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<p>Immune checkpoint inhibitors (ICIs) have significantly changed cancer therapy, yet their response rates remain relatively low. Identifying methods for robust prediction is crucial. This study evaluates the efficacy of gene-based methods for deriving predictive tumor-microenvironment scores in cancer patients, focusing on their performances in predicting survival outcomes and response to ICI therapy across various cancer types. The TIP Hot method demonstrated robustness as a predictive method for ICI response, particularly in Non-Small Cell Lung Cancer, Head and Neck Squamous Cell Carcinoma, and Urothelial Cancer. However, no score is robustly applicable to all cancer types. Therefore, significant challenges remain due to the variability of tumor biology and host immune responses, and universally applicable method should be further explored. Future research should aim to refine these predictive scoring methods through larger and more diverse datasets, and integrate advanced computational techniques to enhance predictive accuracy and utility in personalized cancer treatment.</p>

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Towards the tumor microenvironment scoring methods for immune checkpoint inhibitor response

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Qilu Zhou,
  • Arkadz Kirshtein,
  • Leili Shahriyari

摘要

Immune checkpoint inhibitors (ICIs) have significantly changed cancer therapy, yet their response rates remain relatively low. Identifying methods for robust prediction is crucial. This study evaluates the efficacy of gene-based methods for deriving predictive tumor-microenvironment scores in cancer patients, focusing on their performances in predicting survival outcomes and response to ICI therapy across various cancer types. The TIP Hot method demonstrated robustness as a predictive method for ICI response, particularly in Non-Small Cell Lung Cancer, Head and Neck Squamous Cell Carcinoma, and Urothelial Cancer. However, no score is robustly applicable to all cancer types. Therefore, significant challenges remain due to the variability of tumor biology and host immune responses, and universally applicable method should be further explored. Future research should aim to refine these predictive scoring methods through larger and more diverse datasets, and integrate advanced computational techniques to enhance predictive accuracy and utility in personalized cancer treatment.