dataesr/scientific-paragraphs-categorization huggingface dataset:
Because we noted cGAMP induced type I IFN and ISG expression in CD4 T cells in vivo (online supplemental figure S2C), we investigated whether cGAMP was intrinsically triggering a global transcriptional program in differentiating T H 1 and T H 9 cells that was linked with the induction of a typical type I IFNdriven response. For this, we performed GSEA for type I IFN production (GO:0032479), ISGs and regulation of inflammatory response signatures (GO:0050727). GSEA revealed that these three gene sets were enriched in both T H 1 and T H 9 cells stimulated with cGAMP 16 and 48 hours after in vitro polarization (figure  4E  and online supplemental figure  S4E ). Finally, Trim30a, which was recently identified as a negative feedback regulator of the STING pathway in DCs,  42  and Usp18, which was shown to be involved in STING deubiquitination and stabilization,  43  were strongly induced in both T H 1 and T H 9 cells stimulated with cGAMP (online supplemental table S2).
Acknowledgements This work was possible with help from Flow Cytometry Core Facility/INSERM  UMR1231 , at the  University of Burgundy  and the  Biomax Facility/ Centre de Recherche d'Immunologie et d'Hématologie Strasbourg. Contributors IB-L, EJ, TRV, AR, LDZ, TM, AM, SHS, and CP performed in vitro and/or in vivo experiments; SG, RC, CR and RB performed and analyzed NGS experiments; ABG, DV, FV, RAF, BR, ERN, PS-S, and TL provided materials and suggestions for the study. IB-L, EJ, TRV, and LA wrote the paper and supervised the study. LA acts as the guarantor of the study. Funding The authors were supported by grants from the  Fondation de France  (LA, TRV, and ABG), the  Fondation pour la Recherche Médicale  ( ARF20170938687 ) (EJ), the  Conseil Régional de Bourgogne  and  FEDER (LA) , the  Agence Nationale de la Recherche  ( ANR-11-LABX-0021 ) (TM, AR, AM, ABG, and LA), the  LabEx MAbImprove  ( ANR-10-LABX-53-01 ) (LA), as well as ( ANR-14-CE14-0026-04 ,  Lumugene ; and  ANR   19-CE15-0028-01 , Lympho-sting) (PS-S), The  Fondation ARC  ( DOC20190509200 ) (IB-L), the  National Cancer Institute of the National Institutes of Health  ( R01CA234025 ) (ERN and SHS), the  Department of Defense Breast Cancer Research Program  ( BC171214 ) (ERN and SHS), and the  Embassy of France  in the USA, 2018-2019  STEM Chateaubriand Fellowship (SHS).
11 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) Biological Process 2018 database. Pathways ranked according to combined score. Adjusted p value indicated for each pathway. on this supplemental material which has been supplied by the author(s) BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Immunother Cancer J Immunother Cancer 12 , et al. Benoit-Lizon I , et al. Benoit-Lizon I J Immunother Cancer J Immunother Cancer 2022; 2022; :e003459. 10 :e003459. 10 doi: 10. 1136/jitc-2021-003459 doi: 10.
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.
Understanding such diversity in CAF phenotypes conditions the efficiency of future antistroma therapies. In a previous report  [10] , using primary cultures of human CAFs from resected PDAC, we described four CAF subtypes (A to D) with specific molecular and functional features. Based on transcriptomic analysis (using NanoString technology) and immunohistochemistry (IHC), we identified periostin (POSTN), myosin-11 (MYH11), and podoplanin (PDPN) as markers of subtypes A, B, and C, respectively. POSTN-positive CAFs (subtype A) were the most frequently present and were associated with poor prognosis in patients with resected PDAC. In this study, we further refined our CAF subtype signatures using IHC and RNAseq with the goal of defining bioinformatically phenotypic stromal and tumor epithelial states associated with such CAF diversity in a variety of models: (1) a large panel of primary CAF cultures grown from patient PDAC tissue, (2) resected-PDAC patient data sets (public and in-house) and genetically engineered KPC mouse tissues, and (3) PDAC patientderived xenografts (PDX) grown in mice (a hybrid model where RNA sequences from human tumor epithelial and murine stromal cells can be distinguished)  [24].
Histochemistry and immunohistochemistry Whole sections of tumors were obtained from resected chemotherapy-naive PDAC FFPE samples from the Beaujon patient cohort and KPC mice. All the slides of the human cases were reviewed by a pathologist with expertise in pancreatic tumors (JC) to select one representative block. Hematoxylin-eosin-saffron and Picro-Sirius Red staining was performed on an automated platform (Benchmark Ultra, Ventana, Tucson, AZ, USA). Details of antibodies used are provided in supplementary material, Table  S3. Stained slides were scanned using a computer-controlled capture device (AT turbo, Aperio; Leica Biosystems, Nanterre, France) and quantified using Halo software (Indica Labs, Albuquerque, NM, USA). For Picro-Sirius Red, the percentage of positive red pixels in the tumor area was computed after visual inspection of the threshold (JC).
RNAseq analysis A CAF subtype gene set signature was derived for POSTN-positive (subtype A), MYH11-positive (subtype B), and PDPN-positive (subtype C) CAFs using the following approach. Differential gene expression (DGE) analysis was performed on both the CAF cultures, comparing one subtype to all others, and the tumor tissues, comparing CAF marker-positive tumors versus others. DGE was performed using limma following the voom transformation  [30]. Gene signatures were defined by the sets of genes that were found to be significantly overexpressed in both in vitro CAF cultures and in situ tumor tissue (alpha 5% and logfold-change >0). The three CAF subtype gene set signatures (POSTN-, MYH11-, and PDPN-RNAsign) were used to quantify each CAF subtype in each of the transcriptome data sets using the average of gene expression z-score.
Basal-like and classical phenotypes were quantified on tumor profiles or on the epithelial compartment of PDX using the original independent component analysis from the Puleo et al  [28]  study by computing a projection of every sample on the components. Immune deconvolution tools were applied to PDX stromal compartment and whole tumor transcriptome profiles; XCELL and MCPcounter as available from the immunedeconv package  [31]. Gene set enrichment analyses (GSEA) were performed using the fast-GSEA implementation from Bioconductor (fgsea) using 1,000 permutations  [32]. Normalized enrichment scores (NESs) were used for heatmap representation. Singlegene expression levels (e. g. MKI67) were analyzed using log2 + 1 upper-quartile normalized counts.
Single-cell analysis To compare our built CAF signatures distinguishing CAF subtypes, we downloaded single-cell RNAseq raw data from a recent publication  [33]. Using the Seurat R package  [34] , data preprocessing functions were run with default parameters. Low-quality cells (<200 genes/cell, <3 cells/gene, and >5% mitochondrial genes) and cells from the normal tissue counterpart were excluded. The Seurat package implemented in R was applied to identify major cell types. Highly variable genes were generated and used to perform principal component analysis (PCA). Significant principal components were determined using JackStraw. Cell clusters were projected onto t-distributed stochastic neighbor embedding (t-SNE) analysis, using previously computed principal components. Cell-type identification was based on established marker genes  [35]  (supplementary material, Table  S5 ).
1e-08), respectively (Figure  1D-F ). At the individual patient level (n = 50), multigene RNAseq signatures were also correlated with protein IHC expression of POSTN, PDPN, or MYH11, as shown in the correlative heatmap (Figure  1G ). There was no significant association between RNAseq signatures and classical prognostic factors, i. e. tumor T stage, N stage, or margin R status, or with pancreatic adenocarcinoma molecular gradient (PAMG) defined according to the classical and basal-like tumor cell transcriptomic subtypes  [37]  (Figure  1G ). Consistent with the previously described poor prognosis associated with POSTN expression using IHC  [10] , we showed that tumors rich in POSTN-positive CAFs based on POSTN-RNAsign were associated with significantly shorter patient OS, in two human PDAC databases, i.
GSEA of RNAseq data from the two independent sets of CAF primary cultures (Set 1, n = 16, and Set 2, n = 23), from PDX stroma (n = 29), and from bulk resected human PDAC samples (Beaujon, n = 50, and ICGC, n = 269), revealed that tumors rich in PDPNpositive CAFs, as defined with PDPN-RNAsign, are also significantly enriched in multiple immune-related pathways, involved in both innate (e. g. Toll-like receptors) and adaptive (e. g. PD-1, interferon gamma) immunity (Figure  4A ). Regarding immune cells, although PDPN-positive CAFs displayed immune-related signatures, PDPN-high tumors (as assessed by IHC) were not significantly associated with monocyte/macrophage RNA signatures or CD163 immunostaining (Figure  4B-D ) or with T cell RNA signature or CD8 immunostaining (supplementary material, Figure  S3A, B ).
We further explored, in five RNAseq databases, representing a total of 934 PDAC patient tumors (TCGA, Moffitt et al  [29] , ICGC, Puleo et al  [28] ) and 29 PDXs  [24] , the differential enrichment of several immune cell type RNA signatures according to the combination of POSTN-positive CAFs and PDPN-positive CAFs, as defined using the POSTN-RNAsign and PDPN-RNAsign (Figure  5G  and supplementary material, Table  S2 ). Global immune score (i. e. composite score of immune cell types) was the highest in tumors rich in PDPN-positive CAFs (PDPN+), this subtype being a prerequisite for the presence of immune cells (absent in PDPNÀ tumors, whatever the status of POSTN). Interestingly, in tumors rich in PDPN-positive CAFs (PDPN+), the coenrichment in POSTN-positive CAFs is required to reach the highest levels for the infiltration with monocytes/macrophages (PDPN+/ POSTN+ versus PDPN+/POSTNÀ).
Discussion Our results add to the growing evidence that CAFs impact PDAC prognosis and tumor phenotypic heterogeneity. Thanks to the refinement of the gene signatures of our previously described subtype A-C CAFs, we were able to interrogate our in-house data set (n = 50, with extensive IHC characterization) and public bulk RNAseq databases comprising 934 PDAC patients and 29 PDX, to confirm that POSTN-positive CAFs and PDPN-positive CAFs were associated with patient OS (in particular, POSTN-positive CAFs) and TME features (ECM abundance, immune cell infiltrates). Further, we showed that PDPN-positive CAF resembled the published iCAF subset molecularly (based on immunogenic RNA signatures and single-cell analysis showing positive correlations between PDPN-RNAsign and iCAF RNA signature), yet that POSTN-positive CAF identified a novel type of fibroblast emerging at early steps of PDAC carcinogenesis (detected in adjacent tissue of human tumors and surrounding preneoplastic lesions in KPC), with low αSMA expression, which does not fit with the 'mature' myCAF/iCAF phenotypes.
Using IHC, we showed that, in addition to being expressed at the invading front of tumors  [10] , POSTN expression was also found in peritumoral pancreatitis area in human tumors and surrounding preneoplastic lesions (ADMs and PanINs) in genetically engineered KPC mouse models. In addition, we previously showed that PSC 'cancer education' induced a transition from POSTN-high to POSTN-low subtypes  [10]. Taken together, these data suggested that POSTN-positive CAFs may be the initial reactive CAFs emerging at early steps of PDAC carcinogenesis from the peri-acinar PSCs. POSTN expression is associated with aggressive tumor features, i. e. with poor prognosis and metastatic dissemination, in a variety of cancers, including pancreatic  [43] , biliary tract  [44] , colorectal  [45] , oesophageal  [46] , head and neck  [47] , lung  [48] , breast  [49] , ovarian  [50] , and genito-urinary  [51]  carcinomas, and melanoma  [52].
Acknowledgements We would like to acknowledge the following collaborators for their contribution to this work:  Minoti Apte ,  Pancreatic Research Group, South Western Sydney Clinical School, University of New South Wales, and Ingham Institute for Applied Medical Research, Australia; Nathalie Colnot, Department of Pathology, Beaujon Hospital (APHP), Clichy/Paris, France; Francesca Romana Delvecchio, Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK ;  Safi Dokmak ,  Department of Hepato-Pancreato-Biliary Surgery, Beaujon Hospital (APHP), Clichy-La-Garenne, France ;  Mert Erkan ,  Department of Surgery, Koc University School of Medicine, Istanbul, Turkey; Pascal Hammel, Department of Medical and GI Oncology, Paul Brousse Hospital (APHP), Villejuif, France ;  Jörg Kleeff ,  Department of Visceral, Vascular and Endocrine Surgery, Martin-Luther-University Halle-Wittenberg, Halle, Germany ;  Chanthirika Ragulan ,  Division of Molecular Pathology, The Institute of Cancer Research, London, UK ;  Anguraj Sadanandam ,  Division of Molecular Pathology, The Institute of Cancer Research, London, UK ;  Alain Sauvanet ,  Department of Hepato-Pancreato-Biliary Surgery, Beaujon Hospital (APHP), Clichy-La-Garenne, France; Abigail Shirley Wilson (nee Coetzee), Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK ;  Jeremy Wilson ,  Pancreatic Research Group, South Western Sydney Clinical School, University of New South Wales and Ingham Institute for Applied Medical Research, Australia.
This work was funded by grants from the  Ligue Nationale Contre le Cancer  (Labellisation to CB), the  Institut National du Cancer (INCa ,  2018-080 ), the  Fondation pour la Recherche Médicale (FRM) , the  French National Society of Gastroenterology (SNFGE) , the  ARCAD Foundation , and the  French Pancreatic Club (CFP). We acknowledge NHS funding to the  NIHR Biomedical Research Centre at The Royal Marsden  and the  Institute of Cancer Research London. We are grateful for core facility support from  Cancer Research UK  ( C16420/A18066 ). We are grateful to the non invasive exploration service ( UMS006/CREFRE, Anexplo platform Toulouse ) for giving access to the Aixplorer, with financial support from  ITMO Cancer AVIESAN. These funders had no involvement in the study design, in the collection, analysis, and interpretation of the data, in the writing of the report, or in the decision to submit the paper for publication.
Objectives: The clinical and cost-saving benefits of transcatheter aortic valve implantation (TAVI) over surgical aortic valve replacement (SAVR) in patients with severe aortic stenosis who are at high or intermediate risk of surgical mortality are supported by a growing evidence base. The PARTNER 3 trial (Placement of AoRTic TraNscathetER Valve Trial) demonstrated clinical benefits with SAPIEN 3 TAVI compared with SAVR in selected patients at low risk of surgical mortality. This study uses PARTNER 3 outcomes in combination with a French national hospital claim database to inform a cost-utility model and examine the cost implications of TAVI over SAVR in a low-risk population. Methods: A 2-stage cost-utility analysis was developed to estimate changes in both direct healthcare costs and health-related quality of life using TAVI with SAPIEN 3 compared with SAVR.
Early adverse events associated with TAVI were captured using the PARTNER 3 data set. These data fed into a Markov model that captured longer-term outcomes of patients, after TAVI or SAVR intervention. Results: TAVI with SAPIEN 3 offers meaningful benefits over SAVR in providing both cost saving (V12 742 per patient) and generating greater quality-adjusted life-years (0. 89 per patient). These results are robust with TAVI with SAPIEN 3 remaining dominant across several scenarios and deterministic and probabilistic sensitivity analyses. Conclusions: This model demonstrated that TAVI with SAPIEN 3 was dominant compared with SAVR in the treatment of patients with severe symptomatic aortic stenosis who are at low risk of surgical mortality. These findings should help policy makers in developing informed approaches to intervention selection for this patient population.
Introduction Since the first intervention was performed in Rouen, France, by Alain Cribier in 2002, transcatheter aortic valve implantation (TAVI) has emerged as the first option therapy in inoperable or patients at high risk of surgical mortality with severe aortic stenosis. Currently, the number of TAVIs exceeds the number of surgical aortic valve replacement (SAVR) in many countries, including France. 1  There exists real-world evidence supporting the beneficial performance of SAVR in patients at low risk of surgical mortality. 2 ] [6]  There is a trend toward using TAVI in patients at lower risk of surgical mortality, and support for this approach was presented in two 5-year data sets after TAVI procedures in patients at high or intermediate surgical risk. 7, 8 Nevertheless, before PARTNER 3, there was a scarcity of evidence in patients at low risk.
The health states and events included in our model were similar to those modeled previously  16  and were aligned with events from the PARTNER 3 trial experienced by a meaningful proportion of patients or with significant differences between treatment arms. A lifetime time horizon (30 years) was chosen to reflect all potential consequences to people with aortic stenosis over their lifetime. The model had 2 stages. Early adverse events (AEs) linked to the TAVI procedure were first captured using the 30-day AE data set from the PARTNER 3 trial in a decision tree (Fig. 1A ). 3  These data were fed into a subsequent Markov model that captures longer-term outcomes of patients, after TAVI or SAVR intervention (Fig. 1B ). 3  The model is constructed with 4 distinctive health states: HRQOL was included in the analysis using quality-adjusted life-years (QALYs) measured using EQ-5D for the different health states in the model, using decrements taken from a published study and age-adjusted population norms.
22 The PARTNER 3 data were not used to inform these "treated AF" to "disabling stroke" estimates because of the very low number of stroke events in the trial. The survival estimates using parametric survival analysis from the 2-year outcomes of the PARTNER 3 trial produced clinically implausible estimates in the model because of a very low rate of death in the study. 3 Nevertheless, they are included as options in the model and are explored in scenario analyses in combination with general mortality data for France to ensure that patients in the model cannot survive for longer than would be expected for the general population. All-cause mortality in the base case was determined from general population normal mortality risk, with relative risks applied from published literature corresponding to each health state (see Appendix Table  2  in Supplemental Materials found at https://doi.
org/10. 1016/j. jval. 2021. 10. 003). Transitions to "treated AF" and "disabling stroke" were assumed to remain constant over the time horizon from year 2 onward, regardless of time in the model or patient age. Hospitalization data used in the model were based on 1-and 2-year data from the PARTNER 3 trial and were assumed to remain constant over the time horizon of the model after 2 years. Similarly, the rate of reinterventions was assumed to remain constant after 22 years,  23  because of a paucity of longer-term data. In the base case, the same reintervention rate was applied to all treatment options. Nevertheless, the impact of increased risk of reintervention with SAPIEN 3 was explored in scenario analyses, and reinterventions were associated with an increased risk of procedure-related mortality.
Cost inputs The cost perspective was based on the French Programme de Médicalisation du Système d'Information (PMSI) national hospital claim database. Using PMSI, both procedure ("hospitalisation en médecine, chirurgie, obstétrique et odontologie [MCO]") and rehabilitation ("soins de suite et de réadaptation [SSR]") costs were estimated from an exhaustive data collection from 2017 to 2018 for TAVI and SAVR (isolated), valued using the 2017 French national frame of reference for hospital costs (Echelle Nationale des Coûts) and actualized to 2020. TAVI and pacemaker device costs were determined from the liste des produits et prestations remboursables (LPPR) French reimbursement prices. Follow-up cost, cost linked to postoperative complications, and acute and chronic rehospitalization costs have been estimated from the PMSI/Echelle Nationale des Coûts or relevant publications and are summarized in Table  1.
Scenarios A series of scenario analyses were conducted to assess the impact of changing various assumptions on the results of the model. TAVI with SAPIEN 3 remained dominant regardless of the time horizon considered because of the cost savings generated in the acute phase once rehabilitation was considered (Table  2 ). In model scenarios, SAPIEN 3 TAVI was also more cost-effective (dominant) than SAVR, despite: 1. Changes to the discount rates used 2. Increasing the risk of reintervention for device replacement with SAPIEN 3 3. Using HRQOL data by treatment from PARTNER 3 4. Artificially increasing the risk of stroke with SAPIEN 3 to produce results aligned with outcomes in PARTNER 3 5. Including AE costs for those occurring within the first 30 days after surgery (considering events occurring after discharge from index hospitalization) 6.
Using survival data from the PARTNER 3 study and extrapolating this over the model time horizon (after adjusting for background mortality) 7. Alternative severity level 1 used to cost TAVI and SAVR procedures Results of each of the scenario analyses conducted are presented in Appendix Table  5  in Supplemental Materials found at https://doi. org/10. 1016/j. jval. 2021. 10. 003.
26 For the SAPIEN 3 TAVI outcomes, this is likely to represent a conservative assumption because it is anticipated that the risk of stroke would be higher in the SAVR treatment arm because of increased incidence of treated AF; therefore, this would likely increase the incremental difference in stroke incidence between treatment arms. Hospitalization data used in the model are based on 1-and 2year data from the PARTNER 3 trial. A simplifying assumption was used whereby this rate remained constant over the time horizon of the model after 2 years; the impact of this assumption is unknown because people in both treatment arms in the model are at risk of hospitalization. The rate of reinterventions is assumed to remain constant after 22 years, because of a paucity of longer-term data. It is expected that at this point this would have very little impact on modeled outcomes because it is expected that only approximately 11% of the patients would still be alive in the model, with a limited need for reintervention.