Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer

Abstract

Yanan Gao,1,* Qiong Lyu,2,* Peng Luo,2,* Mujiao Li,3 Rui Zhou,3 Jian Zhang,2 Qingwen Lyu4 1Department of Radiotherapy, Affiliated Cancer Hospital, Zhengzhou University, Zhengzhou, People’s Republic of China; 2Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China; 3School of Biomedical Engineering, Southern Medical University, Guangzhou, People’s Republic of China; 4Department of Information, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qingwen Lyu; Jian Zhang Email [email protected]; [email protected]: 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

Keywords

• lung cancer
• machine learning
• svms
• biomarkers