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Updated: May 20, 2025
Intramucosal Inoculation of Squamous Cell Carcinoma Cells in Mice for Tumor Immune Profiling and Treatment Response Assessment
Published on: April 22, 2019
Histological tumor necrosis predicts decreased survival after neoadjuvant chemotherapy in head and neck squamous cell
A R Koskenniemi1, T Huusko2, J Routila3
1Department of Pathology, Laboratory Division, Turku University Hospital and University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland.
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.
Insights
Tumor necrosis predicts outcomes for head and neck cancer patients receiving neoadjuvant radio- (RT) or chemoradiotherapy (CRT). Artificial intelligence (AI) effectively identifies necrosis, aiding treatment response prediction in HNSCC.
Area of Science:
- Oncology
- Immunology
- Medical Imaging
Background:
- Neoadjuvant therapies like radio- (RT) and chemoradiotherapy (CRT) are increasingly used for head and neck squamous cell carcinoma (HNSCC).
- Predictive biomarkers for RT/CRT response in HNSCC are currently lacking.
- Understanding the impact of neoadjuvant RT/CRT on the tumor immune microenvironment is crucial.
Purpose of the Study:
- To investigate the effects of neoadjuvant RT/CRT on the tumor immune landscape in HNSCC.
- To identify biomarkers that predict patient survival after neoadjuvant RT/CRT.
- To develop and validate an artificial intelligence (AI) method for predicting treatment response.
Main Methods:
- Retrospective analysis of 53 HNSCC patients treated with neoadjuvant RT/CRT from a cohort of 1033 patients.
- Analysis of pre- and post-treatment tumor samples for immune markers including CD8, CD68, CD206, Clever-1, PD-L1, and histologic tumor necrosis.
- Development and validation of an AI deep learning model for identifying tumor necrosis.
Main Results:
- Significant alterations in the immune landscape were observed following neoadjuvant RT/CRT.
- Pretreatment tumor necrosis was a strong predictor of poor overall survival (OS) (14.3% with necrosis vs. 48.5% without).
- An AI deep learning method for identifying tumor necrosis demonstrated successful prediction of neoadjuvant RT/CRT response.
Conclusions:
- Histologic tumor necrosis is a key biomarker for predicting outcomes in HNSCC patients undergoing neoadjuvant RT/CRT.
- AI-driven deep learning offers a promising approach for effectively predicting neoadjuvant RT/CRT response in HNSCC.
- These findings can inform personalized treatment strategies for HNSCC.
