Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10⁻³ for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.

吉西他滨/卡铂化疗通常会引起骨髓抑制，包括中性粒细胞减少症、白细胞减少症和血小板减少症。预测有这些药物不良反应 (ADR) 风险的患者并相应地调整治疗方法是个性化医疗的长期目标。本研究使用来自 96 名吉西他滨/卡铂治疗的非小细胞肺癌 (NSCLC) 患者的血液样本的全基因组测序 (WGS) 和基因网络模块来预测骨髓抑制。PLINK 中遗传变异的关联发现 4594、5019 和 5066 个常染色体 SNV/INDEL，p  ≤ 1 × 10 ^-3分别用于中性粒细胞减少症、白细胞减少症和血小板减少症。基于 SNV/INDEL，我们确定了毒性模块，由分别从 MCODE 生成的 350、345 和 313 个基因的基因网络模块推断出的 215 个独特的重叠基因组成。这些模块基因在大鼠骨髓、人骨髓和暴露于卡铂和吉西他滨的人细胞系中显示出差异表达基因的富集（p < 0.05)。然后使用 80% 的患者作为训练数据，随机 LASSO 将毒性模块中 SNVs/INDELs 的数量减少到一个可行的预测模型，该模型由 62 个 SNVs/INDELs 组成，可以准确预测训练和测试（剩余 20%）数据具有高（CTCAE 3-4）和低（CTCAE 0-1）最大骨髓抑制毒性完全，受试者工作特征（ROC）曲线下面积（AUC）为100％。本研究展示了 WGS、基因网络模块和随机 LASSO 可如何用于开发可行且经过测试的模型来预测骨髓抑制毒性。尽管所提出的模型在本研究中预测了骨髓抑制，但需要在其他研究中进一步评估以确定其可重复性、可用性和临床效果。