Establishing a Prediction Model for the Efficacy of Platinum—Based Chemotherapy in NSCLC Based on a Two Cohorts GWAS Study

Author:

Xiao Qi1234,Mao Chenxue1234,Gao Ying5,Huang Hanxue1234,Yu Bing1234,Yu Lulu1234,Li Xi1234,Mao Xiaoyuan1234ORCID,Zhang Wei1234,Yin Jiye1234,Liu Zhaoqian1234

Affiliation:

1. Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China

2. Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, China

3. National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, China

4. Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China

5. Department of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha 410008, China

Abstract

Platinum drugs combined with other agents have been the first-line treatment for non-small cell lung cancer (NSCLC) in the past decades. To better evaluate the efficacy of platinum–based chemotherapy in NSCLC, we establish a platinum chemotherapy response prediction model. Here, a total of 217 samples from Xiangya Hospital of Central South University were selected as the discovery cohort for a genome-wide association analysis (GWAS) to select SNPs. Another 216 samples were genotyped as a validation cohort. In the discovery cohort, using linkage disequilibrium (LD) pruning, we extract a subset that does not contain correlated SNPs. The SNPs with p < 10−3 and p < 10−4 are selected for modeling. Subsequently, we validate our model in the validation cohort. Finally, clinical factors are incorporated into the model. The final model includes four SNPs (rs7463048, rs17176196, rs527646, and rs11134542) as well as two clinical factors that contributed to the efficacy of platinum chemotherapy in NSCLC, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.726.

Funder

National Natural Science Foundation of China

Project Program of National Clinical Research Center for Geriatric Disorders of China

Science and Technology Program of Changsha of China

Publisher

MDPI AG

Subject

General Medicine

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