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Pancreatic ductal adenocarcinoma (PDA) is characterized by an immune-suppressive tumor microenvironment that renders it largely refractory to immunotherapy. We implemented a multimodal analysis approach to elucidate the immune landscape in PDA. Using a combination of CyTOF, single-cell RNA sequencing, and multiplex immunohistochemistry on patient tumors, matched blood, and non-malignant samples, we uncovered a complex network of immune-suppressive cellular interactions. These experiments revealed heterogeneous expression of immune checkpoint receptors in individual patient's T cells and increased markers of CD8 T cell dysfunction in advanced disease stage. Tumor-infiltrating CD8 T cells had an increased proportion of cells expressing an exhausted expression profile that included upregulation of the immune checkpoint , a finding that we validated at the protein level. Our findings point to a profound alteration of the immune landscape of tumors, and to patient-specific immune changes that should be taken into account as combination immunotherapy becomes available for pancreatic cancer.

Spatial pattern modelling concepts are being increasingly used in capturing disease heterogeneity. Quantification of heterogeneity in the tumor microenvironment is extremely important in pancreatic ductal adenocarcinoma (PDAC), which has been shown to co-occur with other pancreatic diseases and neoplasms with certain attributes that make visual discrimination difficult. In this paper, we propose the GaWRDenMap framework, that utilizes the concepts of geographically weighted regression (GWR) and a density function-based classification model, and apply it to a cohort of multiplex immunofluorescence images from patients belonging to six different pancreatic diseases. We used an internal cohort of 228 patients comprised of 34 Chronic Pancreatitis (CP), 71 PDAC, 70 intraductal papillary mucinous neoplasm (IPMN), 16 mucinous cystic neoplasm (MCN), 29 pancreatic intraductal neoplasia (PanIN) and 8 IPMN-associated PDAC patients. We utilized GWR to model the relationship between epithelial cells and immune cells on a spatial grid. The GWR model estimates were used to generate density signatures which were used in subsequent pairwise classification models to distinguish between any two pairs of disease groups. Image-level, as well as subject-level analysis, were performed. When applied to this dataset, our classification model showed significant discrimination ability in multiple pairwise comparisons, in comparison to commonly used abundance-based metrics, like the Morisita-Horn index. The model was able to best discriminate between CP and PDAC at both the subject- and image-levels. It was also able to reasonably discriminate between PDAC and IPMN. These results point to a potential difference in the spatial arrangement of epithelial and immune cells between CP, PDAC and IPMN, that could be of high diagnostic significance. Further validation on a more comprehensive dataset would be warranted.

Multiplexed immunofluorescence (mIF) imaging has revolutionized the study of cellular interactions within tissue microenvironments, enabling complex pattern analysis critical to understanding disease biology. However, current analytical methods assume uniform cellular patterns across tissues, overlooking the spatial heterogeneity that characterizes tumor microenvironments. Here, we introduce ISPat (Informed Spatially aware Patterns), a fully Bayesian framework that identifies both shared and region-specific interaction patterns while integrating domain knowledge to enhance spatial pattern estimation. ISPat models spatial cellular densities through kernel density estimation, then constructs interaction networks from precision matrices that capture conditional dependencies between cell types while controlling for confounding effects. The resulting networks reveal direct cellular relationships, with non-zero precision matrix entries indicating significant interactions. We applied ISPat to analyze 119 pancreatic ductal adenocarcinoma (PDAC) and 53 intraductal papillary mucinous neoplasm (IPMN) patients, partitioning tissues into five regions based on tumor intensity gradients. Our analysis revealed fundamentally distinct immune architectures: PDAC maintains a rigid, stable immunosuppressive microenvironment across tumor heterogeneity gradients, whereas IPMN exhibits dynamic spatial remodeling with marked regional variability. Critically, we identified multiple ligand-receptor (LR) interactions that consistently differ between disease conditions specifically in intermediate tumor intensity regions, while extreme conditions showed no significant differences. These include interactions spanning multiple functional axes of anti-tumor immunity: antigen presentation and T cell activation (APC CTL, THelper APC, Epithelial APC), effector function and tumor cell killing (Epithelial CTL, CTL Treg), and immune regulation (Epithelial Treg, Treg APC, THelper Treg). Notably, the APC CTL interaction, fundamental for adaptive immunity activation, differs significantly in high tumor density regions, alongside Epithelial CTL interactions critical for direct tumor elimination. These spatially resolved signatures provide quantitative evidence for distinct immune evasion mechanisms and represent promising biomarker candidates for disease classification and risk stratification. Through simulation studies, we demonstrated ISPat’s accuracy in pattern recovery and its computational efficiency, achieving 8-10 fold speedup over comparable methods through variational Bayesian inference. The framework exhibits robust scalability and handles naturally occurring partition size imbalances, making it well-suited for analyzing heterogeneous tissues. Our findings demonstrate that spatial context fundamentally shapes cellular interactions in pancreatic cancer, with critical implications for understanding immune evasion mechanisms and developing spatially informed therapeutic strategies. The identification of differential interactions across antigen presentation, effector function, and immune regulation pathways suggests that therapeutic interventions must address multiple axes of immune dysfunction rather than single targets. ISPat provides a generalizable framework for spatial analysis applicable to emerging technologies, enabling precision oncology approaches guided by spatially resolved biomarkers.

Immune checkpoint inhibitors (ICI) with anti-PD-1/PD-L1 agents have improved the survival of patients with metastatic non-small cell lung cancer (mNSCLC). Tumor PD-L1 expression is an imperfect biomarker as it does not capture the complex interactions between constituents of the tumor microenvironment (TME). Using multiplex fluorescent immunohistochemistry (mfIHC), we modeled the TME to study the influence of cellular distribution and engagement on response to ICI in mNSCLC. We performed mfIHC on pretreatment tissue from patients with mNSCLC who received ICI. We used primary antibodies against CD3, CD8, CD163, PD-L1, pancytokeratin, and FOXP3; simple and complex phenotyping as well as spatial analyses was performed. We analyzed 68 distinct samples from 52 patients with mNSCLC. Patients were 39–79 years old (median 67); 44% were male and 75% had adenocarcinoma histology. The most used ICI was atezolizumab (48%). The percentage of PD-L1 positive epithelial tumor cells (EC), degree of cytotoxic T lymphocyte (CTL) engagement with EC, and degree of CTL engagement with helper T lymphocytes (HTL) were significantly lower in non-responders versus responders ( p  = 0.0163, p  = 0.0026 and p  = 0.0006, respectively). The combination of these 3 characteristics generated the best sensitivity and specificity to predict non-response to ICI and was also associated with shortened overall survival ( p  = 0.0271). The combination of low CTL engagement with EC and HTL along with low expression of EC PD-L1 represents a state of impaired endogenous immune reactivity. Together, they more precisely identified non-responders to ICI compared to PD-L1 alone and illustrate the importance of cellular interactions in the TME.

Metastatic colorectal cancer (mCRC) remains a common and highly morbid disease, with a recent increase in incidence in patients younger than 50 years. There is an acute need to better understand differences in tumor biology, molecular characteristics, and other age-related differences in the tumor microenvironment (TME). 111 patients undergoing curative-intent resection of colorectal liver metastases were stratified by age into those <50 years or >65 years old, and tumors were subjected to multiplex fluorescent immunohistochemistry (mfIHC) to characterize immune infiltration and cellular engagement. There was no difference in infiltration or proportion of immune cells based upon age, but the younger cohort had a higher proportion of programmed death-ligand 1 (PD-L1) expressing antigen presenting cells (APCs) and demonstrated decreased intercellular distance and increased cellular engagement between tumor cells (TCs) and cytotoxic T lymphocytes (CTLs), and between TCs and APCs. These trends were independent of microsatellite instability in tumors. Age-related differences in PD-L1 expression and cellular engagement in the tumor microenvironment of patients with mCRC, findings which were unrelated to microsatellite status, suggest a more active immune microenvironment in younger patients that may offer an opportunity for therapeutic intervention with immune based therapy.

The majority of colorectal cancer patients are not responsive to immune checkpoint blockade (ICB). The interferon gamma (IFNγ) signaling pathway drives spontaneous and ICB-induced antitumor immunity. In this review, we summarize recent advances in the epigenetic, genetic, and functional integrity of the IFNγ signaling pathway in the colorectal cancer microenvironment and its immunological relevance in the therapeutic efficacy of and resistance to ICB. Moreover, we discuss how to target IFNγ signaling to inform novel clinical trials to treat patients with colorectal cancer.

Pancreatic ductal adenocarcinoma (PDA) is accompanied by reprogramming of the local microenvironment, but changes at distal sites are poorly understood. We implanted biomaterial scaffolds, which act as an artificial premetastatic niche, into immunocompetent tumor-bearing and control mice, and identified a unique tumor-specific gene expression signature that includes high expression of C1qa , C1qb , Trem2 , and Chil3 . Single-cell RNA sequencing mapped these genes to two distinct macrophage populations in the scaffolds, one marked by elevated C1qa , C1qb , and Trem2 , the other with high Chil3 , Ly6c2 and Plac8 . In mice, expression of these genes in the corresponding populations was elevated in tumor-associated macrophages compared with macrophages in the normal pancreas. We then analyzed single-cell RNA sequencing from patient samples, and determined expression of C1QA , C1QB , and TREM2 is elevated in human macrophages in primary tumors and liver metastases. Single-cell sequencing analysis of patient blood revealed a substantial enrichment of the same gene signature in monocytes. Taken together, our study identifies two distinct tumor-associated macrophage and monocyte populations that reflects systemic immune changes in pancreatic ductal adenocarcinoma patients.

Background The 8th edition of the AJCC staging system for pancreatic cancer incorporated several significant changes. This study sought to evaluate this staging system and assess its strengths and weaknesses relative to the 7th edition AJCC staging system. Methods Using the Surveillance, Epidemiology and End Results (SEER) database (2004–2013), 8960 patients undergoing surgical resection for non-metastatic pancreatic adenocarcinoma were identified. Overall survival was estimated using the Kaplan–Meier method and compared using log-rank tests. Concordance indices (c-index) were calculated to evaluate the discriminatory power of both staging systems. The Cox proportional hazards model was used to determine the impact of T and N classification on overall survival. Results The c-index for the AJCC 8th staging system [0.60; 95% confidence interval (CI), 0.59–0.61] was comparable with that for the 7th edition AJCC staging system (0.59; 95% CI, 0.58–0.60). Stratified analyses for each N classification system demonstrated a diminishing impact of T classification on overall survival with increasing nodal involvement. The corresponding c-indices were 0.58 (95% CI, 0.55–0.60) for N 0, 0.53 (95% CI, 0.51–0.55) for N 1, and 0.53 (95% CI, 0.50–0.56) for N 2 classification. Conclusion This is the first large-scale validation of the AJCC 8th edition staging system for pancreatic cancer. The revised system provides discrimination similar to that of the 7th-edition system. However, the 8th-edition system allows for finer stratification of patients with resected tumors according to extent of nodal involvement.

Esophageal adenocarcinoma (EAC) develops from Barrett’s esophagus (BE), a chronic inflammatory state that can progress through a series of transformative dysplastic states before tumor development. While molecular and genetic changes of EAC tumors have been studied, immune microenvironment changes during Barrett’s progression to EAC remain poorly understood. In this study, we identify potential immunologic changes that can occur during BE-to-EAC progression. RNA sequencing (RNA-Seq) analysis on tissue samples from EAC patients undergoing surgical resection demonstrated that a subset of chemokines and cytokines, most notably IL6 and CXCL8 , increased during BE progression to EAC. xCell deconvolution analysis investigating immune cell population changes demonstrated that the largest changes in expression during BE progression occurred in M2 macrophages, pro–B cells, and eosinophils. Multiplex immunohistochemical staining of tissue microarrays showed increased immune cell populations during Barrett’s progression to high-grade dysplasia. In contrast, EAC tumor sections were relatively immune poor, with a rise in PD-L1 expression and loss of CD8 + T cells. These data demonstrate that the EAC microenvironment is characterized by poor cytotoxic effector cell infiltration and increased immune inhibitory signaling. These findings suggest an immunosuppressive microenvironment, highlighting the need for further studies to explore immune modulatory therapy in EAC.

Comprehensive characterization of the tumor microenvironment (TME) is essential for understanding cancer progression and developing effective, patient-specific therapies. Spatial context of the TME is particularly important, and exists across multiple scales—from the molecular to cellular to tissue levels. However, current methods are modality-specific and lack flexibility in effectively modeling the TME. We introduce SPIFEE, a flexible graph deep learning framework designed to model the TME and uncover spatial insights across multiple levels of biological organization. SPIFEE increases the expressivity of graph-based representations by directly encoding spatially varying functional vectors into graph edges. Additionally, it represents graph nodes as unique TME entities ( e.g ., cell types, phenotypic clusters, molecular pathways). This general formulation is modality-agnostic and also offers cross-modality integration. We demonstrate the versatility of SPIFEE across multiplex immunofluorescence, H&E histopathology, and spatial transcriptomics datasets, enabling rich characterization of cellular, phenotypic, and pathway-level interactions. SPIFEE shows improved performance when leveraging function-based edge representations and outperforms existing spatial modeling approaches. Moreover, by integrating graph attention mechanisms, SPIFEE reveals multi-scale spatial interactions most associated with disease state and patient survival. Overall, SPIFEE enhances the flexibility and representational power of spatial graph modeling, and enables deeper interrogation of the TME, advancing the potential for personalized cancer analysis.