Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response

TL;DR

This paper introduces CIWM, a neuro-symbolic framework combining machine learning and reasoning to improve drug response prediction and mechanistic understanding in colorectal cancer, especially with limited data.

Contribution

The novel CIWM framework integrates a machine learning emulator with an LLM-based reasoning layer for explainable oncology predictions.

Findings

Achieved a predictive correlation of r=0.447 on GDSC dataset.

Identified a Symbolic Scaffold effect improving data-sparse modeling.

Validated in silico CRISPR perturbations against clinical profiles.

Abstract

Precision oncology is currently limited by the small-N, large-P paradox, where high-dimensional genomic data is abundant but pharmacological response samples are sparse. While deep learning achieves predictive accuracy, it frequently fails to provide the mechanistic clarity required for clinical adoption. We present the Contextual Invertible World Model (CIWM), a Neuro-Symbolic Agentic Framework that bridges this gap by integrating a quantitative machine learning emulator with an LLM-based reasoning layer. Utilising a zero-leakage forensic pipeline on the Sanger GDSC dataset (N = 83), we achieve a robust predictive correlation (r = 0.447, p = 2.30e-05). We identify a Symbolic Scaffold effect, where the explicit modelling of clinical context (MSI status) provides a 3.6 percent gain in fidelity in data-sparse regimes. Through Inverse Reasoning, we perform in silico CRISPR perturbations across the colorectal landscape, identifying a hierarchical dominance of the APC/Wnt-axis over the p53 apoptotic pathway. Validated against human clinical profiles (TCGA-COAD proxy, p = 0.0357), our framework provides a transparent, invertible, and biologically grounded path towards explainable AI in oncology.