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<p>Cancer remains a leading cause of death worldwide, with its heterogeneous and patient-specific nature complicating treatment. Immune checkpoint blockades (ICB) show promising potential by reactivating the immune system to detect and destroy cancer cells. Despite the success of ICBs in treating numerous cancers, response rates among patients remain highly variable, with a significant proportion showing no benefit. Therefore, predicting ICB success and identifying potential biomarkers that significantly affect response are essential for assessing prognostic outcomes. This study aims to develop an integrative and interpretable pipeline to identify clinically relevant biomarkers associated with ICB response across multiple cancer types. This study proposes a multi-step approach that integrates clinical, genomic, transcriptomic, and immune-related data to identify response-associated features. The proposed pipeline employed a feature selection scheme with four independent strategies to robustly identify response-related features, and machine learning models to validate their relevance for predicting immune responses. Network-based analyses, including the construction of gene regulatory and co-mutational networks, were then performed to identify hub genes specific to responders and non-responders, providing system-level insights into ICB response mechanisms. Additionally, multivariate Cox survival analysis was used to identify features with prognostic significance. The proposed pipeline identified 482 response-related features across the different data modalities, with Logistic Regression achieving mean classification accuracy of 89% and F1-Score of 0.86. Network analyses highlighted significant differences in transcriptional regulation and co-mutations between the two groups. By intersecting network-derived hub genes with prognostic, survival-associated features, eleven candidate biomarkers with potential clinical and mechanistic relevance to ICB response were identified: EEA1, RFX5, SALL2, TBX2, DLGAP2, INSRR, MBD5, PIK3C2G, RYR2, SCN1A, and SLITRK3. These results provide further biological insights into the mechanisms underlying ICB response. The proposed approach establishes a distinctive integrative framework for biomarker discovery, with potential applications in personalized cancer immunotherapy.</p>

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Integrative multi-omics pipeline for analysis of checkpoint blockade therapy

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Ahmed H. Saadawy,
  • Omar Loay,
  • Eman Badr

摘要

Cancer remains a leading cause of death worldwide, with its heterogeneous and patient-specific nature complicating treatment. Immune checkpoint blockades (ICB) show promising potential by reactivating the immune system to detect and destroy cancer cells. Despite the success of ICBs in treating numerous cancers, response rates among patients remain highly variable, with a significant proportion showing no benefit. Therefore, predicting ICB success and identifying potential biomarkers that significantly affect response are essential for assessing prognostic outcomes. This study aims to develop an integrative and interpretable pipeline to identify clinically relevant biomarkers associated with ICB response across multiple cancer types. This study proposes a multi-step approach that integrates clinical, genomic, transcriptomic, and immune-related data to identify response-associated features. The proposed pipeline employed a feature selection scheme with four independent strategies to robustly identify response-related features, and machine learning models to validate their relevance for predicting immune responses. Network-based analyses, including the construction of gene regulatory and co-mutational networks, were then performed to identify hub genes specific to responders and non-responders, providing system-level insights into ICB response mechanisms. Additionally, multivariate Cox survival analysis was used to identify features with prognostic significance. The proposed pipeline identified 482 response-related features across the different data modalities, with Logistic Regression achieving mean classification accuracy of 89% and F1-Score of 0.86. Network analyses highlighted significant differences in transcriptional regulation and co-mutations between the two groups. By intersecting network-derived hub genes with prognostic, survival-associated features, eleven candidate biomarkers with potential clinical and mechanistic relevance to ICB response were identified: EEA1, RFX5, SALL2, TBX2, DLGAP2, INSRR, MBD5, PIK3C2G, RYR2, SCN1A, and SLITRK3. These results provide further biological insights into the mechanisms underlying ICB response. The proposed approach establishes a distinctive integrative framework for biomarker discovery, with potential applications in personalized cancer immunotherapy.