AI Reasoning Enhances Biomarker Discovery in Cancer Research

A new study demonstrates how large language model reasoning can improve biomarker discovery in cancer research. Researchers trained a Mamba SSM model on TCGA-BRCA RNA-seq data and extracted the top 50 genes using gradient saliency methods. The raw 50-gene set performed worse than a 5,000-gene variance baseline, achieving an AUC of 0.832 compared to 0.903. DeepSeek-R1 then evaluated each candidate gene using structured chain-of-thought reasoning to produce a refined 17-gene set. This LLM-filtered set achieved superior performance with an AUC of 0.927 while using 294 times fewer features than the baseline. A faithfulness audit using COSMIC CGC, OncoKB, and PAM50 databases revealed that 6 of the 17 selected genes (35.3%) are validated BRCA biomarkers. The study, documented in arXiv preprint 2604.14334v2, explores whether reasoning quality correlates with downstream performance in filtering tissue-composition confounders. Gradient saliency from deep sequence models efficiently surfaces candidate biomarkers but can be contaminated by confounders that degrade classifier performance. The research addresses whether LLM chain-of-thought reasoning can effectively filter these contaminants. Among the input genes, 10 of 16 known BRCA genes were present before filtering.