Introduction

Obesity is a well-known risk factor for the incidence of pancreatic ductal adenocarcinoma (PDAC), and obesity is a poor prognostic factor for PDAC as well. However, the underlying mechanisms remain unclear. Since visceral adipose tissue is a major component in abdominal organs, adipocytes may induce the development of PDAC. There have been very few reports that investigated the tumor microenvironment focusing on obese adipocytes.

Aims & Methods

The aim of this study is to elucidate the relationship between fat tissues in the obese state and PDAC progression.We used a mouse PDAC cell line and 2 human PDAC cell lines. We established mouse precursor adipocytes from the visceral fat tissues of obese mice (M O-PrAd), and purchased human precursor adipocytes derived from the visceral fat of an obese patient (H O-PrAd). Each line of PrAd cells was differentiated to mature adipocytes (M O-Ad and H O-Ad), and these obese adipocytes were used to analyze interactions with PDAC cells. Endothelial cells, HMVECs, were also used for in vitro angiogenesis assay. A xenograft mouse model with subcutaneously injected human PDAC cells was used for animal studies. The relationship between obese fat tissues and prognosis was analyzed using resected tissues from PDAC patients with and without obesity.

Results

Conditioned media (CM) from O-Ad significantly increased PDAC cell growth and migration and angiogenic capacity in both human and mice cells, whereas blocking osteopontin (OPN) in O-Ad suppressed cell growth, migration and angiogenic capability of PDAC cells to recruit EC and form tube connections. In addition, O-Ad directly increased the migratory and tube-forming capacities of endothelial cells, while blocking OPN canceled these effects. O-Ad increased AKT phosphorylation and VEGFA expression in both PDAC and endothelial cells, and OPN inhibition in O-Ad canceled those O-Ad-induced effects. In the xenograft model, PDAC tumor volume was significantly increased in obese mice compared with lean mice, whereas neutralizing anti-OPN antibody significantly inhibited obesity-accelerated tumor growth. OPN expression in normal adipose tissues adjacent to human PDAC tumor was significantly higher in obese patients than in non-obese patients. In PDAC patients with obesity, high OPN expression in adipose tissues was significantly associated with poor prognosis.

Conclusion

Obese adipocytes trigger aggressive transformation in PDAC cells to induce PDAC progression and accelerate angiogenesis via OPN secretion.

Introduction

More than 90% of pancreatic cancers are pathologically classified as pancreatic ductal adenocarcinoma (PDAC). Most PDAC patients have not yet benefited from current standard treatments in terms of pathological response.

Aims & Methods

This study aimed to identify druggable targets with translational research potential on PDAC. Multimodal proteomics datasets were included, encompassing mouse PDAC cells, spheroids, organoids and tumors, as well as human PDAC organoids and tumors. Systems modeling workflow of knowledge discovery in datasets of proteomics (KDD) was used to identify the common/matched proteins between human PDAC and the research models. Multi-bioinformatics and computational molecular docking were used to identify targets and repurposed drugs.

Results

A total of 1,975 proteins characterized with degree centrality and topological coefficient were identified across the research models and patients by KDD. 32 out of 1,975 proteins were confirmed by immunohistochemistry with available antibodies in PDAC patients at the Human Protein Atlas (HPA). 3D molecular structures of the 32 proteins were obtained from the RCSB PDB and AlphaFold databases and further validated through blind docking simulations using 7,509 3D-structured ligands sourced from PubChem. The 32 protein-encoding genes included VIL1, GSN, MVB12A, SFXN2, AHNAK2, ANXA3, PACSIN2, ATP6V1F, OTUB1, SERPINB6, SNAP23, SURF1, PCCA, PLP2, ACADSB, ACTR1A, ANXA2, EIF2A, HPCAL1, MYH14, MYO1C, PCK2, PDHB, POLR2H, SCO1, SCPEP1, SH3BGRL3, SPTAN1, STAM, TIMM50, VTI1B, and YWHAQ. Of note, EIF2A had high affinity to Padnarsertib (orally small molecular drug) with -11.4 kcal/mol, whereas STAMBP had affinity to Bemcentinib (orally small molecular drugs) with -11.8 kcal/mol, and ANXA2 and AHNAK2 to Zavegepant (CGRP receptor antagonist as migraine drug) with -12.1 kcal/mol and -11.4 kcal/mol, respectively, suggesting druggable targets on endocytosis/exocytosis/phagocytosis and CGRP signaling pathways. Validation by docking network analysis (string enrichments with p<0.05 by Benjamini-Hochberg) showed that EIF2A exhibited neighborhood connectivity (Nc) at 189.72 with topological coefficient (Tn) at 0.09, whereas STAMBP was with Nc at 106.41 and Tn at 0,13, ANXA2 with Nc at 132.83 and Tn at 0.09 and AHNAK2 with Nc at 56.64 and Tn at 0.06. Specific clusters of the 32 proteins included disinhibition of SNARE formation pathway (involved machinery required for membrane fusion) (60% associated genes), negative feedback regulation of MAPK pathway (67%) and ERK/MAPK signaling pathway (involved CGRP) (100%). EIF2A, STAMBP, ANXA2 and AHNAK2 were expressed in the pancreatic ductal cells (c-6, c-12. c-13) whereas EIF2A and AHNAK2 were located in cytoplasm, STAMBP in nucleus and ANXA2 in plasma membrane according to signal cell atlas and protein-protein interactions (Ingenuity Pathway Analysis). HPA survival analysis of 176 PDAC patients with median follow-up time of 1.27 years showed that EIF2A, STAMBP, ANXA2 and AHNAK2 were strong prognostic biomarkers with p<0.001 (high expression associated with low survival probability).

Conclusion

Potential druggable targets for PDAC were identified by KDD, particularly including the key proteins with repurposed drugs acting on the ERK/MAPK signaling pathway (involved CGRP) and the membrane fusion.

Introduction

Pancreatic Ductal Adenocarcinoma (PDAC) is a lethal cancer accounting for over 90% of pancreatic cancer cases and it aggressively develops metastases. It is currently the 4^th leading cause of all cancer deaths in both males and females, with expectations to become the 2^nd most by 2030. PDAC prognosis is grim with most being unresectable at the time of diagnosis.

Aims & Methods

Our objective was to develop machine learning algorithms based on clinicopathologic characteristics and biomarker levels to determine a prognostication model for PDAC assessment. A total of 76 patients diagnosed by EUS-FNB sampling with PDAC were included in the study. Clinical, paraclinical, and specific biomarker expressions were collected. We considered the following: alcohol consumption, smoking, diabetes type II, CRP, albumin, neutrophil/lymphocyte, thrombocyte/lymphocyte, CRP/albumin, CEA, CA 19.9, CA125, plasma levels of mir107 and CD63 immunohistochemistry expression. The endpoint was to compare T1-T2 with T3-T4 stages by using various algorithms. We calculated error rates for Latent Dirichlet Allocation (LDA), Quadratic Discriminant Analysis (QDA), and Logistic Regression (LR). To evaluate the model and check how well it performs on unseen data, we took a validation set and checked the model's performance on it. Then modeled K-Nearest Neighbors (K-NN), where we demonstrated the validation set performance check.

Results

The 4 models had high accuracy ranging from 0.77 to 1, the best model was QDA and the worst was K-NN. LDA obtained an accuracy of 85% showing that the Neutrophil/Lymphocyte ratio will most influence a T1+T2 stage, and the PRC/Albumin and Thrombocyte/Lymphocyte ratio will lead to a T3+T4 stage. LR obtained an accuracy of 83%. Platelet/Lymphocyte ratio, PRC/Albumin, and CD63 indicators can be used for high prediction of T3+T4 stage, and Neutrophil/Lymphocyte ratio, PRC, and mir107 are suitable indicators for T1+T2 stage prediction.

Conclusion

Machine-learning methods based on clinicopathologic and biomarkers may serve as an auxiliary method to diagnose and predict PDAC prognosis.

Introduction

Alterations in body composition and metabolic factors may serve as a potential biomarker for early-detection of pancreatic ductal adenocarcinoma (PDAC).

Aims & Methods

This study aimed to capture the longitudinal changes occurring in body composition and metabolic factors preceding a diagnosis of PDAC. All patients (≥18 years) diagnosed with PDAC between 2000 and 2021 were identified. We collected data on demographics, comorbidities, PDAC characteristics, all abdominal CT scans and blood-based markers up to 3 years prior to date of diagnosis (index date). We applied a fully-automated abdominal segmentation algorithm previously developed by our group (Weston et al. Radiology 2019 [1]) for three-dimensional quantification of body composition on CT scans. Longitudinal trends of body composition and laboratory markers prior to a diagnosis of PDAC were estimated by linear mixed models and visualized using spline regression.

Results

We included 1,825 PDAC patients in body composition analysis, of which 581 (31.8%) had ≥2 pre-diagnostic CT exams. For analysis of longitudinal trends of blood-based markers, 4,846 patients were included. Mean age at diagnosis was 68.4 (SD 11.3) years, 43.6% were female and the mean BMI was 27.0 (SD 5.5). The distribution of PDAC stage was as follows: 12.5% stage I, 32.6% stage II, 19.7% stage III, and 35.2% stage IV. Prior to a PDAC diagnosis, we observed significant decreases in visceral fat volume, subcutaneous fat volume, bone volume, and a variety of blood markers including low-density lipoprotein and hemoglobin (Table 1). Concurrently, patients developed significant increases in HbA1c, fasting blood glucose, high-density lipoprotein and white blood cells prior to a clinical PDAC diagnosis. Most body composition and metabolic markers showed a linear decreasing or increasing trend over time to diagnosis of PDAC.

Table 1. Trends of body composition and laboratory markers over time prior to a diagnosis of PDAC estimated by linear mixed models.

Conclusion

This study identified significant alterations in a variety of soft tissue and metabolic markers that occur in the development of PDAC. These metabolic changes may become apparent several years before diagnosis, which may serve as a significant window of opportunity for early detection.

References

1. Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, Sugimoto M, Takahashi N, Erickson BJ. Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning. Radiology. 2019 Mar;290(3):669-679. doi: 10.1148/radiol.2018181432. Epub 2018 Dec 11. PMID: 30526356.

Disclosure

On behalf of all authors, I declare no conflicts of interest.

Introduction

KRAS, TP53, CDKN2A, and SMAD4 have been the main driver mutations in pancreatic ductal adenocarcinoma (PDAC). Studies on the clinical significance and treatment response to 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) regimen in terms of the presence of these mutations remain inconclusive.

Aims & Methods

This study analyzed patients diagnosed with pancreatic adenocarcinoma using a targeted next-generation sequencing platform at two tertiary referral hospitals from January 2016 to March 2022. Patients receiving FOLFIRINOX chemotherapy as the initial treatment were retrospectively investigated.

Results

The analysis included 100 patients. KRAS mutation was identified in 92 (92.0%) patients, followed by TP53, CDKN2A, and SMAD4 in 63 (63.0%), 18 (18.0%), and 17 (17.0%) patients, respectively. The TP53 wild type group demonstrated longer overall survival (OS) than the TP53 mutated group (median OS: 29 months vs. 19 months, p = 0.03), and TP53 served as a prognostic factor for survival (hazard ratio = 1.74, 95% confidence interval: 1.00–3.00, p = 0.048). The difference in OS according to TP53 mutation was intensified in localized pancreatic adenocarcinoma (37 months vs. 19 months, p = 0.01). The TP53 wild type group demonstrated a higher objective response rate to FOLFIRINOX chemotherapy than the TP53 mutation group in localized pancreatic adenocarcinoma (50.0% vs. 17.6%, p = 0.024).

Conclusion

PDAC patients with wild type TP53 demonstrated longer OS than those with TP53 mutation, and this trend was intensified in patients with localized disease. This result may be due to an impaired response to FOLFIRINOX chemotherapy in patients with TP53 mutation.

Introduction

Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis. This bleak outlook is due to the lack of effective drug therapies for PDAC. Therefore, there is an urgent need to develop new and effective medications for PDAC.
Protein kinase R (PKR) is a ubiquitously expressed serine-threonine kinase encoded by EIF2AK2 in humans. Recent studies have uncovered a dual role for PKR in cancer; it can act as either a promoter or suppressor, depending on the type of cancer. This paradoxical effect is thought to be due to variations in PKR expression and their diverse effects on cellular regulation in different tumor microenvironments.
Our study also revealed that PKR promotes the growth of hepatocellular carcinoma cells infected with hepatitis C virus. However, the role of PKR in PDAC remains to be elucidated.

Aims & Methods

This study aimed to identify the roles and mechanisms of action of PKR in PDAC by analyzing clinical evidence and in vitro data obtained from PDAC cell lines.
We used the human pancreatic cancer cell lines (PANC-1 and MIA PaCa-2) and verified PKR expression in PDAC cell lines using RT-PCR and Western blotting. MTS assay was used to determine the effect of PKR on cell proliferation. To elucidate the mechanisms underlying the effect of PKR on PDAC, RNA-seq analysis was conducted. Flow cytometry was performed to investigate the effects of PKR knockdown on cell cycle progression and apoptosis in PDAC cells. Additionally, we used the GEPIA database to compare the expression levels of PKR (EIF2AK2) in pancreatic cancer and normal pancreatic tissues. The Kaplan-Meier Plotter tool was used to analyze the relationship between individual PKR mRNA expression and disease-free survival (DFS) in patients with PDAC.

Results

PDAC cell lines exhibited significantly reduced proliferation when transfected with PKR-targeting siRNAs or treated with PKR inhibitors. RNA-seq analysis revealed a substantial upregulation of GADD45A expression when PKR expression was inhibited. After PKR silencing, cell cycle analysis indicated a pronounced accumulation of cells in the G1 phase, which is consistent with the known role of GADD45A as a cell cycle regulator. Additionally, the inhibition of proliferation resulting from PKR knockdown was reversed upon downregulation of GADD45A, indicating an interactive effect between PKR and GADD45A in regulating PDAC cell growth. Furthermore, as mentioned above, PKR knockdown increased the percentage of cells in the G1 phase, and knockdown of both GADD45A and PKR returned the percentage of cells in the G1 phase to the same level as that in the control. These results indicated that the interaction between PKR and GADD45A plays a critical role in the proliferation of PDAC cells by regulating the cell cycle.
PKR mRNA expression levels in tumor and normal tissues were compared using the GEPIA database. Elevated PKR mRNA expression in tumor tissues compared with that in normal tissues was more pronounced in PDAC than in other carcinomas. We performed a prognostic analysis using the Kaplan-Meier Plotter database to explore the clinical significance of PKR in PDAC. The results showed that patients with elevated PKR mRNA expression had shorter disease-free survival (DFS; n=278; HR=1.84, 95% CI 1.36-2.5). In contrast, the group with high GADD45A expression showed a prolonged DFS. These data were consistent with our in vitro experimental results.

Conclusion

PKR promotes PDAC cell proliferation by modulating cell cycle via regulation of GADD45A expression. These results suggest that PKR is a potential novel therapeutic target for PDAC.