Histological tumor necrosis predicts decreased survival after neoadjuvant chemotherapy in head and neck squamous cell carcinoma

  • A. R. Koskenniemi*
  • , T. Huusko
  • , J. Routila
  • , S. Jalkanen
  • , M. Hollmén
  • , P. Vainio
  • , S. Ventelä
  • *Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

Objective: Despite growing interest in neoadjuvant therapies, there are no methods to predict radio- (RT) or chemoradiotherapy (CRT) response in head and neck squamous cell carcinoma (HNSCC). The aim of this research was to study the effect of neoadjuvant RT or CRT on the tumor immune landscape and patient survival in HNSCC. Methods: All HNSCC patients treated with neoadjuvant RT or CRT (n = 53) were identified from a retrospective cohort of 1033 patients. Pre- and post-neoadjuvant cancer samples from the same patient were analyzed with biomarkers related to cancer immunology: tumor-infiltrating lymphocytes (CD8), tumor-associated macrophages (CD68, CD206, Clever-1), immune response regulator (PD-L1) and histologic tumor necrosis. Outcomes of interest were individual immune landscape profiling and its impact on 5-year overall survival (OS) in HNSCC patients treated with neoadjuvant RT/CRT. Results: Results from 588 whole-section stainings revealed multiple statistically significant alterations in immune landscape in response to RT/CRT. Pretreatment tumor necrosis was the most useful biomarker in predicting poor outcome, as the OS was 14.3% with necrosis and 48.5% without necrosis (HR 2.87; 95% CI: 1.23 to 6.66, p=0.014). In addition, an artificial intelligence-based (AI) deep learning method for identifying tumor necrosis from histopathological specimens was successfully developed. The predictive role of histological necrosis in neoadjuvant RT/CRT was validated in additional samples from 171 HNSCC patients untreated with neoadjuvant therapy. Conclusions: Detection of tumor necrosis and AI-driven deep learning effectively predict neoadjuvant RT/CRT responses in HNSCC.

Original languageEnglish
Article number107287
JournalOral Oncology
Volume165
DOIs
Publication statusPublished - Jun 2025
MoE publication typeA1 Journal article-refereed

Funding

This work was supported by Finnish National Research Funding, Finnish Foundation for Promotion of Laboratory Medicine, Finnish ORL-HNS Research Foundation, Cancer Society of South-West Finland, Finnish Medical Foundation, Jane and Aatos Erkko Foundation, Academy of Finland and the Finnish Cultural Foundation. We thank Heikki Irjala and Ilmo Leivo for critical revision of the manuscript and for valuable scientific advice. All authors contributed to the study conception and design. ARK, PV, JR, TH and SV performed study concept and design and performed development of methodology, original drafting, review, and revision of the paper; SV provided acquisition of data; ARK, PV, JR, and SV provided analysis and interpretation of data; JR performed statistical analysis of data; SJ and MH participated in the design of the study and commented on the results and its interpretation; ARK, JR and SV obtained funding supports; SV and PV supervised the study. ARK had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. The first draft of the manuscript was written by ARK and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. This work was supported by Finnish National Research Funding, Finnish Foundation for Promotion of Laboratory Medicine, Finnish ORL-HNS Research Foundation, South-Western Finnish Cancer Society, Finnish Medical Foundation, Jane and Aatos Erkko Foundation, Academy of Finland and the Finnish Cultural Foundation.

Keywords

  • Chemoradiotherapy
  • HNSCC
  • Immune landscape
  • Neoadjuvant therapy
  • Radiotherapy
  • Tumor necrosis

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