Pancreatic adecarcinoma fibroblast subtype using RNAseq

Creators

• Nicolle Rémy
• Neuzillet Cindy
• Bousquet Corinne

Description

Cancer-associated fibroblasts (CAFs) are orchestrators of the pancreatic ductal adenocarcinoma (PDAC) microenvironment. Previously we described four CAF subtypes with specific molecular and functional features. Here, we have refined our CAF subtype signatures using RNAseq and immunostaining with the goal to define bioinformatically the phenotypic stromal and tumor epithelial states associated with CAF diversity. We used primary CAF cultures grown from patient PDAC tumors, human datasets (in-house and public, including single-cell analyses), genetically engineered mouse PDAC tissues, and patient-derived xenografts (PDX) grown in mice. We found that CAF subtype RNAseq signatures correlated with immunostaining. Tumors rich in periostin-positive CAFs were significantly associated with shorter overall survival of patients. Periostin-positive CAFs were characterized by high proliferation and protein synthesis rates, low αSMA expression, and were found in peri-/pre-tumoral areas. They were associated with highly cellular tumors and with macrophage infiltrates. Podoplanin-positive CAFs were associated with immune-related signatures and recruitment of dendritic cells. Importantly, we showed that the combination of periostin-positive CAFs and podoplanin-positive CAFs was associated with specific tumor microenvironment features in terms of stromal abundance and immune cell infiltrates. Podoplanin-positive CAFs identified an iCAF-like subset whereas periostin-positive CAFs were not correlated with the published myCAF/iCAF classification.

Taken together, these results suggest that a periostin-positive CAF is an early, activated CAF, associated with aggressive tumors, whereas a podoplanin-positive CAF is associated with an immune-related phenotype. These two subpopulations cooperate to define specific tumor microenvironment and patient prognosis, and are of putative interest for future therapeutic stratification of patients.

Material and methods

Total RNA was extracted from FFPE sections using a high pure FFPE RNA isolation kit (Roche®, Basel, Switzerland) following the manufacturer’s protocol. RNA yield and quality was determined using a NanoDrop™ One spectrophotometer and fragment size was analyzed using an RNA ScreenTape assay run on a 4200 Bioanalyzer (Agilent Technologies®, Santa Clara, CA, USA . DV200 values representing the percentage of RNA fragments above 200 nucleotides in length were estimated, and cases with DV200 more than 30% were included for library preparation.
Library preparation was performed using QuantSeq 3’ mRNA-Seq REV (Lexogen® , Vienna, Austria) with an input of 150 ng of total FFPE RNA. The pool was sequenced on a NovaSeq 6000 system flow cell SP (Illumina Inc., San Diego, CA) using a 75-cycle, paired-end protocol providing approximately 10 million reads per sample. Base call files were converted to fastq format using Bcl2Fastq (Illumina®, San Diego, CA). All RNA-seq reads were aligned to the human reference genome (GRCh37, hg19) using STAR (version 2.6.1a_08-27), quantified using FeatureCount and Upper-Quartile normalized.