AI Insight
Researchers developed a Stromal Activity Score (SAS) that integrates five dimensions of the tumor microenvironment to predict cancer outcomes across 12 cancer types using 1,303 patient samples. The score showed statistically significant association with overall survival in pan-cancer analysis and correlated well with existing stromal measures, but demonstrated only modest predictive performance for immunotherapy response and did not maintain statistical significance after multiple testing correction in individual cancer types. Time-dependent analysis revealed declining predictive accuracy over longer follow-up periods, and the score performed best when the angiogenesis component was removed.
Why it matters
This work addresses the need for integrated biomarkers that capture complex tumor microenvironment biology across multiple cancer types. While the current version shows limited standalone clinical utility for predicting treatment outcomes, it provides a foundation for developing combination biomarkers that could help personalize cancer treatment decisions, particularly for immunotherapy selection.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background: The tumor microenvironment (TME) plays a critical role in cancer progression and treatment response. Stromal components, including cancer-associated fibroblasts (CAFs), extracellular matrix (ECM), and angiogenesis, contribute to tumor aggressiveness. However, a comprehensive stromal activity score integrating multiple stromal dimensions for pan-cancer prognosis prediction is lacking. Methods: We developed a Stromal Activity Score (SAS) integrating five stromal dimensions: CAF signature (12 genes), ECM remodeling (15 genes), TGF-{beta} signaling (13 genes), angiogenesis (12 genes), and complement activation (11 genes). SAS was calculated using single-sample Gene Set Enrichment Analysis (ssGSEA) on TCGA pan-cancer data comprising 1,303 samples across 12 cancer types. Prognostic value was evaluated using Kaplan-Meier analysis and Cox regression. Immunotherapy response prediction was validated in two independent cohorts (IMvigor210, n=88; Liu2019, n=105). Results: Pan-cancer Cox regression demonstrated a significant association between SAS and overall survival (HR = 1.165, 95% CI: 1.065-1.275, P = 0.001). Per-cancer analysis identified BRCA (HR = 1.942, P = 0.022), STAD (HR = 1.684, P = 0.024), and LUSC (HR = 1.552, P = 0.038) as significant, though none survived FDR correction. SAS correlated strongly with ESTIMATE Stromal Score (Spearman {rho} = 0.835) and moderately with Immune Score ({rho} = 0.396). Immunotherapy validation showed consistent trends (IMvigor210: AUC = 0.602; Liu2019: AUC = 0.617). Time-dependent ROC analysis showed 1-year AUC = 0.596, 3-year = 0.579, 5-year = 0.559. Leave-one-out analysis identified angiogenesis removal as enhancing prognostic signal (HR = 3.737, P = 0.0002). Three distinct TME subtypes were identified with differential SAS profiles. Conclusions: SAS is a novel pan-cancer stromal activity score that captures TME biology with strong construct validity. Its clinical utility as a standalone biomarker remains modest, but it may complement existing immunotherapy biomarkers.