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An estrogen response-related signature predicts response to immunotherapy in melanoma.
Lin, Min; Du, Tian; Tang, Xiaofeng; Liao, Ying; Cao, Lan; Zhang, Yafang; Zheng, Wei; Zhou, Jianhua.
Afiliação
  • Lin M; Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Du T; Department of Breast Surgery, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Tang X; Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Liao Y; Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Cao L; Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Zhang Y; Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Zheng W; Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Zhou J; Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
Front Immunol ; 14: 1109300, 2023.
Article em En | MEDLINE | ID: mdl-37251404
ABSTRACT

Background:

Estrogen/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.

Methods:

RNA 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).

Conclusion:

In 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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Melanoma Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Melanoma Idioma: En Ano de publicação: 2023 Tipo de documento: Article