School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China.
Department of Breast Surgery, Institute of Breast Disease, Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
PLoS One. 2024 Jul 31;19(7):e0306343. doi: 10.1371/journal.pone.0306343. eCollection 2024.
Due to the heterogeneity of cancer, precision medicine has been a major challenge for cancer treatment. Determining medication regimens based on patient genotypes has become a research hotspot in cancer genomics. In this study, we aim to identify key biomarkers for targeted therapies based on single nucleotide variants (SNVs) and copy number variants (CNVs) of genes. The experiment is carried out on 7 cancers on the Encyclopedia of Cancer Cell Lines (CCLE) dataset. Considering the high mutability of driver genes which result in abundant mutated samples, the effect of data sparsity can be eliminated to a large extent. Therefore, we focus on discovering the relationship between driver mutation patterns and three measures of drug response, namely area under the curve (AUC), half maximal effective concentration (EC50), and log2-fold change (LFC). First, multiple statistical methods are applied to assess the significance of difference in drug response between sample groups. Next, for each driver gene, we analyze the extent to which its mutations can affect drug response. Based on the results of multiple hypothesis tests and correlation analyses, our main findings include the validation of several known drug response biomarkers such as BRAF, NRAS, MAP2K1, MAP2K2, and CDKN2A, as well as genes with huge potential to infer drug responses. It is worth emphasizing that we identify a list of genes including SALL4, B2M, BAP1, CCDC6, ERBB4, FOXA1, GRIN2A, and PTPRT, whose impact on drug response spans multiple cancers and should be prioritized as key biomarkers for targeted therapies. Furthermore, based on the statistical p-values and correlation coefficients, we construct gene-drug sensitivity maps for cancer drug recommendation. In this work, we show that driver mutation patterns could be used to tailor therapeutics for precision medicine.
由于癌症的异质性，精准医学一直是癌症治疗的一大挑战。根据患者基因型确定治疗方案已成为癌症基因组学的研究热点。在这项研究中，我们旨在基于基因的单核苷酸变体（SNVs）和拷贝数变异（CNVs）来识别靶向治疗的关键生物标志物。该实验是在癌症细胞系百科全书（CCLE）数据集上的 7 种癌症上进行的。考虑到驱动基因的高突变率导致大量突变样本，数据稀疏性的影响可以在很大程度上消除。因此，我们专注于发现驱动突变模式与三种药物反应测量值（即曲线下面积（AUC）、半最大有效浓度（EC50）和对数倍变化（LFC））之间的关系。首先，应用多种统计方法评估药物反应在样本组之间差异的显著性。接下来，对于每个驱动基因，我们分析其突变对药物反应的影响程度。基于多假设检验和相关分析的结果，我们的主要发现包括验证了一些已知的药物反应生物标志物，如 BRAF、NRAS、MAP2K1、MAP2K2 和 CDKN2A，以及具有巨大潜力推断药物反应的基因。值得强调的是，我们确定了一系列基因，包括 SALL4、B2M、BAP1、CCDC6、ERBB4、FOXA1、GRIN2A 和 PTPRT，它们对药物反应的影响跨越多种癌症，应优先作为靶向治疗的关键生物标志物。此外，根据统计 p 值和相关系数，我们构建了用于癌症药物推荐的基因-药物敏感性图。在这项工作中，我们表明驱动突变模式可用于为精准医学定制治疗方法。
