Tumour and stroma RNA signatures predict more accurately distant recurrence than clinicopathological factors in resected pancreatic adenocarcinoma

Prognostic Factors for Early Recurrence After Resection of Pancreatic Cancer: A Systematic Review and Meta-Analysis

BACKGROUND & AIMS

More than half of pancreatic ductal adenocarcinomas (PDACs) recur within 12 months after curative-intent resection. This systematic review and meta-analysis was conducted to identify all reported prognostic factors for early recurrence in resected PDACs.

METHODS

After a systematic literature search, a meta-analysis was conducted using a random effects model. Separate analyses were performed for adjusted vs unadjusted effect estimates as well as reported odds ratios (ORs) and hazard ratios (HRs). Risk of bias was assessed using the Quality in Prognostic Studies tool, and evidence was rated according to Grading of Recommendations Assessment, Development and Evaluation recommendations.

RESULTS

After 2903 abstracts were screened, 65 studies were included. Of these, 28 studies (43.1%) defined early recurrence as evidence of recurrence within 6 months, whereas 34 (52.3%) defined it as evidence of recurrence within 12 months after surgery. Other definitions were uncommon. Analysis of unadjusted ORs and HRs revealed 41 and 5 prognostic factors for early recurrence within 6 months, respectively. When exclusively considering adjusted data, we identified 25 and 10 prognostic factors based on OR and HR, respectively. Using a 12-month definition, we identified 38 (OR) and 15 (HR) prognostic factors from unadjusted data and 38 (OR) and 30 (HR) prognostic factors from adjusted data, respectively. On the basis of frequency counts of adjusted data, preoperative carbohydrate antigen 19-9, N status, nondelivery of adjuvant therapy, grading, and tumor size based on imaging were identified as key prognostic factors for early recurrence.

CONCLUSIONS

Reported prognostic factors of early recurrence vary considerably. Identified key prognostic factors could aid in the development of a risk stratification framework for early recurrence. However, prospective validation is necessary.

Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma

Two tumor (Classical/Basal) and stroma (Inactive/active) subtypes of Pancreatic adenocarcinoma (PDAC) with prognostic and theragnostic implications have been described. These molecular subtypes were defined by RNAseq, a costly technique sensitive to sample quality and cellularity, not used in routine practice. To allow rapid PDAC molecular subtyping and study PDAC heterogeneity, we develop PACpAInt, a multi-step deep learning model. PACpAInt is trained on a multicentric cohort (n = 202) and validated on 4 independent cohorts including biopsies (surgical cohorts n = 148; 97; 126 / biopsy cohort n = 25), all with transcriptomic data (n = 598) to predict tumor tissue, tumor cells from stroma, and their transcriptomic molecular subtypes, either at the whole slide or tile level (112 µm squares). PACpAInt correctly predicts tumor subtypes at the whole slide level on surgical and biopsies specimens and independently predicts survival. PACpAInt highlights the presence of a minor aggressive Basal contingent that negatively impacts survival in 39% of RNA-defined classical cases. Tile-level analysis ( > 6 millions) redefines PDAC microheterogeneity showing codependencies in the distribution of tumor and stroma subtypes, and demonstrates that, in addition to the Classical and Basal tumors, there are Hybrid tumors that combine the latter subtypes, and Intermediate tumors that may represent a transition state during PDAC evolution.

Poor-prognosis molecular subtypes in adenocarcinomas of pancreato-biliary and gynecological origin: A systematic review

Pancreato-biliary and gynecological adenocarcinomas need better tools to predict clinical outcome. Potential prognostic mesenchymal(-like) transcriptome-based subtypes have been identified in these cancers. In this systematic review, we include studies into molecular subtyping and summarize biological and clinical features of the subtypes within and across sites of origin, searching for suggestions to improve classification and prognostication. PubMed and Embase were searched for original research articles describing potential mesenchymal(-like) mRNA-based subtypes in pancreato-biliary or gynecological adenocarcinomas. Studies limited to supervised clustering were excluded. Fourty-four studies, discussing cholangiocarcinomas, gallbladder, ampullary, pancreatic, ovarian, and endometrial adenocarcinomas were included. There was overlap in molecular and clinical features in mesenchymal(-like) subtypes across all adenocarcinomas. Approaches including microdissection were more likely to identify prognosis-associating subtypes. To conclude, molecular subtypes in pancreato-biliary and gynecological adenocarcinomas share biological and clinical characteristics. Furthermore, separation of stromal and epithelial signals should be applied in future studies into biliary and gynecological adenocarcinomas.

Prognostic Relevance of Pancreatic Adenocarcinoma Whole-Tumor Transcriptomic Subtypes and Components

PURPOSE

Our team previously defined six quantitative transcriptomic components, and a classification in five subtypes by association of these components. In this study, we compared the robustness of quantitative components and qualitative classifications from different transcriptomic profiling techniques, investigated their clinical relevance, and proposed a new prognostic model.

EXPERIMENTAL DESIGN

A total of 210 patients from a multicentric cohort and 149 patients from a monocentric cohort were included in this study. RNA microarray profiles were obtained from 165 patients of the multicentric cohort. RNA sequencing (RNA-seq) profiles were obtained from all the patients.

RESULTS

For the patients with both RNA microarray and RNA-seq profiles, the concordance in subtype assignment was partial with an 82.4% coherence rate. The correlation between the two technique projections of the six components ranged from 0.85 to 0.95, demonstrating an advantage of robustness. On the basis of the Akaike information criterion, the RNA components showed more prognostic value in univariate or multivariate models than the subtypes. Using the monocentric cohort for training, we developed a multivariate Cox regression model using all six components and clinicopathologic characteristics (node invasion and resection margins) on disease-free survival (DFS). This prognostic model was highly associated with DFS (P < 0.001). The evaluation of the model in the multicentric cohort showed significant association with DFS and overall survival (P < 0.001).

CONCLUSIONS

We described the advantage of the prognostic value and robustness of the whole-tumor transcriptomic components than subtypes. We created and validated a new DFS-based multivariate Cox regression prognostic model, including six pancreatic adenocarcinoma transcriptomic component levels and pathologic characteristics.

Anti-tumor effect of statin on pancreatic adenocarcinoma: From concept to precision medicine

A statin is a cholesterol-lowering agent, which inhibits HMG-CoA (3-hydroxy-3-methylglutaryl-coenzyme A) reductase and subsequently reduces the cholesterol precursor, and was first used commercially in 1987. The concept of cholesterol restriction leading to cancer cell dysfunction was proposed in 1992. The interruption of different signaling pathways has been proved in preclinical experiments to elucidate the anti-tumor mechanism of statins in pancreatic adenocarcinoma. Observational studies have shown that the clinical use of statins is beneficial in patients with pancreatic adenocarcinoma, including a chemoprevention effect, post-surgical resection follow-up and therapeutic prognosis of advanced cancer stage. Arrest of the cancer cell cycle by the combined use of gemcitabine and statin was observed in a cell line study. The effect of microbiota on the tumor microenvironment of pancreatic adenocarcinoma is a new therapeutic approach as statins can modulate the gut microbiota. Hence, further randomized trials of statins in pancreatic adenocarcinoma treatment will be warranted with application of precision medicine from microbiota-derived, cell cycle-based and signaling pathway-targeted research.