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

Alberto Delaidelli 1, 2, 3, 4
Fares Burwag 1, 4
Susana Ben-Neriah 5
Yujin Suk 6, 7, 8, 9
Taras Shyp 1, 4
Suzanne Kosteniuk 10, 11
Christopher Dunham 12
Sylvia Cheng 13
Konstantin Okonechnikov 14, 15
Daniel Schrimpf 16
Andreas von Deimling 16
Benjamin Ellezam 17
Sébastien Perreault 18
Sheila Singh 6, 7, 8, 9, 19, 20
Cynthia E. Hawkins 21, 22, 23
Marcel Kool 14, 15, 24, 25, 26, 27, 28, 29
Stefan M. Pfister 14, 15, 30
Christian Steidl 2, 3, 5
Christopher Hughes 31
Andrey Korshunov 16
Poul H. Sorensen 1, 2, 3, 4
1
 
Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver , BC, V5Z 1L3,
4
 
Department of Molecular Oncology, British Columbia Cancer Research Centre , Vancouver, British Columbia ,
5
 
Centre for Lymphoid Cancer, BC Cancer , Vancouver, British Columbia ,
28
 
National Center for Tumor Diseases (NCT) , Heidelberg ,
30
 
Department of Pediatric Hematology and Oncology, University Hospital, and National Center for Tumor Diseases (NCT) , Heidelberg ,
Publication typeJournal Article
Publication date2025-02-18
scimago Q1
wos Q1
SJR6.974
CiteScore30.2
Impact factor13.4
ISSN15228517, 15235866
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 2 groups of 3/4 medulloblastoma clinical cohorts (n = 362) treated with standard therapies.

Results

After the 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.

Conclusions

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.

Found 
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Delaidelli A. et al. High-resolution proteomic analysis of medulloblastoma clinical samples identifies therapy resistant subgroups and MYC immunohistochemistry as a powerful outcome predictor // Neuro-Oncology. 2025.
GOST all authors (up to 50) Copy
Delaidelli A. et al. High-resolution proteomic analysis of medulloblastoma clinical samples identifies therapy resistant subgroups and MYC immunohistochemistry as a powerful outcome predictor // Neuro-Oncology. 2025.
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TY - JOUR
DO - 10.1093/neuonc/noaf046
UR - https://academic.oup.com/neuro-oncology/advance-article/doi/10.1093/neuonc/noaf046/8052127
TI - High-resolution proteomic analysis of medulloblastoma clinical samples identifies therapy resistant subgroups and MYC immunohistochemistry as a powerful outcome predictor
T2 - Neuro-Oncology
AU - Delaidelli, Alberto
AU - Burwag, Fares
AU - Ben-Neriah, Susana
AU - Suk, Yujin
AU - Shyp, Taras
AU - Kosteniuk, Suzanne
AU - Dunham, Christopher
AU - Cheng, Sylvia
AU - Okonechnikov, Konstantin
AU - Schrimpf, Daniel
AU - Deimling, Andreas von
AU - Ellezam, Benjamin
AU - Perreault, Sébastien
AU - Singh, Sheila
AU - Hawkins, Cynthia E.
AU - Kool, Marcel
AU - Pfister, Stefan M.
AU - Steidl, Christian
AU - Hughes, Christopher
AU - Korshunov, Andrey
AU - Sorensen, Poul H.
PY - 2025
DA - 2025/02/18
PB - Oxford University Press
SN - 1522-8517
SN - 1523-5866
ER -
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@article{2025_Delaidelli,
author = {Alberto Delaidelli and Fares Burwag and Susana Ben-Neriah and Yujin Suk and Taras Shyp and Suzanne Kosteniuk and Christopher Dunham and Sylvia Cheng and Konstantin Okonechnikov and Daniel Schrimpf and Andreas von Deimling and Benjamin Ellezam and Sébastien Perreault and Sheila Singh and Cynthia E. Hawkins and Marcel Kool and Stefan M. Pfister and Christian Steidl and Christopher Hughes and Andrey Korshunov and Poul H. Sorensen and others},
title = {High-resolution proteomic analysis of medulloblastoma clinical samples identifies therapy resistant subgroups and MYC immunohistochemistry as a powerful outcome predictor},
journal = {Neuro-Oncology},
year = {2025},
publisher = {Oxford University Press},
month = {feb},
url = {https://academic.oup.com/neuro-oncology/advance-article/doi/10.1093/neuonc/noaf046/8052127},
doi = {10.1093/neuonc/noaf046}
}