Multiple systemic therapy options are clinically approved for breast cancer, but selection of regimens is often complex and based on limited molecular biomarkers. Functional precision medicine could improve treatment decisions with added insights from ex vivo drug sensitivity data. Optim.AI™, a combinatorial functional precision medicine platform, has previously demonstrated clinical feasibility in identifying effective treatments for hematological cancers. In this study, we evaluated the technical feasibility and potential clinical utility of Optim.AI™ in breast cancer using patient-derived organoids (PDOs).
Tumor cells were isolated from breast cancer samples, including biopsies, resections and malignant pleural effusions. Dissociated tumor cells (DTCs) were allowed to form PDOs prior to combinatorial drug treatment using a 12-drug panel comprising chemotherapy and targeted agents. After drug incubation, cell viability was measured and used for Optim.AI™ analysis, which generates a report containing the top-ranked treatments for each sample.
A total of 29 samples were evaluated in this study, with 65.5% primary and 35.5% metastatic cases. The DTCs were highly viable post-processing, with mean viability of 87%. Reports were generated for 20 samples with sufficient cell yield, with a median turnaround time from sample receipt to report dissemination of 6 business days. Optim.AI™ analysis identified gemcitabine- or doxorubicin-based combinations as common top ranked treatments for primary breast cancer samples. These PDOs also showed a wide range of responses to standard first-line regimens, highlighting potential utility of Optim.AI™ in guiding neoadjuvant/adjuvant therapy selection. For metastatic cases, Optim.AI™ predicted results for prior line treatments were 80% concordant with previously observed clinical outcomes.
This preliminary study demonstrates the feasibility of Optim.AI™ in guiding treatment options for breast cancer patients, and its potential application at multiple stages of therapy. Future clinical concordance analysis with retrospective or prospective data on Optim.AI™-guided treatments would further establish its predictive value and utility in breast cancer management.
J.J. Lim, M.M.A. Rashid, S.P.Y. Chan: Financial Interests, Personal, Full or part-time Employment: KYAN Technologies Pte. Ltd.
E.K. Chow: Financial Interests, Personal, Full or part-time Employment: KYAN Technologies Pte. Ltd.; Financial Interests, Personal, Stocks/Shares: KYAN Technologies Pte. Ltd.
All other authors have declared no conflicts of interest.