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000299528 1001_ $$aDelaidelli, Alberto$$b0
000299528 245__ $$aHigh-resolution proteomic analysis of medulloblastoma clinical samples identifies therapy resistant subgroups and MYC immunohistochemistry as a powerful outcome predictor.
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000299528 520__ $$aWhile 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.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.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.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.
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000299528 650_7 $$2Other$$aFFPE proteomics
000299528 650_7 $$2Other$$aMYC
000299528 650_7 $$2Other$$aMedulloblastoma
000299528 650_7 $$2Other$$abiomarker
000299528 650_7 $$2Other$$arisk-stratification
000299528 7001_ $$aBurwag, Fares$$b1
000299528 7001_ $$aBen-Neriah, Susana$$b2
000299528 7001_ $$aSuk, Yujin$$b3
000299528 7001_ $$aShyp, Taras$$b4
000299528 7001_ $$aKosteniuk, Suzanne$$b5
000299528 7001_ $$00000-0002-6244-0584$$aDunham, Christopher$$b6
000299528 7001_ $$aCheng, Sylvia$$b7
000299528 7001_ $$0P:(DE-He78)34b3639de467b2c700920d7cbc3d2110$$aOkonechnikov, Konstantin$$b8$$udkfz
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000299528 7001_ $$0P:(DE-He78)a8a10626a848d31e70cfd96a133cc144$$avon Deimling, Andreas$$b10$$udkfz
000299528 7001_ $$aEllezam, Benjamin$$b11
000299528 7001_ $$aPerreault, Sébastien$$b12
000299528 7001_ $$aSingh, Sheila$$b13
000299528 7001_ $$00000-0003-2618-4402$$aHawkins, Cynthia$$b14
000299528 7001_ $$0P:(DE-He78)4c28e2aade5f44d8eca9dd8e97638ec8$$aKool, Marcel$$b15$$udkfz
000299528 7001_ $$0P:(DE-He78)f746aa965c4e1af518b016de3aaff5d9$$aPfister, Stefan$$b16$$udkfz
000299528 7001_ $$aSteidl, Christian$$b17
000299528 7001_ $$aHughes, Christopher$$b18
000299528 7001_ $$0P:(DE-He78)8d9c904a6cea14d4c99c78ba46e41f93$$aKorshunov, Andrey$$b19$$udkfz
000299528 7001_ $$aSorensen, Poul H$$b20
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