基础模型引导的放射基因组学发现：将癌症基因组与癌症扫描联系起来
Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans
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中文总结 AI 辅助
研究将基于Evo~2的基因组分析与临床成像结合，全基因组识别基因-表型关联。对TCGA队列体细胞突变用Evo~2预测严重程度评分，与放射组学特征关联。在TCGA-cRCC中恢复已知驱动基因，发现新的显著基因，证明该方法可发现传统方法难以察觉的基因-成像关联。
中文摘要 AI 辅助
许多基因的功能仍不明确，传统的驱动基因发现方法依赖基因的突变频率，无法评估很少受影响的基因。本文将基于Evo~2的基因组分析与常规临床成像相结合，在全基因组范围内识别基因-表型关联。对于三个TCGA队列中的每个体细胞突变，Evo~2预测一个严重程度评分。然后将每个基因的严重程度总结与从配对肿瘤分割中提取的放射组学特征相关联，控制总突变负担。在TCGA-cRCC中，该方法恢复了已确定的肾癌驱动基因，并识别出46个在精选癌症基因面板中不存在的达到错误发现率（FDR）显著性的额外基因。这些结果表明，将基因组语言模型与广泛可用的临床成像相结合，可以作为一种无假设的发现工具，用于发现传统方法不可见的基因-成像关联。
英文摘要
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.
发表机构
- University Hospital RWTH Aachen（RWTH亚琛大学医院）
- TU Dresden（德累斯顿技术大学）
- TUD Dresden University of Technology（德累斯顿技术大学）
- Heidelberg University Hospital（海德堡大学医院）
机构由 AI 辅助整理，请以论文原文为准。
