Standard care for gynecological malignancies, particularly ovarian and endometrial cancers, often involves platinum-based chemotherapy. While genetic testing for BRCA mutations or homologous recombination deficiency (HRD) can guide the use of PARP inhibitors, these actionable markers are only present in a subset of patients. This leaves a significant clinical gap for those with HRD-negative status or recurrent disease.
The Role of Functional Precision Medicine
Functional precision medicine offers a complementary approach by testing drug responses directly on live patient cells. Unlike genomic sequencing, which predicts response based on historical data, functional platforms measure actual phenotypic sensitivity.
Optim.AI™ is a combinatorial functional precision medicine platform designed to identify effective drug pairings. While previously validated in hematological cancers and sarcomas, a recent feasibility study presented at AACR 2026 explored its application in gynecological oncology.
Methodology and Workflow
The Optim.AI™ process integrates advanced bioengineering with artificial intelligence to manage molecular heterogeneity. The workflow consists of four primary stages:
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Cell Isolation: Tumor cells are extracted from resections, biopsies, or body fluids.
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Organoid Generation: Short-term patient-derived organoids (PDO) are formed using specialized culture media.
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Combinatorial Screening: PDOs are exposed to a panel of up to 12 drugs in various combinations.
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Cell Viability Measurement: Post 96-hour treatment, cell viability is quantified and analyzed to generate a clinical report.
Feasibility in Recurrent Ovarian Cancer
The study demonstrated high technical success rates across various sample types. For ovarian cancer, reports were successfully generated for 92% of viable samples. Notably, the median turnaround time from sample receipt to report generation was only seven working days.
Research into endometrial cancer drug sensitivity also revealed significant inter-patient variability. Heatmap analyses showed differential sensitivities to standard-of-care (SOC) options like Carboplatin and Paclitaxel. In some cases, the platform identified synergistic drug-drug interactions that significantly outperformed conventional monotherapies.
Clinical Case Study: Heavy Pre-treatment
A key highlight of the study involved a patient with mucinous ovarian cancer who had failed three prior lines of therapy. Using the Optim.AI™ platform, clinicians identified a novel four-drug regimen:
A four-drug regimen, Cobimetinib, Gemcitabine, Irinotecan (SN-38), and Pazopanib, was chosen for subsequent treatment, yielding a partial response with duration of seven months.
This prospective application underscores the platform’s potential as a clinical decision support system for complex, relapsed cases.
Conclusion
The study concludes that Optim.AI™ is a viable tool for guiding treatment in gynecological cancers lacking actionable mutations. Ongoing clinical concordance analyses aim to further validate the platform’s utility, particularly for HRD-negative patients who typically have limited targeted therapy options.
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