Development and validation of a novel defined mutation classifier based on Lasso logistic regression for predicting the overall survival of immune checkpoint inhibitor therapy in renal cell carcinoma

Minyu Chen; Pengju Li; Haohua Yao; Fei Liu; Liangmin Fu; Yinghan Wang; Jiangquan Zhu; Quanhui Xu; Hui Liang; Yayun Zhou; Zhu Wang; Qiong Deng; Wei Chen; Jiazheng Cao; Xu Chen; Junhang Luo

doi:10.21037/tau-23-21

Development and validation of a novel defined mutation classifier based on Lasso logistic regression for predicting the overall survival of immune checkpoint inhibitor therapy in renal cell carcinoma

Transl Androl Urol. 2023 Mar 31;12(3):406-424. doi: 10.21037/tau-23-21.

Authors

Minyu Chen^#¹, Pengju Li^#^{1

2}, Haohua Yao^#¹, Fei Liu³, Liangmin Fu¹, Yinghan Wang¹, Jiangquan Zhu¹, Quanhui Xu¹, Hui Liang⁴, Yayun Zhou⁵, Zhu Wang⁴, Qiong Deng⁴, Wei Chen¹, Jiazheng Cao⁶, Xu Chen¹, Junhang Luo^{1

2}

Affiliations

¹ Department of Urology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
² Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
³ Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
⁴ Department of Urology, Affiliated Longhua People's Hospital, Southern Medical University, Shenzhen, China.
⁵ Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
⁶ Department of Urology, Jiangmen Central Hospital, Jiangmen, China.

^# Contributed equally.

Abstract

Background: Currently, immune checkpoint inhibitor (ICI)-based therapy has become the first-line treatment for advanced renal cell carcinoma (RCC). However, few biomarkers have been identified to predict the response to ICI therapy in RCC patients. Herein, our research aimed to build a gene mutation prognostic indicator for ICI therapy.

Methods: This multi-cohort study explored the mutation patterns in 2 publicly available advanced RCC ICI therapy cohorts, the Memorial Sloan Kettering Cancer Center (MSKCC) advanced RCC ICI therapy cohort and the CheckMate ICI therapy cohort. A total of 261 patients in the CheckMate ICI therapy cohort were randomly assigned to either the training or validation set. Least absolute shrinkage and selection operator (Lasso) logistic regression analysis was subsequently used to develop a mutation classifier utilizing the training set. The classifier was then validated internally in the validation set and externally in 2 ICI therapy cohorts and 2 non-ICI therapy cohorts. Survival analysis, receiver operator characteristic curves and Harrell's concordance index were performed to assess the prognostic value of the classifier. Function and immune microenvironment analysis in each subgroup defined by the classifier were performed.

Results: A 10-gene mutation classifier was constructed based on the CheckMate ICI therapy cohort to separate patients into 2 risk groups, with patients in the high-risk group showing significantly lower overall survival probability than those in the low-risk group [the training set (HR: 1.791; 95% CI: 1.207-2.657; P=0.003), the validation set (HR: 1.842; 95% CI: 1.133-2.996; P=0.012) and combination set (HR: 1.819; 95% CI: 1.339-2.470; P<0.001)]. Further validation confirmed that the mutation classifier only showed predictive value for patients receiving ICI therapy instead of non-ICI therapy. Combined with the clinical characteristics, the risk score was proven to be an independent prognostic factor for overall survival in ICI therapy by multivariate Cox regression analysis. Functional and immune infiltration analysis demonstrated that lower risk scores tended to associate with immunologically "hot" status in RCC.

Conclusions: Our 10-gene mutation classifier was found to be a biomarker for predicting the overall survival of patients with advanced RCC to ICI therapy.

Keywords: Renal cell carcinoma (RCC); immune checkpoint inhibitor (ICI); immunotherapy; mutation classifier; tumor immune microenvironment.