PO.BCS01.13 · 生物信息与计算
用于稳健的通路感知特征选择的分层Dorfman筛选识别NSCLC中MEK抑制剂响应的预测因子
Hierarchical Dorfman screening for robust pathway-aware feature selection identifies predictors of MEK-inhibitor response in NSCLC
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摘要 Abstract
中文摘要
背景：靶向治疗响应的基因表达预测因子通常表现出强烈的通路内相关性、重尾噪声以及来自批次效应的污染。这种结构严重降低了传统特征选择方法（LASSO、弹性网络、SIS）和现有的组正则化方法（包括组LASSO、稀疏组LASSO和组SIS/AR2）的性能。
方法：我们引入了Dorfman筛选，一种用于通路结构化基因组数据的计算高效的分层特征选择框架。该方法（1）使用Hallmark注释和动态树切割聚类将基因分组为生物学通路，（2）执行全局通路层面的检验，（3）进行通路内基因筛选，以及（4）应用最终的正则化选择（Dorfman-LASSO或Dorfman-EN）。稳健的Dorfman变体还额外纳入了Huber加权回归，以处理重尾误差、离群值、杠杆点和批次污染。所有调优都通过交叉验证的RMSE以数据驱动的方式进行。
结果：在具有不同相关结构和严重污染的广泛模拟中（p=1000，n=200），Dorfman方法始终优于LASSO/EN、SIS、SIS-LASSO、组LASSO、稀疏组LASSO和组AR2-gpLASSO。Dorfman-EN在强相关性（ρ=0.8）下实现了最高的准确性，而Dorfman-LASSO在较低相关性水平下表现出色。稳健变体在非线性失真和重尾噪声下表现出显著的抗干扰能力，保持了最低的假发现率。应用于癌症药物敏感性基因组学（GDSC）中非小细胞肺癌（NSCLC）曲美替尼（trametinib）响应的RNA-seq数据，Dorfman方法相比LASSO/EN（RMSE=2.53-2.59）和组正则化方法（RMSE=2.63-3.66）实现了显著改善的预测准确性（RMSE=2.41-2.45）。当基因按文献支持的生物标志物层级进行分层时，Dorfman选择显著富集了高置信度基因，并揭示了竞争方法未能发现的先前未报道的候选基因。
结论：Dorfman筛选为通路结构化基因组数据提供了一种可扩展、稳健且具有生物学可解释性的方法，在NSCLC中MEK抑制剂响应的预测性能和生物标志物发现两方面均取得了改进。
查看英文原文 English abstract
Background: Gene expression predictors of targeted therapy response often exhibit strong within-pathway correlation, heavy-tailed noise, and contamination from batch effects. Such structure severely degrades performance of conventional feature selection methods (LASSO, elastic net, SIS) and existing group-regularized approaches including group LASSO, sparse group LASSO, and group SIS/AR2.
Methods: We introduce Dorfman Screening, a computationally efficient hierarchical feature selection framework for pathway-structured genomic data. The method (1) groups genes into biological pathways using Hallmark annotations and dynamic tree cut clustering, (2) performs global pathway-level testing, (3) conducts within-pathway gene screening, and (4) applies a final regularized selection (Dorfman-LASSO or Dorfman-EN). Robust Dorfman variants additionally incorporate Huber-weighted regression to handle heavy-tailed errors, outliers, leverage points, and batch contamination. All tuning is data-driven through cross-validated RMSE.
Results: In extensive simulations (p=1000, n=200) with varying correlation structures and severe contamination, Dorfman methods consistently outperformed LASSO/EN, SIS, SIS-LASSO, group-LASSO, sparse-group-LASSO, and group-AR2-gpLASSO. Dorfman-EN achieved the highest accuracy under strong correlations (ρ=0.8), while Dorfman-LASSO excelled at lower correlation levels. Robust variants showed marked resilience under nonlinear distortions and heavy-tailed noise, maintaining the lowest false discovery rates. Applied to Genomics of Drug Sensitivity in Cancer (GDSC) RNA-seq data for trametinib response in non-small cell lung cancer (NSCLC), Dorfman methods achieved substantially improved predictive accuracy (RMSE=2.41-2.45) compared to LASSO/EN (RMSE=2.53-2.59) and group-regularized methods (RMSE=2.63-3.66). When genes were stratified into literature-supported biomarker tiers, Dorfman selections were significantly enriched for high-confidence genes and revealed previously unreported candidates not discovered by competing methods.
Conclusions: Dorfman Screening provides a scalable, robust, and biologically interpretable approach for pathway-structured genomic data, yielding improvements in both prediction performance and biomarker discovery for MEK-inhibitor response in NSCLC.
利益披露 Disclosure
W. Guo, None..
J. Xie, None.