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Immune Cell Densities Predict Response to Immune Checkpoint-Blockade in Head and Neck Cancer.

Daniel A Ruiz-Torres^1,2,3, Michael E Bryan^1,2, Shun Hirayama^1,2

¹Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA 02115, USA.

Medrxiv : the Preprint Server for Health Sciences

|September 24, 2024

Summary

Tertiary Lymphoid Structures (TLS) are more prevalent in head and neck squamous cell carcinoma (HNSCC) patients who respond to immune checkpoint blockade (ICB). TLS density predicts ICB response and survival, potentially outperforming PD-L1 Combined Positive Score (CPS).

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Area of Science:

Immunology
Oncology
Biomarker Discovery

Background:

Immune checkpoint blockade (ICB) is a standard treatment for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), but its efficacy is limited.
Current predictive biomarkers, such as PD-L1 Combined Positive Score (CPS), have limited predictive value due to a lack of cell-type differentiation.
Tertiary Lymphoid Structures (TLS) show promise as a more accurate predictor of ICB response, but their role in HNSCC requires further investigation.

Purpose of the Study:

To investigate the composition, size, and spatial biology of TLS in HNSCC.
To evaluate the association between TLS characteristics and response to ICB.
To explore the potential of TLS as a predictive biomarker for ICB therapy in HNSCC.

Main Methods:

Multi-immunofluorescence (mIF) staining was used to characterize immune cells, tumor cells, and immunoregulatory molecules in pre-ICB HNSCC tumor tissues from responders and non-responders.
Spatial metrics of immune cells and TLS were analyzed.
A machine learning model was employed to assess the predictive value of these metrics for ICB response.

Main Results:

Responders exhibited a higher density of B lymphocytes (CD20+) compared to non-responders.
TLS density positively correlated with mIF and pathologist identification, trending higher in responders.
The presence of TLS near the tumor was associated with improved overall and progression-free survival, with TLS density achieving 80% accuracy in predicting ICB response via a machine learning model.

Conclusions:

Immune cell densities and TLS spatial location are critical in the HNSCC immune response to ICB.
TLS show potential as a superior predictive biomarker for ICB response in HNSCC compared to CPS.
Further understanding of TLS biology may lead to improved patient selection for ICB therapy.