Genomics-Driven Precision Medicine for Advanced Pancreatic Cancer: Early Results from the COMPASS Trial

Abstract

To perform real-time whole genome sequencing (WGS) and RNA sequencing (RNASeq) of advanced pancreatic ductal adenocarcinoma (PDAC) to identify predictive mutational and transcriptional features for better treatment selection. Patients with advanced PDAC were prospectively recruited prior to first-line combination chemotherapy. Fresh tumor tissue was acquired by image-guided percutaneous core biopsy for WGS and RNASeq. Laser capture microdissection was performed for all cases. Primary endpoint was feasibility to report WGS results prior to first disease assessment CT scan at 8 weeks. The main secondary endpoint was discovery of patient subsets with predictive mutational and transcriptional signatures. Sixty-three patients underwent a tumor biopsy between December 2015 and June 2017. WGS and RNASeq were successful in 62 (98%) and 60 (95%), respectively. Genomic results were reported at a median of 35 days (range, 19-52 days) from biopsy, meeting the primary feasibility endpoint. Objective responses to first-line chemotherapy were significantly better in patients with the classical PDAC RNA subtype compared with those with the basal-like subtype ( = 0.004). The best progression-free survival was observed in those with classical subtype treated with m-FOLFIRINOX. expression in tumor measured by RNA hybridization was found to be a robust surrogate biomarker for differentiating classical and basal-like PDAC subtypes. Potentially actionable genetic alterations were found in 30% of patients. Prospective genomic profiling of advanced PDAC is feasible, and our early data indicate that chemotherapy response differs among patients with different genomic/transcriptomic subtypes. .

Network‐based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer

“…It is essential and urgent to crack the secret of the carcinogenesis and progression of PC. Although several transcriptomic (including both mRNA and miRNA) studies conducted previously have provided preliminary assessments of gene expression profiles in PC tissues, systematic analysis using network‐based approaches is quite limited, which would allow us for a better understanding of the pathogenesis of PC.…”

“…Almost all of these studies used either cell lines or animal models of PC, or microarray‐based methods that have apparent limitations when compared with RNA‐seq techniques. Very recently, a small number of RNA‐seq‐based transcriptome analyses have been conducted to investigate the gene expression profile changes in pancreatic tissues between different groups of patients (eg, tumor vs non‐tumor, short‐survivor vs long‐survivor) . However, those preliminary studies did not fully demonstrate the complex mRNA‐miRNA interaction network and failed to identify genes and molecular mechanisms that may drive the pathogenesis of PC.…”

“…Very recently, a small number of RNA-seq-based transcriptome analyses have been conducted to investigate the gene expression profile changes in pancreatic tissues between different groups of patients (eg, tumor vs non-tumor, short-survivor vs long-survivor). [37][38][39] However, those preliminary studies did not fully demonstrate the complex mRNA-miRNA interaction network and failed to identify genes and molecular mechanisms that may drive the pathogenesis of PC. Thus, a network-based comprehensive analysis of both mRNA and miRNA profiles using tissue samples from patients with PC is urgently needed.…”

Reciprocal regulation of pancreatic ductal adenocarcinoma growth and molecular subtype by HNF4α and SIX1/4

“…This integrative analysis found significant overlap between the different subtypes, ultimately suggesting that PDAC can fundamentally be classified into two major subtypes based on cancer cell autonomous properties: Classical and Basal-like. These subtypes appear to be clinically relevant because the Classical subtype is associated with a better overall survival and better response to first line chemotherapy than the Basal-like subtype (2,4,6).…”

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

“…Despite its overall dismal prognosis, recent research has identified specific molecular subtypes with distinct therapy response and outcome. Among these, the so-called classical phenotype shows improved chemotherapy response and survival compared to the so-called quasimesenchymal or basal-like subtype underlining the urgent requirement for advanced techniques for precise pretherapeutic patient stratification (2,3). This is key to both, adequate patient management, based on informed decision processes, clinical trial design and outcome interpretation.…”