Dataset Information

Robust immunoscore model to predict the response to anti-PD1 therapy in melanoma.

ABSTRACT: This study aimed to construct immune-related predictors to identify responders to anti-PD1 therapy of melanoma through CIBERSORT algorithm. Using the least absolute shrinkage and selection operator (LASSO) logistic regression, we constructed an immunoscore consisting of 8 immune subsets to predict the anti-PD1 response. This score achieved an overall accuracy of AUC = 0.77, 0.80 and 0.73 in the training cohort, validation cohort and on-anti-PD1 cohort, respectively. Patients with high immunoscores had significantly higher objective response rates (ORRs) than did those with low immunoscores (ORR: 53.8% vs 17.7%, P < 0.001 for entire pre-anti-PD1 cohort; 42.1% vs 15.1%, P = 0.022 for on-anti-PD1 cohort; 66.7% vs 16.7%, P = 0.038 for neoadjuvant anti-PD1 cohort). Prolonged survival trends were observed in high-immunoscore group (1-year PFS: 42.4% vs 14.3%, P = 0.059; 3-year OS: 41.5% vs 31.6%, P = 0.057). Furthermore, we found that high-immunoscore group exhibited higher fractions of tumor-infiltrating lymphocytes and an increased IFN-? response. Analysis of the results of the GSEA indicated a significant enrichment of antitumor immunity pathways in the high-immunoscore group. Therefore, this study indicated that we constructed a robust immunoscore model to predict the anti-PD1 response of metastatic melanoma and the neoadjuvant anti-PD1 response of resectable melanoma.

SUBMITTER: Nie RC

PROVIDER: S-EPMC6932919 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Robust immunoscore model to predict the response to anti-PD1 therapy in melanoma.

Nie Run-Cong RC Yuan Shu-Qiang SQ Wang Yun Y Chen Ying-Bo YB Cai Yan-Yu YY Chen Shi S Li Shu-Man SM Zhou Jie J Chen Guo-Ming GM Luo Tian-Qi TQ Zhou Zhi-Wei ZW Li Yuan-Fang YF

Aging 20191203 23

This study aimed to construct immune-related predictors to identify responders to anti-PD1 therapy of melanoma through CIBERSORT algorithm. Using the least absolute shrinkage and selection operator (LASSO) logistic regression, we constructed an immunoscore consisting of 8 immune subsets to predict the anti-PD1 response. This score achieved an overall accuracy of AUC = 0.77, 0.80 and 0.73 in the training cohort, validation cohort and on-anti-PD1 cohort, respectively. Patients with high immunoscor ...[more]

PMID: 31796647

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Project description:PurposeAnti-programmed cell death protein 1 (PD1)±anti-cytotoxic T-lymphocyte associated protein 4 (CTLA4) immune checkpoint inhibitors (ICIs) are standard therapeutic options for metastatic melanoma. We assessed whether biologic subtype according to primary tumor type or genomic subtype can function as predictive biomarkers for anti-PD1±anti-CTLA4 ICI in patients with advanced melanoma.MethodsWe performed a single-center retrospective cohort analysis of patients who received anti-PD1±anti-CTLA4 ICI for advanced melanoma between 2012 and 2019. Primary tumor type, BRAF and NRAS mutation status, and other covariates were abstracted from chart review. Log-rank tests and multivariable Cox regression models were used to assess differences in clinical progression-free (cPFS) and overall survival (OS).ResultsWe identified 230 patients who received 249 lines of anti-PD1±anti-CTLA4 ICI for unresectable or metastatic disease. Of these patients, 74% were cutaneous, 11% mucosal, 8% unknown primary and 7% acral. BRAF and NRAS mutations were identified in 35% and 28% of patients, respectively. In multivariable analyses of the entire cohort, acral or mucosal primary tumor type, >3 metastatic sites, elevated LDH were predictive of shorter cPFS and OS. Combination ICI therapy was associated with longer cPFS (HR 0.57, 95% CI 0.38 to 0.86, p=0.007) and OS (HR 0.42, 95% CI 0.28 to 0.65, p<0.001). Combination ICI was significantly associated with longer OS in unknown primary and mucosal melanoma. There was a non-significant trend toward longer OS with anti-PD1+anti-CTLA4 in cutaneous melanoma, but not in acral melanoma. In multivariable analyses, combination ICI was associated with longer OS in NRAS (HR 0.24, 95% CI 0.10 to 0.62, p=0.003, n=69) and BRAF V600E/K (HR 0.47, 95% CI 0.24 to 0.90, p=0.024, n=86) mutant melanoma but not BRAF/NRAS wild-type (n=94) melanoma.ConclusionsIn our cohort, primary melanoma tumor type and genomic subtype were independent predictive markers of cPFS and OS for patients with metastatic melanoma receiving anti-PD1 ICI. Primary tumor type and genomic subtype-including NRAS-should be further evaluated in prospective clinical trials to determine their value as predictive markers. Biologic subtypes may facilitate clinical decision-making when recommending combination ICI treatment (anti-PD1±anti-CTLA4) versus anti-PD1 alone for patients with metastatic melanoma.

Dataset Information

Robust immunoscore model to predict the response to anti-PD1 therapy in melanoma.

Publications

Robust immunoscore model to predict the response to anti-PD1 therapy in melanoma.

Similar Datasets

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