those receiving clinician-chosen standard care. Specifically, the hazard ratio (HR) was 0.559 ($P=0.0191$).
In the 2.0 study, researchers analyzed NHL patient samples (N=10) to rank sensitivities to various immunotherapies. The results identified novel drug combinations that outperformed the standard of care (SOC).
Expanding to Solid Tumors: Colorectal Cancer (CRC)
The platform’s utility extends beyond hematologic malignancies. Testing on colorectal cancer (CRC) samples demonstrated its ability to rank the top five 2-drug and 3-drug combinations. This capability is vital for patients who have failed standard therapies or lack actionable molecular biomarkers.
Conclusions and Future Directions
The study concludes that Optim.AI™ 2.0 offers a feasible method for evaluating real-world clinical utility in immunotherapy. When combined with molecular profiling, this functional assay may further refine treatment selection. It serves as a powerful tool for identifying effective drug combinations and elucidating mechanisms of treatment sensitivity.