The prognosis for pancreatic cancer remains a significant clinical challenge, with a five-year survival rate of less than 10%. Standard treatments primarily involve chemotherapy, yet targeted therapies and immunotherapies remain limited to specific patient subsets. Currently, treatment selection is often empirical because of a lack of reliable predictive biomarkers. Combinatorial functional precision medicine offers a promising alternative by using live patient cells to guide therapeutic decisions.
Real-World Concordance Analysis in Solid Cancers
To validate the clinical relevance of the Optim.Al™ clinical decision support platform, a retrospective concordance analysis was conducted on a diverse cohort of solid tumors. This analysis included a total of 97 cases. The study evaluated the platform’s ability to mirror clinical outcomes across several cancer types, including:
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Other solid tumor indications
Using an established normalized cell viability (NCV) cut-off of 0.5, 89% of the cases demonstrated concordance to prior lines of treatment ($p < 0.0001$). Statistical significance was determined using the Wilcoxon rank sum test. These results suggest that the platform accurately reflects the therapeutic resistance and sensitivity observed in real-world clinical settings.
Feasibility and Outcomes in Pancreatic Cancer
In a prospective observational case series, researchers focused on the application of functional drug sensitivity testing specifically for pancreatic cancer. The workflow utilizes the OncoPro™ Tumoroid Culture Medium Kit to form short-term patient-derived organoids (PDOs) from various sample types, such as biopsies, surgical resections, and body fluids like ascites or pleural fluid.
The study highlighted strong performance outcomes for the platform in this challenging indication:
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71% of total pancreatic cancer samples received had sufficient, viable cells to proceed with testing.
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Reports were successfully generated for 90% of these viable samples.
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The platform demonstrated a median turnaround time of 6 working days from sample receipt to report generation.
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Optim.Al™ achieved a predictive accuracy of 85% in identifying individualized drug response profiles.
The platform also identified novel drug combinations that outperformed the standard of care (SOC). For example, the combination of Gemcitabine and Romidepsin was identified as a top-ranked doublet, a pairing previously supported by clinical trial data (NCT00379639). By identifying these synergies, the platform supports the advancement of precision medicine for pancreatic cancer by offering alternative, evidence-based treatment options.
Conclusion
This study presents Optim.AI™ as a viable clinical-decision support platform in providing alternative treatment options for solid cancers. Additionally, it also demonstrates the clinical relevance of Optim.AI™ in pancreatic cancer. Optim.AI™ revealed individualized drug response profiles, including cases in which standard treatments may be resistant, achieving a predictive accuracy of 85%. These findings are consistent with results from pan-cancer retrospective concordance analysis.