An Integrated Multi-Compartment Biomarker Ecosystem for the Discrimination of True Tumor Progression from Treatment Effect in High-Grade Glioma: Pooled Analysis of Seven Multicenter Prospective Studies (n = 212)

Background Distinguishing true tumor progression from treatment effect—encompassing pseudoprogression (PsP) and radiation necrosis—remains one of the most pressing and unresolved challenges in high-grade glioma (HGG) management. Conventional contrast-enhanced MRI is frequently ambiguous in the post-chemoradiation period, and blood–brain barrier dynamics constrain single-compartment liquid biopsy assays. We hypothesized that an integrated multi-compartment biomarker ecosystem would overcome these limitations. Methods Pooled analysis of seven multicenter prospective studies (n = 212; 140 newly diagnosed, 72 recurrent; 160 IDH-wildtype GBM, 52 other HGG). The ecosystem comprised plasma cfDNA methylation profiling (EPIC 850K array), paired CSF/plasma data-independent acquisition (DIA) mass spectrometry proteomics, multiparametric MRI radiomics with ComBat harmonization, and a stacked AI ensemble classifier. Ground truth: histopathological confirmation in 67% of cases (n = 142); pre-specified radiographic consensus (independent adjudication, modified RANO, ≥6-month follow-up) in 33% (n = 70). A plasma-only ecosystem variant (no CSF) was pre-specified for the subgroup without CSF availability. Feature selection stability was assessed by outer-fold selection frequency across nested cross-validation. The dynamic biomarker axis is reported as an exploratory analysis. Results The full ecosystem achieved AUC 0.92 (95% CI 0.88–0.96), sensitivity 91.5%, and specificity 84.1% (Table 2). In the sensitivity analysis restricted to histopathologically confirmed cases (n = 142), AUC was 0.89 (95% CI 0.83–0.95), confirming that performance was not substantially dependent on radiographically assigned ground truth. The plasma-only ecosystem achieved AUC 0.86. Median lead-time over radiographic consensus was 6.2 weeks. Ten of 32 retained features demonstrated ≥80% fold-selection stability. Proteomic myeloid inflammation markers (IL-6, CXCL1) were enriched in treatment effect; correlation with cfDNA-estimated TMB (r ≈ 0.42) is reported as hypothesis-generating. The dynamic biomarker axis (exploratory, n = 89 longitudinal cases) resolved 76% of cross-sectionally indeterminate cases and requires prospective validation. Conclusions This integrated ecosystem achieves robust internal performance for treatment effect discrimination in HGG. The histopathologically confirmed sensitivity analysis, plasma-only model, feature stability data, and formal dynamic axis algorithm address key methodological concerns. External prospective validation is the essential and pre-planned next step.