dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was supported, in part, by the  National Eye Institute ,  NIH , grants  R01 EY028203 ,  R01 EY028954 ,  R01 EY029315 , and  P30 19007  ( Core Grant for Vision Research ); the  Foundation Fighting Blindness USA , grant no. PPA-1218-0751-COLU ; and the unrestricted grant to the  Department of Ophthalmology, Columbia University , from  Research to Prevent Blindness. The authors acknowledge  Joseph Terwilliger  for helpful discussions and  Jimmy Duong  and  Wei-Yann Tsai  of the  Biostatistics, Epidemiology, and Research Design, Irving Institute for Clinical and Translational Research , for helpful statistical consultation on the project. Last, all authors would like to thank the patients and their families for their immeasurable contributions to our ABCA4 studies over the years.
Using the IPD-IMGT/ HLA Database, 2 nucleotide sequence alignment with HLA-DQA1 alleles shows that this new allele has one nucleotide change from DQA1*02:01:01:02 in codon 105 in exon 3, where C ! A, (CCC ! CCA, Figure 1), not resulting in a coding change. This nucleotide change was confirmed by performing the typing twice in two different laboratories. We were confident in the phasing as the sample displayed a mean read length of 335 base pairs over all the loci, the mismatched A base was attributed 299 times to the new HLA-DQA1*02:01. The nucleotide sequence of the exons 1 to 4 of the new allele has been submitted to the GenBank database (Accession No. OP393480) and to the IPD-IMGT/HLA Database (Submission No. HWS10062890). The name DQA1*02:01:14 has been officially assigned by the WHO Nomenclature Committee for Factors of the HLA System in September 2022.
In addition to NET's contribution to the occurrence of thrombosis  (Wolach et al. , 2018) , anti-PL such as aCL were associated with thrombosis in various diseases, in particular with chronic diseases  (Abdel-Wahab et al. , 2020; Leal Rato et al. , 2021). While aPL thrombophilic process or direct association is unclear, it has been suggested that aPL plasma high level may trigger thrombosis in patients with cancer  (Gomez-Puerta et al. , 2006; Islam, 2020). Thus, concurrent with NET markers and cirDNA, we determined aCL levels in order to investigate their association. To explore our hypothesis, we will exploit data and plasma samples from patients screened within the UCGI 28 PANIRINOX clinical trial (NCT02980510), which uses cirDNA analysis to determine their RAS and BRAF status. This ongoing clinical study is the first interventional study to use cirDNA as a companion test for selecting patients with mCRC (metastatic colorectal cancer) toward anti-EGFR targeted therapy.
Human subjects In this study, we included 219 mCRC patients (51 females (41. 1%) and 73 males (28. 9%) (Table  1 ) from the screening procedure of the ongoing UCGI 28 PANIRINOX study (NCT02980510/EudraCT n 2016-001490-33). We investigated the correlation of MPO concentrations, NE concentrations and the nuclear cell-free DNA (cirDNA) concentrations to demonstrate that a very significant fraction of cirDNA derives from the degradation of neutrophil extracellular traps (NETs). PANIRINOX is the first interventional study to use cirDNA as a companion test for selecting mCRC patients towards anti-EGFR targeted therapy by the IntPlexâ method. Briefly: eligible patients were recruited in the PANIRINOX screening procedures at diagnosis; patients accepted for inclusion were male or female, aged between 18 and 75 years old, with a ECOG performance status 0 or 1, a histologically confirmed colo-rectal adenocarcinoma, an untreated synchronous or metachronous metastatic disease deemed unresectable with curative intent; a KRAS  (codons 12, 13, 61, 117, 146) , NRAS (codons 12, 13, 61) and BRAFV600E wild type (WT) tumor status according to plasma analysis of cirDNA by Intplex technology; a measurable disease according to RECIST version 1.
Quantification of cirDNA Analysis of cirDNA was done by IntPlexâ, an allele-specific blocker quantitative PCR (ASB Q-PCR),  (Mouliere et al. , 2014; Thierry et al. , 2014) , according to the MIQE guidelines  (Bustin, 2010; Bustin et al. , 2009). This IntPlex system specifically detect nuclear cirDNA. Q-PCR amplifications were carried out in at least two replicates in a total volume of 25 mL on a CFX96 instrument using the CFX manager software (Bio-Rad). Each PCR reaction was composed of 12. 5 mL of IQ Supermix Sybr Green (Bio-Rad), 2. 5 mL of DNase-free water (Qiagen) or specific oligoblocker, 2. 5 mL of forward and reverse primers (0. 3 pmol/mL), and 5 mL of template. Thermal cycling comprised three repeated steps: a hot-start activation step at 95 C for 3 minutes, followed by 40 cycles of denaturation-amplification at 95 C for 10 seconds, then at 60 C for 30 seconds.
Samples, controls and standards were removed from the microplates, and wells were washed three times with 300 mL of WB. Detection antibodies were diluted at the working concentrations in the RD, and then added by 100 mL per well, for one hour at RT. Detection antibodies were removed from the microplates, and wells were washed three times with 300 mL of WB. Then, 100 mL of Streptavidin-HRP was added to each well and microplates were incubated at RT for 30 minutes. Repeat wash three times. Finally, 100mL per well of substrate solution was added and incubated for 15 minutes, and the Optical Density (O. D) of each well was read immediately at 450 nm with the PHERAstar FS instrument using the PHERAstar control software.
QUANTIFICATION AND STATISTICAL ANALYSIS The Mann-Whitney U test was used for non-parametric data and the Student t-test was used for parametric data. Correlation analysis was performed using the Spearman test (Graph Pad Prism 8. 3. 1 software). A probability of less than 0. 05 was considered to be statistically significant; *p <0. 05, **p <0. 01; ***p < 0. 001; ****p < 0. 0001.
ACKNOWLEDGMENTS The PANIRINOX study is funded by AMGEN. B. Pastor was partially supported by  SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553 , and AR Thierry by INSERM. PANIRINOX study is sponsored by R&D  Unicancer. The sponsor had a role in the collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The funder had no role in the design and conduct of this present study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. We thank our patients and their families for their trust; all the participating physicians and supporting staff; Datacenter Unicancer members ( Institut Regional du Cancer de Montpellier ) and  Marie Bergeaud  (R&D Unicancer).
For analyse of PANoptosome formation, slides were incubated with donkey anti-goat IgG secondary antibodies conjugated with AlexaFluor488 (1:200, Invitrogen) and goat anti-rat secondary antibodies Alexa Fluor 546. Slides were stained using DAPI for 10 min. For NETs and ZBP1 visualization, cells were observed using a Leica DM 6000B microscope (Leica Camera, Wetzlar, Germany), images were acquired using Meta-Morph ® software, and were treated using ImageJ software. For PANoptosme visualization, cells were observed using a Zeiss LSM 980 microscope coupled with a Zeiss Airyscan 2 device (Carl Zeiss Co. Ltd. , Jena, Germany). Images were acquired using Zeiss LSM Image Browser (Carl Zeiss Co. Ltd. , Jena, Germany).
Measurement of cytokine levels MPO, IL-6, TNFα, CXCL1, IL-10, and CXCL10 concentrations in BALF or cell culture supernatant were measured by ELISA (R&D System, Minneapolis). IFNα and IFNβ levels were measured in BALF or cell culture supernatant by multiplex immunoassay according to manufacturers' instructions (Procar-taPlex, Life Technologies, Massachusetts). Data were acquired on Luminex equipment (MagPix, Bio-Rad, California) and analyzed using Bioplex Manager software (Bio-Rad).
Statistical analysis Statistic analysis was performed with GraphPad Prism 8. 0 software (San Diego). Statistical significance was determined by one-way ANOVA followed by Kruskal-Wallis multiple-comparisons tests or two-way ANOVA followed by Dunn post test as indicated in figure legends. P value < 0. 05 was considered significant. *p < 0. 05, **p < 0. 01, ***p < 0. 001 and ****p < 0. 0001. All data were shown as mean ± SEM.
ACKNOWLEDGEMENTS The authors thank  Glen N. Barber  for sharing the STING-deficient mice, to  Zhijian J. Chen  for providing cGAS-deficient mice ( University of Texas Southwestern Medical Center, Dallas ), to  Vijay Dixit  and  Thomas Henry  for sharing AIM2-deficient mice ( Max Planck Institute of Biochemistry, 82152 Martinsried, Germany ). The skillful assistance of  David Gosset , running the  P@CYFIC platform (CNRS UPR 4301, Orleans)  is gratefully acknowledged.
Because individuals were sampled several times over the years, we included individual's identities as random intercepts. Our models also included the interaction between "average age" and sex as well as the interactions between sex and both the linear and quadratic effect of "delta age" to test for sex differences in the leukocyte variation with age. We also conducted the same models as above, but using a subset of our data for which we have both the leukocyte concentration and the relative number of each leukocyte type for the same individuals in the same year (n = 75 from 33 individuals). These analyses provided comparable results than the analyses using the whole dataset and are presented in Table  S2.
Among-individual variation in immune phenotype with age Models including average and delta age cannot properly investigate the occurrence of selective appearance and disappearance of peculiar individual phenotypes from the population  (Fay et al. , 2022; van de Pol & Verhulst, 2006). For this purpose, we replaced the "average age" and "delta age" (linear and quadratic) effects in the previously selected within-individual models by the actual age, the "age at access to dominance", to assess the selective appearance, and the "age at last observation", to assess selective disappearance  (Fay et al. , 2022; van de Pol & Verhulst, 2006). The functions "lmer" and "glmer" in the package "lme4"  (Bates et al. , 2015)  were used to fit the models  (Bolker et al. , 2009). Final models were selected using a backward elimination procedure.
| Immune phenotype and survival probability We tested whether the mortality risk depended on leukocyte characteristics with mixed effects Cox right-censored regression models  (Nenko et al. , 2018; Ripatti & Palmgren, 2000; Therneau et al. , 2003). These models included leukocyte concentration or profiles as time-dependent covariates and survival as a response variable using the "coxme" function in the "coxme" R package  (Therneau, 2018). The age at first sampling and the sex were also included as fixed effects. Individual identity and year of birth were added as random effects to take into account repeated measurements and cohort effects (Table  2 ). The data were encoded with a zero as the starting point for all individuals and with the years to death, to the end of the study, or to the next capture (for individuals with repeated data) as right-censor stop points  (Therneau, 2018).
| DISCUSS ION The immune parameters (leukocyte concentration and leukocyte profile) measured on the marmots varied with their age. At the within-individual level, although the leukocyte concentration remains stable over the course of an individual life, the relative number of lymphocytes decreased, while the relative numbers of neutrophils and eosinophils increased with age. These results were consistent when a subset of our data for which we have both the leukocyte concentration and the relative number of each leukocyte type for the same individuals in the same year was used in the analyses (Table  S2 ). These results were also corroborated by the absence of correlation between the leukocyte concentration and any relative numbers of leukocyte types (Table  S1 ). The decrease in the number of lymphocytes with age is often interpreted as a consequence of the gradual decline over age in the generation, in the thymus, of new naïve T lymphocytes, responsible for generating new input in the immune memory.
ACK N OWLED G M ENTS We thank all the volunteers involved in marmot catching. We thank O. Vedder and S. Bouwhuis for their helpful comments and F. Kirkpatrick Baird for editing this manuscript. We also warmly thank M. Moiron for her advice regarding statistical analyses. The ANR-13-JSV7-0005 supported this work. The protocol was conducted under the permit AP n82010/121 and approved by the national ethical committee (n8BH2012-92 V1). The last author is authorized for animal experimentation (n8R45GRETAF110).
Background: During cancer development, the normal tissue microenvironment is shaped by tumorigenic events. Inflammatory mediators and immune cells play a key role during this process. However, which molecular features most specifically characterize the malignant tissue remains poorly explored. Methods: Within our institutional tumor microenvironment global analysis (T-MEGA) program, we set a prospective cohort of 422 untreated breast cancer patients. We established a dedicated pipeline to generate supernatants from tumor and juxta-tumor tissue explants and quantify 55 soluble molecules using Luminex or MSD. Those analytes belonged to five molecular families: chemokines, cytokines, growth factors, metalloproteinases, and adipokines. Results: When looking at tissue specificity, our dataset revealed some breast tumor-specific characteristics, as IL-16, as well as some juxta-tumor-specific secreted molecules, as IL-33.
The Cancer Genome Atlas Program is today a reference in terms of genomic characterization of different types of cancer and matched normal tissue. However, we hypothesized that genomic data may only partially reflect specific tumor inflammatory elements, such as cell composition and soluble mediators that can be transiently secreted or produced at particularly low levels. Studies at the protein level are necessary to precisely define specific features of cancer inflammation. This requires a systematic comparative approach of the tumor tissue with the non-tumor counterpart, in large patient cohorts. In the attempt to comprehensively understand cancer complexity, integrating data from different sources is of primary importance because it allows to mathematically reduce this complexity and formalize specific cancer signatures.
e. , mastectomy, tumorectomy) or molecular breast cancer subtypes (Table  S6 , data not shown). The Internal Review Board and Clinical Research Committee of the Institut Curie approved this study, named T-MEGA (tumor microenvironment global analysis), and samples were included prospectively between 2011 and 2016. All patients gave informed consent for research use of their biologic material in accordance with the declaration of Helsinki. The T-MEGA study was conducted in a laboratory that operates under exploratory research principles. Samples were collected based on the following inclusion criteria: age > 18 years, pathological diagnosis of breast cancer, untreated tumors, absence of immune-modulating factors (including steroids) within the past month, and absence of neoadjuvant therapy. Patient characteristics are summarized in Table  1.
Data were acquired using a MESO QUICKPLEX SQ 120 plate reader and analyzed with the DISCOVERY WORKBENCH 4. 0 software. For each analyte, concentrations below the lower limit of detection of the corresponding batch were imputed by a common value. This value was set as half of the mean lower detection limits over the batches that included out-ofrange samples. Similarly, for each analyte, concentrations above the upper limit of detection of the corresponding batch were imputed by the maximum of the upper detection limits of the analyte. These lower and upper detection limits are reported in Table  S2. Some concentrations were missing for some samples because of beads aggregation, which led to an insufficient number of acquired beads.
Colorimetric assay Glucose and lactate concentrations were measured from supernatants using a colorimetric assay. The Glucose Assay Kit and the Lactate Assay Kit were purchased from Abnova (KA0831 and KA0833) and used according to the manufacturer's recommendations. To fit into the linear concentration range of the assay, samples were diluted at 1:200 for glucose assay and 1:50 for lactate assay. Data were acquired using a Fluostar OPTIMA BMG Labtech plate reader and analyzed with the optima data analysis software (version 3. 32).
Tumor-specific signature Preliminary tests were conducted using the secretome dataset to compare classification models. The elastic-net model was selected as the best compromise between performance on the data (cross-validation error), the possibility of interpreting the classifier, and computing time (compared with random forest, linear discriminant analysis, K-svm, kernel-KNN, XGBoost, bartMachine, using the SuperLearner wrapper). To build a tumor signature, the dataset was first randomly separated into train and test sets (70%-30% of the sample size). Paired tissues were attributed to the same set. On the train and test sets separately, near-zero variance variables were discarded, and multiple imputation was performed. Some clinical variables (surgery type, age at diagnosis, molecular class, number of invaded lymph nodes, presence of vascular emboli) and the outcome (tissue type) were included in the imputation model.
To avoid automatic exclusion of variables, the predictor matrix was modified using mice::quickpred, with clinical features forced in the model. Thirty imputations were obtained with a maximum of 10 iterations, using predictive mean matching for continuous variables and logistic regression for categorical variables. Then, highly correlated variables were removed using the Caret methodology  [13] , with a correlation threshold of 0. 8. Parameters of the elastic-net models were selected on the train set. The Lambda parameter was selected by tenfold cross-validation for each imputed dataset. The alpha parameter was selected based on AUC, accuracy, and numbers of parameters retained by the model. The final signature was obtained by keeping only variables selected in more than half of the imputed datasets.
Specifically, the final estimate for a variable was the median of the estimates from the models that selected that variable. A classifier was obtained from the signature using the cutoff minimizing the distance between the ROC curve and point (0. 1). Performances of the classifier were evaluated on the train set, as a control, and on the test set. Predicted probabilities, ROC curves, AUC, accuracy, sensitivity, specificity values, and confusion matrices were considered. The secretome-based classifier was also applied to a dataset of non-malignant tumors as a control. Associations of clinical features with the secretome-based signature were evaluated with ANOVA. Representations of the patients' values were generated from the first imputed dataset.
Tumor-specific secretome pattern The tumor-specific secretion pattern was obtained by analyzing the tumor/juxta-tumor ratios of each analyte independently. Based on those data, we then performed a PCA to better understand the multivariate data structure of the tumor-specific secretome (Fig. 3A ). The correlation circle from the PCA shows different types of associations: 1) strong positive associations, such as CXCL5 and GRO, 2) independent behaviors such as Leptin and CXCL5, and 3) a few negative associations, such as Leptin and FGF-2. (Fig. 3A  and B ). Then, we performed an unsupervised clustering analysis of the concentration ratios to identify groups of molecules with similar tumor-specific secretion behavior. We identified five clusters on the dendrogram generated from a hierarchical clustering (Fig.
Tumor secretome defines a breast cancer signature We evaluated if the secretome could segregate breast tumors from non-malignant breast tissues. We performed a supervised learning analysis using an elastic-net modelization with a train/test strategy. The classifier was built on the train set, representing 70% of the secretome dataset (Fig. S3 ), and its performances were evaluated on the test set. Fourteen molecules were selected by the model: IL-16, IL-33, TGF-β3, CXCL9, SCF, Adiponectin, VEGF, CCL8, IL-1RA, FGF-2, TGF-β1, TGF-β2, GRO, and CCL2 (Fig. 4A ). IL-33, Adiponectin, CCL8, GRO, and CCL2 contributed to the juxta-tumor type; the other analytes contributed to the tumor type. Altogether, this defined a tumor signature that can be interpreted as a "tumorness" score. Among the 14 molecules defining the signature, IL-16 was one of the most important in discriminating tumor versus juxta-tumor tissues, as shown by its high tumor signature coefficient (Fig.
Finally, the juxta-tumor secretome was not impacted by cancer molecular subtype. Cytokines and other soluble mediators are often measured in serum samples of cancer patients  [18, 19]. This represents a noninvasive approach, which allows collecting relatively large amounts of biological material and to perform multiplex measurements of soluble analytes. However, the quantification of circulating mediators has limitations because it does not reflect the changes occurring at the tissue microenvironment level. Breast tumor-protein studies have been mostly performed on the malignant tissue itself, without taking into account normal breast tissue composition, and without integrating data on immune-related molecules  [20] [21] [22] [23]. Several studies have compared tumor tissues and healthy counterparts relying exclusively on transcriptomic datasets and no information on secreted inflammatory proteins  [9] [10] [11].
Our dataset could further be used to help answering other biomedical questions, in particular according to histopathological and molecular subtypes. Overall, our study opens new horizons for personalized anticancer therapy design by providing a reference on primary untreated breast cancer secretome. Differences in secretome profile could be used for diagnostic, prognostic, or as predictive biomarker. Changes in secretome profile can also be used for drug assessment  [27, 28]  or could allow identification of new therapeutic targets in treatment resistant patients. The T-MEGA framework also opens broad perspectives for the study of other cancer types and should help better delineating biological features that are specific of the tumoral process.
Conclusions Our study represents a comprehensive and systematic evaluation of the breast cancer tissue and the normal breast tissue. We have used a systematic experimental approach on a large (> 400) breast tumor patient cohort (T-MEGA) to generate a unique secretome dataset. In order to precisely define tissue "tumorness, " we established a novel and widely applicable breast cancer-specific classifier by mathematically formalizing. Our dataset is a freely accessible resource to the biomedical community to help advancing scientific knowledge on breast cancer. Moreover, the key elements of the signature that we have identified could open new approaches to the design of targeted strategies in the context of breast cancer. The pipeline that we have built is applicable to other cancer type studies as well as diverse inflammatory diseases.
Acknowledgements We thank  Annick Viguier  and  Sophie Grondin  from the  Curie Institut Cytometry Platform. We thank  Haroun Linda ,  Christophe Marie-Georges ,  Falcou Marie-Christine  and  Fernandes Xosé  from the  "Direction des Données" of the Institut Curie  for the clinical data collection and updates. We thank the team of the Pathology department for the sampling of the fresh samples right after surgery every day, the medical doctors and surgeons who helped at different levels for patient identification and for facilitating circuit of samples. We thank  Cristina Ghirelli  for the critical reading of the manuscript.