MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641050246 A) filed by Dr. Latha Kiran Krishna Rajendran, Bangalore, Karnataka, on April 20, for 'ai-driven predictive and therapeutic systems for overcoming immunotherapy resistance in colorectal cancer liver metastases.'
Inventor(s) include Dr. Latha Kiran Krishna Rajendran.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "Colorectal cancer liver metastases (CRCLM) represent the leading cause of mortality among colorectal cancer patients, with hepatic dissemination occurring in approximately 50 percent of diagnosed cases and conferring a median overall survival of fewer than 30 months under standard therapeutic regimens. Immune checkpoint inhibitor therapies that have achieved transformative outcomes in microsatellite instability-high colorectal tumors demonstrate near-complete therapeutic futility in the microsatellite-stable subtype accounting for over 85 percent of metastatic colorectal cancer cases, attributable to a profoundly immunosuppressive liver metastatic microenvironment characterized by tolerogenic Kupffer cell populations, regulatory T-cell accumulation, myeloid-derived suppressor cell infiltration, and hepatic stellate cell-mediated fibrotic barrier formation that collectively neutralize cytotoxic T-lymphocyte anti-tumor activity. [510] Existing therapeutic strategies for immunotherapy-resistant colorectal cancer liver metastases exhibit critical deficiencies in their capacity to prospectively identify patients at highest risk of primary resistance, characterize the specific resistance mechanisms operative in individual patient tumors at molecular resolution, rationally select resistance-overcoming combination therapeutic regimens personalized to individual microenvironmental immunosuppression profiles, and dynamically monitor treatment-induced immune reprogramming to guide adaptive therapeutic adjustments before radiological progression events confirm treatment failure. These limitations result in protracted exposure of patients to ineffective therapies, missed windows for timely therapeutic escalation, and systematic underutilization of emerging resistance-overcoming combination strategies whose efficacy is critically dependent on precise patient selection. [515] The integration of Artificial Intelligence capabilities including multi-omic resistance mechanism deconvolution, spatially resolved tumor immune landscape modeling, reinforcement learning-guided combination therapy optimization, liquid biopsy-based dynamic resistance monitoring, and generative AI-driven novel combination hypothesis generation presents transformative opportunities for systematically dismantling immunotherapy resistance barriers in colorectal cancer liver metastases. AI systems trained on comprehensive multi-institutional datasets capturing the molecular, cellular, and spatial dimensions of hepatic metastatic immune microenvironments can identify actionable resistance mechanisms, predict combination therapy response probabilities, and generate individualized therapeutic blueprints that restore immune surveillance competency in previously refractory tumors. [520] The present invention describes a comprehensive AI-Driven Predictive and Therapeutic System for Overcoming Immunotherapy Resistance in Colorectal Cancer Liver Metastases that integrates multi-omic resistance mechanism deconvolution modules, spatially resolved immune landscape reconstruction engines, reinforcement learning combination therapy optimization controllers, longitudinal liquid biopsy resistance evolution monitoring frameworks, and generative AI novel resistance-overcoming strategy design systems within a unified personalized immunotherapy resistance management platform. The system continuously analyzes patient tumor molecular profiles, spatial immune architecture data, and dynamic liquid biopsy signals to identify operative resistance mechanisms, predict combination therapy outcomes, and generate individualized resistance-overcoming therapeutic strategies with unprecedented molecular precision. [525] Validation studies conducted across multiple academic oncology centers encompassing 2,847 colorectal cancer liver metastasis patients demonstrated that the AI-Driven Predictive and Therapeutic System achieved a 58.4 percent improvement in primary resistance prediction accuracy, 64.7 percent improvement in combination therapy response prediction, 49.2 percent acceleration in resistance mechanism characterization timelines, and 71.8 percent improvement in overall survival prediction accuracy at treatment initiation compared to conventional clinicopathological resistance assessment and empirical combination therapy selection methodologies. [530] These research findings confirm that the AI-Driven Predictive and Therapeutic System for Overcoming Immunotherapy Resistance in Colorectal Cancer Liver Metastases constitutes a foundational technological advancement for precision oncology immunotherapy management, with deployment potential spanning academic oncology centers, community cancer treatment facilities, pharmaceutical clinical development programs, and healthcare systems requiring systematic, molecularly-informed approaches to individualized immunotherapy resistance prediction, characterization, and therapeutic intervention in metastatic colorectal cancer."
Disclaimer: Curated by HT Syndication.
