2 research outputs found
Integrated MRI–Immune–Genomic Features Enclose a Risk Stratification Model in Patients Affected by Glioblastoma
Simple Summary: Despite crucial scientific advances, Glioblastoma (GB) remains a fatal disease with
limited therapeutic options and a lack of suitable biomarkers. The unveiled competence of the brain
immune system together with the breakthrough advent of immunotherapy has shifted the present
translational research on GB towards an immune-focused perspective. Several clinical trials targeting
the immunosuppressive GB background are ongoing. So far, results are inconclusive, underpinning
our partial understanding of the complex cancer-immune interplay in brain tumors. High throughput
Magnetic Resonance (MR) imaging has shown the potential to decipher GB heterogeneity, including
pathologic and genomic clues. However, whether distinct GB immune contextures can be deciphered
at an imaging scale is still elusive, leaving unattained the non-invasive achievement of prognostic
and predictive biomarkers. Along these lines, we integrated genetic, immunopathologic and imaging
features in a series of GB patients. Our results suggest that multiparametric approaches might
offer new efficient risk stratification models, opening the possibility to intercept the critical events
implicated in the dismal prognosis of GB.
Abstract: Background: The aim of the present study was to dissect the clinical outcome of GB patients
through the integration of molecular, immunophenotypic and MR imaging features. Methods: We
enrolled 57 histologically proven and molecularly tested GB patients (5.3% IDH-1 mutant). Two-
Dimensional Free ROI on the Biggest Enhancing Tumoral Diameter (TDFRBETD) acquired by MRI
sequences were used to perform a manual evaluation of multiple quantitative variables, among which
we selected: SD Fluid Attenuated Inversion Recovery (FLAIR), SD and mean Apparent Diffusion
Coefficient (ADC). Characterization of the Tumor Immune Microenvironment (TIME) involved the
immunohistochemical analysis of PD-L1, and number and distribution of CD3+, CD4+, CD8+ Tumor
Infiltrating Lymphocytes (TILs) and CD163+ Tumor Associated Macrophages (TAMs), focusing on
immune-vascular localization. Genetic, MR imaging and TIME descriptors were correlated with
overall survival (OS). Results: MGMT methylation was associated with a significantly prolonged OS
(median OS = 20 months), while no impact of p53 and EGFR status was apparent. GB cases with high
mean ADC at MRI, indicative of low cellularity and soft consistency, exhibited increased OS (median
OS = 24 months). PD-L1 and the overall number of TILs and CD163+TAMs had a marginal impact
on patient outcome. Conversely, the density of vascular-associated (V) CD4+ lymphocytes emerged
as the most significant prognostic factor (median OS = 23 months in V-CD4high vs. 13 months in
V-CD4low, p = 0.015). High V-CD4+TILs also characterized TIME of MGMTmeth GB, while p53mut
appeared to condition a desert immune background. When individual genetic (MGMTunmeth), MR
imaging (mean ADClow) and TIME (V-CD4+TILslow) negative predictors were combined, median OS was 21 months (95% CI, 0–47.37) in patients displaying 0–1 risk factor and 13 months (95% CI
7.22–19.22) in the presence of 2–3 risk factors (p = 0.010, HR = 3.39, 95% CI 1.26–9.09). Conclusion:
Interlacing MRI–immune–genetic features may provide highly significant risk-stratification models
in GB patients