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Multimodel preclinical platform predicts clinical response of melanoma to immunotherapy.

ABSTRACT: Although immunotherapy has revolutionized cancer treatment, only a subset of patients demonstrate durable clinical benefit. Definitive predictive biomarkers and targets to overcome resistance remain unidentified, underscoring the urgency to develop reliable immunocompetent models for mechanistic assessment. Here we characterize a panel of syngeneic mouse models, representing a variety of molecular and phenotypic subtypes of human melanomas and exhibiting their diverse range of responses to immune checkpoint blockade (ICB). Comparative analysis of genomic, transcriptomic and tumor-infiltrating immune cell profiles demonstrated alignment with clinical observations and validated the correlation of T cell dysfunction and exclusion programs with resistance. Notably, genome-wide expression analysis uncovered a melanocytic plasticity signature predictive of patient outcome in response to ICB, suggesting that the multipotency and differentiation status of melanoma can determine ICB benefit. Our comparative preclinical platform recapitulates melanoma clinical behavior and can be employed to identify mechanisms and treatment strategies to improve patient care.

SUBMITTER: Perez-Guijarro E

PROVIDER: S-EPMC8482620 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Multimodel preclinical platform predicts clinical response of melanoma to immunotherapy.

Pérez-Guijarro Eva E Yang Howard H HH Araya Romina E RE El Meskini Rajaa R Michael Helen T HT Vodnala Suman Kumar SK Marie Kerrie L KL Smith Cari C Chin Sung S Lam Khiem C KC Thorkelsson Andres A Iacovelli Anthony J AJ Kulaga Alan A Fon Anyen A Michalowski Aleksandra M AM Hugo Willy W Lo Roger S RS Restifo Nicholas P NP Sharan Shyam K SK Van Dyke Terry T Goldszmid Romina S RS Weaver Ohler Zoe Z Lee Maxwell P MP Day Chi-Ping CP Merlino Glenn G

Nature medicine 20200413 5

Although immunotherapy has revolutionized cancer treatment, only a subset of patients demonstrate durable clinical benefit. Definitive predictive biomarkers and targets to overcome resistance remain unidentified, underscoring the urgency to develop reliable immunocompetent models for mechanistic assessment. Here we characterize a panel of syngeneic mouse models, representing a variety of molecular and phenotypic subtypes of human melanomas and exhibiting their diverse range of responses to immun ...[more]

PMID: 32284588

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Project description:BackgroundEstrogen/estrogen receptor signaling influences the tumor microenvironment and affects the efficacy of immunotherapy in some tumors, including melanoma. This study aimed to construct an estrogen response-related gene signature for predicting response to immunotherapy in melanoma.MethodsRNA sequencing data of 4 immunotherapy-treated melanoma datasets and TCGA melanoma was obtained from open access repository. Differential expression analysis and pathway analysis were performed between immunotherapy responders and non-responders. Using dataset GSE91061 as the training group, a multivariate logistic regression model was built from estrogen response-related differential expression genes to predict the response to immunotherapy. The other 3 datasets of immunotherapy-treated melanoma were used as the validation group. The correlation was also examined between the prediction score from the model and immune cell infiltration estimated by xCell in the immunotherapy-treated and TCGA melanoma cases.Results"Hallmark Estrogen Response Late" was significantly downregulated in immunotherapy responders. 11 estrogen response-related genes were significantly differentially expressed between immunotherapy responders and non-responders, and were included in the multivariate logistic regression model. The AUC was 0.888 in the training group and 0.654-0.720 in the validation group. A higher 11-gene signature score was significantly correlated to increased infiltration of CD8+ T cells (rho=0.32, p=0.02). TCGA melanoma with a high signature score showed a significantly higher proportion of immune-enriched/fibrotic and immune-enriched/non-fibrotic microenvironment subtypes (p<0.001)-subtypes with better response to immunotherapy-and significantly better progression-free interval (p=0.021).ConclusionIn this study, we identified and verified an 11-gene signature that could predict response to immunotherapy in melanoma and was correlated with tumor-infiltrating lymphocytes. Our study suggests targeting estrogen-related pathways may serve as a combination strategy for immunotherapy in melanoma.

Project description:Ferroptosis is a non-apoptotic regulated cell death process, and much research has indicated that ferroptosis can induce the non-apoptotic death of tumor cells. Ferroptosis-related genes are expected to become a biological target for cancer treatment. However, the regulation of ferroptosis-related genes in skin cutaneous melanoma (SKCM) has not been well studied. In the present study, we conducted a systematic analysis of SKCM based on RNA sequencing data and clinical data obtained from The Cancer Genome Atlas (TCGA) database and the FerrD database. SKCM patients from the GSE78220 and MSKCC cohorts were used for external validation. Applying consensus clustering on RNA sequencing data from TCGA the generated ferroptosis subclasses of SKCM, which were analyzed based on the set of differentially expressed ferroptosis-related genes. Then, a least absolute shrinkage and selection operator (LASSO)-Cox regression was used to construct an eight gene survival-related linear signature. The median cut-off risk score was used to divide patients into high- and low-risk groups. The time-dependent receiver operating characteristic curve was used to examine the predictive power of the model. The areas under the curve of the signature at 1, 3, and 5 years were 0.673, 0.716, and 0.746, respectively. Kaplan-Meier survival analysis showed that the prognosis of high-risk patients was worse than that of low-risk patients. Univariate and multivariate Cox regression analyses showed that the risk signature was a robust independent prognostic indicator. By incorporating risk scores with tumor staging, a nomogram was constructed to predict prognostic outcomes for SKCM patients. In addition, the immunological analysis showed different immune cell infiltration patterns. Programmed-death-1 (PD-1) immunotherapy showed more significant benefits in the low-risk group than in the high-risk group. In summary, a model based on ferroptosis-related genes can predict the prognosis of SKCM and could have a potential role in guiding targeted therapy of SKCM.

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Multimodel preclinical platform predicts clinical response of melanoma to immunotherapy.

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Multimodel preclinical platform predicts clinical response of melanoma to immunotherapy.

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