dataesr/scientific-paragraphs-categorization huggingface dataset:
57 x more frequent, Chi-square statistic = 11. 2346, p = 0. 0008) (Supplementary Table  S3 ). We evaluated the biological significance of the bCSC metagene, using the GSEA analysis (Fig. 2d, e , and Supplementary Figures  S3-S6 ) in NACT and TCGA-BRCA datasets. This metagene was correlated with stem cell, EMT and aggressiveness signatures (Fig. 2d  and Supplementary Figures  S3  and S5 ), and inversely correlated with the immune response signature (Fig. 2e  and Supplementary Figures  S4  and S6 ). We designated the SNAI2, TWIST1, BNC2, PRRX1 and TBX5 TFs and their regulons as "mesenchymal transcription module" and the SCML4, ZNF831, SP140 and IKZF3 genes and their regulons as "immune response transcription module". The analysis of the PAM50 breast cancer molecular subtypes in the TCGA-BRCA showed that the pattern of expression of the two groups of TFs and the related GSEA annotations were independent of the molecular subtype (Fig.
3  and Supplementary Figures  S6-S10 ). The expression of the TFs in the immune transcription network correlated positively with that of the ESTIMATE immune score and of the immune response proteins in the TCGA-BRCA dataset (Fig. 4 ). The ESTIMATE immune score showed that increased expression of the immune  module TFs was related to a greater infiltration of immune cells into tumour tissues (r 2 = 0. 73, p < 0. 01), independently of the molecular subtype (Fig. 4a ). Using RPPA data from TCGA-BRCA we found that the expression of the Tyrosine-protein kinases SYK and LCK as well as of the Caspase 7 protein (cleaved at D198) was correlated with the expression of the immune metagene, independently of the molecular subtype (Fig. 4b ; r = 0. 41, r = 0. 51 and r = 0. 51, respectively; p < 0. 001; Supplementary Table  S4 ).
Association of the bCSC Transcription Factor Modules expression with the clinical outcome of breast cancer patients. We evaluated clinical associations with the activation of the two TF networks in a large compendium of breast cancer transcriptomes using the Kmplot tool  21. We noted that expression of the mesenchymal TF module was not correlated to any patient outcome difference in the dataset, although the expression TWIST1 and BNC2 had an impact in the HER2-enriched and Basal subtypes (Supplementary Figures  S11-S14 ). Contrastingly, expression of each TF from the immune response transcription module in the Basal tumours correlate with improved recurrence free survival (RFS) in a 10 year end point (Fig. 5 ; logrank p ≤ 0. 01). It is also interesting to note that the high expression ZNF831 has the lower hazard ratio (HR) in all molecular subtypes (Supplementary Figures  S15-S17 ).
05). On the other hand, genes of the EMT/ CSC signature TBX5, SNAI2, TWIST1, BNC2 and SCML4 did not show any significant expression difference between the two groups. However, the expression of the regulons of the mesenchymal module are enriched in the human tumours giving rise to tumour in mice and inversely, the regulons of the immune module are enriched in the No Takegroup (GSEA analysis based on rank, Supplementary Figure  S18  and Supplementary Table  S9  and Supplementary Figure  S19  and Supplementary Table  S10 ). We then annotated the whole transcriptome differences between successfully Take and no take tumours, to delineate their prominent characteristics. Consistent with our previous result using other datasets, human tumours giving rise to tumour in mice present a mesenchymal profile (Supplementary Figure  S20  and Supplementary Table  S11 ) and No Take tumours depict an immune profile (Supplementary Figure  S21  and Supplementary Table  S12 ).
These characteristics formed a strong rationale to identify CSCs and design therapeutic strategies that specifically target them. In this study, based on transcriptome analyses from sorted ALDH1-positive cell subpopulations, we first identified a set of TFs differentially expressed between bCSC and bulk breast cancer. The regulons of these TFs were determined by reverse-engineering on a large dataset of breast cancer. Finally, we demonstrated that regulons from 9 TFs contributing significantly to the bCSC transcriptome, were organized in two networks showing inverse expression patterns. This pattern of expression could be confirmed in the NACT and the TCGA-BRCA datasets taken as a whole or stratified in breast cancer molecular subtypes. The first network is controlled by the SNAI2, TWIST1, BNC2, PRRX1 and TBX5 TFs.
We, therefore, named this network "immune response transcription module". IKZF3 is the third member of the Ikaros family of transcription factors, which controls cell fate decision in haematopoiesis, via chromatin remodeling  27, 28. SP140 is part of the promyelocytic leukaemia protein nuclear body (PML-NB)  29, 30. PML-NB is a tumour suppressor, linked to regulation of apoptosis and senescence  29, 31. Recently SP140 and IKZF3 were recognized as master regulators of immune response in ovarian cancer  32. SCML4, Sex Comb On Midleg-Like Protein 4, is related to the Polycomb group complexes which are known to act in chromatin remodelling and play important roles in stem cell plasticity, cell fate decision and cancer  33, 34. Information about ZNF831 is scarce in the literature  35. The expression of the TFs from this immune response module is correlated with an increase in the number of Tumour-infiltrating lymphocytes in the TCGA-BRCA dataset and the expression of activated Caspase-7, LCK and SYK proteins indicating an active immune response.
The association with prolonged RFS strongly suggest an active immune editing mechanism modulating the tumour microenvironment  2, 41. Aggressive tumours subvert the immune response, inducing naïve T cells to differentiate into Treg cells, rather than T helper 1 cells. This results in immune tolerance, which orchestrates the local adaptive immune response to the tumour  2, 41. We wondered what are the clinical implications of the expression of the immune module. Reduced expression of the mesenchymal module associated to increased expression of the immune module is associated with improved response to chemotherapy in NACT patients. This is in agreement with the data from Teschendorff et al. 23  who reported an antagonism between Th1 response pathways and TGF-β pathway classically associated with EMT and cancer stem cells  2, 7, 23.
They demonstrated a better prognosis in patients with low activation of the TGF-β pathway and high activation Th1 response, in particular in Basal and HER2-enriched. In a larger dataset, we demonstrated that the expression of the immune module was strongly associated with longer RFS in patients with Basal breast cancer. The good prognosis emphasizes the possible therapeutic or prognostic value of these genes. PDXs are being used as preclinical models because they efficiently reflect the genetic and phenotypic structure of the tumours they originated from  42, 43. The relationship of the tumour with its microenvironment, including the presence and active state of the hematopoietic cells, is one of the factors involved in the engraftment frequency and growth rate of implanted tumours  43. Du Manoir and co-workers identified an IL8 signature in engrafted Basal-Like samples  42  , showing the importance of the immune system for engraftment.
The flow cytometry assay was performed in the FACSAria II (BD Biosciences, San Jose, CA) and the analysis of the data was performed using the FlowJo software (TreeStar, USA). The total RNA was extracted using the Mirvana Kit (Ambion, USA) as specified by the manufacturer. We estimated the RNA quantity and purity by UV spectrometry by A220/A260 and A260/A230 ratios. RNA integrity was evaluated using the RNA 6000 Nano Kit, RNA 6000 Pico Kit and 2100 Bioanalyzer (Agilent Technologies, USA). Of the initial 40 patient samples, for logistical problems we were not able to realise the cell sorting in 19 of them. From the 21 sorted samples, we had to exclude 18 samples: 2 because the diagnostic of the patients were not IDC, 4 because of RNA degradation, 11 because of the low quantity of RNA and 1 because of low quality of chip hybridization.
Datasets Discovery Datasets. bCSC/Bulk dataset. We extracted the RNA from the patients' samples, both from bCSC and from the bulk of the tumour, and analysed utilizing the GeneChip ® Human Gene 2. 0 ST Array (Affymetrix, USA) in the International Research Center (CIPE), A. C. of the Camargo Cancer Center, São Paulo, SP, Brazil. This analysis was performed with the transcriptome information from 3 samples (ER+/HER2+, HER2+ and TN) paired as bCSC against the bulk, and the two groups were analysed one against the other, as described below. The clinical data from patients is depicted in Supplementary Table  S6. The dataset was deposited in GEO repository under the accession number GSE83811. bCSC/CC dataset. The "bCSC/CC" dataset was taken from the public available GEO repository (GSE52327)  56.
It is composed of paired samples of 8 patients. The samples were divided into bCSC (ALDH1+/LIN-/ESA+) and cancer cells (CC, ALDH1-/LIN-/ESA+) and the two groups were analysed one against the other, as described below. The RNA extract was analysed with the Human Genome U133 Plus 2. 0 Array chip (Affymetrix, USA). The Immunohistochemical status of the samples is described in Supplementary Table  S7. Neoadjuvant chemotherapy (NACT) dataset. The "NACT dataset" was taken from public available GEO repository (GSE32646). The dataset consists of transcriptome information acquired with Human Genome U133 Plus 2. 0 Array chip (Affymetrix, USA) of 115 tissue samples from patients with breast cancer acquired by core biopsy prior to chemotherapy  19. The patients were treated with neoadjuvant chemotherapy and divided by response two groups, the group with pathological complete response (pCR) and the group with non-pathological complete response (nCR)  19.
The Immunohistochemical status of the samples is described in Supplementary Table  S8. Validation Datasets. TCGA -BRCA Dataset. From "The Cancer Genome Atlas" (TCGA), we used public available RNAseq data from Illumina platform. The normalised RNAseqV2 data from 1198 patients was downloaded and processed using the TCGA-Assembler package in R environment. Clinical, pathological and proteomic data were downloaded with the TCGA-Assembler package and we selected 621 samples with histological confirmed invasive ductal carcinoma from females. We classified the samples in the PAM50 subset using the "intrinsic. cluster" function in the "genefu" R package. The proteomic Data were downloaded already normalised, in the level 3. The RPPA technology was used. The ESTIMATE is a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples and its immune score were used as describe previously (check Supplementary material and methods).
Survival Dataset. The survival analysis was made using the web tool "Kaplan-Meyer Plotter"  21  , which uses 4142 breast samples from the GEO repository, the technology used was the HGU133 Plus 2. 0 array from Affymetrix. To make the survival curve we used the median of the expression of the SCML4, IKFZ3, SP140, ZNF831 genes. Xenografts. We used the data from breast cancer samples generated in the Theillet group at the Institut de Recherche en Cancérologie de Montpellier (IRCM) for the building of a collection of PDXs  42. We compared the initial transcriptome profile of human tumour samples before the implantation in the mouse, that have generated PDXs (Take samples) against those that did not (Not Take samples). The xenografts were generated by the implantation of a single fragment of fresh human tumour (approximately 8 mm3) into the inter-scapular fat pads of 3 to 4-week-old female Swiss-nude mice (NU/NU, Crl:NU-Foxn1nu Charles Rivers, France)  42.
The project was approved by the IRCM internal ethics committee and by the University of Montpellier animal ethics committee under registration number 1144. Molecular Profile Analysis of the Datasets. We normalised the samples values by robust multi-array average (RMA) using the R package "affy" for GeneChip ® Human Gene U133 Plus 2 and "oligo" package for GeneChip ® Human Gene 2. 0 ST. The annotation of the genes was made using the information accessible in the Affymetrix website (http://www. affymetrix. com/support/technical/annotationfilesmain. affx). We summarised the data by max strategy. Using the "limma" function on the POMELOS2 tool. We applied a paired t-test in the "bCSC/Bulk" and "bCSC/CC" datasets, then selected the transcription factor genes as described by Vasquerizas and colleagues in 2009  15, 16  -with p ≤ 0.
05, a fold change ≥2, considering the size of datasets, 6 and 16, respectively, we accept an FDR ≥ 0. 2, and with the same pattern, up or down-regulated, in both datasets. The regulons were inferred using the ARACNE algorithm using genes selected from the intersection between DE genes in each dataset  15, 20. We used the Adaptive Partitioning Algorithm with using p ≤ 0. 01 correct by the number of markers as the threshold and not accepting DPI tolerance. Each regulon was analysed by Fisher's exact test method of master regulator analysis (MRA-FET)  15. Expression networks were constructed based on the relationships of the TFs inferred by ARACNE and confirmed by MRA-FET. The value of the metagene, based on the expression of nine TFs evaluated as master regulators, were calculated in the NATC dataset.
We used as a coefficient for each gene its fold change estimated in bCSC/CC dataset using the genefu package in R. We ordered the samples by that score and depicted them as a heatmap using the gplots package in R Hierarchical clustering was performed using the complete linkage method. We divided the ordered dataset into two halves and calculated the difference in pathological complete response between them using Chi-square test. Finally, we used the score of the metagene as a continuous phenotype (using Pearson metrics) in GSEA analyses. Same methods were used to analyse the "TCGA-BRCA" dataset. The technical references are listed in the Supplementary document. We downloaded the complete list of "cytokines and growth factors" from GSEA gene families documentation  57, 58.
bCSC specific Transcription Factors and Master Regulator. The 11 transcription factors differentially expressed and with the same pattern, up or down regulated, in both "bCSC/Bulk" and "bCSC/CC" datasets and selected as master regulators in the "bCSC/CC" dataset by MRA-FET. Mode = Positively (+) or negatively (-) correlated with the bCSC phenotype, FET p-Values = P value as possible Master regulator in MRA-FET analysis, bCSC/CC = Fold Change in the bCSC/CC dataset of the determined gene, bCSC/Bulk = Fold Change in the bCSC/Bulk dataset of the determined gene.
Acknowledgements We would like to thank  FAPESP  who funded this project under the number  11/19758-5. We would like to thank the  University of São Paulo (Brazil)  and the  Institut de Recherche en Cancérologie de Montpellier (France)  for Institutional support. The group at IRCM was supported by  INCa  grants " MOPRECLI" and "Role of cancer stem cells during metastatic progression of breast cancer ". The PDX collection was established with the support of the  Comprehensive Cancer Center of Montpellier (SIRIC Montpellier-Cancer). We would also like to thank the  A. C. Camargo Cancer Center, Brazil. We would like to thank  Laetitia Linares  (IRCM) from fruitful discussion and  Gary Hardiman  ( MUSC ) for the support.
Effect of VPA treatment on tumor growth and CXCL12 re-expression in a xenografted human colon tumor The impact of HDAC inhibition on tumor growth was evaluated in vivo in xenografted human colon tumors treated (n = 5) with 500 mg/kg VPA for 30 days. The control group (n = 5) only received vehicle. The VPA treatment resulted in a 50% decrease in tumor volume (Figure  9A ) with extensive necrosis and few residual tumor cells (data not shown). As expected, CXCL12 expression was almost absent in the tumor cells of the untreated mice while we observed that CXCL12 expression was restored in the residual tumor cells after VPA treatment (Figure  9B-9C ).
Transcriptome analysis CXCL12 mRNA expression was evaluated in a gene expression data set of 519 MSS and MSI colon carcinomas  [16] , 47 adenomas (32 from GSE8671, 15 from GSE4183) and 59 non-tumor tissue samples (19 from GSE33582, 32 from GSE8671 and 8 from GSE4183). Thirty additional carcinomas from the GSE4183 data set were also integrated. The three data sets were normalized together by RMA normalization (R package affy). An unsupervised Gaussian mixture model clustering approach  [38]  (R package mclust) was used to define the best number of sample groups for CXCL12 expression data. Associations with annotations were assessed by an ANOVA, t-test or Fisher's exact test depending on the type of variable tested (R package stats).
Methylome analysis Whole-genome DNA methylation was analyzed in a subset of 87 tumors (Table  2 ) using the Illumina Infinium HumanMethylation450 Beadchips. Hybridizations were performed at Integragen (www. integragen. com) according to the manufacturer's specifications (Illumina). The chips were scanned using the Illumina HiScan SQ scanner, and the raw image data were imported into the GenomeStudio (v2011. 1) methylation module (v1. 9. 2. ). Only CXCL12 CpG sites within CpG islands were considered, as reported in Illumina annotations; sites with probes that contained a SNP were removed. Unsupervised hierarchical clustering, Ward linkage and Euclidian distance were performed to identify samples with different methylation profiles for these sites. For each site, β-values were assigned to the methylated/unmethylated status using an unsupervised mixture modeled fits approach (R package mclust).
1, 1 mM EDTA, 1% deoxycholate, 1% NP40, 0. 25 M LiCl), and TE. Samples were further processed using iPure Kit (Diagenode) according to the manufacturer's instructions. DNA recovered after ChIP was used for PCR detection of CXCL12 promoter and a nonrelated region in the CXCL12 exon 2 as a control (see the primer sequences in Supplementary Table  4 ). ChIP-qPCR data were analysed relative to total histone H3 1% input adjusted to 100%, fold enrichment was calculated according to the 2 -∆∆Ct method and PCR efficiency was calculated after serial dilutions of standard sample (slope: -3,25; Efficiency : 103%).
Acknowledgments Author contributions: E. A. , P. K. , J. G. -M. , K. R. , G. G. , P. E. , F. A. , D. B. , M. v. B. -B. , J. W. , and M. S. met at an advisory board to discuss thrombotic microangiopathies in critically ill patients. Together, they defined the content of this article, the scope of the systematic review, the elements needed to make this review didactical and thoughtful for the readers, and to select the content of each tables and figures. E. A. and M. S. drafted the review, and all authors added their contribution and suggested changes. A medical writer edited the sentences where needed and improved the language. Before submission, the article circulated several times within authors, and each single suggestion was taken into account. All authors were involved in drafting and critical revision of the manuscript.
t-SNE analysis The t-Distributed Stochastic Neighbor Embedding (t-SNE)  [17]  analysis was performed in R using the packages flowCore  [18]  and Rtsne  [19]. For the t-SNE analysis, gated events representing living NK cells (CD56+ CD16+/-) from liver and peripheral blood from 19 patients were independently merged and randomly subsampled to 100,000 events for comparative purposes. Subsequently, liver NK cell and peripheral NK cell files were combined into one single FCS file containing 200,000 events. Fluorescence channels were then scaled according to the logicle display method  [20]  and t-SNE analysis was run considering the markers DNAM1, CXCR3, NKG2C, CD56, CXCR4, CD16, CD57, CD49a, CD25, CD34 and CXCR6. After analysis, the results were plotted using the R package ggplot2  [21]  into merged plots of liver and PBMCs and into plots containing cells derived from only liver or only PBMCs, respectively.
Statistical methods Percentages of cell sub-populations within the same individual were compared between PBMC and IHLs, and between CD49a+ vs CD49a-cell populations within IHLs using Wilcoxon signed rank tests. Test multiplicity was controlled by a false discovery rate (FDR) procedure accounting for dependency among statistical tests  [22]. FDR-adjusted p-values <0. 05 were considered statistically significant. Statistical analyses were done with SAS, version 9. 3 (SAS Institute, Cary, North Carolina, USA).
Acknowledgments The authors wish to thank all the donors from the LTX cohort at the  University Medical Center Hamburg-Eppendorf (UKE) in Hamburg  and from the AKB cohort at the  Askeplios Hospital Barmbek in Hamburg  for their contribution to our study. The authors are very grateful to the team of nurses and surgeons from the  Department of Hepatobiliary and Transplant Surgery (UKE)  and from the  Department of General & Abdominal Surgery (Asklepios Hospital Barmbek)  for their involvement.
The long length of the reads allowed for their precise mapping on switch regions, previously reported as error prone with shorter reads due to the highly repetitive structure of these sequences  34. Quantification of genes was then performed using feature Counts release subread-1. 4. 6-p1-Linux-x86_64 with "-primary -g gene_name -p -s 1 -M" options based on Ensembl GTF release 77 annotations. Accession number. Data were deposited in Gene Expression Omnibus under the accession number GSE90760.
Acknowledgements This work was supported by grants from  Comité d'Orientation de la Recherche en Cancérologie du Limousin  ( CORC:FJA/NP-2015-109  and  CORC:FJA/NP-2014-126 ),  Association pour la Recherche sur le Cancer  ( PJA 20141201649 ),  Ligue Contre le Cancer  ( Comité de la Corrèze et Haute Vienne ),  Agence Nationale de la Recherche  (ANR: projet EpiSwitch-3′RR 2016). Epigenetic experiments were supported by a specific grant from  INCa-Cancéropôle  GSO (2014). A. Saintamand was supported by a grant from fondation  ARC  ( DOC20150602943 ). N. Ghazzaui was supported by a grant from  Association de Spécialisation et d'Orientation Scientifique (Lebanon)  and the municipality of Khiam (Lebanon). M. Cogné is supported by  Institut Universitaire de France  and  Fondation pour la Recherche Médicale.
We acknowledge the technological expertise of the  Nice Sophia-Antipolis Functional Genomics Platform , supported by  MICROENVIMET ,  FP7-HEALTHF2-2008-201279 , the  ARC , and the  INCa. We thank  Dr. P. Barbry  for helpful discussions and support during this work. We thank  S. Desforges  and  B. Remerand  for help with animal care.
0. 9) (Supplementary Figure  9 ). We then aligned all reads that did not align to the human genome to the P. vivax reference genome (PlasmoDB-29) with the following options: -g 1 (to randomly choose a single location for multiple mapped reads), -I 5000 (to only consider introns shorter than 5,000 bases), and -library-type fr-firststrand (to specify mapping of stranded libraries). We then removed all potential PCR duplicates using the samtools rmdup. While this approach might bias estimates of coverage for a few very highly expressed genes, we considered it essential for removing artefacts introduced by the low amount of starting material. We then used custom Perl scripts to calculate read coverage for all annotated exons using the most recent genome annotations of the human (Hg38) and P. vivax (PlasmoDB-29) genomes.