dataesr/scientific-paragraphs-categorization huggingface dataset:
This has been widely described in retrospective studies assessing the prognosis of localized breast cancer patients according to gBRCA status. In the present study, the setting is metastatic disease, prognosis is assessed according to baseline status, which avoids most of these biases. Furthermore, time varying analyses include both the onset of new mutant statuses. Several sensitivity analyses as well as Landmark analyses have been deployed to limit these potential biases. Finally, our database was built by the collection of data treated within.
French Epidemiological Strategy and Medical Economics (ESME) database of MBC patients, established in 2014 by Unicancer, represents a nation-wide, multicenter real world retrospective cohort treated in the French Comprehensive Cancer Centers (FCCCs). The ESME research program collects patients' demographic, clinical and genetic characteristics, tumor' features, detailed treatment patterns and outcome data from all MBC patients treated in the 18 FCCCs since 2008.
18Comprehensive Cancer Centers only in France. Perhaps our results may not be fully transposable to all medical institutions worldwide. A major strength of the present study is the size of the cohort, the ESME database being among the largest database of MBC. This comprehensive retrospective study is the largest analysis of the prognosis of BRCA in MBC in a multicentric real-life cohort, with a coverage of almost all of MBC patients treated in the national network of 18 French Comprehensive Cancer Centers which treats more than one-third of all French breast cancer patients. Other strengths are the long follow up, its update, the high-quality levels of the database, the robust quality control methodology and the statistical power higher than other previous reported studies. Besides, the academic independent nature of our work has to be noted.
Finally, our analyses are performed in a country where access to cancer treatments is provided to all patients and fully covered by the national insurance system. In conclusion, this large series establishes benchmark outcome data among gBRCA carriers with metastatic breast cancer. gBRCAm patients from the ESME-metastatic breast cancer database overall had identical outcomes as patients with WT statuses or nontested ones. However, outcomes differed among patients with different tumor subtypes. Patients with TNBC had a better PFS1 and OS if gBRCA carriers, as compared with WT patients. On the opposite, gBRCA carriers with luminal cancers had lower PFS1 and a trend toward lower OS. Our data further highlight the need to have information on gBRCA status early in the metastatic setting. Clinical oncology is progressing toward a targeted and more efficient treatment for gBRCAm patients, raising hopes for important improvements of the outcomes noted in the present study.
The French Epidemiological Strategy and Medical Economics (ESME) database of MBC patients, established in 2014 by Unicancer, represents a nation-wide, multicenter real world retrospective cohort treated in the French Comprehensive Cancer Centers (FCCCs). The ESME research program collects patients' demographic, clinical and genetic characteristics, tumor' features, detailed treatment patterns and outcome data from all MBC patients treated in the 18 FCCCs since 2008.
We used extensions of linear mixed models (package lme4,  Bates et al. 2014 ) in R ver. 3. 5. 2 (www. rproject. org) to conduct neighborhood analyses of focal seed set and survival. We assumed binomial errors for the analyses of plant survival and Poisson errors for analyses of seed set. The mixed models described interactions among plants by including neighborhood indices as explanatory variables at two spatial scales in separate models. Neighborhood indices are spatial density effects of surrounding neighborhood plants that affect focal seed set and survival. For each plant, we used the Euclidian distance between the focal plant and the neighboring plants to compute response effects of within MA line or between MA line neighbors in a given radius around focal plants  (Nottebrock et al. 2017b ).
This is a valid assumption since each MA line differs from the other by approximately 20 + 20 = 40 mutations, while any two replicates within a MA line or the founder lines will differ by one generation, < 2 mutations. On average founders should differ from the individual MA lines by roughly half the difference of the number of mutations differentiating MA lines. Thus, we simulated line effects from parameters derived from MA lines as random effects with the R package 'merTools' and the function 'plotREsim' in R 2018. Not surprisingly, because the founder performance was always the average of all MA lines, we found no difference between founder and MA line effects on a focal plant (see results). Consequently, in many of our analyses we refer to the founder line and MA lines as just lines. We used the trait values of neighbors to calculate trait-based neighborhood indices including plant biomass as a trait (Goldberg & Fleetwood, 1987; Goldberg & Landa, 1991;  Cahill et al.
However, the genetic trade-off between intra-and interspeci c competitive abilities due to mutation remains unknown because our dataset only includes performance of A. thaliana individuals without quantifying the surrounding intraspeci c environment  (Chesson 2000 , Adler et al. 2007 ). Competitive Hierarchies of MA lines and Founder lines Our result demonstrates scale dependent competition and facilitation  (Nottebrock et al. 2017b ) providing further evidence that in a relative short evolutionary time scale spontaneous mutations may change the competitive hierarchies between founders and speci c MA lines due to advantageous or deleterious mutations  (Rutter et al. 2010 ). In contrast to  Masclaux et al. 2010 , we found differential responses to similar genotypes vs different genotypes in A.
149. Dip the tip of the needle into a droplet of superglue and attach to the furthest end of the muscle construct, having a couple of millimetres of overlap between the needle and construct so that both are in tandem. CRITICAL STEP: this step must be performed rapidly as superglue sets quickly. 150. Leave to set for 30 seconds and then attach the needle to the syringe. 151. Slowly mount the sample holder disk onto the microscope stage and into the upper opening. 152. Using the Zen software specimen locate sample tool, nd and centre the specimen along the 3 axes. 153. In the acquisition tab, setup the light path by de ning the necessary tracks. 154. Activate multi-view checkbox in multidimensional input eld, use the now-available z-stack tool window to select the upper and lower limit of imaging using the "set rst" and "set last" buttons, the interval (recommended 1 μm) and the number of rotations (recommended 3).
Analysis: 220. Export time series from microscope and open in Fiji software (also known as ImageJ). 221. Select areas (each representing a myotube and covering a maximum of 10% of its area to avoid interfering signals from other cells) using the "Lasso" tool. 222. From the "Analyse" menu, choose "Set measurements" and select "Mean grey value". 223. From the "Image" menu, select "Stacks", "Plot z axis pro le" and save as an xls le.
ACKNOWLEDGEMENTS We thank all co-authors of the original article describing this technology, the  EU  FP7 projects  Biodesign  ( 262948 ) and  Plurimes  ( 602423 ),  G. Cossu ,  H. Redl  and  T. Eschenaghen  for their initial support and contribution. We are grateful to  CureCMD, Cellular Dynamics International, Inc. ,  M. Oshimura , and  T. VandenDriessche  for cell lines used in our original papers, to  J. Ng  for AAV9 vector particles, to DSHB for providing MF20, F5D and titin antibodies, and to all lab members for helpful discussions. This work was supported by the  European Research Council  ( 759108 -HISTOID ),  MDUK  ( 19GRO-PS48-0188 ;  17GRO-PS48-0093-1 ), the  BBSRC London Interdisciplinary Biosciences Consortium  (LIDo;  BB/M009513/1  to FST, PSZ, LP and NK),  AFM-Telethon  ( 21687  and  23782 ),  Duchenne Parent Project NL  ( 19.
We associated single cell RNA sequencing (scRNAseq) to spatial mapping by imaging using a multiple transgenic fluorescent reporter mouse to discriminate mammary macrophage subsets in a spontaneous and a transplanted breast tumor model  (Engelhardt et al. , 2012; Guy et al. , 1992). Our analysis reveals that ductal and stromal TAMs follow distinct differentiation pathways. Further functional diversity of these TAM subsets was detected according to different anatomical structures of the stroma and tumor malignancy. DM associated with advanced tumor stage were potent phagocytes but did not show efficient capacity to activate CD8 T cells compared to SM. This spatial heterogeneity was lost in an orthotopic tumor model. Finally, similar subsets were identified in human breast tumor using a published scRNAseq dataset  (Pal et al.
3B, FigS. 2C, D). In order to support our findings based on microscopy, we first applied to our dataset the steady state DM and SM signatures as well as lactation-and weaning-associated macrophage signatures defined by Dawson and colleagues  (Dawson et al. , 2020) (Fig. 3C , Table  S1 ). DM signature mostly covered the CD11b -compartment including clusters 0, 4, 5, 8, 9, and 11, but also clusters 2 and 3 that belonged to a mix of CD11b + and CD11b -compartments. The lactation macrophage signature which reflects an active proliferation state of DM covered the same clusters than the steady state DM. However the weaning macrophage signature reflecting DM participating in the involution of the mammary tissue through phagocytosis of milk-producing cells  (Dawson et al. , 2020) , covered mostly cluster 2 and a fraction of cluster 3 (Fig.
3C ). SM signature mainly covered cluster 1 (Fig. 3C ), but also a fraction of clusters 8 and 2 suggesting that these two clusters could be composed of both ductal and stromal macrophages or macrophages ongoing a transitory state between the stroma and the mammary duct. Dawson and colleagues also identified DC1 and DC2 signatures from sorted DC of the mammary tissue (Table  S1 ). These signatures applied to the PyMT dataset associated cluster 7 to the DC2 signature, and cluster 10 and 13 to the DC1 signature (FigS. 2E, F). Finally, cluster 12 corresponded to a contamination of granulocytes. Beyond the relationship established through the DM and SM signature overlaps, we next sought to match subsets identified by scRNAseq with the different TAM locations identified by imaging. We conclude that cluster 6 fits with ECFP + Ly6C high classical monocytes (Cyan circle) based on the correlation Itgam + /CD11b + , Ly6c2 + /Ly6C + , Cx3cr1 low /EGFP low (Fig.
To test this model in our data, we selected a list of genes commonly related to macrophage polarization  (Biswas and Mantovani, 2010; Cassetta et al. , 2019; Cheng et al. , 2021; Roberts et al. , 2016 ) (Table  S2 ). To refine the analysis and seek further markers of TAM heterogeneity, we selected a set of transcripts encoding cell surface molecules involved in homing, adhesion, and migration, obtained from the HUGO database (Table  S3 ). Hierarchical clustering of the relative expression of their transcripts confirmed that TAM subsets display specific patterns changing with tissue localization and tumor progression (Fig. 4B ). We sought to identify the inherent TF involved according to the transcriptomic profile of each cluster (FigS. 4E). Putative role STAT1, STAT2, IRF7 and IRF9, associated to IFNγresponse  (Kovarik et al.
Human breast TAMs exhibit similar diversity As the MMTV-PyMT model has been described to recapitulate the different stages of development of human breast cancer, we sought to determine whether the TAM heterogeneity identified in this model could be reflected in human tumors. We used scRNAseq data from Visvader's group containing a diversity of cell types sequenced from different breast tumors  (Pal et al. , 2021). We focused our analysis on monocytes and macrophages by selecting them based on CSF1R, CD68 or FCGR1A expression and generated a UMAP representation of the concatenated tumor-associated myeloid cells. Louvain graph-based clustering identified 10 clusters (Fig. 6A ). We applied the combination of markers that were used to identify distinct TAM subsets in PyMT mouse to the human dataset (Fig.
, 2013; Faria et al. , 2020; Picon-Ruiz et al. , 2017). In the orthotopic model CD206 is expressed in a high frequency of TAMs regardless of their localization but in human tumors, Mrc1 + stromal TAMs were present in a proportion similar to the spontaneous model. This shows that orthotopic models tend to artificially bias TAM phenotype. Indeed, TAM diversity was more restricted in the orthotopic model which we argue to be linked to the lack of niche variety and to a model representative of very progressive tumor. In the human dataset, we were able to recover a similar TAM diversity including the HES1 + , FOLR2 + and TREM2 + main subsets with functional hallmarks comparable to the ones present in the mouse PyMT tumor. These subsets were recently identified in human breast tumors to reside in distinct niches  (Nalio Ramos et al.
Flow cytometry acquisition was performed on the flow cytometer FACS LSRFortessa X-20 (BD Biosciences, Franklin Lakes, NJ, USA) with DIVA (BD Biosciences) Flow Cytometry software. Flow cytometry data were analyzed with FlowJo software (Tree Star Inc. , Ashland, OR, USA) or OMIQ (Omiq inc. ) data analysis software for opt-SNE representations (https ://omiq. ai, Santa clara CA).
Single Cell Data Processing Sequencing data was processed using 10X Genomics Cell Ranger V1. 2 pipeline. The Cell Ranger subroutine mkfastq converted raw, Illumina bcl files to fastqs. Reads were then aligned with count, using the STAR aligner  (Dobin et al. , 2013)  against the mm10 mouse genome. After filtering reads with redundant unique molecular identifiers (UMI), count generated a final gene-cellular barcode matrix. Both mkfastq and count were run with default parameters.
Cellular Identification and Clustering For each sample, the gene/barcode matrix was passed to the R software package Seurat (v3. 1. 5) (Stuart et al. , 2019)  for all downstream analyses. We then filtered on cells that expressed a minimum of 200 genes and required that all genes be expressed in at least 3 cells. We also removed cells that contained > 9% reads associated with cell cycle genes  (Kowalczyk et al. , 2015; Macosko et al. , 2015). Count data was then log2 transformed and scaled using each cell's proportion of cell cycle genes as a nuisance factor (implemented in Seurat ScaleData function) to correct for any remaining cell cycle effect in downstream clustering and differential expression analyses. For the spontaneous PyMT model, Bulk, CD11b + and CD11b -enriched myeloid compartment data sets were merged for downstream analysis.
Principal component (PC) analysis was performed on a set of highly variable genes defined by Seurat FindVariableGenes function. Genes associated with the resulting PCs were then used for graph-based cluster identification and subsequent dimensionality reduction using UMAP. Cluster-based marker identification and differential gene expression (DGE) were performed using Seurat FindAllMarkers for all between-cluster comparisons. Few clusters enriched for Epcam-Krt identified as tumor cells and enriched in ribosomal transcripts considered as dying cells were discarded from the analysis. Subclustering was performed by selecting cells from clusters of interest and the whole same processing was applied.
M1/M2 gene correlation The list of commonly M1 or M2-associated genes was obtained from  (Biswas and Mantovani, 2010; Cassetta et al. , 2019; Cheng et al. , 2021; Roberts et al. , 2016)  (Table  S2 ). From this complete gene list (69 genes), 61 were recovered from the dataset and 50 out of the 61 were conserved using the Seurat FindVariableGenes function. Genes with lowest sct. mean (<0. 02) for the dataset were discarded. We used the R package Stats (version 4. 0. 3) to calculate the correlation scores for each gene in individual cells. The cor function was used to compute gene correlation score with "spearman" as method. Resulting heatmap was generated with pheatmap. The same list was used to identify specific pattern in each cluster. The heatmap was generated using Doheatmap.
Transcription factor analysis We applied murine version of DoRothEA (version 1. 2. 1) package in combination with VIPER package (version 1. 24. 0) on single sample matrices (genes in rows and single cells in columns) containing normalized gene expression scores scaled gene-wise. Transcription factors with lowest confidence levels listed in the corresponding gene expression matrix were discarded from the analysis. VIPER provides a normalized enrichment score (NES) for each TF which we consider as a metric for the activity. Resulting heatmap was generated with pheatmap.
Human data processing scRNAseq dataset were obtained from  (Pal et al. , 2021 )(GSE161529). All 32 files corresponding to female tumor samples of all types were pre-processed with the same pipeline than mouse scRNAseq. After cleaning the data, myeloid cells identified by the expression of PTPRC, CD68, CSF1R, FCGR1A of each individual tumor were subsampled. Tumors containing less than 300 myeloid cells were excluded for downstream analyses. Samples were combined using anchor-based integration method from Seurat. The integrated data were used for downstream analysis, for which the same pipeline than mouse analysis was applied.
For live imaging, mice were anaesthetized and maintained during the imaging period with 2% isoflurane in medical air. An incision of the skin was performed at the level of the breast tumor and an imaging window was positioned to stabilize the tissue from breathing and drifting artefacts. Local temperature was monitored and maintained at 32°C using an incubation chamber. To define the tumor vasculature, Evans Blue was injected i. v. before the imaging session. Real time movies were performed by imaging every 30s by 5 consecutive 3μm z spacing image stack (total 12μm thickness). Static 3D images were performed on 100µm thick cryosections by 1μm z spacing image stack (up to 20μm thickness). For all images the objective was a water immersion, plan apochromat ×20 (numerical aperture = 1). 3D reconstructions and mask rendering were done using Imaris software (Bitplane).
Acknowledgments This work was supported by  Association pour la Recherche contre le Cancer ARC  " Projet labellisé ", by  Cancéropole Ile-de-France  " programme Emergence 2020". INCa  ( 2018-1-PLBIO-06-1 ) supported research activity and M. P. funding. M. L. was funded by  Sorbonne University program  " Idex SUPER " and  Fondation pour la Recherche Médicale FRM. The authors thank  UMS28  for the animal core facility and the two-photon microscopy, and  Doriane Foret  and  Thomas Van Hecke  for animal breeding. Research in the D. A. H laboratory was supported by the  Mater Foundation. AJ. C, TC and MF. K were supported by  UCSF Bakar ImmunoX  and  NIH  ( R01 U01 ).
Lipid A isolation and mass spectrometry Isolation of lipid A for analysis was performed as previously described  [56]  with slight modifications. To analyze lipid A after meropenem treatment, overnight cultures grown in BHI broth were diluted 1:10 in pre-warmed media with or without meropenem statically for 3 h. To assess Rilu-dependent modification of lipid A, 12. 5 mL of E. cloacae was grown to OD 600 1. 0. Rilu-2 was used at a final concentration of 200 μM. Bacteria were harvested and lipid A extraction was carried out by mild-acid hydrolysis as previously described  [57]. For mass spectrometry (MS), data were collected on a MALDI-TOF (Axima Confidence, Shimadzu) mass spectrometer in the negative mode, as previously done  [22]. For quantification of lipid A, cultures were grown with 2. 5 μCi/mL of 32 P ortho-phosphoric acid ( 32 P) (Perkin Elmer) and lipid A was extracted.
For single myofibres, an additional step was added to minimize formalin background by using 1% glycine in PBS. Coverslips were mounted with 60% glycerol in Tris HCl 0. 2 M pH 9. 3. Photographs were acquired using an Axio imager A2 system equipped with an Axiocam HRc, with Axiovision Release 4. 8. 2 software (Zeiss). At least n = 3 biological samples per genotype were analysed. For neuromuscular junction (NMJ) evaluation, freshly isolated GA muscles were fixed in 10% neutral buffered formalin solution (Sigma-Aldrich), overnight at 4°C. Small bundles of myofibres were isolated under the dissecting microscope as previously described. 21 The pre-synaptic terminal was labelled with mouse anti-neurofilament (1:200; SMI-312; BioLegend) and rabbit antisynaptophysin (1:200; Thermo Fisher Scientific).
Morphometric analyses Photographs were acquired using an Axio imager A2 system equipped with an Axiocam HRc, with Axiovision Release 4. 8. 2 software (Zeiss), at standard 1300 × 1030 pixel resolution. The myofibre cross-sectional area (CSA) was quantified on stained sections from the mid-belly of GA or diaphragm muscles by using ImageJ software. n = 3-6 mice per condition were used. The entire muscle section was quantified for each replicate. Ten fields per sample were analysed for the in vitro analyses. Muscle cell terminal differentiation was quantified by differentiation index, that is, the number of myonuclei in MHC+ cells, over total nuclei, and fusion index, that is, the number of myonuclei within myotubes, over total nuclei. A myotube was defined as having ≥2 nuclei in the same cytoplasm.
The gels were transferred to nitrocellulose membranes and protein bands were visualized under UV light. The whole signal of the entire lane has been used for loading and transfer control. Membranes were blocked with 5% BSA in TBST and blotted with different primary antibodies. After washing in TBST, membranes were incubated with HRP-conjugated secondary antibodies (BIO-RAD), and signals were detected by using ECL chemistry (Cyanagen). Images were acquired on a ChemiDoc MP imaging system (BIO-RAD) with Image Lab 5. 2. 1 software. The following primary antibodies were used: HDAC4 (clone H-92, Santa Cruz), Gapdh (clone 6C5, Santa Cruz), Dysferlin (clone JAI-1-49-3, Abcam), Trim72 [Polyclonal, Synthetic peptide corresponding to Human MG53 aa 288-337 (internal sequence), Abcam], Rip3 [Polyclonal, Synthetic peptide corresponding to Mouse RIP3 (C terminal), Abcam], Myh3 (clone F1.
Substrates were added (5 μL of 1 mM DMSO stock solution), and the plates were returned to the 37°C incubator for 3 h. Then, 50 μL per well of developer/stop solution were added (PBS with 1. 5% Triton X-100, 3 μM TSA, and 0. 75 mg/mL trypsin), allowing an additional 20 min incubation at 37°C. To detect fluorescent signal, the Glowmax (Promega) instrument was used, with excitation and emission filters of 360 and 460 nm, respectively. Background signals from buffer blanks were subtracted, and data were normalized as needed using appropriate controls. n = 6 mice per condition were used.
Statistics and program Statistical significance was determined by using two-tailed Student's t-test when two conditions needed to be compared, or with one-way analysis of variance (ANOVA), followed by Tukey's HSD test as a post hoc test, when more than two conditions needed to be compared. All values were expressed as mean ± standard error of the mean (SEM). VassarStats, a statistical computation website (http://vassarstats. net/), was used for the statistical analyses. All the graphs were created by using GraphPad Prism 6 program. Figure  8  was created with BioRender (https://biorender. com/). Differences between mdx and mdx;KO mice were confirmed in independent experiments, that is, the analyses were carried out using independent litters; in addition, n represents an independent biological sample (a distinct mouse) and is not a mere technical replicate.