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| Home > Publications database > Integrating MALDI-MSI-Based Spatial Proteomics and Machine Learning to Predict Chemoradiotherapy Outcomes in Head and Neck Cancer. > print |
| 001 | 304976 | ||
| 005 | 20251001115231.0 | ||
| 024 | 7 | _ | |a 10.3390/ijms26189084 |2 doi |
| 024 | 7 | _ | |a pmid:41009656 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC12469958 |2 pmc |
| 024 | 7 | _ | |a 1422-0067 |2 ISSN |
| 024 | 7 | _ | |a 1661-6596 |2 ISSN |
| 037 | _ | _ | |a DKFZ-2025-01967 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 540 |
| 100 | 1 | _ | |a Grzeski, Marta |0 0009-0002-7189-5617 |b 0 |
| 245 | _ | _ | |a Integrating MALDI-MSI-Based Spatial Proteomics and Machine Learning to Predict Chemoradiotherapy Outcomes in Head and Neck Cancer. |
| 260 | _ | _ | |a Basel |c 2025 |b Molecular Diversity Preservation International |
| 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 1759230950_31256 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Head and neck squamous cell carcinoma (HNSCC) is often diagnosed at advanced stages. Due to pronounced intratumoral heterogeneity (ITH), reliable risk stratification and prediction of treatment response remain challenging. This study aimed to identify peptide signatures in HNSCC tissue that are associated with treatment outcomes in HPV-negative, advanced-stage HNSCC patients undergoing 5-fluorouracil/platinum-based chemoradiotherapy (CDDP-CRT). We integrated matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) of tryptic peptides with univariate statistics and machine learning approaches to uncover potential prognostic patterns. Formalin-fixed, paraffin-embedded whole tumor sections from 31 treatment-naive, HPV-negative HNSCC patients were digested in situ with trypsin, and the generated peptides were analyzed using MALDI-MSI. Clinical follow-up revealed recurrence or progression (RecPro) in 20 patients, while 11 patients showed no evidence of disease (NED). Classification models were developed based on the recorded peptide profiles using both unrestricted and feature-restricted approaches, employing either the full set of m/z features or a subset of the most discriminatory m/z features, respectively. The unrestricted model achieved a balanced accuracy of 71% at the patient level (75% sensitivity, 66% specificity), whereas the feature-restricted model reached a balanced accuracy of 72%, showing increased specificity (92%) but reduced sensitivity (52%) in the CDDP-CRT cohort. In order to assess treatment specificity, models trained on the CDDP-CRT cohort were tested on an independent patient cohort treated with mitomycin C-based CRT (MMC-CRT). Neither model demonstrated prognostic performance in the MMC-CRT patient cohort, suggesting specificity for platinum-based therapy. Presented findings highlight the potential of MALDI-MSI-based proteomic profiling to identify patients at elevated risk of recurrence following CDDP-CRT. This approach may support more personalized risk assessment and treatment planning, ultimately contributing to improved therapeutic outcomes in HPV-negative HNSCC. |
| 536 | _ | _ | |a 899 - ohne Topic (POF4-899) |0 G:(DE-HGF)POF4-899 |c POF4-899 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de |
| 650 | _ | 7 | |a MALDI-MSI |2 Other |
| 650 | _ | 7 | |a chemoradiotherapy outcome |2 Other |
| 650 | _ | 7 | |a head and neck cancer |2 Other |
| 650 | _ | 7 | |a machine learning |2 Other |
| 650 | _ | 7 | |a prognostic classifier |2 Other |
| 650 | _ | 7 | |a spatial proteomics |2 Other |
| 650 | _ | 7 | |a Cisplatin |0 Q20Q21Q62J |2 NLM Chemicals |
| 650 | _ | 7 | |a Fluorouracil |0 U3P01618RT |2 NLM Chemicals |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization: methods |2 MeSH |
| 650 | _ | 2 | |a Machine Learning |2 MeSH |
| 650 | _ | 2 | |a Male |2 MeSH |
| 650 | _ | 2 | |a Female |2 MeSH |
| 650 | _ | 2 | |a Head and Neck Neoplasms: therapy |2 MeSH |
| 650 | _ | 2 | |a Head and Neck Neoplasms: metabolism |2 MeSH |
| 650 | _ | 2 | |a Head and Neck Neoplasms: pathology |2 MeSH |
| 650 | _ | 2 | |a Head and Neck Neoplasms: diagnosis |2 MeSH |
| 650 | _ | 2 | |a Middle Aged |2 MeSH |
| 650 | _ | 2 | |a Proteomics: methods |2 MeSH |
| 650 | _ | 2 | |a Chemoradiotherapy: methods |2 MeSH |
| 650 | _ | 2 | |a Aged |2 MeSH |
| 650 | _ | 2 | |a Squamous Cell Carcinoma of Head and Neck: therapy |2 MeSH |
| 650 | _ | 2 | |a Squamous Cell Carcinoma of Head and Neck: metabolism |2 MeSH |
| 650 | _ | 2 | |a Squamous Cell Carcinoma of Head and Neck: pathology |2 MeSH |
| 650 | _ | 2 | |a Adult |2 MeSH |
| 650 | _ | 2 | |a Prognosis |2 MeSH |
| 650 | _ | 2 | |a Treatment Outcome |2 MeSH |
| 650 | _ | 2 | |a Cisplatin: therapeutic use |2 MeSH |
| 650 | _ | 2 | |a Fluorouracil: therapeutic use |2 MeSH |
| 700 | 1 | _ | |a Jensen, Patrick Moeller |0 0000-0002-8479-4885 |b 1 |
| 700 | 1 | _ | |a Hempel, Benjamin-Florian |0 0000-0002-1998-4033 |b 2 |
| 700 | 1 | _ | |a Thiele, Herbert |0 0000-0003-2913-8305 |b 3 |
| 700 | 1 | _ | |a Lellmann, Jan |0 0000-0002-5243-0331 |b 4 |
| 700 | 1 | _ | |a Schallenberg, Simon |0 0000-0002-7897-7116 |b 5 |
| 700 | 1 | _ | |a Budach, Volker |b 6 |
| 700 | 1 | _ | |a Keilholz, Ulrich |0 P:(DE-He78)db014277a1d4005afd93fa95bc6a1001 |b 7 |
| 700 | 1 | _ | |a Tinhofer, Ingeborg |0 0000-0002-0512-549X |b 8 |
| 700 | 1 | _ | |a Klein, Oliver |b 9 |
| 773 | _ | _ | |a 10.3390/ijms26189084 |g Vol. 26, no. 18, p. 9084 - |0 PERI:(DE-600)2019364-6 |n 18 |p 9084 |t International journal of molecular sciences |v 26 |y 2025 |x 1422-0067 |
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