Publication Database

Integrating MALDI-MSI-based spatial proteomics and machine learning to predict chemoradiotherapy outcomes in head and neck cancer (2025)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Grzeski, Marta

Jensen, Patrick Moeller

Hempel, Benjamin-Florian (Tiermedizinisches Zentrum für Resistenzforschung)

Thiele, Herbert

Lellmann, Jan

Schallenberg, Simon

Budach, Volker

Keilholz, Ulrich

Tinhofer, Ingeborg

Klein, Oliver

Quelle

International journal of molecular sciences

Bandzählung: 26

Heftzählung: 18

Seiten: Artikel 9084 (20 Seiten)

ISSN: 1422-0067

Sprache

Englisch

Verweise

URL (Volltext): https://www.mdpi.com/1422-0067/26/18/9084

DOI: 10.3390/ijms26189084

Pubmed: 41009656

Kontakt

Tiermedizinisches Zentrum für Resistenzforschung

Robert-von-Ostertag-Str. 8
14163 Berlin

Abstract / Zusammenfassung

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.