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| 005 | 20251016152844.0 | ||
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| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Delaidelli, Alberto |b 0 |
| 245 | _ | _ | |a High-resolution proteomic analysis of medulloblastoma clinical samples identifies therapy resistant subgroups and MYC immunohistochemistry as a powerful outcome predictor. |
| 260 | _ | _ | |a Oxford |c 2025 |b Oxford Univ. Press |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1760621297_219688 |2 PUB:(DE-HGF) |
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| 500 | _ | _ | |a 2025 Oct 14;27(9):2431-2444 |
| 520 | _ | _ | |a 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.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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| 650 | _ | 7 | |a FFPE proteomics |2 Other |
| 650 | _ | 7 | |a MYC |2 Other |
| 650 | _ | 7 | |a Medulloblastoma |2 Other |
| 650 | _ | 7 | |a biomarker |2 Other |
| 650 | _ | 7 | |a risk-stratification |2 Other |
| 700 | 1 | _ | |a Burwag, Fares |b 1 |
| 700 | 1 | _ | |a Ben-Neriah, Susana |b 2 |
| 700 | 1 | _ | |a Suk, Yujin |b 3 |
| 700 | 1 | _ | |a Shyp, Taras |b 4 |
| 700 | 1 | _ | |a Kosteniuk, Suzanne |b 5 |
| 700 | 1 | _ | |a Dunham, Christopher |0 0000-0002-6244-0584 |b 6 |
| 700 | 1 | _ | |a Cheng, Sylvia |b 7 |
| 700 | 1 | _ | |a Okonechnikov, Konstantin |0 P:(DE-He78)34b3639de467b2c700920d7cbc3d2110 |b 8 |u dkfz |
| 700 | 1 | _ | |a Schrimpf, Daniel |0 P:(DE-He78)e54a1e0999c1d8c95869ef9188b794cc |b 9 |u dkfz |
| 700 | 1 | _ | |a von Deimling, Andreas |0 P:(DE-He78)a8a10626a848d31e70cfd96a133cc144 |b 10 |u dkfz |
| 700 | 1 | _ | |a Ellezam, Benjamin |b 11 |
| 700 | 1 | _ | |a Perreault, Sébastien |b 12 |
| 700 | 1 | _ | |a Singh, Sheila |b 13 |
| 700 | 1 | _ | |a Hawkins, Cynthia |0 0000-0003-2618-4402 |b 14 |
| 700 | 1 | _ | |a Kool, Marcel |0 P:(DE-He78)4c28e2aade5f44d8eca9dd8e97638ec8 |b 15 |u dkfz |
| 700 | 1 | _ | |a Pfister, Stefan |0 P:(DE-He78)f746aa965c4e1af518b016de3aaff5d9 |b 16 |u dkfz |
| 700 | 1 | _ | |a Steidl, Christian |b 17 |
| 700 | 1 | _ | |a Hughes, Christopher |b 18 |
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| 700 | 1 | _ | |a Sorensen, Poul H |b 20 |
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