Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response
This addresses the need for explainable AI in precision oncology, where current deep learning models lack causal mechanisms for clinical decision-making, though it appears incremental as it builds on existing neuro-symbolic approaches.
The paper tackles the problem of predicting drug response in colorectal cancer by developing a neuro-symbolic framework that integrates a machine learning world model with an LLM-based agentic reasoning layer, achieving a predictive correlation of r=0.504 and validating findings against clinical data with p=0.023.
Precision oncology is currently limited by the small-N, large-P paradox, where high-dimensional genomic data is abundant, but high-quality drug response samples are often sparse. While deep learning models achieve high predictive accuracy, they remain black boxes that fail to provide the causal mechanisms required for clinical decision-making. We present a Neuro-Symbolic Agentic Framework that bridges this gap by integrating a quantitative machine learning World Model with an LLM-based agentic reasoning layer. Our system utilises a forensic data pipeline built on the Sanger GDSC dataset (N=83), achieving a robust predictive correlation (r=0.504) and a significant performance gain through the explicit modelling of clinical context, specifically Microsatellite Instability (MSI) status. We introduce the concept of Inverse Reasoning, where the agentic layer performs in silico CRISPR perturbations to predict how specific genomic edits, such as APC or TP53 repair, alter drug sensitivity. By distinguishing between therapeutic opportunity and contextual resistance, and validating these findings against human clinical data (p=0.023), our framework provides a transparent, biologically grounded path towards explainable AI in cancer research.