Purpose: Lung cancer, mainly lung adenocarcinoma, lung squamous cell carcinoma and small cell lung cancer, has the highest incidence and cancer-related mortality worldwide. Platinum-based chemotherapy plays an important role in the treatment of various lung cancer subtypes, but not all patients benefit from this treatment regimen; thus, it is worth identifying lung cancer patients who are resistant or sensitive to platinum-based therapy.
Methods: The drug response and sequencing data of 170 lung cancer cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database, and support vector machines (SVMs) and beam search were used to select an optimal gene panel that can predict the sensitivity of cell lines to cisplatin. Then, we used available cell line data to explore the potential mechanisms.
Results: In this work, the drug response and sequencing data of 170 lung cancer cell lines were downloaded from the GDSC database, and SVMs and beam search were used to screen a panel of genes related to lung cancer cell line resistance to cisplatin. A final panel of nine genes (PLXNC1, KIAA0649, SPTBN4, SLC14A2, F13A1, COL5A1, SCN2A, PLEC, and ALMS1) was identified, and achieved an area under the curve (AUC) of 0.873 ± 0.004. The natural logarithm of the half maximal inhibitory concentration (lnIC50) values of the mutant-type (panel-MT) group was significantly higher than that of the wild-type (panel-WT) group, regardless of the lung cancer subtype. The differentially expressed pathways between the two groups may explain this difference.
Conclusion: In this study, we found that a panel of nine genes can accurately predict sensitivity to cisplatin, which may provide individualized treatment recommendations to improve the prognosis of patients with lung cancer.

Keywords: lung cancer, machine learning, SVMs, biomarkers


中文翻译：

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机器学习在预测肺癌顺铂耐药中的应用

目的：肺癌，主要是肺腺癌、肺鳞状细胞癌和小细胞肺癌，是全球发病率和癌症相关死亡率最高的癌症。铂类化疗在各种肺癌亚型的治疗中发挥着重要作用，但并非所有患者都能从这种治疗方案中受益；因此，有必要确定对铂类治疗耐药或敏感的肺癌患者。
方法：从癌症药物敏感性基因组学（GDSC）数据库下载170个肺癌细胞系的药物反应和测序数据，并使用支持向量机（SVM）和beam search来选择可以预测敏感性的最佳基因panel细胞系顺铂。然后，我们使用可用的细胞系数据来探索潜在的机制。
结果：本工作从GDSC数据库中下载了170个肺癌细胞系的药物反应和测序数据，利用SVMs和beam search筛选了一组肺癌细胞系顺铂耐药相关基因。最后一组九个基因（PLXNC1、KIAA0649、SPTBN4、SLC14A2、F13A1、COL5A1、SCN2A、PLEC 和 ALMS1）被鉴定，曲线下面积 (AUC) 为 0.873 ± 0.004。无论肺癌亚型如何，突变型（panel-MT）组的半数最大抑制浓度（lnIC50）值的自然对数显着高于野生型（panel-WT）组。两组之间差异表达的途径可以解释这种差异。
结论：在这项研究中，我们发现一组九个基因可以准确预测对顺铂的敏感性，这可能为改善肺癌患者的预后提供个体化的治疗建议。

关键词：肺癌，机器学习，SVM，生物标志物