Introduction
Immunotherapies have improved the management of various cancers, with response rates observed in approximately 20-40% of patients. However, the variable response and lack of reliable biomarkers pose challenges in predicting responses to immunotherapy, thus limiting its efficacy. Further complicating use of immunotherapy in cancer treatment is the proliferation of immunotherapy-based combinations that serve to enhance treatment response as well as expand efficacy across a broader patient population.
The emerging field of functional precision medicine integrates primary cell culture systems with ex vivo drug screening assays and analytics to predict clinical response from ex vivo drug sensitivity data. Ex vivo drug sensitivity data can be used alone or as part of a multi-omic approach to guide treatment. Optim.AI™ is a combinatorial functional precision medicine platform that has previously demonstrated clinical utility in predicting effective combination therapies for hematological cancers and sarcoma. These studies prospectively demonstrated both good clinical concordance between Optim.AI™ treatment outcomes as well as the use of the platform to successfully guided chemo- and targeted-based therapies. While promising, the absence of immune cells limited the types of drugs that can be evaluated. This study introduces Optim.AI™ 2.0, which incorporates high-content imaging of tumor-immune co-culture models to also evaluate ex vivo immunotherapy response. We aim to evaluate its potential to functionally predict responses to immunotherapy combinations across both solid and hematological cancers.
Fill the form to download the full poster ->