用患者数据验证基因扰动的下游效应，精准预测表达变化方向。
CASCADE: An Agentic Regulatory Network Framework for Patient-Data-Validated Downstream Perturbation Prediction
- 基于ARACNe网络和真实肿瘤数据，通过拷贝数扩增反推基因敲低效果。
- MYC扰动预测在三种癌症中准确率达72%-90%，显著优于随机猜测。
- 适用于调控通路明确的增殖相关基因，对谱系因子预测失败。
CASCADE是一个智能代理框架，利用预先计算的ARACNe调控网络（通过MCP暴露），预测基因扰动对下游转录的影响。现有方法仅验证预测基因是否为已知癌基因；本研究改用焦点基因拷贝数扩增作为剂量性代理，对比真实TCGA肿瘤患者数据中表达变化方向。针对MYC，CASCADE预测的敲低靶点在乳腺癌（BRCA: 90.0%）、结肠癌（COAD: 72.0%）、胃癌（STAD: 85.7%）中与扩增/非扩增肿瘤表达一致性极高（均p<0.0013），显著高于置换基线，并通过了PAM50亚型控制，在独立队列METABRIC中复现（87.2%）。与MSigDB基因集比较，其准确性未超越已有生物学知识，但方向判断显著优于随机猜测。扩展至15个基因发现：增殖相关因子多可复现，而谱系决定因子和一个周期素D同源物（CCND2）持续失败，此模式被提出为后验假设。此外，评估基于LLM的代理将自然语言请求转化为真实MCP调用的能力：35个查询中，本地模型准确匹配率为71.4%（大模型85.7%），尽管可通过规模或服务端修正语法和别名问题，但两者在模糊查询时仍会错误默认为特定扰动类型，该缺陷无法通过针对性修复解决，因触发条件从未发生。
原文摘要 · Abstract (English)
CASCADE is an agentic framework that predicts downstream transcriptional effects of gene perturbation from precomputed ARACNe regulatory networks, exposed via MCP. Prior work validates such tools by checking whether predicted genes are known cancer genes (membership); we instead test whether the predicted direction of change matches reality, using focal-gene copy-number amplification as a dosage-based proxy for the inverse of knockdown against real TCGA patient tumor data. For MYC, CASCADE's predicted knockdown targets show strong concordance with real amplified-vs-non-amplified tumor expression across three cancer types (BRCA: 90.0%, COAD: 72.0%, STAD: 85.7%; all p<0.0013), well above permutation baselines, surviving a PAM50 subtype control and replicating in an independent cohort (METABRIC, 87.2%). Compared against curated MSigDB gene-set baselines via Fisher's exact test, CASCADE's accuracy is not shown to exceed existing public knowledge of MYC- or E2F-driven biology, though its gene-specific direction-calling clearly outperforms a naive uniform guess. Extending to fifteen additional genes, validation proves gene-specific rather than universal: proliferation-machinery regulators mostly replicate, while lineage-identity transcription factors and one cyclin-D paralog (CCND2) consistently fail, a pattern we discuss as a hedged, post-hoc hypothesis. We separately benchmark whether an LLM-based agent correctly grounds natural-language requests into CASCADE's real MCP tool calls. Across 35 queries, a documented local model reaches 71.4% exact match (85.7% for a larger model); schema and gene-alias failures are resolved by scale or server-side correction, but both models confidently default to the wrong perturbation type on ambiguous queries, a failure a targeted fix could not resolve because its trigger condition never occurs.
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