Predictive Value of the TP53/PIK3CA/ATM Mutation Classifier for Patients With Bladder Cancer Responding to Immune Checkpoint Inhibitor Therapy.

Only a proportion of patients with bladder cancer may benefit from durable response to immune checkpoint inhibitor (ICI) therapy. More precise indicators of response to immunotherapy are warranted. Our study aimed to construct a more precise classifier for predicting the benefit of immune checkpoint inhibitor therapy. This multi-cohort study examined the top 20 frequently mutated genes in five cohorts of patients with bladder cancer and developed the TP53/PIK3CA/ATM mutation classifier based on the MSKCC ICI cohort. The classifier was then validated in a validation set consisting of IMvigor210 cohort and Broad/Dana-Farber cohort. The molecular profile and immune infiltration characteristics in each subgroup as defined by this classifier were explored. Among all 881 patients with bladder cancer, the mutation frequency of TP53, PIK3CA, and ATM ranked in the top 20 mutated genes. The TP53/PIK3CA/ATM mutation classifier was constructed based on the Memorial Sloan Kettering Cancer Center (MSKCC) ICI cohort and only showed predictive value for patients with bladder cancer who received ICI therapy (median overall survival: low-risk group, not reached; moderate-risk group, 13.0 months; high-risk group, 8.0 months; P<0.0001). Similar results were found in subgroups of MSKCC ICI cohort defined by tumor mutation burden. Multivariate Cox analysis revealed that the risk group defined by the classifier served as an independent prognostic factor for overall survival in patients with bladder cancer. Efficacy of the classifier was verified in a validation set consisting of IMvigor210 cohort and Broad/Dana-Farber cohort. Lower expression of PD-1/PD-L1 and less tumor immune infiltration were observed in the high-risk group than the other two groups of the TCGA cohort and the IMvigor210 cohort. Our study constructed a TP53/PIK3CA/ATM mutation classifier to predict the benefit of immune checkpoint inhibitor therapy for patients with bladder cancer. This classifier can potentially complement the tumor mutation burden and guide clinical ICI treatment decisions according to distinct risk levels.

Pan YH ，Zhang JX ，Chen X ，Liu F ，Cao JZ ，Chen Y ，Chen W ，Luo JH ... -  《Frontiers in Immunology》

Association of Tumor Protein p53 and Ataxia-Telangiectasia Mutated Comutation With Response to Immune Checkpoint Inhibitors and Mortality in Patients With Non-Small Cell Lung Cancer.

Chen Y ，Chen G ，Li J ，Huang YY ，Li Y ，Lin J ，Chen LZ ，Lu JP ，Wang YQ ，Wang CX ，Pan LK ，Xia XF ，Yi X ，Chen CB ，Zheng XW ，Guo ZQ ，Pan JJ ... -  《JAMA Network Open》

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.

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

Chen M ，Li P ，Yao H ，Liu F ，Fu L ，Wang Y ，Zhu J ，Xu Q ，Liang H ，Zhou Y ，Wang Z ，Deng Q ，Chen W ，Cao J ，Chen X ，Luo J ... -  《-》

Alterations in TP53 Are a Potential Biomarker of Bladder Cancer Patients Who Benefit From Immune Checkpoint Inhibition.

Lyu Q ，Lin A ，Cao M ，Xu A ，Luo P ，Zhang J ... -  《-》

Prognostic effect of coexisting TP53 and ZFHX3 mutations in non-small cell lung cancer patients treated with immune checkpoint inhibitors.

In recent years, immune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of patients with advanced-stage non-small cell lung cancer (NSCLC). The relationship between TP53 mutation and prognosis of non-small cell lung cancer (NSCLC) remains controversial. We aimed to identify advanced-stage NSCLC patients harboring TP53 mutation who would benefit from ICI treatment. Gene mutations and tumor mutational burden (TMB) data of NSCLC patients who received at least one dose of ICI therapy at the Memorial Sloan Kettering Cancer Center between 2013 and 2017 were extracted from the cBioPortal online platform. Gene clustering analyses were performed for patients with short and long overall survival (OS). The top ten significantly different mutated genes were identified. Furthermore, we analyzed the different OS of coexisting TP53 and other significantly different mutated genes to identify NSCLC patients with TP53 mutations who would benefit from immunotherapy. A total of 350 patients were enrolled in the study. Of these a total of 219 (62.6%) patients were found to harbor TP53 mutations, whereas 131 (37.4%) had wild-type TP53. There was no statistically significant difference in OS between TP53 mutated or wild-type NSCLC patients who underwent ICI treatment. However, coexisting TP53 and ZFHX3 mutations were independent prognostic factors. Higher somatic TMB (highest 20% in each histology) and combination of anti-CTLA-4 and anti-PD-1/PD-L1 therapy were also associated with longer OS in multivariate analysis. Coexisting TP53 and ZFHX3 mutations are independent prognostic factors for advanced-stage NSCLC patients undergoing ICI treatment. These findings could help identify patients harboring TP53 mutations that would benefit from ICI treatment.

Zhang L ，Zhang T ，Shang B ，Li Y ，Cao Z ，Wang H ... -  《-》