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Background <p>Current tissue-based biomarkers for gastric cancer (GC) immunotherapy face significant limitations due to tumor heterogeneity and sampling constraints. This study explores plasma proteome profiling as a non-invasive strategy to identify dynamic biomarkers predictive of treatment response.</p> Methods <p>In a prospective cohort of 88 advanced GC patients receiving immunotherapy, we performed longitudinal plasma proteomic analysis at baseline and during cycles 2 and 4. Ridge regression was employed to develop composite protein scores, which were validated using Cox models and Kaplan–Meier analyses. Survival outcomes, including progression-free survival (PFS) and overall survival (OS), as well as biomarker dynamics, were assessed over a median follow-up period of 12.9&#xa0;months.</p> Results <p>A baseline composite score integrating IFN-gamma, CSF-1, MIC-A/B, and ANGPT demonstrated superior discriminative power for immunotherapy response (area under the ROC curve [AUC]) = 0.77, 95% confidence intervals [CI]: 0.67–0.88) compared to individual markers. Elevated baseline levels of IFN-gamma correlated with prolonged PFS (upper median <i>vs.</i> lower median: Hazard ratios [HR] = 0.67, <i>P</i> = 0.007) and OS (HR = 0.64, <i>P</i> = 0.012). Longitudinal monitoring revealed the dynamics pattern of IFN-gamma: early elevation predicted durable clinical benefit (DCB) (median PFS: not evaluable <i>vs.</i> 6.7&#xa0;months in no-durable benefit [NDB]), while a persistent decrease post-cycle 4 indicated a risk of relapse (<i>P</i> = 0.028).</p> Conclusion <p>IFN-gamma emerges as a critical biomarker for prognostic assessment and therapeutic monitoring in advanced GC immunotherapy, and its composite model incorporating PD-L1 Combined Positive Score (CPS) demonstrated superior predictive efficacy, highlighting the necessity for validating additional biomarkers in future clinical studies.</p>

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Longitudinal plasma proteome profiling identifies IFN-gamma dynamics as a time-dependent predictor of immunotherapy response in advanced gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月2日

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  • Jingshuai Fang,
  • Yuhui Yu,
  • Yan Sun,
  • Xiaofeng Chen,
  • Caiwang Yan,
  • Qiufen Sun,
  • Guangfu Jin

摘要

Background

Current tissue-based biomarkers for gastric cancer (GC) immunotherapy face significant limitations due to tumor heterogeneity and sampling constraints. This study explores plasma proteome profiling as a non-invasive strategy to identify dynamic biomarkers predictive of treatment response.

Methods

In a prospective cohort of 88 advanced GC patients receiving immunotherapy, we performed longitudinal plasma proteomic analysis at baseline and during cycles 2 and 4. Ridge regression was employed to develop composite protein scores, which were validated using Cox models and Kaplan–Meier analyses. Survival outcomes, including progression-free survival (PFS) and overall survival (OS), as well as biomarker dynamics, were assessed over a median follow-up period of 12.9 months.

Results

A baseline composite score integrating IFN-gamma, CSF-1, MIC-A/B, and ANGPT demonstrated superior discriminative power for immunotherapy response (area under the ROC curve [AUC]) = 0.77, 95% confidence intervals [CI]: 0.67–0.88) compared to individual markers. Elevated baseline levels of IFN-gamma correlated with prolonged PFS (upper median vs. lower median: Hazard ratios [HR] = 0.67, P = 0.007) and OS (HR = 0.64, P = 0.012). Longitudinal monitoring revealed the dynamics pattern of IFN-gamma: early elevation predicted durable clinical benefit (DCB) (median PFS: not evaluable vs. 6.7 months in no-durable benefit [NDB]), while a persistent decrease post-cycle 4 indicated a risk of relapse (P = 0.028).

Conclusion

IFN-gamma emerges as a critical biomarker for prognostic assessment and therapeutic monitoring in advanced GC immunotherapy, and its composite model incorporating PD-L1 Combined Positive Score (CPS) demonstrated superior predictive efficacy, highlighting the necessity for validating additional biomarkers in future clinical studies.