ABSTRACT
Immune checkpoint blockade (ICB) is the standard of care for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), yet efficacy remains low. The current approach for predicting the likelihood of response to ICB is a single proportional biomarker (PD-L1) expressed in immune and tumor cells (Combined Positive Score, CPS) without differentiation by cell type, potentially explaining its limited predictive value. Tertiary Lymphoid Structures (TLS) have shown a stronger association with ICB response than PD-L1. However, their exact composition, size, and spatial biology in HNSCC remain understudied. A detailed understanding of TLS is required for future use as a clinically applicable predictive biomarker. Methods: Pre-ICB tumor tissue sections were obtained 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 designed, optimized, and 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). Spatial metrics were compared among groups. Serial tissue sections were scored for TLS in both H&E and mIF slides. A machine learning model was employed to measure the effect of these metrics on achieving a response to ICB (SD, PR, or CR). Results: A higher density of B lymphocytes (CD20+) was found in responders compared to non-responders to ICB (p=0.022). A positive correlation was observed between mIF and pathologist identification of TLS (R 2 = 0.66, p-value= <0.0001). TLS trended toward being more prevalent in responders to ICB (p=0.0906). 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. Conclusion: Immune cell densities and TLS spatial location within the tumor microenvironment play a critical role in the immune response to HNSCC and may potentially outperform CPS as a predictor of ICB response.
ABSTRACT
Microfluidic devices have been used for decades to isolate cells, viruses, and proteins using on-chip immunoaffinity capture using biotinylated antibodies, proteins, or aptamers. To accomplish this, the inner surface is modified to present binding moieties for the desired analyte. While this approach has been successful in research settings, it is challenging to scale many surface modification strategies. Traditional polydimethylsiloxane (PDMS) devices can be effectively functionalized using silane-based methods; however, it requires high labor hours, cleanroom equipment, and hazardous chemicals. Manufacture of microfluidic devices using plastics, including cyclic olefin copolymer (COC), allows chips to be mass produced, but most functionalization methods used with PDMS are not compatible with plastic. Here we demonstrate how to deposit biotin onto the surface of a plastic microfluidic chips using aryl-diazonium. This method chemically bonds biotin to the surface, allowing for the addition of streptavidin nanoparticles to the surface. Nanoparticles increase the surface area of the chip and allow for proper capture moiety orientation. Our process is faster, can be performed outside of a fume hood, is very cost-effective using readily available laboratory equipment, and demonstrates higher rates of capture. Additionally, our method allows for more rapid and scalable production of devices, including for diagnostic testing.