KEM®-Based Genomic Signature Predicts NADT Response in High-Risk Prostate Cancer
Dernière mise à jour : 1 sept.
Identification of a Predictive Genomic Signature for NADT Response in High-Risk Prostate Cancer Using Real-World Clinical Trial Data Analyzed by the KEM® Explainable AI Platform. Presented as a scientific poster at ESMO-AI 2025, Berlin.
Prostate cancer is one of the most common malignancies worldwide, and treatment decisions for localized high-risk disease increasingly rely on molecular predictors to guide therapy selection. Neoadjuvant androgen deprivation therapy (NADT) can improve outcomes in selected patients, but clinicians have lacked validated genomic indicators to distinguish exceptional responders from poor responders.
Ariana Pharmaceuticals, in collaboration with Philips, conducted an explainable AI–driven study to identify genomic features predictive of NADT response. A total of 58 patients from two clinical studies - NCT02430480 (Wilkinson et al., 2021) and NCT02268175 (Tewari et al., 2021) - were analyzed using the KEM® platform, which applies Formal Concept Analysis (FCA) to systematically evaluate all relationships within a dataset and reveal transparent, interpretable biomarker signatures.
A curated panel of 100 gene symbols was selected for biological relevance, including genes involved in viral–bacterial defense, T-cell receptor signaling, and PDE4D7-associated androgen response pathways. Predictive signatures were generated from association rules acting as voters in a majority-vote system, optimized via 3-fold cross-validation across 50 repeated train–test splits.
The resulting KEM® signature identified four key genes — GUCY1A1, PLS3, ARSD, and CUX2 — whose high expression predicted favorable response to NADT, with patient-level interpretability via a rule-based coverage matrix. Benchmarked against Immunoscore and conventional multivariate approaches, the KEM® signature demonstrated superior predictive performance and enhanced transparency.
This preliminary work lays the foundation for further validation as additional prospective and real-world patient data become available, underscoring the potential of explainable AI for precision stratification and predictive biomarker development in prostate cancer treatment.
