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<p>Gene signatures have been developed to predict the immune checkpoint blockade (ICB) response and prognosis in patients with melanoma. However, most of these signatures are obtained from pre-treatment biopsy samples, and there is no predictive combination of immune and metabolic signatures from patients receiving treatment. In this study, using the Elastic Net Regression (ENLR) algorithm, we built an immunometabolism signature using on-treatment (IMME-ON) tumor specimens based on clinical information and transcriptome data from patients with metastatic melanoma after anti-PD1 and or anti-CTLA4 treatment. The IMME-ON signature was validated in three independent datasets of metastatic melanoma, achieving area under the curve (AUC) values of 0.79–0.86. We also combined all the test samples and obtained an overall AUC of 0.82 for the IMME-ON signature. Based on the IMME-ON signature, subjects were divided into high- and low-scores groups using the mean score. ICB response rates were higher in the high-scoring cohort subjects than the low-scoring subjects. Patient with high scores tended to have better survival outcomes than did those with low scores. In conclusion, we identified and verified an immunometabolism signature that provides a theoretical basis for applying such signatures derived from on-treatment tumor samples to predict therapeutic responses to ICB therapies.</p>

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Immunometabolism signature derived from on-treatment tumor specimens predicts immune checkpoint blockade response in metastatic melanoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Shuzhao Chen,
  • Shutong Liu,
  • Jiangtao Song,
  • Xiaojin Li,
  • Huaze Liao,
  • Shuiqin Chen,
  • Yuanbin Song,
  • Yun Wang,
  • Yang Liang,
  • Qianqian Huang,
  • Weiran Lv

摘要

Gene signatures have been developed to predict the immune checkpoint blockade (ICB) response and prognosis in patients with melanoma. However, most of these signatures are obtained from pre-treatment biopsy samples, and there is no predictive combination of immune and metabolic signatures from patients receiving treatment. In this study, using the Elastic Net Regression (ENLR) algorithm, we built an immunometabolism signature using on-treatment (IMME-ON) tumor specimens based on clinical information and transcriptome data from patients with metastatic melanoma after anti-PD1 and or anti-CTLA4 treatment. The IMME-ON signature was validated in three independent datasets of metastatic melanoma, achieving area under the curve (AUC) values of 0.79–0.86. We also combined all the test samples and obtained an overall AUC of 0.82 for the IMME-ON signature. Based on the IMME-ON signature, subjects were divided into high- and low-scores groups using the mean score. ICB response rates were higher in the high-scoring cohort subjects than the low-scoring subjects. Patient with high scores tended to have better survival outcomes than did those with low scores. In conclusion, we identified and verified an immunometabolism signature that provides a theoretical basis for applying such signatures derived from on-treatment tumor samples to predict therapeutic responses to ICB therapies.