Quantitative Spatial Profiling of Immune Populations in Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Heterogeneity and Prognostic Biomarkers

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with poor 5-year survival rates, necessitating identification of novel therapeutic targets. Elucidating the biology of the tumor immune microenvironment (TiME) can provide vital insights into mechanisms of tumor progression. In this study, we developed a quantitative image processing platform to analyze sequential multiplexed IHC data from archival PDAC tissue resection specimens. A 27-plex marker panel was employed to simultaneously phenotype cell populations and their functional states, followed by a computational workflow to interrogate the immune contextures of the TiME in search of potential biomarkers. The PDAC TiME reflected a low-immunogenic ecosystem with both high intratumoral and intertumoral heterogeneity. Spatial analysis revealed that the relative distance between IL10+ myelomonocytes, PD-1+ CD4+ T cells, and granzyme B+ CD8+ T cells correlated significantly with survival, from which a spatial proximity signature termed imRS was derived that correlated with PDAC patient survival. Furthermore, spatial enrichment of CD8+ T cells in lymphoid aggregates was also linked to improved survival. Altogether, these findings indicate that the PDAC TiME, generally considered immuno-dormant or immunosuppressive, is a spatially nuanced ecosystem orchestrated by ordered immune hierarchies. This new understanding of spatial complexity may guide novel treatment strategies for PDAC. Significance: Quantitative image analysis of PDAC specimens reveals intertumoral and intratumoral heterogeneity of immune populations and identifies spatial immune architectures that are significantly associated with disease prognosis.

A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition

“…While interindividual variability and intraindividual heterogeneity in terms of tumortumor heterogeneity are accounted for in this study, our QSP model ignores intratumoral heterogeneity and assumes that tumors are well-mixed. Spatial immune architecture such as the proximity of certain cell types has been shown to be associated with disease prognosis (63). Agent-based models have been developed and integrated with QSP models to explore the effects of intratumoral heterogeneity on treatment responses (64)(65)(66).…”

Managing the immune microenvironment of osteosarcoma: the outlook for osteosarcoma treatment

“…Osteosarcoma tissue is surrounded by massive immune cell infiltration, resulting in the creation of a complex immune microenvironment that allows osteosarcoma cells to grow within the bone by creating an immunosuppressive microenvironment to maintain their survival and proliferation. [35][36][37] A robust immunosuppressive microenvironment is positively correlated with overactivation of molecules associated with immune suppression, such as indoleamine 2,3-dioxygenase (IDO), programmed cell death protein 1 (PD-1), interleukin-10 (IL-10), transforming growth factor-β (TGF-β), vascular endothelial growth factor (VEGF), and signal transducer and activator of transcription 3 (STAT3), due to their immunosuppressive effects mediated by myeloid-derived suppressor cells (MDSCs), TAMs, and regulatory T lymphocytes (Tregs) [38][39][40][41][42][43] (Fig. 2).…”

Robust biomarker discovery through multiplatform multiplex image analysis of breast cancer clinical cohorts

“…Tissue structures such as tertiary lymphoid structures, identified with multiplex imaging, are predictive biomarkers of immunotherapy response in melanoma 11,12 . Recently, spatial biomarkers involving multiple cell types have been identified that predict response to immune checkpoint inhibitors in cutaneous T cell lymphoma 13 and patient survival pancreatic ductal adenocarcinoma 14 . Employment of multiplex imaging methods in breast cancer has revealed that single-cell spatial context has prognostic relevance and shows correlations with transcriptomic and genomic features of tumors [15][16][17] .…”