Protein Signatures and Tissue Diagnosis of Pancreatic Cancer

Pancreatic stellate cells and the interleukin family: Linking fibrosis and immunity to pancreatic ductal adenocarcinoma (Review)

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive form of cancer with a low survival rate. A successful treatment strategy should not be limited to targeting cancer cells alone, but should adopt a more comprehensive approach, taking into account other influential factors. These include the extracellular matrix (ECM) and immune microenvironment, both of which are integral components of the tumor microenvironment. The present review describes the roles of pancreatic stellate cells, differentiated cancer‑associated fibroblasts and the interleukin family, either independently or in combination, in the progression of precursor lesions in pancreatic intraepithelial neoplasia and PDAC. These elements contribute to ECM deposition and immunosuppression in PDAC. Therapeutic strategies that integrate interleukin and/or stromal blockade for PDAC immunomodulation and fibrogenesis have yielded inconsistent results. A deeper comprehension of the intricate interplay between fibrosis, and immune responses could pave the way for more effective treatment targets, by elucidating the mechanisms and causes of ECM fibrosis during PDAC progression.

Infiltration of CD3+ and CD8+ lymphocytes in association with inflammation and survival in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinomas (PDAC) have heterogeneous tumor microenvironments relatively devoid of infiltrating immune cells. We aimed to quantitatively assess infiltrating CD3+ and CD8+ lymphocytes in a treatment-naïve patient cohort and assess associations with overall survival and microenvironment inflammatory proteins.

METHODS

Tissue microarrays were immunohistochemically stained for CD3+ and CD8+ lymphocytes and quantitatively assessed using QuPath. Levels of inflammation-associated proteins were quantified by multiplexed, enzyme-linked immunosorbent assay panels on matching tumor and tissue samples.

RESULTS

Our findings revealed a significant increase in both CD3+ and CD8+ lymphocytes populations in PDAC compared with non-PDAC tissue, except when comparing CD8+ percentages in PDAC versus intraductal papillary mucinous neoplasms (IPMN) (p = 0.5012). Patients with quantitatively assessed CD3+ low tumors (lower 50%) had shorter survival (median 273 days) compared to CD3+ high tumors (upper 50%) with a median overall survival of 642.5 days (p = 0.2184). Patients with quantitatively assessed CD8+ low tumors had significantly shorter survival (median 240 days) compared to CD8+ high tumors with a median overall survival of 1059 days (p = 0.0003). Of 41 proteins assessed in the inflammation assay, higher levels of IL-1B and IL-2 were significantly associated with decreased CD3+ infiltration (r = -0.3704, p = 0.0187, and r = -0.4275, p = 0.0074, respectively). Higher levels of IL-1B were also significantly associated with decreased CD8+ infiltration (r = -0.4299, p = 0.0045), but not IL-2 (r = -0.0078, p = 0.9616). Principal component analysis of the inflammatory analytes showed diverse inflammatory responses in PDAC.

CONCLUSION

In this work, we found a marked heterogeneity in infiltrating CD3+ and CD8+ lymphocytes and individual inflammatory responses in PDAC. Future mechanistic studies should explore personalized therapeutic strategies to target the immune and inflammatory components of the tumor microenvironment.

A protein-based machine learning approach to the identification of inflammatory subtypes in pancreatic ductal adenocarcinoma

BACKGROUND/OBJECTIVES

The inherently immunosuppressive tumor microenvironment along with the heterogeneity of pancreatic ductal adenocarcinoma (PDAC) limits the effectiveness of available treatment options and contributes to the disease lethality. Using a machine learning algorithm, we hypothesized that PDAC may be categorized based on its microenvironment inflammatory milieu.

METHODS

Fifty-nine tumor samples from patients naïve to treatment were homogenized and probed for 41 unique inflammatory proteins using a multiplex assay. Subtype clustering was determined using t-distributed stochastic neighbor embedding (t-SNE) machine learning analysis of cytokine/chemokine levels. Statistics were performed using Wilcoxon rank sum test and Kaplan-Meier survival analysis.

RESULTS

t-SNE analysis of tumor cytokines/chemokines revealed two distinct clusters, immunomodulating and immunostimulating. In pancreatic head tumors, patients in the immunostimulating group (N = 26) were more likely to be diabetic (p = 0.027), but experienced less intraoperative blood loss (p = 0.0008). Though there were no significant differences in survival (p = 0.161), the immunostimulating group trended toward longer median survival by 9.205 months (11.28 vs. 20.48 months).

CONCLUSION

A machine learning algorithm identified two distinct subtypes within the PDAC inflammatory milieu, which may influence diabetes status as well as intraoperative blood loss. Opportunity exists to further explore how these inflammatory subtypes may influence treatment response, potentially elucidating targetable mechanisms of PDAC's immunosuppressive tumor microenvironment.

The interleukin-1 axis and the tumor immune microenvironment in pancreatic ductal adenocarcinoma

Interleukin-1 (IL-1) plays a key role in carcinogenesis and several IL-1-targeted therapeutics are under investigation for the treatment of pancreatic ductal adenocarcinoma (PDAC). We sought to broaden our understanding of how the family of IL-1 ligands and receptors impact the tumor immune landscape and patient survival in PDAC. Gene expression data and DNA methylation data for IL1A, IL1B, IL1RN, IL1R1, IL1R2, and IL1RAP was attained from The Cancer Genome Atlas (TCGA) database and cross validated using the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Immune cell-type abundance was estimated using CIBERSORTx. Further confirmatory soluble protein analysis and peripheral blood immunophenotyping were performed on available tissue samples from our institution. 169 PDAC patients and 50 benign pancreatic TCGA-based samples were analyzed. IL1A (p < 0.001), IL1RN (p < 0.001), IL1R2 (p < 0.001), and IL1RAP (p = 0.006) were markedly increased in PDAC tumor tissue compared to benign pancreatic tissue. Furthermore, expression of IL1A, IL1B and IL1R1 were positively correlated with gene expression of immune checkpoints PVR, CD274, CD47, CD80, and HLA-A/B/C (p < 0.001). IL1B and IL1R1 were correlated to expression of PDCD1, CD86, CTLA4 and IDO1 (<0.001). Low expression of IL1RN (p = 0.020), IL1R2 (p = 0.015), and IL1RAP (p = 0.003) and high expression of IL1B (p = 0.031) were correlated with increased patient survival. At the protein level, IL-1β was correlated with increased peripheral central memory CD4+ and CD8+ T-cells as well as decreased Th2 cells. These findings suggest that the IL-1 axis plays a complex and pivotal role in the host immune response to PDAC.

Endoscopic Ultrasound-Guided Sampling for Personalized Pancreatic Cancer Treatment

Pancreatic cancer is the most lethal solid malignancy, and the number of patients with pancreatic cancer is increasing. Systemic chemotherapies are often ineffective for such patients, and there is an urgent need for personalized medicine. Unlike other types of cancer, personalized treatments for pancreatic cancer are still in development. Consequently, pancreatic cancer is less sensitive to anticancer drugs and is often refractory to common treatments. Therefore, advances in personalized medicine for pancreatic cancer are necessary. This review examined advances in personalized medicine for pancreatic cancer, including the use of endoscopic ultrasound (EUS)-guided sampling. EUS-guided sampling is widely used for diagnosing pancreatic tumors and is expected to be applied to sampled tissues. Additionally, there has been an increase in clinical research using EUS-guided sampling. The combination of precision medicine using genomic testing and pharmacological profiles based on high-throughput drug sensitivity testing using patient-derived organoids is expected to revolutionize pancreatic cancer treatment.

Spatially resolved proteomics in osteoarthritis: State of the art and new perspectives

Osteoarthritis (OA) is one of the most common diseases worldwide caused by chronic degeneration of the joints. Its high prevalence and the involvement of several tissues define OA as a highly heterogeneous disease. New biological markers to evaluate the progression of the pathology and improve its prognosis are needed. Among all the different -omic strategies applied to OA, solution phase bottom-up proteomics has made an extensive contribution to the field of biomarker research. However, new technologies for protein analysis should be considered for a better understanding of the disease. This review focuses on complementary proteomic methodologies and new technologies for translational research of OA and other rheumatic pathologies, especially mass spectrometry imaging and protein imaging methods not applied by the OA community yet.