Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma

Immune checkpoint blockade (ICB) is the standard of care for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), yet efficacy remains low. The combined positive score (CPS) for PD-L1 is the only biomarker approved to predict response to ICB and has limited performance. Tertiary Lymphoid Structures (TLS) have shown promising potential for predicting response to ICB. However, their exact composition, size, and spatial biology in HNSCC remain understudied. To elucidate the impact of TLS spatial biology in response to ICB, we utilized pre-ICB tumor tissue sections from 9 responders (complete response, partial response, or stable disease) and 11 non-responders (progressive disease) classified via RECISTv1.1. A custom multi-immunofluorescence (mIF) staining assay was applied to characterize tumor cells (pan-cytokeratin), T cells (CD4, CD8), B cells (CD19, CD20), myeloid cells (CD16, CD56, CD163), dendritic cells (LAMP3), fibroblasts (α Smooth Muscle Actin), proliferative status (Ki67) and immunoregulatory molecules (PD1). A machine learning model was employed to measure the effect of spatial metrics on achieving a response to ICB. A higher density of B cells (CD20+) was found in responders compared to non-responders to ICB (p = 0.022). The presence of TLS within 100 µm of the tumor was associated with improved overall (p = 0.04) and progression-free survival (p = 0.03). A multivariate machine learning model identified TLS density as a leading predictor of response to ICB with 80% accuracy. Immune cell densities and TLS spatial location play a critical role in the response to ICB in HNSCC and may potentially outperform CPS as a predictor of response.