Bertrand Mathon

DNA methylation profiling of CNS tumors led to the identification of HPAP (high‐grade glioma with pleomorphic and pseudopapillary features), a recently proposed entity with variable morphology, recurrent MAP‐kinase pathway activating events, and longer survival compared to glioblastoma. We aimed to independently validate and further characterize this entity. We retrieved a multicentric cohort of gliomas compatible with HPAP and performed t‐SNE dimensionality reduction on their DNA methylation profile. Clinical, radiological, histological, and molecular data were reviewed. Twenty tumors clustering with previously reported HPAP cases were identified. Median age at diagnosis was 37 years. Radiologically, the tumors appeared as expansive lesions with heterogeneous enhancement and frequent cysts. Histologically, they were well‐circumscribed, with papillary and pleomorphic features variably present. Marked histological signs of aggressivity were present in seven. Immunostainings showed expression of GFAP, OLIG2, and CD34. All cases were IDH1/2 wildtype and pMGMT unmethylated. Recurrently mutated genes included TP53 , ATRX , RB1, and BRAF . Five patients had germline pathogenic variants in genes associated with hereditary tumor predisposition syndromes ( MLH1 , BRCA2 , CHEK2 , NF2 , RB1 ). All patients underwent surgical resection, but subsequent management was heterogeneous. Ten‐year estimated survival was 85%. CDKN2A homozygous deletion seemed to identify more aggressive tumors. In conclusion, our data suggest the existence of a novel entity of circumscribed gliomas with long‐term survival despite possible high‐grade histological presentation, for which we propose the nomenclature “Gliomas with pleomorphic and pseudopapillary features” with a spectrum encompassing provisory grade 2 and 3. A proper identification could guide treatment choices.

Background: Baseline biomarkers anticipating outcome in primary central nervous system lymphoma (PCNSL) are limited, and classical scores rely on clinical variables alone. We present a plasma proteomic framework for prognostic stratification of newly diagnosed PCNSL. Methods: Baseline plasma from 162 newly diagnosed PCNSL patients (French LOC network; training n=84; test n=78) was profiled using Olink Reveal (1,007 proteins). Consensus k-means clustering applied to the training proteome was projected to the test cohort, evaluated for immune-signature enrichment, and assessed by multivariable Cox regression. An elastic-net classifier predicted early treatment failure, evaluated by held-out and nested cross-validated AUC. Results: Clustering identified 2 proteomic states, Inflamed and Non-inflamed plasma, that replicated in the test cohort. The Inflamed state was enriched for pre-specified immune signatures, most strongly an Innate/Inflammatory signature. High Innate/Inflammatory scores marked shorter overall (OS) and progression-free survival (PFS) in training (log-rank P=.0014 OS, P=.0037 PFS) and test (P=5.4×10⁻⁴ OS, P=.0014 PFS), remaining significant after adjustment for MSKCC class and sex (test OS HR 2.51, P=.0022). Combining signatures with clinical variables improved test discrimination, most clearly for PFS (C-index 0.68 vs. 0.60). A supervised classifier predicted early treatment failure (PFS < 9 months) at test AUC 0.71, driven by the immune signatures rather than a whole-proteome search. Conclusions: Plasma proteomics identifies a reproducible Inflamed state in PCNSL associated with shorter OS and PFS that improves discrimination beyond clinical scores and stratifies the risk of early treatment failure. These findings support prospective evaluation of a focused innate-inflammatory plasma panel for risk stratification.

Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion are key molecular markers for glioma classification. Amide proton transfer weighted (APTw) and nuclear Overhauser effect–weighted (NOEw) markers showed promise for glioma characterization, by probing protein‐related tissue properties. However, their interpretation is confounded by direct water saturation, macromolecules (semi‐solid magnetization transfer—ssMT), and T ₁ relaxation. Here, we aimed to assess the performance of three APTw and NOEw metrics—uncorrected, spillover/ssMT‐corrected (FMC), and fully spillover/ssMT‐ and T ₁ ‐corrected (FMTC)—for glioma stratification. Fifty patients with suspected gliomas were prospectively enrolled (12 IDH‐wild‐type, 38 IDH‐mutant, of which 21 with 1p/19q codeletion). Acquisitions were performed at 3 T using a 3D gradient echo readout with chemical exchange saturation transfer ( B ₁ = 2 μT for APTw, 0.6 μT for NOEw; T _1sat = 2 s), WASABI (WAter Shift And B1) for B ₀ / B ₁ mapping, and saturation recovery for T ₁ mapping. Glioma subtypes were compared using metrics extracted from manually segmented masks, using two‐tailed Mann–Whitney U tests, and the Benjamini–Hochberg false discovery rate (FDR) correction. Effect sizes were quantified using Cliff's δ with 95% bootstrap confidence intervals and classification performance was assessed by receiver operating characteristic analyses (area under the curve, AUC). IDH‐mutant and wild‐type gliomas differed significantly for the uncorrected APTw metric ( p = 0.005, AUC = 0.79), with stronger discrimination following correction—APTw‐FMC ( p < 0.001, AUC = 0.94) and APTw‐FMTC ( p < 0.001, AUC = 0.96), both with large effect sizes. Only APTw‐FMTC distinguished 1p/19q codeleted from non‐codeleted gliomas before FDR correction (uncorrected p = 0.01, AUC = 0.74). The NOEw metrics did not differ between any molecular subgroups, likely due to limited sensitivity of this contrast at 3 T. These results suggest that correcting for fluid, ssMT, and T ₁ effects enhances the accuracy of APTw metrics, offering a more robust and biophysically grounded approach to noninvasive glioma diagnosis.

Individual longitudinal changes in PET MTV and FLAIR volumes during treatment. For each patient, changes in PET-derived MTV (red line) and FLAIR lesion volume (blue line) are plotted over time, with treatment initiation marked as day 0. Numeric values indicate the change in volume at each time point, referenced either to baseline or to the lowest value observed (best response) in cases with multiple follow-ups. Each panel represents the data for a single patient.

Multimodal imaging assessment. A) Comparison of FLAIR volumes obtained from PET MTV with diffusion (apparent diffusion coefficient [ADC], mm²/s) and perfusion (relative cerebral blood volume [rCBV]) maps, shown at baseline and follow-up for patients 2 and 8. B) Longitudinal evolution of T1-weighted gadolinium-enhanced (T1G) lesions during treatment for patients 2, 8, and 18 (images for patient 15 unavailable), displayed alongside their corresponding PET images.

Waterfall Plot of Individual Tumor Responses by PET and MRI Criteria. Individual percentage changes from baseline for metabolic tumor volume (MTV, RANO-PET 1.0, red bars), sum of products of diameters (SPD, light blue bars), and volumetric FLAIR (dark blue bars). Dashed lines indicate the partial response thresholds for each respective criterion. Below the plot, information is provided for each patient regarding the presence of baseline contrast-enhancing T1G lesion, progression during IDH inhibitor therapy according to RANO criteria, PFS in months.

Exploratory comparison of baseline 18F-DOPA-PET values in progressors (n=6) vs non-progressors (n=14) on IDH inhibitor treatment. Parameters include metabolic tumor volume (MTV), total lesion glycolysis (TLG), tumor-to-background maximum ratio (TBRmax), and mean ratio (TBRmean). P-values were calculated using the Mann-Whitney U test.