标题： 癌症药物反应的遗传特征 -- 探讨集成特征选择方法 显示英文标题

标题： Genetic Features for Drug Responses in Cancer -- Investigating an Ensemble-Feature-Selection Approach

摘要： 使用遗传和转录组特征预测药物反应对于提高个性化医疗至关重要。 在本研究中，我们实施了机器学习算法的集成方法，以分析癌细胞系的遗传和转录组特征与IC50值之间的相关性，IC50值是药物疗效的可靠指标。 我们的分析涉及将特征集从原始的38,977个特征减少，显示出遗传特征与各种算法（包括SVR、线性回归和岭回归）的药物反应之间存在强烈的线性关系。 值得注意的是，拷贝数变异（CNVs）比突变更具预测性，这表明需要对药物反应预测的生物标志物进行重大重新评估。 通过严格的统计方法，我们确定了一个高度简化的421个关键特征集。 这个特征集提供了一种与传统癌症驱动基因不同的新视角，强调了这些生物标志物在设计靶向治疗中的潜力。 此外，我们的研究结果倡导将IC50值作为药物反应的可预测测量值，并强调了需要更多能够代表基因组数据维度的药物反应预测数据。 未来的工作将旨在扩大数据集并优化特征选择，以提高预测模型在临床环境中的泛化能力。 显示英文摘要

摘要： Predicting drug responses using genetic and transcriptomic features is crucial for enhancing personalized medicine. In this study, we implemented an ensemble of machine learning algorithms to analyze the correlation between genetic and transcriptomic features of cancer cell lines and IC50 values, a reliable metric for drug efficacy. Our analysis involved a reduction of the feature set from an original pool of 38,977 features, demonstrating a strong linear relationship between genetic features and drug responses across various algorithms, including SVR, Linear Regression, and Ridge Regression. Notably, copy number variations (CNVs) emerged as more predictive than mutations, suggesting a significant reevaluation of biomarkers for drug response prediction. Through rigorous statistical methods, we identified a highly reduced set of 421 critical features. This set offers a novel perspective that contrasts with traditional cancer driver genes, underscoring the potential for these biomarkers in designing targeted therapies. Furthermore, our findings advocate for IC50 values as a predictable measurement of drug responses and underscore the need for more data that can represent the dimensionality of genomic data in drug response prediction. Future work will aim to expand the dataset and refine feature selection to enhance the generalizability of the predictive model in clinical settings.