Establishment of a pancreatic adenocarcinoma molecular gradient (PAMG) that predicts the clinical outcome of pancreatic cancer

Abstract

BACKGROUND: A significant gap in pancreatic ductal adenocarcinoma (PDAC) patient's care is the lack of molecular parameters characterizing tumors and allowing a personalized treatment. The goal of this study was to examine whole PDAC transcriptomic profiles to define a signature that would predict aggressiveness and treatment responsiveness better than done until now. METHODS AND PATIENTS: Tumors were obtained from 76 consecutive resectable (n=40) or unresectable (n=36) tumors. PDAC were transplanted in mice to produce patient-drived xenografts (PDX). PDX were classified according to their histology into five groups, from highly undifferentiated to well differentiated. This classification resulted strongly associated with tumors aggressiveness. A PDAC molecular gradient (PAMG) was constructed from PDX transcriptomes recapitulating the five histological groups along a continuous gradient. The prognostic and predictive value for PMAG was evaluated in: i/ two independent series (n=598) of resected tumors; ii/ 60 advanced tumors obtained by diagnostic EUS-guided biopsy needle flushing and iii/ on 28 biopsies from mFOLFIRINOX treated metastatic tumors. RESULTS: A unique transcriptomic signature (PAGM) was generated with significant and independent prognostic value. PAMG significantly improves the characterization of PDAC heterogeneity compared to non-overlapping classifications as validated in 4 independent series of tumors (e.g. 308 consecutive resected PDAC, HR=0.321 95% CI [0.207;0.5] and 60 locallyadvanced or metastatic PDAC, HR=0.308 95% CI [0.113;0.836]). The PAMG signature is also associated with progression under mFOLFIRINOX treatment (Pearson correlation to tumor response: -0.67, p-value < 0.001). CONCLUSION: We identified a transcriptomic signature (PAMG) that, unlike all other stratification schemas already proposed, classifies PDAC along a continuous gradient. It can be performed on formalin-fixed paraffin-embedded samples and EUS-guided biopsies showing a strong prognostic value and predicting mFOLFIRINOX responsiveness. We think that PAMG could unify all PDAC preexisting classifications inducing a shift in the actual paradigm of binary classifications towards a better characterization in a gradient.

A transcriptomic signature to predict adjuvant gemcitabine sensitivity in pancreatic adenocarcinoma

“…GemPred+ and hENT1 IHC selected independent groups of patients and had independent predictive values on both OS and DFS (Supplementary figure 6), suggesting these potentially select complementary sets of gemcitabine-sensitive patients. The GemPred+ patients were all found to be a subset of the Classical subtype [24] (Figure 5b, 92 of 389 classical, none of 72 Basal-like), supported by their distribution in the upper half of the pancreatic cancer molecular gradient [33] (Figure 5c). A multivariate analysis among patients that have received adjuvant gemcitabine in the pooled cohort showed that GemPred+ was a predictor of OS independent of clinicopathological features, the RNA levels of genes previously described to be associated with response to gemcitabine (i.e.…”

“…An important point is that the genome-wide RNA profiles in this study were obtained from routine FFPE samples with an overall low cost, supporting a wide application of the GemPred signature. Previous work has also shown that sequencing RNA from fine needle aspirates, including for metastatic patients, is feasible [33]. It should also be noted that, while the signature was developed on tumor cells, it performed well on more complex samples such as surgical specimens that have abundant stroma suggesting its relevance on diagnostic biopsies.…”

“…The identified RNA signatures (proliferation and response) can be used on any genome-wide RNA profiling assay to project any new sample on each of these spaces. This approach was shown to give highly robust results and to score J o u r n a l P r e -p r o o f samples independently of technological considerations [33]. A web application is provided to apply the GemPred signature on whole transcription profiles, preferentially using an identical RNAseq methodology: http://cit-apps.ligue-cancer.net/pancreatic_cancer/GemPred.…”