dataesr/scientific-paragraphs-categorization huggingface dataset:
Treg quantification was realized as previously described  (12). For all biopsy samples, the percentage of CD3 cells expressing Foxp3, CTLA-4 or Helios was quantified manually (by two investigators blinded to the experimental conditions) on at least four different pictures from two different areas of each tissue section separated by at least 100 mm. Slides were analyzed with the AxioVision imaging software (Carl Zeiss AG, Oberkochen, Germany).
Statistical analyses Graft survival times were plotted with the Kaplan-Meier representation, and survival time between different groups was evaluated with a log-rank test. Continuous variables were expressed as the mean AE SEM, unless otherwise indicated, and compared with the Mann-Whitney nonparametric test. Multiple comparisons were analyzed with a one-way analysis of variance and a Kruskal-Wallis test. p-Values of <0. 05 were considered statistically significant. All statistical analyses were performed on GraphPad Software (GraphPad Software Inc. , La Jolla, CA).
This was also observed for the frequency of Helios-negative Tregs in the rapamycin monotherapy group, but the number of these cells remained low compared with the association rapamycin with FR104. We also analyzed cell infiltrates in protocol kidney biopsies at 1 month or earlier in the case of rejection. We did not observe significant differences in terms of macrophage, B-lymphocyte or major T-lymphocyte populations in the graft of all groups (data not shown). Stable recipient allografts were also characterized by mild infiltrates at 1 month. Unfortunately, analyses at later time points were compromised by the cleanup of our facility. In order to better qualify T-lymphocytes infiltrates at 1 month, but also quantify the frequency of cells having a Treg compatible phenotype, we performed a triple immunofluorescence staining with antibodies to CD3, Foxp3 and either CTLA-4 or Helios.
The elimination half-life was superior to 1 week, and blood and lymph node receptor occupancy were superior to at least 1 month. Since secondary half-lives are known to be higher in man that in NHP, these data indicate that our pegylated Fab' fragment could be administered on a monthly basis or less in humans. As expected, FR104 alone delayed acute kidney rejection but did not induce long-term allograft survival in this full-mismatch NHP allograft model. However, we noticed that FR104 monotherapy-induced survival was very similar to the allograft survival of subjects treated either by a 2-week induction therapy of corticosteroids and daily administration of MMF, or chronic exposure to clinical doses of rapamycin or low doses of tacrolimus. FR104 immunotherapy associated with CNI-free (rapamycin) or CNI-low (tacrolimus) regimens, without any corticosteroids exposure, induced long-term allograft survival and prevented acute rejection in all recipients.
Acknowledgments The authors thank the SFR MicroPICell imaging platform and the  SFR Integrative Genomic platform (Nantes, France)  for technical assistance. The authors received funding from the  Fondation de la Recherche Medicale  ( FRM grant  # DIM20081013832 ;  Paris, France) , the  Agence Nationale de la Recherche  ( ANR  grant # 09-BIOT-013-02 ;  Paris, France)  and the European project  TRIAD  ( EU-FP7-Health program EC-GA N8281493 ; www. TRIAD-CD28. eu). This work was also supported by the  Progreffe and Centaure foundations (France)  and the  IHU-Cesti  project, supported by  Nantes Metropole  and the  Pays de la Loire Region.
RESULTS 4Mu inhibits HA production by colorectal carcinoma cells in vitro and in vivo We first analyzed the effect of 4Mu on HA expression in vitro in CT26 cells using an enzyme-linked immunosorbent assay (ELISA)-like assay in culture supernatants. When CT26 cells were pretreated with 4Mu for 24 hours, we observed an inhibition on production of HA in a dose-dependent manner. HA production by CT26 cells was slightly reduced at a dose of 0. 125 mmol/l of 4Mu whereas the levels of HA were significantly reduced at a dose of 0. 5 mmol/l (8. 9 ± 1. 9 ng/ml; P < 0. 01 (Figure  1a ). CT26 cells viability was not affected by 4Mu at any doses assayed (data not shown). We also analyzed the effect of 4Mu on the HA production in tumor stroma. BALB/c mice were inoculated with CT26 cells (day 0). On day 6, CT26 tumor-bearing mice were orally treated with 200 mg/kg/day of 4Mu for 8 days or left untreated (control).
TME modulation achieved by 4Mu therapy increases the homing of specific antitumor T lymphocytes into CT26 s. c. tumors We decided to investigate whether the effects on TIP and functional perfusion induced by 4Mu in TME modify the homing of T lymphocyte into tumors. To this end, mice carrying CT26 s. c. tumors were treated or not with 4Mu and then received specific T lymphocytes obtained from cured mice treated with Cy + AdIL-12. Twenty-four hours after receiving CD3+T cells, mice were sacrificed and tumor-associated fluorescence was analyzed. The total fluorescence imaging in both groups of animals were similar, indicating no differences in the amount of injected CD3 + DiR + T cells (data not shown). Importantly, we found that tumors from animals treated with 4Mu showed a significantly higher signal in comparison with control mice (Figure  3a ).
As a consequence of changes in tumor perfusion, we observed an increased delivery of adenoviruses and notably, a higher homing of tumor-specific T lymphocytes, indicating that the delivery of both genes and cells were effectively improved through the modulation of TME. Different approaches targeting factors that contribute to the elevated TIP resulted in an improved intratumoral distribution of anticancer agents. 3] [34] [35] [36]  Many of them demonstrated potential as anticancer agents as well as limitations. For instance, normalization of tumor vessels is usually transient, and treatments with antiangiogenic agents might increase tumor hypoxia. 37 On the other hand, the administration of proteolytic enzymes requires the knowledge of the optimal biologic dose to avoid toxicity. 20, 26, 38, 39 Mu is a vegetal 7 hydroxy-4-methyl coumarin, which has been used safely in humans as a cholagogue as well as in clinical  trials in patients with chronic viral hepatitis (ClinicalTrials.
gov identifier: NCT00225537;  40  ). In addition, the capability of 4Mu to control tumor growth by inhibition of HA synthesis has been demonstrated in many types of tumors including prostate, breast, and liver cancer,  15, 16, 18  but never in combination with immunotherapy. We have previously reported a synergistic antitumor combination of Cy and AdIL-12 in mouse colorectal carcinoma (CRC). This combined immunotherapy showed capacity to revert immunosuppression induced by CD4 + CD25 + FoxP3 + T regulatory cells. 7 he antitumoral activity achieved with this strategy also includes the induction of interferon (IFN)-γ secreting CD4 T-lymphocytes with cytotoxic activity. 6, 7 In order to evaluate whether the modulation of TME in CT26 tumor model is able to improve the efficacy of cancer immunotherapy, we decided to investigate the therapeutic activity of 4Mu plus the combined immunotherapy with Cy + AdIL-12 on advanced CRC.
Ex vivo experiments Efficiency of adenovirus transfection. Subcutaneous CT26 tumors were induced in BALB/c mice as described above. The mice were distributed in experimental groups and treated with saline i. p. or 4Mu (200 mg/kg/day, o. a from day 6 until mice were sacrificed). On day 9, animals were treated with Adβ-gal (10 9 TCID50 i. t) or AdIL-12 (10 9 TCID50 i. t). Forty-eight hours later, mice were sacrificed and tumor samples were taken to determine IL-12 levels by ELISA or β-galactosidase expression by X-gal staining reaction. Briefly, frozen tissue section were fixed for 5 minutes in paraformaldehyde 2% and incubated with 10 mg/ml X-gal (Promega, WI) into staining solution in a humidified chamber at 37˚C for several hours until blue color has developed to a maximum. Positive areas were calculated with ImageJ software.
Detection of perfused blood vessels. Subcutaneous CT26 tumors were induced in BALB/c mice as described above. The mice were distributed in experimental groups and treated with saline (control group) or 4Mu (200 mg/kg/day, o. a, for a week, from day 6). At day 14, animals received 10 mg/kg of Hoechst 33342 dye (Sigma-Aldrich) in PBS via tail vein (total volume of 100 μl). Five minutes later, the animals were sacrificed and tumor samples were taken, embedded in mounting medium (Cryoplast, Biopack, Buenos Aires, Argentina) and 10 μm sections were cut. Images were recorded and Hoechst 33342 profiles were counted using a particle analysis routine in ImageJ software. Ex vivo expansion of splenocytes for CD3+ T cell isolation. BALB/c mice were injected with CT26 cells and treated with Cy + AdIL-12 as described above.
08% ClNH 4 , 5% nickel ammonium sulfate in 0. 2 M AcNa and 0. 05% H 2 O 2. Positive areas were calculated with ImageJ software (National Institutes of Health, Bethesda, MD, USA; NIH). Immunofluorescence. Immunofluorescence for von Willebrand factor was performed on frozen tumor sections. After 1 hour of incubation in blockage buffer (5% normal donkey serum, Jackson ImmunoResearch, PA, 1% BSA, 0. 3% Triton-X in PBS; room temperature), tissue was incubated overnight at 4 °C with a rabbit anti-von Willebrand factor polyclonal antibody (vWF; 1:215; Sigma-Aldrich). After extensive washing, tissue was incubated with FITC-conjugated donkey anti-rabbit IgG secondary antibody (1:450; 2 hours, room temperature; Vector Laboratories). Slides were mounted and analyzed under a fluorescence microscope. Images were captured and quantified using the ImageJ software.
HMEC-1 cells (2 × 10 4 ), fetal bovine serum starved for 18 hours, were seeded on 90 µl of fetal bovine serum-free Dulbecco's Modified Eagle Medium, and stimulated with 10 µl of 4Mu-pretreated CT26 supernatants. After 6 hours of incubation at 37 °C, cells were fixed with 2% paraformaldehyde solution. Quantification was performed by analyzing the number of branching points/well at 20× (Nikon, Buenos Aires, Argentina) with Image J software (National Institutes of Health, Bethesda, MD). Reverse transcription-polymerase chain reaction (RT-PCR). Total RNA from tumor was extracted using Trizol Reagent (Sigma-Aldrich). Total RNA (2 mg) was reverse transcribed with 200 U of SuperScript II Reverse Transcriptase (Invitrogen) using 500 ng of Oligo (dT) primers. cDNAs were subjected to real-time polymerase chain reaction (qPCR) (Stratagene Mx3005p, Stratagene, La Jolla, CA).
At the end of PCR reaction, the temperature was increased from 60 to 95 °C at a rate of 2 °C/minute, and the fluorescence was measured every 15 seconds to construct the melting curve. Values were normalized to levels of glyceraldehyde-3-phosphate dehydrogenase transcript (GAPDH; used as housekeeping) (forward 5′-CATCTCTGCCCCCTCTGCTG-3′ and reverse 5′-GCCTGCTTCACCACCTTCTTG-3′). Data were processed by the ΔΔC t method. The relative amount of the PCR product amplified from untreated cells was set as 1. A nontemplate control (NTC) was run in every assay, and all determinations were performed as triplicates for each animal (n = 5/group) in two separated experiments.
Transcriptome profiling confirms strong intratumor heterogeneity PCA and HCPC performed on the whole microarray dataset highlighted zone-specific profiles with pronounced intra-tumor architecture: from the core to beyond the margin (Figure  2A ). This classification reflected the surgical sampling plan of the study (Figure  2B ), highlighting that the transcriptome heterogeneity was much more important within tumors than between patients. This classification was confronted with histological examinations performed by the central committee of neuropathologists and copy number alteration analyses. Clusters were enriched as follow: tumor and necrotic biopsies (HCPC #1), tumor and interface biopsies (HCPC #2), infiltrated peripheral brain zone (HCPC #3), and, reference control brain biopsies with peripheral brain zone biopsies (HCPC #4).
case). FT08 presented strong tumor heterogeneity with two Mesenchymal and two Classical biopsies (Supplementary File 2). Taken as a whole, the Verhaak GB classes were highly associated with the zone-specific profiles, as determined by the PCA performed on the whole transcriptome dataset (ANOVA, p = 9. 10-9) (Figure  3B ). This highlighted that the definition of GB subtype based on gene expression was related to the biopsy zone.
Gene expression signature of intratumor heterogeneity is marked by correlation with methylation levels PCA performed on the methylome data (27578 CpG sites, 23 samples) showed the presence of particular intratumor architecture as observed at the transcriptome level (Supplementary File 5). There were an estimated 3% (382 genes) of genes showing expression-methylation correlations (r < -0. 6) in the whole dataset. To assess the importance of epigenetics mechanisms in the definition of intratumor heterogeneity, we analyzed the proportion of anti-correlated genes with respect to their contribution to the transcriptome PCA data structure. This proportion was compared to random samplings (n = 1000) of the genes. The percentage of anti-correlated genes significantly increased with the gene contribution to the PCA data structure.
A signature of key cancer-heterogeneity genes highly associated with the intratumor spatial gradient We focused on the difference between infiltrated brain zone and macroscopically normal brain (HCPC #3 and HCPC #4, respectively). We selected the 100 most informative genes in terms of (i) importance in the PCA data structure, (ii) differential expression, and (iii) module membership (25 genes per significant module). Among these genes we found well-known GB genes such as: VEGF, CD44, GFAP, EZH2, CHI3L1, NES, and IGFBP2. Hierarchical clustering based on this selection conserved the inner structure of the data identified by PCA and the biological meaning of the coexpression modules (Figure  5B  and Supplementary File 6). As for the co-expression analysis, this suggested that central genes drove the functional enrichment of each module.
Array CGH profiling and data analysis Array CGH profiling was performed with the Human CGH 4x44K Microarray Kit (Agilent Technologies) according to the manufacturer's recommendations. For each patient, each biopsy DNA and matching patient constitutional DNA were pooled and together to avoid false positives due to individual copy number variation. Genomic profiles were computed and compared using waviCGH software  [49]. Normalization was performed by subtracting the weighted median from the logratios for each array. Duplicated probes were merged by the median. Segmentation was performed using the HaarSeg method  [50]. Each segment was assigned a probability of being lost (-1), normal (0), gained (+1) or amplified (+2) using the CGHcall method  [51]. Minimal common regions (MCRs) were identified using a permutation method taking into account the frequency of calls in each sample and chromosome to calculate statistically significant MCRs.
Gene expression profiling and normalization Gene expression profiling was carried out with the Agilent Whole Human Genome 4x44K Microarray Kit (Agilent Technologies). Total RNA was extracted, labelled and hybridized according to the kit manufacturer's recommendations. Raw intensity data were log2-transformed and normalized (intra-array and inter-array scaling) using the R limma package. Expression arrays have been deposited in the Gene Expression Omnibus repository under the accession number GSEXXXXX.
Sample classification and gene selections We used Principal Component Analysis (PCA) and Hierarchical Clustering on Principal Components (HCPC) to classify samples and select genes. PCA is a linear dimensionality reduction technique (factorial analysis) that seeks to identify a small number of components that capture most of the relevant structure in a dataset. HCPC is an agglomerative hierarchical classification method based on the principal components of the factorial analysis. PCA and HCPC were performed on transcriptome data with the FactoMiner R package and default parameters. Following the rationale of the experimental design of the study, we cut the hierarchical tree in order to obtain four clusters. We also used PCA to identify informative genes as done by Paschou et al. [53]  in the field of Populational Genetics.
They have developed a method to select informative markers related to the data structure revealed by PCA. This selection algorithm first determines the number of significant principal components (and thus the number of informative eigenMarkers) in the data. Subsequently, a score is assigned to each marker, with higher scores corresponding to markers that correlate well with all informative eigenMarkers. The markers are ranked according to this score and the top scoring markers are selected for further analyses. When computing the scores we used the correlation with the first Principal Component only. The gene expression dataset was reduced to the top 8000 genes for subsequent analyses.
The module membership measure being highly related to intramodular connectivity, it was used to identify hub genes (highly connected intramodular genes) displaying characteristic expression profiles for their module of residence. Gene significance (GS) measures the association (correlation) between a gene and external data. Here, GS was based on the adjusted p-values from the analysis of variance performed between HCPC clusters. All network analyses were performed using the WGCNA R package with default parameters except for the minModuleSize that was set to 175. Functional analyses were generated through the use of IPA (Ingenuity ® Systems, www. ingenuity. com) and GOMiner  [56]. For each co-expression module, functional enrichments were tested for the whole module and for a 'core module' defined as the upper quartile of the genes ranked on decreasing module membership.
ACkNOWlEDGMENTS This work was supported by the Institut National contre le Cancer (INCa), the Région Bretagne, the European Union (FEDER) and the Cancéropole Grand Ouest. The contribution of all members of the Grand Ouest Glioma Project is gratefully acknowledged, and in particular surgeons and anatomopathologists: A Hamlat, P Dam Hieu, P François, D Loussouarn, S Michalack, A Rousseau and M Wager. We also thank the Biogenouest Genomics core facilities, professor Thierry Fest, professor André Guillouzo and Camille Savary for their technical support.
Patients and settings We conducted an international, multicenter (N = 30 centers) observational study of ICU patients with evidence of either Aspergillus colonization or IA (AspICU Study). All consecutive adult (>18 years) ICU patients with a culture and/or direct examination and/or histopathologic sample positive for Aspergillus spp at any site between January 2000 and January 2011 were eligible for inclusion. Patients with a post-mortem diagnosis of IA were also eligible. Data were collected prospectively. However, because we anticipated a relative paucity of patients in whom histopathologic data were available, we accepted patients from historical cohorts (starting from January 2000) on the condition that none of the requested data were missing. Clinical suspicion of IA prior to ICU admission was an exclusion criterion.
Data collection and outcomes Data were collected from patient medical records and submitted via a web-based registration system  [24]. Collected patient data included demographics (age, weight, height and sex), underlying diseases and acute illness severity scores, including the Acute Physiology and Chronic Health Evaluation (APACHE) II score on admission  [25]  and the Sequential Organ Failure Assessment (SOFA) score  [26]  on the day of the positive Aspergillus culture. Acute respiratory distress syndrome (ARDS) was defined according to the 1994 Consensus Conference criteria  [27]. Sepsis was defined according to standard criteria  [28]. We also collected clinical data, including signs compatible with invasive fungal disease (that is, refractory or recrudescent fever, pleuritic chest pain or rub, dyspnea, hemoptysis or worsening lung function).
Sampling techniques and sites, as well as mycologic test results (including GM measurements and Aspergillus PCR, whenever available) to support a diagnosis of IA, were recorded. Test results were interpreted as positive if they met international consensus criteria  [5, 9, 29]. In particular, GM was considered abnormal if the optical density index was >0. 5. Organs affected by Aspergillus and species identification were recorded. Radiologic data included findings from chest X-rays or computed tomography (CT) scans of involved organs (chest, sinuses, abdomen and central nervous system). Findings on chest CT scans suggestive of invasive fungal disease were defined as "typical" if at least one of the following was present: wedge-shaped lesion, halo or aircrescent sign, lung cavitation or nodule  [9].
The date of diagnosis of IA was assigned as the date of the first positive Aspergillus culture or as the date of clinical deterioration compatible with fungal disease in cases of postmortem diagnosis. Although the "clinical" diagnosis of IA was reported in the database according to the judgment of the attending physicians, the final diagnosis was obtained using a central adjudication committee for the various diagnostic categories according to (1) the EORTC/Mycosis Study Group (MSG) criteria (proven, probable, possible IA or not classifiable)  [9]  and (2) "validated" criteria to discriminate Aspergillus colonization from IA in critically ill patients (putative or proven IA) (Appendix 2)  [23]. Among those patients with putative or proven IA, we separately analyzed those who had EORTC host factors involved in immunosuppression (that is, recent history of neutropenia (<500 neutrophils/mm 3 ), receipt of an allogeneic stem cell transplant, prolonged use of corticosteroids at a mean minimum dose of 0.
3 mg/kg/day of prednisone equivalent for 13 weeks, treatment with other recognized T cell immunosuppressants and severe inherited immunodeficiency)  [9]. Data were also collected on antifungal therapy and its duration as well as outcomes, including length of ICU stay, 12week survival rate and use of mechanical ventilation and/ or renal replacement therapy (RRT) or vasopressor agents, both at the time of the first Aspergillus-positive culture and during the ICU stay.
Statistical analysis Statistical analyses were performed using the SPSS 18. 0 for Windows NT software package (SPSS, Chicago, IL, USA). Descriptive statistics were computed for all study variables. Discrete variables are expressed as counts (percentage) and continuous variables as mean ± standard deviation or median (first to third interquartile range). Differences between the three study groups were assessed using the χ 2 test, Fisher's exact test, Student's t-test or the Mann-Whitney U test, as appropriate. A time-to-death analysis was performed until 84 days (12 weeks) after the first positive culture for Aspergillus. The Kaplan-Meier method with a log-rank test was used to compare the survival curves of the three populations (colonization, putative IA and proven IA) over time. This long-rank test was then adjusted for all clinically relevant variables that could affect outcome (that is, age, sex, medical admission, sepsis on admission, acute respiratory distress syndrome (ARDS) on admission, diagnostic category, APACHE II score on admission, bone marrow transplant, comorbidities, SOFA score on the day of positive Aspergillus culture, lung involvement, comorbidities, EORTC criteria for IA, antifungal therapy, vasopressors, mechanical ventilation and RRT).