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. 2025 Mar 5:noaf046.
doi: 10.1093/neuonc/noaf046. Online ahead of print.

High-resolution proteomic analysis of medulloblastoma clinical samples identifies therapy resistant subgroups and MYC immunohistochemistry as a powerful outcome predictor

Alberto Delaidelli  1   2 , Fares Burwag  1 , Susana Ben-Neriah  3 , Yujin Suk  4   5 , Taras Shyp  1 , Suzanne Kosteniuk  6 , Christopher Dunham  7 , Sylvia Cheng  8 , Konstantin Okonechnikov  9 , Daniel Schrimpf  10 , Andreas von Deimling  10 , Benjamin Ellezam  11 , Sébastien Perreault  12 , Sheila Singh  4   5   13 , Cynthia Hawkins  14   15   16 , Marcel Kool  9   17   18   19   20 , Stefan M Pfister  9   21 , Christian Steidl  2   3 , Christopher Hughes  22 , Andrey Korshunov  10 , Poul H Sorensen  1   2
Affiliations

High-resolution proteomic analysis of medulloblastoma clinical samples identifies therapy resistant subgroups and MYC immunohistochemistry as a powerful outcome predictor

Alberto Delaidelli et al. Neuro Oncol. .

Erratum in

Abstract

Background: While international consensus and the 2021 WHO classification recognize multiple molecular medulloblastoma subgroups, these are difficult to identify in clinical practice utilizing routine approaches. As a result, biology-driven risk stratification and therapy assignment for medulloblastoma remains a major clinical challenge. Here, we report mass spectrometry-based analysis of clinical samples for medulloblastoma subgroup discovery, highlighting a MYC-driven prognostic signature and MYC immunohistochemistry (IHC) as a clinically tractable method for improved risk stratification.

Methods: We analyzed 56 formalin fixed paraffin embedded (FFPE) medulloblastoma samples by data independent acquisition mass spectrometry identifying a MYC proteome signature in therapy resistant Group 3 medulloblastoma. We validated MYC IHC prognostic and predictive value across two Group 3/4 medulloblastoma clinical cohorts (n=362) treated with standard therapies.

Results: After exclusion of WNT tumors, MYC IHC was an independent predictor of therapy resistance and death [HRs 23.6 and 3.23; 95% confidence interval (CI) 1.04-536.18 and 1.84-5.66; P = .047 and < .001]. Notably, only ~50% of the MYC IHC positive tumors harbored MYC amplification. Accordingly, cross-validated survival models incorporating MYC IHC outperformed current risk stratification schemes including MYC amplification, and reclassified ~20% of patients into a more appropriate very high-risk category.

Conclusion: This study provides a high-resolution proteomic dataset that can be used as a reference for future biomarker discovery. Biology-driven clinical trials should consider MYC IHC status in their design. Integration of MYC IHC in classification algorithms for non-WNT tumors could be rapidly adopted on a global scale, independently of advanced but technically challenging molecular profiling techniques.

Keywords: FFPE proteomics; MYC; Medulloblastoma; biomarker; risk-stratification.

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