dataesr/scientific-paragraphs-categorization huggingface dataset:
Histological and immunohistochemical analysis The left upper lobe was collected for determination of inflammation by histological analysis. The lobes were infused with 10% neutral buffered formalin and embedded in paraffin. Lung sections (5 μm thick) were stained with H&E or Carstair  (51, 52)  to visualize fibrillary collagen. Images were captured using the automated Nanozoomer digital whole-slide imaging system (Hamamatsu Photonics). Regions of inflammatory cell infiltration were delineated using NDP view2 software (Hamamatsu Photonics), and the percentage of inflammation was calculated in relation to the total lung area of each section. All scoring was conducted in a blinded manner, using 3-5 mice per group. Formalin-fixed, paraffin-embedded lung sections were subjected to immunohistochemical analysis using the following antibodies: CD68 (mouse α-CD68, clone PG-M1, Genetex, catalog GTX73723, RRID: AB_375099); PARP9 (rabbit α-PARP9, Thermo Fisher Scientific, catalog PA5-48254, RRID:AB_2633712); mouse α-human macrophages (αhuman macrophages/monocytes/granulocytes, Bio-Rad, catalog MCA874G, RRID:AB_321963); cGAS (rabbit α-C6orf150 [cGAS], Thermo Fisher Scientific, catalog PA5-76367, RRID: AB_2720094); S100A9: (goat α-mouse S100A9, R&D Systems, catalog AF2065, RRID: AB_2184263); SOD (mouse α-SOD, clone 2A1, Abcam, catalog ab16956, RRID: AB_302569); mouse F4/80 (rat α-mouse F4/80, clone Cl:A3-1, Bio-Rad, catalog MCA-497R, RRID: AB_323279); and polyclonal rabbit α-histone H2A.
Acknowledgments We thank  Lan Lu  and  Misty Veschak  for the animal work and technical assistance. We acknowledge  Radha Gopal  of the  Department of Pediatrics at the University of Pittsburgh  for providing H1N1 stock for experimental use. This work was supported by the  Washington University in St. Louis ;  NIH  grants  HL105427 ,  AI111914-02  (to SAK),  AI123780  (to SAK, DK, TJS, and MM), and  AI134236-02  (to SAK and DK); and the  Department of Molecular Microbiology, Washington University in St. Louis. Address correspondence  to:  Shabaana A. Khader , The University of Chicago, Department of Microbiology, 920 East 58th Street, CLSC 1117, Chicago, Illinois 60637,  USA. Phone: 773. 702. 2601;Email: khader@uchicago. edu. SAK's present address is: The  University of Chicago, Department of Microbiology, Chicago, Illinois, USA.
Due to their intracellular localization, targeting iICPs remains challenging. Nonetheless, pharmacological approaches based on systemic administration of small molecules have already been reported  12  (ie, clinical trials NCT04521413, NCT04649385, NCT05128487, NCT05159700, NCT05233436, NCT05370755, NCT05107674, NCT05107739, NCT05662397, NCT05315167). The recent development of PROTACs (Proteolysis Targeting Chimeras) has expanded the toolbox of chemicals available,  10  especially when considering targeting 'undruggable' proteins, that is, without enzymatic activities. Indeed, in vitro gene editing coupled with adoptive cell therapy allows T-cell-specific deletion of iICPs in clinically relevant settings (ie, clinical trials NCT04426669, NCT05566223). Hopefully, future improvements in gene therapies, particularly in delivery, will enable in vivo gene modification.
Acknowledgements The authors wish to thank  Thomas Miller  ( CRCM, Marseille ) for critical reading and to improve language quality of the manuscript. Contributors VL, P-LB, CCDS, GG and JAN planned and wrote the review. Funding VL was supported by a doctoral fellowship from  Aix-Marseille Université , then by the  Fondation ARC. P-LB and CCDS were supported by doctoral fellowships from  La Ligue Nationale contre le Cancer. GG was supported by a post-doctoral fellowship by the  Janssen Horizon Fonds de dotation , then the fondation  Bristol-Myers Squibb. This work is supported by institutional grants from the  Institut National de la Santé et de la Recherche Médicale (Inserm) ,  Centre National de la Recherche Scientifique (CNRS)  and  Aix Marseille Université  to CRCM; by project grants from the  Fondation ARC pour la recherche sur le Cancer  ( PJA20191209406 ), the  Janssen Horizon Fonds de dotation and the fondation Bristol-Myers Squibb.
PCSK9 AND STROKE An obvious detrimental role for PCSK9 on brain health is by increasing LDL-C levels which accelerates intracranial atherosclerosis (in particular of large vessels) leading to ischemic strokes. This was evidenced in carriers of PCSK9 gain-of-function mutations  (69, 70). In line with these observations, PCSK9 inhibition with evolocumab reduced ischemic strokes and major cardiovascular events in the total population of the FOURIER trial as well as in the key subgroup of patients with prior ischemic strokes  (71). Whereas PCSK9 loss-of-function mutations carriers with lifelong reductions in LDL-C and coronary heart disease incidence, were not initially found at significantly reduced risk of stroke  (3, 72) , a much larger study of the UK Biobank database described the first genetic evidence of a protective effect of the R46L loss-of-function mutation on PCSK9 on ischemic stroke  (73).
Funding and additional information A. K. J. is the recipient of a PhD scholarship from  La Fondation pour la Recherche M édicale, Paris, France , and from the  European Union  and  R égion R éunion ,  Saint-Denis , R éunion; GL is the recipient of a project grant funded by the  Agence Nationale de la Recherche, Paris, France  [ KRINGLE2 ANR-20-CE14-0009 ]; SB is the recipient of a program grant funded by  Inserm, Paris, France [ATIP-AVENIR ].
Bioinformatics For transcriptomics analyses, raw sequencing files were processed using the nextflow nf-core/RNAseq pipeline (v3. 8. 1). Reads from different sequencing lanes were concatenated using bash cat utility and trimmed with Cutadapt (v3. 4) and Trimgalore (v. 0. 6. 7). The quality of the resulting reads was assessed with FASTQC-0. 11. 9 using default parameters. Then, paired-end reads were aligned to the mouse genome (GRCm39-v106) and quantified with Salmon v1. 5. 2. Alignment files were indexed using SamTools (v. 1. 15. 1). R (v4. 0. 2) and DESeq2 (v1. 28. 1)  [34]  were used for differential expression analysis using default parameters. Conditions included genotype, cell type and sample preparation day to control for variation in the data due to these parameters. Changes in gene expression with a p value adjusted using Benjamini and Hochberg correction (padj) <0.
ACKNOWLEDGEMENTS We thank all personnel from the  Toulouse animal facility CREFRE  and from the flow cytometry, imaging, transcriptomics and bioinformatics technical platforms of INFINITy. M. D. D-M. is supported by  ATIP-Avenir-Plan Cancer  ( C18003BS ),  ANR  ( ANR-20-CE15-0007 ), foundation  ARSEP R19201BB , foundation  ARC ,  La Ligue Contre Le Cancer and INSPIRE (Region Occitanie ,  Inserm  and  CHU Toulouse). M. T. is supported with a  BBSRC  core funding grant and a  Wellcome Investigator award  ( 200823/Z/16/Z ). D. C. -S. is supported by  Boehringer Ingelheim Fonds.
IFN-III induces TLR7 signalling and pathways involved in IFN-I production To gain further insights into the effects of IFN-III on pDCs, we performed RNA-sequencing on pDCs isolated from PBMCs stimulated with or without IFN-III. After conducting a kinetics experiment, we selected 12 h as the optimal time for the highest ISG expression in response to type III IFN (Supplementary Fig. 1A ). Principal component analysis (PCA) on our dataset revealed that IFN-III stimulation was accountable for 45% of the variability between our samples (Fig. 3A ). A gene expression analysis showed that IFN-III had an important effect on pDCs, as 1,489 differentially expressed genes (DEGs) were upregulated, and 888 were downregulated when comparing IFN-III-stimulated pDCs with non-stimulated pDCs (Fig. 3B ). The upregulation of CD274 (PD-L1) was observed among DEGs, supporting our previous observation at the protein level (Fig.
cDC1 and pDCs are in close contact or in the same discrete area of the TME We previously demonstrated that cDC1 were the only cells able to produce IFN-III in the breast TME  21. We hypothesized that pDCs localized close enough to cDC1 that secrete IFN-III may acquire an IFN-producing phenotype. To determine pDC and cDC1 localization in the TME, we performed multiplex-immunofluorescence (mIF) staining on 10 breast tumors. We observed numerous cDC1 (XCR1+) co-localizing with pDCs (BDCA2+) (Fig. 7A ). By performing image segmentation analysis with Inform software and cell-cell distance calculations with bioinformatics tools, we quantified close contacts between pDCs and cDC1 (determined with a distance from the center of two nuclei < 15µm). 9% of cDC1 were in close contact with at least one pDC (Fig.
RNA Sequencing and analysis RNA sequencing was performed on untreated or IFN-III treated (100 ng/mL, 12 h at 37°C) isolated pDCs. pDCs were lysed with a 1% 2-β-Mercatoethanol (Sigma-Aldrich, M6250-100ML) + TCL Buffer (Qiagen, 1031576) solution. Total RNAs were then extracted from cultured cells using the single cell RNA purification kit (Norgen Biotek, ref: 51800) according to the manufacturer's instruction. The sequencing and quality controls were then carried out by the Cancer Genomics Platform of the Leon Berard Center (CLB). Data normalization was then done with the DEseq2 R packages. PCA and volcano plots were created with DEseq2 R packages. DEGs between pDCs treated or not with IFN-III were defined with a |log (Foldchange)| > 0. 58 and adjusted p-value < 0. 05 and z-scores computed with R studio.
A Zscore heatmap of the complete list of DEGs was then uploaded on the Morpheus website created by the Broad Institute. GSEA analysis was performed from the GSEA-MSigB software using the «h. all. v7. 5. 1. symbols. gmt» gene set. A Venn diagram was created with ggvenn R package on the enriched genes from "Interferon gamma response" and "Interferon alpha response". Normalized counts from specific genes of interest were retrieved from the normalized count table created by DEseq2 and plotted using ggplot2 R package.
cDNA was generated with SmartSeqV4 oligodT primer (Takara), following the manufacturer's protocol. The quantity and quality of cDNA were assessed using Qubit dsRNA high sensitivity (Thermofisher) and DNA HS D5000 (Agilent), respectively. Multiplexed pairend libraries 50nt in length were obtained using Nextera XT kit (Illumina). Sequencing was performed in the same batch in Illumina NovaSeq 6000 using an average depth of 32 million reads. Library, sequencing and quality control of the sequencing were performed by the genomic platform and the Gilles Thomas bioinformatics platform of the CLB. Reads were mapped to the human genome reference (hg19/GRCh38) using STAR software. Finally, the Salmon tool was used to quantify gene expression as read counts.
Multiplexed immunofluorescence and spatial analysis We collected formalin-fixed paraffin-embedded (FFPE) tissues (1 slide of 4 μm) from surgical specimens from 10 breast cancer patients at diagnosis. Markers for tumor-infiltrating DC identification were chosen based on previous data from the literature. In particular, we multiplexed, BDCA2 (Goat Polyclonal, R&D Systems, AF1376) for pDC, XCR1 (Rabbit D2F8T, Cell Signaling, 44665S) for cDC1, CD8 (Mouse C8/144B, DAKO, M7103) for CD8+ T cells and cytokeratin for breast cancer cells. We then used the tyramide-signal amplification (TSA)based Opal Automation Research Detection Kit (Leica, DS9777) on the Bond RX automatic stainer (Leica Biosystems). Secondary antibodies used to amplify the signal included the Anti-Goat IgG HORSERADISH Peroxidase (HRP) Conjugated (Donkey Polyclonal, Life Technologies, A16005), the Anti-Rabbit IgG, horseradish Peroxidase (HRP) Conjugated (Goat Polyclonal, Life Technologies, A1604) and the Anti-Rat IgG, HORSERADISH Peroxidase (HRP) Conjugated (Goat Polyclonal, Thermo Scientific, 31470).
Different antigen retrieval reagent conditions, antibody concentrations and time of incubation were tested for each marker of interest on tonsil FFPE samples and validated on breast tumors. After image acquisition, tissue and cell segmentation was performed with Inform Software. Cell coordinates were then retrieved with the Inform data frame, and 2D projections were made with R studio. We then used SPIAT (R) in order to calculate cell distance between pDC and cDC1.
Acknowledgments We wish to thank the staff of the core facilities at the  Cancer Research Center of Lyon (CRCL)  for technical assistance and the  BRC  of the CLB for providing human samples, as well as N. Gadot  and the research anatomopathology platform of the CLB and the  Ex vivo Platform  ( S. Leon ). We thank  T. Andrieu ,  P. Battiston-Montagne , and  A. Jambon  for assistance in flow cytometry in the CRCL Cytometry platform. We thank  Brigitte Manship  for the reading and editing of the manuscript. We thank the genomic platform for RNAseq generation ( S. Boyault ). We thank the  LICL  and particularly  J. Berthet  and  B. Dubois  for their help in the multiimmunofluorescence staining.
Human subjects After providing written informed consent, patients over 18 years of age with a diagnosis of breast cancer were prospectively enrolled onto an institutional review board (Comité d'Orientation Stratégique [COS], Marseille, France)-approved protocol from the Paoli-Calmettes Institute (BC-BIO-IPC protocol, NCT01521676, 2009 approval). The study protocol complied with the Good Clinical Practice guidelines and the Declaration of Helsinki. Fresh EDTA-anticoagulated blood samples (N = 7) and primary tumor samples (N = 8) were collected prior to any treatment at diagnosis and at debulking surgery, respectively. Fresh normal breast tissues (prophylactic mastectomies, N = 4) were collected at surgery. Fresh EDTA-anticoagulated blood samples (N = 4) from healthy donors were recruited by the Etablissement Français du Sang (EFS Alpes-Méditerranée).
Data collection, filtering, and normalization In this study we reanalyzed 8 publicly available scRNA-seq datasets using BBrowser V3. 5. 26 (BioTuring). Datasets were queried through the BioTuring database or the Gene Expression Omnibus (GEO) database (managed by the National Center for Biotechnology Information  [NCBI] ) and imported into BBrowser. Datasets filtering for exclusion of dropouts, doublets, and apoptotic cells, was performed according to the quality control criteria described in the source publication. For public datasets without a detailed filtering process in the source publication, we excluded genes having at least 1 UMI count in less than three cells, cells with less than 200 genes having at least 1 UMI count, and cells with a mitochondria genes ratio > 5. Raw expression matrices were then subjected to log normalization.
Batch-effect removal, dimensionality reduction, cell clustering, and annotation To avoid donor-driven and study-driven cell clustering, we used the MNN batch-effect removal algorithm when computing dimensionality reduction by UMAP. The top 2000 highly variable genes and the first 30 components of the principal component analysis were used to compute UMAP using the uwot package available in BBrowser. Perplexity for UMAP was set automatically based on the count of cells in a dataset, or the count of cells in clusters selected for subclustering. Unsupervised graph-based clustering (Louvain clustering) were performed with a number of nearest neighbors ≤ 8 using the igraph package available in BBrowser. Clusters were annotated based on the expression of markers identified by differential gene expression analysis using the Venice test.
Single-cell TCR sequencing Already curated scTCR-seq data from Zhang et al. (21)  were queried through the BioTuring database, and analyzed using the BBrowserX software (Talk2Data, BioTuring). The BBrowserX software only considered clonotypes that were full-length (valid V and J annotations) and productive (transcripts translate to a protein with a CRD3 region). Clinical characteristics of patients with TNBC and log-normalized gene expression values from two public bulk RNA-seq datasets were used in this study. Datasets were queried through cBioPortal (The Cancer Genome Atlas |TCGA]-BRCA, Firehose legacy) and the Gene Expression Omnibus (GEO, NCBI) database (GSE177043). Clinical characteristics of patients with TNBC and log-normalized/batch-corrected gene expression values from one public microarray dataset were used in this study.
Patients were censored at a cut-off date of 108 months. Survival times were estimated by the Kaplan-Meier method and compared using the log-rank test. Statistical analyses were generated using BBrowser V3. 5. 26 (BioTuring) and Prism V8. 0. 2 (GraphPad). The data analyzed in this study were obtained from Gene Expression Omnibus (GEO) at GSE164898, GSE169246, GSE179994, GSE140228, GSE164690, GSE108989, GSE177043, and GSE194040, from European Genome-phenome Archive (EGA) at EGAS00001004809, from The Tabula Sapiens Consortium at https://tabula-sapiens-portal. ds. czbiohub. org/, from The Cancer Genome Atlas Program (TCGA) at https://www. cbioportal. org/study/summary?id=brca_tcga, and from BioTuring database at https://bioturing. com/. All other raw data are available upon request from the corresponding author.
S4A ) to TNFRSF9 + IL2RA high eTregs were decreased in primary TNBC compared with HR + tumors (p < 0. 05). Given the clinical issues for patients with TNBC, we decided to conduct this study on the latter indication. We evaluated whether the ratio of CD8A to TNFRSF9 expression is associated with TNBC outcome using bulk RNA-sequencing data from two independent cohorts (TCGA and GSE177043). Primary TNBC samples were stratified into two groups based on the CD8A/TNFRSF9 ratio (Fig. S4B ). A low CD8A/TNFRSF9 ratio was associated with decreased survival in TNBC (p < 0. 05) (Fig. 1H ), independently of TNM stage (data not shown). CD8A/FOXP3 ratio as well as levels of TNFRSF9, FOXP3, and CD8A were not associated with survival in TNBC (Fig. S4C ). Altogether, these data suggest that reversing the ratio of cytotoxic effector αβCD8 T cells to 4-1BB + CD25 high eTregs by depletion of intratumoral eTregs, rather than blocking their recruitment from the periphery or induction from conventional αβCD4 T cells, would represent an attractive therapeutic strategy in patients with TNBC.
We obtained similar results in pretreatment biopsies (data not shown). Only exhausted αβCD8 T cells had decreased TCR diversity after treatment, reflecting engagement of tumor antigen-specific αβCD8 T cells (Fig. 4H ). Intratumoral 4-1BB + CD25 high eTregs hamper responses to PD-1 blockade in TNBC We identified markers restricted to eTregs or proinflammatory/cytotoxic lymphoid cells in scRNA-seq data from primary TNBC (Fig. S9A ). In the TCGA dataset, low ratios of expression of proinflammatory cytotoxic markers to eTreg markers were associated with decreased survival in patients with TNBC (Fig. S9B ). We evaluated whether these ratios are associated with responses to αPD-1 in patients with TNBC using microarray data from the I-SPY-2 trial. (37) Primary tumors were sampled before patients received a combination of NAC (paclitaxel) and αPD-1 (pembrolizumab).
Downloaded from http://aacrjournals. org/cancerres/article-pdf/doi/10. 1158/0008-5472. CAN-23-0613/3344220/can-23-0613. pdf by guest on 03 October 2023 Recent single-cell studies have provided meaningful insight into the biological behavior of scarce, yet crucial, subsets of antigen-experienced αβCD8 T cells in TNBC treated with αPD-1. (2,21) However, Tregs are either disregarded or analyzed as a single population. Therefore, Treg subsets remained to be analyzed in depth in TNBC to (i) clarify the counterintuitive prognostic value of Tregs, and (ii) explore a potential role of Tregs in TNBC resistance to PD-1 blockade. We hypothesized that single-cell technologies could provide such information, and re-analyzed public scRNA-seq datasets. We found that TNBC is associated with an accumulation of CD25 high Tregs harboring canonical markers of eTregs (FOXP3 high BATF + 4-1BB + ) conserved across solid tumors.
Acknowledgments The authors thank all the donors participating in this study. Louise Ball from Angloscribe , an independent scientific language editing service, provided drafts and editorial assistance during preparation of this manuscript. We thank the  Integrative Multi Parametric Cytometry Tools moderators (IMPACT , http://impact-cyto. inserm. fr/). We thank the  CRCM cytometry core facility , as well as the  IPC/CRCM/UMR1068 Tumor Bank , which operates under authorization #AC-2007-33 granted by the  French Ministry of Research (Ministère de la Recherche et de l'Enseignement Supérieur). This work was funded by the  INCa  (grant " 2012-064/2019-038 " for D. O. ), the  SIRIC Marseille  (grant " INCa-DGOS-INSERM 6038 " for D. O. ), the  Fondation de France  (grant " 00076207 " for A. S. C.
MATERIALS AND METHODS Trials comparing acitretin, isotretinoin and imiquimod with placebo or an active comparator were selected; which were included in the electronic databases MEDLINE and CENTRAL during 1982-2016 with at least 1 actor reported to be blinded (patient, care provider or outcome assessor). Extracted data included items related to blinding, the frequency of SCSEs and the primary outcomes of trials that we classified as: (i) objective outcomes (such as complete regression of lesions on imaging), (ii) subjective outcomes (pain or quality of life for example), and (iii) unclear  (7).
Cytotoxicity assay Cytotoxicity was determined by assessing the luciferase activity following a co-culture of luciferase expressing target cells with CAR-T as described  15. Cell viability was calculated relative to baseline killing by non-transduced (NT) T-cells according to the following formula: % of viable cells = RLU[E/T CAR]/RLU[E/T NT]*100% (RLU: relative light units). The detailed method is provided in the supplementary information. Long term proliferation test CAR-T were co-cultured on Mitomycin C treated NIH-3T3 feeder cells at an initial ratio of 1/4 (2. 5x10 4 :1x10  5  ) CAR-T to feeder. Every 2-3 days, the CAR-T were transferred to fresh feeder cells. Proliferation was determined by counting the viable cells using the Countess II (Invitrogen). Dead cells were excluded by trypan blue (Invitrogen) staining, Cell numbers were quanti ed using ImageJ software.
Intravenous MM. 1Sluc xenograft model Animal protocols were compliant with regulations of the ethical committee Paris-Nord/N°121 (agreement # APAFIS#19755-2019031316181939 v2). Six to twelve-week-old female NOD/SCID/gc -/-mice were inoculated i. v. with 3x10 6 MM. 1Sluc cells at day 0. Fourteen days later 1x10 6 CAR-T were infused i. v. and bioluminescence was measured with the IVIS Imaging System (PerkinElmer). Data was further analysed using Aura imaging software (Spectral Instruments Imaging). Subcutaneous MM. 1Sluc xenograft model 0. 5x10 6 luciferase expressing MM. 1S wt and MM. 1S CS1ko cells were subcutaneously injected in the left and right anks of each NSG mouse, respectively. CAR-T (1x10 6 cells per mouse) were administered i. v 4 days post tumour injection. Tumour progression was followed by luminescent imaging using the IVIS Imaging System.
However, as CD138 is expressed at the surface of epithelial and endothelial cells 26 , toxicity may remain an issue for this system. The CS1 antigen, which is mainly expressed on tumour PC, dendritic cells, NK cells and activated T-cells, is also a potential target for T cell based therapies in MM, and preclinical studies have shown that CS1 CAR-T may represent an alternative to BCMA CAR-T in MM  27, 28. While ongoing phase 1/2 clinical trials (NCT03958656, NCT04499339) are evaluating the toxicity and the e cacy of second generation CS1 CAR-T therapy, our results indicate that CS1 may also serve as a costimulation antigen for different CAR therapies. The association of rst generation CD38 CAR with anti-CS1 CCR in the DCAR system preserves the anti-MM activity initiated by CD38 in vitro and in vivo (Fig.