dataesr/scientific-paragraphs-categorization huggingface dataset:
(i)). To assess specific TLR induced immunity, namely TLR3 and TLR4, we stimulated whole blood with the widely used ligands, polyIC and LPS. PolyIC is a dsRNA mimic that acts via TLR3 to upregulate a type I interferon response  [21]. LPS is a bacterial ligand that can activate both the type I interferon response, as well as other pro-inflammatory pathways leading to upregulation of key mediators including IL6, TNFα and COX2  [22]. Using linear regression, we found the response to both polyIC and LPS to be similar in RhD-negative and -positive individuals whether male or female (Fig. 3 (ii, iii)). Data are presented as a heatmap with 100 randomly selected genes shown. Differences in cell counts can have a major impact on the whole blood transcriptional response  [13]. Therefore, we compared major circulating immune populations between RhD-positive and -negative individuals.
The MI study was approved by the Comité de Protection des Personnes-Ouest 6 (Committee for the protection of persons) on June 13, 2012 and by French Agence nationale de sécurité du médicament (ANSM) on June 22, 2012. The study was sponsored by Institut Pasteur (Pasteur ID-RCB Number: 2012-A00238-35) and was conducted as a single centre interventional study without an investigational product. The original protocol was registered under ClinicalTrials. gov (study# NCT01699893). The samples and data used in this study were formally established as the MI biocollection (NCT03905993), with approvals by the Comité de Protection des Personnes-Sud Méditerranée and the Commission nationale de l'informatique et des libertés on April 11, 2018. The study was designed and conducted in accordance with the Declaration of Helsinki and good clinical practice, with all subjects giving informed consent.
ACKNOWLEDGEMENTS This work was funded by awards to COF through a  Science Foundation Ireland   Investigator Award  ( 12/IA/1667 ) and under the  Science Foundation Ireland Phase 2   COVID-19 Rapid Response Call  ( 20/COV/8487 ). This work benefited from support of the  French government's Invest  in the Future programme. This programme is managed by the  Agence Nationale de la Recherche , reference  ANR-10-LABX-69-01. We thank the Cytometry and  Biomarkers UTechS  for generation of Nanostring gene expression datasets.
Study Design This parallel design randomized clinical trial was conducted in one province (133,000 inhabitants) in Finland. The study received ethical approval from the Coordinating Ethics Committee of Helsinki University Hospital (Finland) in November 2014 and was registered at ClinicalTrials. gov (NCT02305433) in December 2014. The study was conducted in accordance with the Declaration of Helsinki, and all participants signed a written consent form. The study protocol  [17] , the results of the primary outcome  [62] , and of the secondary outcome functioning have been published  [63].
Instrument Description Spectra were obtained with a 500 MHz SB Bruker Avance Neo spectrometer at 11. 75 Tesla (BioSpin ® , Wissenbourg, France). The magnet was equipped with a 5 mm BBO cryoprobe prodigy (Bruker BioSpin ® , Wissebourg, France). Tuning and matching were automated. Automated gradient shimming for Z coils was used and manual optimization was performed if needed. The spectrometer was controlled with TopSpin 4. 0 software (Bruker BioSpin ® , Wissenbourg, France).
Spectrum Acquisition Parameters Samples were thermostated at 293 K without spinning. One-dimensional (1-D) 1 H NMR spectra were obtained at 500. 09 MHz using a 1-D experiment impulsion acquisition sequence with a noesygppr1d pulse sequence with a presaturation delay during the relaxation delay (d1 = 5 s) and mixing time (d8 = 10 ms). The homospoil/gradient pulse p16 was 1 ms and the delay for homospoil/gradient recovery was 200 µs. Spectra were obtained in 12 min and 15 s by accumulating 64 free induction decay (FID) and 4 dummy scans. The acquisition time was 5. 57 s with a spectral width of 6 kHz collected at 64 K data points. The 90 • pulse delay was 12 µs with a power level of -12. 14 dB. In addition, the residual water resonance was presaturated with a 40. 26 dB field strength irradiation.
Spectrum Processing Parameters All spectra were processed with TopSpin 4. 0 software (Bruker BioSpin ® , Wissenbourg, France). Typical processing parameters were the application of a Fourier transform without line broadening or zero filling. Chemical shifts (δ in ppm) were reported relative to the signal of TSP at 0 ppm. Phase correction and baseline correction were performed manually. The relative values obtained for each metabolite were obtained by integrating the signal relative to the TSP signal. Identification of the metabolites was performed using the Human Metabolome Database [66], the Magnetic Resonance Metabolomics Database  [67] , our database performed at pH = 7. 4, and literature on urinary NMR analysis  [68, 69]. According to the Metabolite Identification Task Group from the Metabolomics Standards Initiative, the level of identification was 2  [70].
Multivariate Analysis We performed automatic bucketing with a bucket width of 0. 05 ppm, normalized to the total integral (from 0. 6 to 10 ppm with the exclusion of water signal and urea from 4. 5 to 6. 5 ppm). We used the Amix viewer from Bruker ® to generate matrices. We used Pareto scaling before generating the score and loading plots from PCA and PLS-DA using Simca ® 17 software from Sartorius AG (Goettingen, Germany). We also generated contribution plots to help find the most discriminative variables.
Pathway Analysis We performed a pathway analysis using MetaboAnalyst 5. 0 on 20 July 2021 (https://www. metaboanalyst. ca), an easy-to-use web-based tool suited to comprehensive and integrative metabolomics data analysis  [73]. The metabolite table was autoscaled. We used the KEGG (Kyoto Encyclopedia of Genes and Genomes) library for Homo sapiens and we have launched two forms of pathway analysis, enrichment (global test) and topology (relative-betweenness centrality), to assess the p-values and the pathway impact values, respectively.
Our study has the inherent limitations of any cohort study, with potential selection bias, residual confounds, retrospective analysis of prospectively collected data etc. The calculation of the BED was made, as already stated, by the basic BED approach as previously described in the literature and not by the voxel-to-voxel approach, which is a method rather complicated and not currently available on accurate basis. In this sense, some approximations might exist. Another limitation is the small number of subjects in this cohort. However, they benefitted from a rigorous follow-up, at precise time-points. A third limitation is that posterior fossa meningiomas can cause a variety of different symptoms depending on location. Here, we have included all patients with clinical improvement due to the limited sample size, rather than focusing on one anatomical location.
Thus, interrogation of leukemic hierarchies may provide an opportunity to potentially integrate features of the genetic and stem cell models of AML  28. Single-cell RNA-sequencing (scRNAseq) has emerged as a powerful tool for dissecting cellular hierarchies  29, 30  , however prohibitive costs restrict these studies to a limited number of patients. Without measuring these cellular hierarchies at scale in large clinical datasets, the relationship of hierarchy composition to therapy response remains unknown. Here, we were able to characterize the cellular hierarchies of >1000 AML patients through gene expression deconvolution on bulk AML transcriptomes using single-cell reference profiles of distinct AML stem, progenitor, and mature types. This approach to characterizing AML heterogeneity enabled integration of both the genomic and functional models of AML resulting in a novel framework for understanding disease biology and predicting drug response.
Classifiers trained on immunophenotype were consistently outperformed by those trained on leukemia cell composition (median AUCs = 0. 71 vs 0. 86, p < 2e-16) and were even outperformed by models trained from Quiescent LSPC abundance as a single variable (median AUCs = 0. 71 vs 0. 77, p < 2e-16) (Fig. 1F , Table  S3 ). Finally, we found Quiescent LSPC to be associated with high LSC frequency in an independent dataset of bulk AML samples assessed through limiting dilution analysis  38  (Fig. 1G ). Collectively, these findings establish a new link between transcriptomic LSPC states and functionally defined LSC at the apex of the hierarchy, suggesting that LSC activity can be inferred through deconvolution of patient hierarchies.
Primitive vs GMP axis captures patient prognosis In line with the observed associations with favorable and adverse cytogenetics, patients with different hierarchy subtypes also differed in their survival outcomes, with Primitive hierarchies associated with the worst outcomes and GMP-dominant hierarchies associated with the best outcomes (Fig. 2F -G, Table  S6 ). We validated these findings using microarray data from a cohort of genetically diverse adult AML patients (GSE6891  42, 43  ; Fig. S5A-B ) as well as a cohort of pediatric AML patients (TARGET-AML  44  ; Fig. S5C-D ). To identify the leukemic cell types linked to patient survival, we performed regularized cox regression on the TCGA and BEAT-AML datasets using leukemia cell type abundances. Quiescent LSPC and Cycling LSPC abundance were predictive of adverse outcomes (coefficients: 0.
34 and 0. 72, respectively) and GMP-like abundance was predictive of favorable outcomes (coefficient: -1. 54). Strikingly, the composite survival score that included all three of these populations was highly anti-correlated with PC1 (r = -0. 99, Fig. S5E ). Indeed, PC1 was highly associated with survival outcomes in TCGA, BEAT-AML, and GSE6891 cohorts (Fig. S5F ), and retained significance in a multivariate meta-analysis incorporating all three datasets (p = 0. 007, Table  S7 ). Moreover, both pediatric and adult AML patients who did not achieve complete remission following induction chemotherapy had higher Quiescent LSPC abundance and lower GMP-like abundance compared to patients who achieved remission (Fig. S5G ). In contrast, the Primitive vs Mature axis was not associated with patient survival (p = 0.
Primitive vs Mature axis predicts sensitivity to investigational drugs Having shown that survival outcomes following chemotherapy are tied to hierarchy composition (i. e. Primitive vs GMP axis), we asked whether AML samples with different cellular compositions were vulnerable to newer investigational therapies. Ex vivo drug sensitivity data from two public datasets  40, 53  were integrated with cell type abundance to generate drug sensitivity profiles for each leukemic cell type (Fig. 4A ). This revealed large differences in drug responses between primitive blasts and mature blasts, with separation of drug responses occurring primarily along the Primitive vs Mature axis, in which PC2 significantly correlated (FDR < 0. 05) with response to 37 drugs in the BEAT-AML screen and 64 drugs in a separate screen from Lee et al (Total = 101; Fig.
A hierarchy composition framework to guide preclinical studies of AML drugs We next sought to determine how our leukemic hierarchy framework can be deployed in the context of drug development. Drug candidates are often identified based on reduction in viability of bulk leukemia cells or cell lines, yet this measure lacks critical information pertaining to the subpopulations of cells that are targeted or that persist after treatment. To understand how drug treatment affects cellular composition, we deconvoluted RNA-seq data from 43 datasets in GEO and ArrayExpress with human AML cells sequenced before and after drug treatment (Fig. 5A-B ). The changes in cellular composition following drug treatment were visualized in low-dimensional UMAP space and treatments were clustered based on the changes they induced (Fig.
JAGuaR 2. 1 was used to incorporate reads spanning multiple exons into the alignment by introducing large alignment gaps. All transcripts of a given gene were collapsed into a single gene model such that exonic bases were the union of exonic bases that belonged to all known transcripts of the gene. Read counts and subsequently RPKM counts were obtained by counting the fraction of each read that overlapped with an exonic region for that gene. To align RNA-seq reads from functionally annotated LSC fractions, sequence data was aligned against GRCh38 and transcript sequences downloaded from Ensembl build 90 using STAR 2. 5. 2a. Default parameters were used except for the following: "-chimSegmentMin 12 -chimJunctionOverhangMin 12 -alignSJDBoverhangMin 10 -alignMatesGapMax 100000 -alignIntronMax 100000 -chimSegmentReadGapMax parameter 3 -alignSJstitchMismatchNmax 5 -1 5 5.
Re-Clustering of Leukemia Stem and Progenitor Cells (LSPCs) Single-cell RNA-sequencing data from 12 AML patients at diagnosis was obtained from van Galen et al 29 (GSE116256). scRNA-seq count data was normalized using the R package 'scran'  63  , log-transformed with an offset value of 1, and scaled. Leukemic cells labeled as "HSC-like" and "Prog-like" (hereafter LSPCs) from the original study were subject to re-analysis using the Self-Assembling Manifolds (SAM) algorithm  31. SAM was applied individually to the four patient samples with the highest number of LSPCs (based on a cutoff of >100 HSC-like cells) to assign weights to each gene based on how well they can demarcate emerging transcriptomic states. Feature weights for each gene were averaged across the four samples and subsequently applied to LSPCs from all 12 patients.
Evaluation of LSPC Clustering To evaluate the new cluster assignments, cell type classifiers were built and evaluated for the new and prior classifications using the R package "SingleCellNet"  67. For each classification, scran normalized gene expression values were used as input and 800 cells from each leukemic cell type were used as a training set. For each cell type, paired products of the top 25 genes for each cell type were calculated and the 50 top gene pairs for each cell type were used to train the Random Forest based model with nTrees = 1000. Models trained on the new and prior cell type classification were subsequently evaluated on a held-out dataset of at least 250 remaining cells for each cell type.
Regulon Analysis and Signature Enrichment To infer transcription factor (TF) regulon activity in scRNA-seq data, regulon analysis was performed using SCENIC  68. The Docker image of pySCENIC was run as per the guidelines from Van de Sande et al 69 : log-transformed counts from leukemic AML cells were used as the input and candidate transcription factors were identified using a list of human transcription factors from Lambert et al  70  , with default parameters. To prune putative TF-target links within each regulon using annotations of TF motifs, CisTarget was applied using databases of known human TF motifs annotated at 500bp, 5kb, and 10kb of transcriptional start sites. Drop-out masking was also applied during this step. Enrichment of refined TF regulons was inferred using AUCell, and enrichment scores were scaled for visualization.
Clinical AML Datasets Publicly available clinical RNA-seq datasets used for deconvolution analysis are outlined in Table  S4. All gene expression data was subject to TPM normalization prior to deconvolution with CIBERSORTx. Clinical and mutational data was extracted from the GDC Data Portal for TCGA (https://portal. gdc. cancer. gov/projects/TCGA-LAML) and from supplemental materials in Tyner et al  40  for BEAT-AML. For the BEAT-AML cohort, we focused exclusively on pretreatment samples collected at AML diagnosis (n = 281). For the Leucegene cohort, clinical and mutational annotations were extracted from supplemental materials of 13 papers  38, [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] [86] [87]  and linked based on sample ID.
Mapping and Clustering AML Hierarchy Composition To map AML patients based on the composition of their leukemic hierarchies, only deconvolution results pertaining to leukemic AML populations were used. In these cases, estimated abundances from leukemic populations were normalized to 1, such that the value associated with each cell type represents the proportion of total leukemic blasts that it constitutes. Patients from TCGA, BEAT-AML, and Leucegene were used. PCA was performed on the normalized leukemic cell type compositions of these patients. Neighbours were calculated using euclidean distance with a local neighborhood size of 30. To determine the optimal number of clusters, the package "NbClust" was used to calculate 30 clustering metrics for values of k from 2 to 10, and k = 4 was selected by majority rule.
Leiden clustering was subsequently performed at a resolution of 0. 4 to obtain four hierarchy clusters. Cluster assignments, hierarchy compositions, and genomic annotations for TCGA, BEAT-AML, and Leucegene are included in Table  S5. To project hierarchies onto the reference map from the three AML cohorts (TCGA, BEAT-AML, Leucegene), normalized leukemic cell type abundances from the query dataset was combined with the reference dataset, and batch correction was applied using ComBat  88. Following this, the ingest function from scanpy was used to project the batch corrected query dataset onto the principal components of the batch corrected reference dataset and assign cluster labels.
Hierarchy Classification of Microarray Cohorts To enumerate patient hierarchy composition from microarray data, we first compared the confidence of CIBERSORTx deconvolution between RNA-seq and microarray data from the TCGA cohort, using the correlation between the original transcriptomes and the synthetic transcriptomes reconstructed from pooling each cell type signature at their estimated frequencies. Deconvolution from RNA-seq achieved the highest correlation (median = 0. 95) while deconvolution from Robust Multichip Average (RMA)  89  normalized microarray data performed poorly (median correlation = 0. 58). However, we found that a single-sample microarray normalization approach (SCAN)  90  yielded better deconvolution results (median correlation = 0. 80). We next projected the deconvolution results from the microarray samples in TCGA onto the hierarchy map from the reference cohorts using ComBat and the ingest function from scanpy as previously described and compared the assigned cluster labels with the true labels from the corresponding TCGA samples for which RNA-seq data was available.
This yielded weighted multi-class accuracies of 0. 67 for RMA-normalized deconvolution results and 0. 75 for SCAN-normalized deconvolution results. We thus restricted deconvolution of microarray data exclusively to SCAN-normalized data for new cohorts. Given the comparatively low accuracy (0. 75) of our standard approach for hierarchy projection using reference cohorts in the case of microarray data, we employed additional approaches to classify microarray samples from the query cohort as Primitive, Intermediate, Mature, or GMP. A second approach involved projection onto deconvoluted microarray data from TCGA, for which cluster assignments were available from bulk RNA-seq for the same samples. Deconvolution results from the query cohort were batch corrected with the TCGA reference data using ComBat and cluster labels were projected using the ingest function from scanpy.
As a third approach, cluster classifiers were trained from the microarray expression data from the TCGA cohort, using the top 10 marker genes for each cluster based on a Wilcoxon test. Microarray expression data from the query cohort was batch corrected with the TCGA reference data using ComBat, and L1-penalty Logistic Regression (L1-LR), L2-penalty Logistic Regression (L2-LR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) classifiers were subsequently trained from these marker genes with hyperparameter tuning performed through a grid search with 10-fold cross-validation for each model. For cluster assignment based on gene expression data in the query cohort, the majority vote of all five models was used. To obtain the final cluster assignments, the predictions from all three approaches (projection using deconvoluted RNA-seq reference cohorts, projection using deconvoluted microarray data from TCGA, and gene expression-based classification using microarray data from TCGA) were combined.
Within the GSE6891 cohort  42, 43  , most samples (372/537, 69%) were assigned to a cluster unanimously by all three approaches, while the remaining samples (165/537, 31%) had conflicting assignments between approaches. These ambiguous samples were primarily positioned at the boundary between the Intermediate cluster and other clusters (Primitive, Mature, GMP). These ambiguous cases were reclassified through a KNN approach trained on the 372 high confidence samples using the ingest function from scanpy to obtain the final classifications.
Survival Analysis Overall survival (OS) was defined as the time from diagnosis until death or last follow-up. Differences in OS between hierarchy classes in each cohort were evaluated using Mantel-Cox Log-Rank tests using the R package "survival", and survival curves for each cluster were visualized using Kaplan-Meier plots using the R package "survminer". Univariate and pairwise hazard ratios for each cluster were derived from Cox proportional hazards regression. For combined hazard ratios, individual patient data were pooled and stratified Cox regression was performed with the patient cohort (TCGA, BEAT-AML, GSE6891) set as the stratifier. For multivariate survival meta-analysis, we included covariates that were available across all three cohorts (Cytogenetic Risk, Age, WBC, NPM1 status, and FLT3-ITD status) and performed multivariate stratified cox regression, with patient cohort as the stratifier.
Rather than AUC, model performance was estimated through the mean likelihood ratio test statistic across the five outer cross-validation splits. This was repeated for 1000 iterations. To identify the leukemic cell types associated with survival, we performed stratified cox regression on the TCGA and BEAT-AML cohorts using an L1 (LASSO) penalty with partial likelihood deviance loss. Because this process was not repeated over multiple iterations, this score was trained on the full dataset and leave-one-out cross-validation was employed to determine the optimal lambda value. Coefficients for each leukemic population were subsequently used to determine feature importance. For GSEA analysis of genes ranked by their association with overall survival, only genes that were detected in TCGA and BEAT-AML were evaluated.
Calculation of Prognostic AML Scores We calculated LSC17 and other prognostic AML scores using log(TPM+1) normalized expression values from TCGA, BEAT-AML, and Leucegene, and normalized microarray expression values from Lee et al  53. In cases where specific genes were missing from a dataset, we calculated the score with those genes removed. To ensure high concordance of these partial scores, we calculated the correlation between each partial score and the full score in other datasets to ensure high concordance: we observed a median correlation of r = 0. 99 between partial and full scores, with the lowest correlation being r = 0. 95. Patients were classified into high and low groups for each score based on a median split within each cohort.
Clonal Evolution Analysis Clonal analysis of paired diagnosis and relapse samples from four independent cohorts was performed using annotated single nucleotide variant calls derived from targeted sequencing  48  , whole exome sequencing  50  , or whole genome sequencing  18, 49  data. Genetic clones were identified using PhyloWGS  93  , selecting the phylogenetic tree with the highest log likelihood (LLH) value. In cases of tied LLH values, the simplest tree with the most representative branching patterns among the top candidates was manually selected. Graphical representations of evolution of genetic clones were depicted using the R package "Fishplot"  94  while representations of changes in cell type composition were depicted using the R package "ggAlluvial".