Introduction

Recent studies propose that pancreatic ductal adenocarcinoma (PDAC) can be morphologically classified into biologically relevant categories each associated with known molecular subtypes with prognostic and possibly predictive significance (1). Two distinct morphological PDAC categories ('gland forming' and 'non-gland forming') and four morphological patterns were thus recognized. We aimed to validate the prognostic significance of the newly proposed morpho-molecular classification of PDAC.

Aims & Methods

We reviewed all tumour containing histological slides from 50 chemotherapy-naive PDAC resection specimens from 50 patients (32 male, median age 71, range 30-97) treated in our centre between 2016-2019 and followed up for up to 85 months (mean follow up 35.1 months, mean time to death 23 months). Cases were assessed for the presence of four morphological patterns (conventional, tubulopapillary, squamous and composite) that grouped into two categories “gland forming” and “non-gland forming” according to the presence of well or not of well-formed tubular or compact structures. Tumour histological grading and TNM staging according to WΗΟ 2019 and AJCC 2017, respectively, was performed. Morphological patterns and tumour categories were correlated with clinical-pathological variables and patient overall survival. Statistical analysis was performed using Pearson correlation coefficient and chi-squared test and overall survival analysis was conducted using the log rank test and Kaplan Meier curves. The level of significance was set at p=0.05.

Results

Our PDAC cohort included 9(19.6%) grade 1, 17(37%) grade 2, 20(43.5%) grade 3 and 12(24.5%) pT1, 25(51%) pT2, 10(20.4%) pT3 and 2(4.1%) pT4 cases. There were 31(62%) gland forming and 19(38%) non-gland forming PDAC, subclassified in 21(42%) with conventional, 10(20%) with tubulopapillary, 2(4%) with squamous and 17(34%) composite pattern. Morphological patterns were positively correlated with lymph node status (r=0.384, p=0.008). The non-gland forming category was positively correlated with higher tumour grade (r=0.435, p=0.003) and inversely correlated with the presence of pancreatic intraepithelial neoplasia-PanIN (r=-0.394, p=0.006). A weak negative association was observed between vascular invasion and female sex (r=-0.301, p=0.034) and perineural invasion with increasing age (r=-0.328, p=0.021). There were no other significant correlations observed. At the end of follow up 12/44 (27.3%) patients were alive. The two morphological categories and four morphological patterns did not correlate with overall survival (log rank test p=0.732 and p=0.843, respectively).

Conclusion

We have confirmed the applicability of the newly proposed binary morphological classification in PDAC. In our cohort the most common morphological category is “gland forming” with the majority of PDAC showing conventional pattern while “non-gland forming” PDAC are of higher histological grade. The recently proposed prognostic significance of the binary morphological classification has not been confirmed. Further studies in cohorts with larger number of cases with molecular subtyping are required to further assess the prognostic implications of the morphomolecular classification in PDAC.

References

1. N Kalimuthu S, Wilson GW, Grant RC et al. Morphological classification of pancreatic ductal adenocarcinoma that predicts molecular subtypes and correlates with clinical outcome. Gut 2020;69(2):317-328. doi: 10.1136/gutjnl-2019-318217.

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

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) 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.