Multiplex immunofluorescence (mIF) allows simultaneous antibody-based detection of multiple markers with a nuclear counterstain on a single tissue section. Recent studies have demonstrated that mIF is becoming an important tool for immune profiling the tumor microenvironment, further advancing our understanding of the interplay between cancer and the immune system, and identifying predictive biomarkers of response to immunotherapy. Expediting mIF discoveries is leading to improved diagnostic panels, whereas it is important that mIF protocols be standardized to facilitate their transition into clinical use. Manual processing of sections for mIF is time consuming and a potential source of variability across numerous samples. To increase reproducibility and throughput we demonstrate the use of an automated slide stainer for mIF incorporating tyramide signal amplification (TSA). We describe two panels aimed at characterizing the tumor immune microenvironment. Panel 1 included CD3, CD20, CD117, FOXP3, Ki67, pancytokeratins (CK), and DAPI, and Panel 2 included CD3, CD8, CD68, PD-1, PD-L1, CK, and DAPI. Primary antibodies were first tested by standard immunohistochemistry and single-plex IF, then multiplex panels were developed and images were obtained using a Vectra 3.0 multispectral imaging system. Various methods for image analysis (identifying cell types, determining cell densities, characterizing cell-cell associations) are outlined. These mIF protocols will be invaluable tools for immune profiling the tumor microenvironment.

The epidermal growth factor receptor (EGFR) inhibitor cetuximab is the only FDA-approved oncogene-targeting therapy for head and neck squamous cell carcinoma (HNSCC). Despite variable treatment response, no biomarkers exist to stratify patients for cetuximab therapy in HNSCC. Here, we applied unbiased hierarchical clustering to reverse-phase protein array molecular profiles from patient-derived xenograft (PDX) tumors and revealed 2 PDX clusters defined by protein networks associated with EGFR inhibitor resistance. In vivo validation revealed unbiased clustering to classify PDX tumors according to cetuximab response with 88% accuracy. Next, a support vector machine classifier algorithm identified a minimalist biomarker signature consisting of 8 proteins - caveolin-1, Sox-2, AXL, STING, Brd4, claudin-7, connexin-43, and fibronectin - with expression that strongly predicted cetuximab response in PDXs using either protein or mRNA. A combination of caveolin-1 and Sox-2 protein levels was sufficient to maintain high predictive accuracy, which we validated in tumor samples from patients with HNSCC with known clinical response to cetuximab. These results support further investigation into the combined use of caveolin-1 and Sox-2 as predictive biomarkers for cetuximab response in the clinic.

Cancers display significant heterogeneity with respect to tissue of origin, driver mutations, and other features of the surrounding tissue. It is likely that individual tumors engage common patterns of the immune system-here "archetypes"-creating prototypical non-destructive tumor immune microenvironments (TMEs) and modulating tumor-targeting. To discover the dominant immune system archetypes, the University of California, San Francisco (UCSF) Immunoprofiler Initiative (IPI) processed 364 individual tumors across 12 cancer types using standardized protocols. Computational clustering of flow cytometry and transcriptomic data obtained from cell sub-compartments uncovered dominant patterns of immune composition across cancers. These archetypes were profound insofar as they also differentiated tumors based upon unique immune and tumor gene-expression patterns. They also partitioned well-established classifications of tumor biology. The IPI resource provides a template for understanding cancer immunity as a collection of dominant patterns of immune organization and provides a rational path forward to learn how to modulate these to improve therapy.