Recent studies have offered ample insight into genome-wide expression patterns to define pancreatic ductal adenocarcinoma (PDAC) subtypes, although there remains a lack of knowledge regarding the underlying epigenomics of PDAC. Here we perform multi-parametric integrative analyses of chromatin immunoprecipitation-sequencing (ChIP-seq) on multiple histone modifications, RNA-sequencing (RNA-seq), and DNA methylation to define epigenomic landscapes for PDAC subtypes, which can predict their relative aggressiveness and survival. Moreover, we describe the state of promoters, enhancers, super-enhancers, euchromatic, and heterochromatic regions for each subtype. Further analyses indicate that the distinct epigenomic landscapes are regulated by different membrane-to-nucleus pathways. Inactivation of a basal-specific super-enhancer associated pathway reveals the existence of plasticity between subtypes. Thus, our study provides new insight into the epigenetic landscapes associated with the heterogeneity of PDAC, thereby increasing our mechanistic understanding of this disease, as well as offering potential new markers and therapeutic targets.

SUMMARYPreclinical models based on patient-derived xenografts have remarkable specificity in distinguishing transformed human tumor cells from non-transformed murine stromal cells computationally. We obtained 29 pancreatic ductal adenocarcinoma (PDAC) xenografts from either resectable or non-resectable patients (surgery and endoscopic ultrasound-guided fine-needle aspirate, respectively). Extensive multiomic profiling revealed two subtypes with distinct clinical outcomes. These subtypes uncovered specific alterations in DNA methylation and transcription as well as in signaling pathways involved in tumor-stromal cross-talk. The analysis of these pathways indicates therapeutic opportunities for targeting both compartments and their interactions. In particular, we show that inhibiting NPC1L1 with Ezetimibe, a clinically available drug, might be an efficient approach for treating pancreatic cancers. These findings uncover the complex and diverse interplay between PDAC tumors and the stroma and demonstrate the pivotal role of xenografts for drug discovery and relevance to PDAC.

Background Pancreatic neuroendocrine tumors (NETs) and intraductal pancreatic mucinous neoplasia (IPMN) with worrisome features are surgically managed. Endoscopic ultrasound (EUS)-guided radiofrequency ablation (RFA) has recently been developed. The safety of EUS-RFA was the primary end point of this study, its efficacy the secondary end point. Methods This was a prospective multicenter study that was planned to include 30 patients with a 1-year follow-up with either a NET < 2 cm or a pancreatic cystic neoplasm (PCN), either a branch duct IPMN with worrisome features or a mucinous cystadenoma (MCA). EUS-RFA was performed with an 18G RFA cooling needle. Results 12 patients had 14 NETs (mean size 13.1 mm, range 10 – 20 mm); 17 patients had cystic tumors (16 IPMNs, 1 MCA; mean size 28 mm, range 9 – 60 mm). Overall three adverse events occurred (10 %), two of these in the first two patients (one pancreatitis, one small-bowel perforation). After these initial patients, modifications in the protocol resulted in a decrease in complications (3.5 %), with one patient having a pancreatic ductal stenosis. Among the 14 NETs, at 1-year follow-up 12 had completely disappeared (86 % tumor resolution), with three patients having a delayed response. Among the 17 PCNs, at 12 months, there were 11 complete disappearances and one diameter that decreased by > 50 % (significant response rate 71 %). All 12 mural nodules showed complete resolution. Conclusions EUS-RFA of pancreatic NETs or PCNs is safe with a 10 % complication rate, which can be decreased by improved prophylaxis for the procedure.

Geriatric factors (MMSE and IADL) are predictive of severe toxicity or unexpected hospitalization (MMSE) in a randomized prospective phase III study in mCRC. These results suggest that cognitive function and autonomy impairment should be taken into account when choosing a regimen for chemotherapy.

This article has an accompanying continuing medical education activity, also eligible for MOC credit, on page e19. Learning Objective: Upon completion of this CME activity successful learners will be able to (1) evaluate the probability of a venous thromboembolism (VTE) in patients with newly diagnosed pancreatic ductal adenocarcinoma (PDAC); (2) identify the risk factors for VTE in patients with PDAC; and (3) assess the impact of VTE on survival in patients with PDAC. Venous Thromboembolism and Pancreatic CancerThe BACAP-VTE Study : pancreatic cancer patients prospectively followed-up from time of enrollment until last visit or death 152 patients (20.79%) developed a VTE during a median follow-up of 19.3 months Patients developing VTE during follow-up had lower PFS (HR 1.74, 95%CI 1.19-2.54, P=.004) Patients developing VTE during follow-up had lower OS (HR 2.02, 95%CI 1.57-2.60, P<.001).

ObjectiveDiagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.DesignWe have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes.ResultsWithin the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated ‘DGMuneS’, outperformed Immunoscore when used in estimating patients’ prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk.ConclusionThese findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients’ prognosis.

No abstract

No abstract