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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 of defining bioinformatically the phenotypic stromal and tumor epithelial states associated with CAF diversity. We used primary CAF cultures grown from patient PDAC tumors, human data sets (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 and low α-smooth muscle actin 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 inflammatory CAF (iCAF)-like subset, whereas periostin-positive CAFs were not correlated with the published myofibroblastic CAF (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. © 2022 The Authors. The Journal of Pathology published by John WileySons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Pancreatic ductal adenocarcinoma (PDAC) aggressiveness results from its high metastatic and chemoresistance potential. Pancreatic cancer cells are immersed in an exuberant tumor microenvironment (or stroma), which represents up to 80% of PDAC tumor volume, and is hijacked by cancer cells for their own survival and metastasis. Additionally, PDAC tumors are heterogeneous, e.g. transcriptomic (RNAseq) analyses of human PDAC tumors have allowed patients to be stratified into two tumor subgroups (basal and classical) with different prognoses. Therefore, we aimed to understand which tumor-stroma crosstalks are critical for PDAC aggressiveness in the context of its heterogeneity, and how they can be pharmacologically targeted, to then propose more effective personalized therapeutic strategies. By performing bioinformatical analysis on RNAseq data from PDX (Patient Derived Xenograft), a hybrid tumor model from which human-derived tumor cell sequences are distinguishable from murine-derived stromal cell sequences, we searched for different stromal behaviors linked to the tumor aggressiveness, to be then validated in published PDAC patient databases. According to NMF (Non-negative matrix factorization) and GSEA (Gene Set Enrichment Analyses), we identified two components, which classify the samples according to a stromal gradient. Interestingly, these components are characterized by distinct functional signatures of aggressiveness and they are highly prognostic (correlated with survival). In addition, these components stratify patients independently on the described molecular tumor (basal or classical) classification. Importantly, we validated those results on other PDAC published databases. Moreover, ligand-receptor bioinformatics analyses identified novel players of the TGFβ superfamily in the most aggressive stromal component. Indeed, we have highlighted the involvement of the ligand activin a member of this large superfamily. Using in situ hybridization coupled with immunofluorescence, we were able to demonstrate that activin is only expressed by stromal cells, and more specifically by cancer associated fibroblasts (CAF), with however a strong heterogeneity (inter and intra-tumor). In vitro, activin strongly increases the migratory capacity of fibroblasts but also of specific cancer cells, this effect being effectively blocked pharmacologically with an activin receptor-targeting drug that is now tested in a pre-clinical PDAC murine model. Therapeutic targeting of the stroma must take into account its functional heterogeneity. We have identified in specific aggressive PDAC tumors novel stromal players involved in the tumor-stroma symbiosis, and which presumably play a critical role in CAFs and cancer cells migration and invasion. Our work should enable to delineate which stroma-targeting drug to co-deliver, in addition to chemotherapies, in stratified patients, to improve PDAC prognosis. Citation Format: Alexia Brunel, Jérôme Raffenne, Remy Nicolle, Ilse Rooman, Elisa Espinet, Marjorie Fanjul, Aurélie Perraud, Muriel Mathonnet, Sophie Vasseur, Nelson Dusetti, Yvan Martineau, Christine Jean, Corinne Bousquet. Bioinformatical identification of cancer-associated-fibroblasts with tumor cells pro-migratory crosstalks depending on activin [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C034.

Multi-omics high throughput analyses lead to the description of multiple molecular subtypes of pancreatic adenocarcinoma with major prognostic impact for most of them. There is no consensual multilevel integrative classification yet like in colon or breast cancers. Genomic classifications have identified a tumor subtype (15% of the patients) with deficient homologous DNA repair-system leading to increase sensitivity to platinum-based therapies and possibly to PARP inhibitors and immunotherapies. Transcriptomic classifications are still debated but all have identified an aggressive subtype with a very poor prognosis, presumably unfit for a surgical approach. Finally, approaches based on metabolomic or proteomic profiling have identified subtypes with a particular sensitivity to compounds targeting the hallmarks metabolomics or oncogenic pathways of each subtype. These classifications were mostly based on tumor cell but the micro-environment is also very heterogeneous and several types of stroma will be described soon. Subtype determination in daily practice remains a major challenge as most technologies used to build these classifications are very expensive, requires dedicated bio-informatics analysis pipelines and are not adapted to routine samples that are mostly formal in fixed paraffin embedded biopsies, in which tumor cells are highly contaminated by the cell from the microenvironment and the clot.

Multi-omics high throughput analyses lead to the description of multiple molecular subtypes of pancreatic adenocarcinoma with major prognostic impact for most of them. There is no consensual multilevel integrative classification yet like in colon or breast cancers. Genomic classifications have identified a tumor subtype (15% of the patients) with deficient homologous DNA repair-system leading to increase sensitivity to platinum-based therapies and possibly to PARP inhibitors and immunotherapies. Transcriptomic classifications are still debated but all have identified an aggressive subtype with a very poor prognosis, presumably unfit for a surgical approach. Finally, approaches based on metabolomic or proteomic profiling have identified subtypes with a particular sensitivity to compounds targeting the hallmarks metabolomics or oncogenic pathways of each subtype. These classifications were mostly based on tumor cell but the micro-environment is also very heterogeneous and several types of stroma will be described soon. Subtype determination in daily practice remains a major challenge as most technologies used to build these classifications are very expensive, requires dedicated bio-informatics analysis pipelines and are not adapted to routine samples that are mostly formal in fixed paraffin embedded biopsies, in which tumor cells are highly contaminated by the cell from the microenvironment and the clot.