Hannah L. Williams

PURPOSE: Although abundant myeloid cell populations in the pancreatic ductal adenocarcinoma (PDAC) microenvironment have been postulated to suppress antitumor immunity, the composition of these populations, their spatial locations, and how they relate to patient outcomes are poorly understood. EXPERIMENTAL DESIGN: To generate spatially resolved tumor and immune cell data at single-cell resolution, we developed two quantitative multiplex immunofluorescence assays to interrogate myeloid cells (CD15, CD14, ARG1, CD33, HLA-DR) and macrophages [CD68, CD163, CD86, IFN regulatory factor 5, MRC1 (CD206)] in the PDAC tumor microenvironment. Spatial point pattern analyses were conducted to assess the degree of colocalization between tumor cells and immune cells. Multivariable-adjusted Cox proportional hazards regression was used to assess associations with patient outcomes. RESULTS: immunosuppressive granulocytic cells and M2-polarized macrophages were associated with worse patient survival. Moreover, beyond cell density, closer proximity of M2-polarized macrophages to tumor cells was strongly associated with disease-free survival, revealing the clinical significance and biologic importance of immune cell localization within tumor areas. CONCLUSIONS: A diverse set of myeloid cells are present within the PDAC tumor microenvironment and are distributed heterogeneously across patient tumors. Not only the densities but also the spatial locations of myeloid immune cells are associated with patient outcomes, highlighting the potential role of spatially resolved myeloid cell subtypes as quantitative biomarkers for PDAC prognosis and therapy.

Pancreatic ductal adenocarcinoma (PDAC) has been classified into classical and basal-like transcriptional subtypes by bulk RNA measurements. However, recent work has uncovered greater complexity to transcriptional subtypes than was initially appreciated using bulk RNA expression profiling. To provide a deeper understanding of PDAC subtypes, we developed a multiplex immunofluorescence (mIF) pipeline that quantifies protein expression of six PDAC subtype markers (CLDN18.2, TFF1, GATA6, KRT17, KRT5, and S100A2) and permits spatially resolved, single-cell interrogation of pancreatic tumors from resection specimens and core needle biopsies. Both primary and metastatic tumors displayed striking intratumoral subtype heterogeneity that was associated with patient outcomes, existed at the scale of individual glands, and was significantly reduced in patient-derived organoid cultures. Tumor cells co-expressing classical and basal markers were present in > 90% of tumors, existed on a basal-classical polarization continuum, and were enriched in tumors containing a greater admixture of basal and classical cell populations. Cell-cell neighbor analyses within tumor glands further suggested that co-expressor cells may represent an intermediate state between expression subtype poles. The extensive intratumoral heterogeneity identified through this clinically applicable mIF pipeline may inform prognosis and treatment selection for patients with PDAC. SIGNIFICANCE: A high-throughput pipeline using multiplex immunofluorescence in pancreatic cancer reveals striking expression subtype intratumoral heterogeneity with implications for therapy selection and identifies co-expressor cells that may serve as intermediates during subtype switching.

The clinical utility of next-generation sequencing (NGS) for a diverse range of targets is expanding, increasing the need for multiplexed analysis of both DNA and RNA. However, translation into daily use requires a rigorous and comprehensive validation strategy. The aim of this clinical validation was to assess the performance of the Ion Torrent Personal Genome Machine (IonPGM™) and validate the Oncomine™ Focus DNA and RNA Fusion panels for clinical application in solid tumour testing of formalin-fixed, paraffin-embedded (FFPE) tissue. Using a mixture of routine FFPE and reference material across a variety of tissue and specimen types, we sequenced 86 and 31 samples on the Oncomine™ Focus DNA and RNA Fusion assays, respectively. This validation considered a number of parameters including the clinical robustness of the bioinformatics pipeline for variant detection and interpretation. The Oncomine™ Focus DNA assay had a sample and variant-based sensitivity of 99.1 and 97.1%, respectively, and an assay specificity of 100%. The Oncomine™ Focus Fusion panel had a good sensitivity and specificity based upon the samples assessed, however requires further validation to confirm findings due to limited sample numbers. We observed a good sequencing performance based upon amplicon, gene (hotspot variants within gene) and sample specific analysis with 92% of clinical samples obtaining an average amplicon coverage above 500X. Detection of some indels was challenging for the routine IonReporter™ workflow; however, the addition of NextGENe® software improved indel identification demonstrating the importance of both bench and bioinformatic validation. With an increasing number of clinically actionable targets requiring a variety of methodologies, NGS provides a cost-effective and time-saving methodology to assess multiple targets across different modalities. We suggest the use of multiple analysis software to ensure identification of clinically applicable variants.