用基因语言模型+医学影像，发现癌症基因与影像的隐藏关联
Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans
- 用Evo²模型预测基因突变严重度，无需任务训练
- 在340例癌症患者中发现46个新显著基因，含纤毛与细胞骨架相关基因
- 适合探索罕见突变基因功能，尤其对无传统驱动基因的癌症研究者
许多基因的功能仍未知，传统驱动基因发现方法依赖突变频率，无法识别罕见突变基因。本文结合基于Evo²的基因组分析与常规临床影像，实现全基因组范围内的基因-表型关联发现。针对三个TCGA队列（cRCC=透明细胞肾癌，HCC=肝细胞癌，BC=乳腺癌；n=340）中的每个体细胞突变，Evo²预测其严重度评分，无需任务特定训练。随后，将每基因的严重度汇总与配对肿瘤分割图像提取的放射组学特征进行相关性分析，并控制总突变负荷。在TCGA-cRCC队列（n=162）中，该方法复现了已知肾癌驱动基因，并发现46个未收录于主流癌症基因面板且达到错误发现率（FDR）显著性的新基因，其中若干为单基因遗传性纤毛病和细胞骨架疾病基因。结果表明，将基因语言模型与广泛可用的临床影像结合，可作为无需假设的基因-影像关联发现工具，揭示传统方法难以捕捉的关联。
原文摘要 · Abstract (English)
The function of many genes is still unknown, and conventional driver-discovery methods, which rely on how frequently a gene is mutated, cannot assess genes that are only rarely affected. Here we pair Evo~2-based genome analysis with routine clinical imaging to identify gene--phenotype associations at genome-wide scale. For every somatic mutation across three TCGA cohorts (cRCC=clear cell renal cell carcinoma, HCC=hepatocellular carcinoma, and BC=breast cancer; $n = 340$ total), Evo~2 predicts a severity score, with no task-specific training. Per-gene severity summaries are then correlated with radiomic features extracted from paired tumor segmentations, controlling for total mutation burden. In TCGA-cRCC ($n = 162$), this sweep recovers established renal-cancer drivers and identifies 46 additional genes reaching false discovery rate (FDR) significance absent from curated cancer-gene panels, several of which are Mendelian ciliopathy and cytoskeletal-disease genes. These results demonstrate that pairing a genomic language model with widely available clinical imaging can serve as a hypothesis-free discovery tool for gene--imaging associations invisible to conventional approaches.
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