Advancing the Care of Pancreatic Cancer Patients: Moving Beyond Just Tumour Tissue.

Biobanking efforts, to establish and grow the pool of available tissue from which evidence on aetiology, therapeutic susceptibility and prognosis of various diseases, have been underway for decades. This is illustrated nowhere better than in cancer. High incidence cancers such as breast, colorectal and lung have seen massive increases in their requisite formularies that have yielded improved prognoses. These discoveries, on a very fundamental level, were made by scientists who had access to tumour tissue and associated clinical data from patient donors. As the research space for higher incidence malignancies became increasingly crowded, attention has turned towards those malignancies with lower incidence. In the same time span, technology has continued to evolve, allowing the next generation of scientists and clinicians to ask more nuanced questions. Inquiries are no longer limited to the -omics of tumour tissue but also include biomarkers of blood and excretory products, concurrent disease status and composition of the gut microbiome. The impact of these new technologies and the questions now facing researchers in low-incidence cancers will be summarized and discussed. Our experience with pancreatic ductal adenocarcinoma will be used as a model for this review.

Stroma vs epithelium-enhanced prognostics through histologic stratification in pancreatic ductal adenocarcinoma.

The mixture of epithelial and stromal components in pancreatic ductal adenocarcinoma (PDAC) may confound sequencing-based studies of tumor gene expression. Virtual microdissection has been suggested as a bioinformatics approach to segment the aforementioned components, and subsequent prognostic gene sets have emerged from this research. We examined the prognostic signature from the epithelial gene set of one such study using laser capture microdissected (LCM) epithelial samples. We also examined this gene set in matched stromal samples to determine whether prognostic findings were specific to the epithelium. LCM samples from 48 long-term and 48 short-term PDAC survivors were obtained. The resultant epithelial and stromal components were subjected to direct mRNA quantification using a 49 gene published PDAC classifier. Component-specific unsupervised hierarchical clustering was used to derive groups and survival differences were quantified. Immunohistochemical validation of particular genes was performed in an independent cohort. Clustering in the epithelial component yielded prognostic differences in univariable analysis (P = .02), but those differences were not significant when controlled for other clinicopathologic covariates (P = .06). Clustering in the stromal component yielded prognostic differences that persisted in the presence of other clinicopathologic covariates (P = .0005). Validation of selected genes in the epithelium (KRT6A-negative prognostic [P = .004]) and stroma (LY6D-improved prognostic [P = .01] and CTSV-negative prognostic [P = .0002]) demonstrated statistical independence in multivariable analysis. Although the genes used in this study were originally identified as being representative of the epithelial component of PDAC, their expression in the stroma appears to provide additional information that may aid in improved prognostication.

Altered Gene Expression along the Glycolysis-Cholesterol Synthesis Axis Is Associated with Outcome in Pancreatic Cancer.

PURPOSE: Identification of clinically actionable molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcome. Intertumoral metabolic heterogeneity contributes to cancer survival and the balance between distinct metabolic pathways may influence PDAC outcome. We hypothesized that PDAC can be stratified into prognostic metabolic subgroups based on alterations in the expression of genes involved in glycolysis and cholesterol synthesis. EXPERIMENTAL DESIGN: We performed bioinformatics analysis of genomic, transcriptomic, and clinical data in an integrated cohort of 325 resectable and nonresectable PDAC. The resectable datasets included retrospective The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) cohorts. The nonresectable PDAC cohort studies included prospective COMPASS, PanGen, and BC Cancer Personalized OncoGenomics program (POG). RESULTS: On the basis of the median normalized expression of glycolytic and cholesterogenic genes, four subgroups were identified: quiescent, glycolytic, cholesterogenic, and mixed. Glycolytic tumors were associated with the shortest median survival in resectable (log-rank test P = 0.018) and metastatic settings (log-rank test P = 0.027). Patients with cholesterogenic tumors had the longest median survival. KRAS and MYC-amplified tumors had higher expression of glycolytic genes than tumors with normal or lost copies of the oncogenes (Wilcoxon rank sum test P = 0.015). Glycolytic tumors had the lowest expression of mitochondrial pyruvate carriers MPC1 and MPC2. Glycolytic and cholesterogenic gene expression correlated with the expression of prognostic PDAC subtype classifier genes. CONCLUSIONS: Metabolic classification specific to glycolytic and cholesterogenic pathways provides novel biological insight into previously established PDAC subtypes and may help develop personalized therapies targeting unique tumor metabolic profiles.See related commentary by Mehla and Singh, p. 6.