dataesr/scientific-paragraphs-categorization huggingface dataset:
To this end, we have developed an alphaLISA-derived approach allowing us to recapitulate all the interactions engaged in the retroviral intasome/nucleosome complex, including the IN/target DNA, IN/viral DNA, IN/IN and IN/histone bonds. In addition to providing an experimental model for further depicting the role of these interactions, our approach was used to select new molecules that dissociate the functional complex by targeting each or all of these interfaces. Since chromatin is a target for anticancer therapy, the OncoSET library was first screened. Among the 133 drugs in the OncoSET database, four were selected as significant inhibitors of both the AS intasome/MN interaction signal and in vitro integration. Most of the selected drugs were anthracycline derivates, including doxorubicin, and are known as DNA intercalating agents.
Interestingly, inhibition of HIV-1 integrase activity by doxorubicin has been previously reported  (18). However, while the inhibition of partial strand-transfer activity onto naked short DNA has been shown with IC50 values between 1-10 µM, we reported here a new mechanism of action for this molecule acting specifically on the physiological nucleosomal DNA substrate with IC50<1 µM. Interestingly, not all the intercalating agents represented in the OncoSET library, such as actinomycin D, showed AS signal inhibition effects. More strikingly, among the anthracyclines in the OncoSET database, not all were selected. For instance, epirubicin was not selected as an intasome/nucleosome modulator, in contrast to doxorubicin, idarubicin, pidorubicin and daunoribicin, suggesting a specific action mechanism for these anthracycline derivatives, leading us to further investigate the nucleosome dissociation property of doxorubicin as a mechanism for the reported inhibition.
This established a final concentration of 20 μg/mL for each bead. The plate was then incubated for 1 h at RT in the dark before the AlphaLISA signal was detected using an EnSpire Multimode Plate Reader (PerkinElmer). Negative control with binding buffer or only with one of the complexes were used to control the assay quality. Data were analyzed with GraphPad Prism 5. 01 software. To evaluate tolerance for dimethyl sulfoxide (DMSO), the assay was performed as describe above with addition of 0. 15 to 20 % (v/v) of DMSO during the binding step. For the competition experiment, increasing concentrations of untagged intasome were titrated out in the 1 nM DIG-intasome/biotinylated nucleosome interaction assay. For the AlphaLISA screening assay with the NIH OncoSET library, the binding conditions were optimized for use in 384-well plate (Optiplate, reference 6007290, PerkinElmer) with the same concentrations for complexes and beads in a final reaction volume of 20.
MD simulation set-up and parametrisation To evaluate the impact of the presence of doxorubicin on mononucleosome stability two models were prepared. One model of the nucleosome complex and another including the nucleosome complex plus 20 doxorubicin molecules. The model for the nucleosome was prepared from the 5MLU structure  (26) , available in the Protein Databank  (27) , with a resolution of 2. 80 Å. Protonation of all the amino acid residues was predicted using Propka version 3. 0 at pH 7. 0  (28). For the model with the doxorubicin molecules, packmol (29) was used to place and randomly distribute 20 doxorubicin molecules (1 for each 7 base pairs) around the volume defined by the two planes aligned with the enveloping DNA chains of the nucleosome. Both systems were further prepared for molecular dynamics simulations using the AMBER18 software package and Xleap, using the ff14SB for the amino acid residues (30), the OL15 force field for DNA  (31)  and the General Amber Force Field (GAFF) for doxorubicin  (32).
Charges on the systems were neutralized through the addition of counter-ions and the system was placed in with TIP3P water boxes with a minimum distance of 12 Å between the nucleosome surface and the side of the box, using the LEAP module of AMBER. Doxorubicin was parameterized with Antechamber using GAFF with RESP charges determined at HF/6-31G(d) using Gaussian16. Both systems were submitted to four consecutive minimizations stages to remove clashes prior to the MD simulation. In these four stages the minimization procedure was applied to the following atoms of the system: 1-water molecules (2500 steps); 2-hydrogens atoms (2500 steps); 3-side chains of the amino acid residues and DNA (2500 steps); 4-full system (10. 000 steps). The minimized systems were then subject to a molecular dynamics equilibration procedure, which was divided into two stages: In the first stage (50 ps), the systems were gradually heated to 310.
Copy number of 2-LTR circles was determined in reference to a standard curve prepared by amplification of quantities ranging from 10 to 1 × 10 6 copies of a plasmid comprising the HIVLAI 2-LTR junction (37) by using Light Cycler quantification software, version 4. 1 (Roche Diagnostics). Results are expressed as copy number of 2-LTR circles per 1× 10 6 cells. Integrated DNA was first amplified by Alu PCR performed in a 50-μl reaction mixture containing 200 ng total DNA, 1× HF Phusion mix, 200 nM deoxynucleoside triphosphates (dNTP), 500 nM primer PBS (5′-TTTCAAGTCCCTGTTCGGGCGCCA-3′), located in the PBS sequence of the viral genome, and 500 nM primer Alu-164 (5′-TCCCAGCTACTGGGGAGGCTGAGG-3′), located in the Alu sequence of the cellular genome. After an initial denaturation step (98°C for 30 s), the heterogeneously sized population of integrated DNA was amplified for 35 cycles (98°C for 10 s, 60°C for 20 s, 72°C for 2 min 30 s).
Cell equivalents were calculated according to amplification of the β-globin gene (two copies per diploid cell) with commercially available materials (Control Kit DNA; Roche Diagnostics). 2-LTR circles, total and integrated HIV-1 DNA levels were determined as copy numbers per 10 6 cells. Two-LTR circles and integrated cDNA were also expressed as a percentage of the total viral DNA. Sup data 4. Counter select assay. The counter select assay was performed using short DNA fragment carrying a biotin and a DIG tag on each side (A). The Alphascreen interaction signal (AU) was monitored using increasing concentration the double tagged DNA (B). Each selected compound was then tested in optimized conditions at 10µM. Data are reported as mean of three independent experiments ±SD.
Data analysis For each village, data related to household number, geographic coordinates, presence or absence of domestic animals, type of support (banco or cement walls) and number of occupants in the sampled room were entered into an Excel workbook, and statistical analyses were performed using R software version 4. 2. 0. The proportions of blood meals taken on each of the five vertebrate hosts tested were estimated by the percentage of the number of blood meals taken on each host out of the total number of blood meals identified. The human blood index (HBI) was calculated from the proportion of mosquitoes feeding on humans out of all identified blood meals. Analysis of the spatio-temporal variation of the human blood meals was performed using Moran's index  34  to determine autocorrelation by measuring the correlation between the households to identify the degree of clustering based on the assessed HBI (clustered, scattered, or random).
Households with low or high human blood meal rates, were identified using the Getis-Ord Gi statistic*  35. Clustered sites with high and low HBI were identified with Z scores > 1. 96 and Z scores < 1. 96 respectively. The selection ratio of Manly et al. 36  was used to determine whether vectors selected human and other hosts in proportion to their relative abundance. It is estimated as the proportion of a given host among other hosts divided by the proportion of available hosts among all selectable hosts in each village. A ratio of 1 indicates that the host selection is proportional to its availability, greater than 1 if a host is selected above its proportional availability, and less than 1 if a host is selected below its proportional availability. Estimates of the selection ratio and its 95% confidence interval were calculated using the adehabitat package available in the R statistical software.
Disappointingly, these trials have been negative  [8] [9] [10] [11] [12]  most likely as limited patient stratification methods were available to rationally select patients who might benefit most from treatment. Nevertheless, a small multicentre randomised control trial J o u r n a l P r e -p r o o f conducted in the setting of recurrent GBM (rGBM) suggests that neo-adjuvant anti-PD-1 blockade may elicit enhanced immune responses and survival benefits  13. In the current study, the GLIOTRAIN consortium (www. gliotrain. eu), together with US collaborators, have interrogated tissue-infiltrating immune and stromal cell populations of selected GBM patients using a modified targeted microenvironment cell-population counter (MCP-counter) RNA-seq computational method  14. Unsupervised partition around medoids (PAM) clustering identified three novel TME-associated subtypes designated TME low , TME med , and TME high which have been validated in publicly available datasets.
To provide insight into novel subtype-specific biology, we analyzed TME functional orientation markers and differentially expressed genes. Moreover, we performed mutational analysis and neoantigen prediction across novel subtypes, and have longitudinally assessed subtype switching events in primary and recurrent tumours. Finally, the capacity of novel TME-subtypes to predict outcome was assessed in retrospective immunotherapy clinical trial datasets. Our findings lay the foundation for a novel subtyping approach which may be applied, to direct novel combinatorial immunotherapy strategies in the brain tumour setting.
GLIOTRAIN Discovery cohort Informed consent for use of multi-omics data and associated clinical annotation was obtained via appropriate institutional channels. The GLIOTRAIN cohort was comprised of J o u r n a l P r e -p r o o f 123 retrospectively collected fresh frozen (FF) GBM samples, acquired at time of surgery, with corresponding clinical follow-up data. Patient samples were collected based on the GLIOTRAIN biobank inclusion criteria (Table  1 ). FF tumour samples from three participating institutions were collected (Table  2 ) and clinical data associated with GLIOTRAIN samples are described in Table  3.
Cohorts Transcriptomic and clinical data from the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) data collection was downloaded from the National Cancer Institute Genomic Data Commons (GDC) data portal (TCGA RNA-seq cohort)  15. mRNAseq_693 (batch 1) dataset and clinical data was downloaded from the Chinese Glioma Atlas database (http://www. cgga. org. cn)(CGGA RNA-seq cohort)  16. The DUKE cohort comprised GBM patients treated at Duke's Preston Robert Tisch Brain Tumor Center (RNA sequencing performed by Caris Life Sciences). The GLASS dataset (GLASS cohort) was downloaded from Synapse (https://www. synapse. org/#!Synapse:syn17038081/wiki/585622)  17. Clinical annotation for DUKE and GLASS datasets provided from collaborators upon request and described in Table  3  and 4 respectively.
Immunotherapy Trial Cohorts Transcriptomic data for GSE121810 were provided upon request (Cloughesy cohort  13  ). Transcriptomic data was downloaded from SRAPRJNA482620 (Zhao cohort  18  ). RNAseq bam files for the PVSRIPO clinical trial were downloaded from Genotypes and J o u r n a l P r e -p r o o f Phenotypes (dbGaP) database (PVSRIPO cohort). Clinical annotation for all datasets was also provided. 19 (Table  5  and 6 ). Discovery, validation, GLASS longitudinal and immunotherapy clinical trial cohorts were filtered as outlined (Supplementary figure  1 ). For detailed descriptions of next generation sequencing methods, MCP-counter modification, interrogation of TME composition and validation of novel TME subtypes, Wang subtype classification, neoantigen prediction, multiplexed immunohistochemistry methods, IvyGAP dataset analysis, gene ontology analysis and statistical methods, see supplementary materials, at Annals of Oncology online.
Identification of novel TME-subtypes Partition around medoids (PAM) clustering, based on patient GBM-MCP-counter scores, was performed on the GLIOTRAIN cohort. Clustering identified 3 distinct, novel subtypes with significantly different TME compositions (silhouette statistic methods and principal component analysis (PCA) (Supplementary Figure  3A-D ). These subgroups were defined as TME Low , 'Immune-Low', (24%), TME Med , 'heterogenous immune populations', (46%) and TME High , 'Immune-High' (30%) (Figure  1A ). These findings were reproduced in TCGA, CGGA and DUKE datasets (Figure  1B -D, Supplementary Figure  4 , Supplementary Figure  5 ). A representative cohort (N=26), from /span>GLIOTRAIN (GLIOTRAIN-IHC cohort) was assigned to TME-subtypes (Supplementary Figure  6A ) and quantitative IHC data orthogonally validated each TME-subtype (Supplementary Figure  6B ,C)  14.
Notably, B7 homolog 3 protein (B7-H3/CD276  22  ) expression was significantly downregulated in TCGA cohort TME High patients (P=0. 012; Figure  S7A ). No significant difference in B7-H3 expression was observed across novel TME subgroups in other cohorts (Figure  2A , Supplementary Figure  8B, C ). Interestingly, several previous J o u r n a l P r e -p r o o f studies assessing B7-H3 expression in GBM have observed similar diverse expression patterns  [23] [24] [25]. We further interrogated TME-subtype mutational landscape within the TCGA RNA-seq cohort, where matching WES data were available. As expected, tumour mutational burden (TMB) was low (median: 48 mutations)[Data not shown]. Moreover, mutational analysis revealed no difference in neo-antigen prediction or mutation count across TMEsubtypes (Kruskal-Wallis, P=0.
TME-subtypes may inform treatment outcome in retrospective immunotherapy trial datasets We subsequently examined whether patient stratification based on TME-subtype could predict response to immune checkpoint blockade. To this end, we accessed RNA-seq and clinical annotation data from the recent neoadjuvant anti-PD1 multi-institution clinical trial (Cloughesy cohort)  13. This trial evaluated immune responses and survival following neoadjuvant and/or adjuvant therapy with pembrolizumab in patients with recurrent, surgically-resectable GBM. Firstly, IDHmt samples (N=4) were identified and excluded. Subsequently, TME classifiers were assigned to the trial cohort (Figure  4A ). TME High tumour-bearing patients displayed a trend towards improved OS when compared with TME Low and TME Med tumour-bearing patients (P=0.
29)(Figure 4B ). Importantly, TME High patients treated with neoadjuvant anti-PD1 exhibited a significantly increased OS compared with neoadjuvant anti-PD1 treated non-TME High (TME Low and TME Med ) patients and TME Med patients treated with adjuvant anti-PD1 (P=0. 028)(Figure 4C, D ). Next, to further study the relationship between TME-subtype and response to ICI, we accessed RNA-seq data from the Zhao study (Zhao cohort) which evaluated immune responses and survival of longitudinally profiled patients during standard therapy and following treatment with PD-1 inhibitors (nivolumab or pembrolizumab)  18. Firstly, the GBM-MCP-counter was applied to pre-and post-anti-PD1 treated tumour samples (n=24)(Figure  5A ). 5B ). We subsequently assessed how TME-subtype proportion changes in pre-and post-anti-PD1 treatment samples, and in responders and non-responders (responders defined as those which revealed an inflammatory response, few tumour cells upon sampling and stable or shrinking tumour volume).
Sequencing and clinical data were accessed from the Desjardins et al  19  2018 phase 1 clinical trial (NCT01491893) which evaluated convectionenhanced, intratumoral delivery of recombinant non-pathogenic polio-rhinovirus chimera (PVSRIPO) in rGBM patients (PVSRIPO cohort). Samples were first assigned to TMEsubtypes (Figure  5E ). Tentatively, TME High patients treated with PVSRIPO showed a trend J o u r n a l P r e -p r o o f towards improved OS (P=0. 056) when compared with TME Low and TME Med tumours (Figure  5F ).
No significant relationship was observed between MGMT methylation status and novel TME subtype. amplification, rather than classical chromosomal alterations  33. Further interrogation of TME Low ecDNA landscape is required to uncover potential resistance mechanisms which may be hallmarks of this subtype. TME Med GBM is associated with an abundance of immune populations, functional orientation markers, immune checkpoint and endothelial cell markers. TME subtype analysis of IvyGAP anatomical samples revealed TME Med patients comprised the highest proportion of samples defined as 'pseudopalisading cells around necrosis'. Interestingly, pseudopalisades are associated with microvascular hyperplasia and angiogeneisis, and may serve as predictors of poor prognosis in GBM  34. Thus, despite negative outcomes following anti-angiogenic therapy (NCT00884741 and Checkmate-143/NCT02017717), our data tentatively suggests that patients identified in the 'colder' TME Med subtype might antiangiogenic treatment combined with immunotherapy due to high endothelial cell abundance, vascularity and diverse immune cell population.
46, 47. Overall, this pathway may harbour potential therapeutic avenues for the treatment of patients with tumours which transition from TME Med to TME High upon recurrence. To further understand subtype evolution and treatment resistance, scRNA-seq analysis and construction of dynamic cellular models to inform TME plasticity, cellular lineage and trajectory, is now required. It will also be important to consider whether therapeutic pressure may truly drive subtype switching  41, 48. Additional analyses of biopsies in primary and recurrent tumours (post-treatment) may further unravel the impact of intra-tumoural heterogeneity on TMEsubtype classification and TME-subtype specific treatment resistance mechanisms  37. Finally, the predictive potential of novel TME-subtypes was retrospectively assessed in interventional immunotherapy clinical trial datasets.
Firstly, our analysis of the small Cloughesy trial dataset tentatively suggests that TME High patients who receive neoadjuvant anti-PD1 might show improved OS compared to patients receiving adjuvant anti-PD1 alone. Nevertheless we acknowledge that TME stratification prior to neoadjuvant treatment is not without complexity. However, we hypothesize that in the future, TME subtyping might be performed prior to surgery by employing a blood based cell free RNA (cfRNA) liquid biopsy method  49  , or a robust TME subtype specific MRI radiomic signature  50, 51. Secondly, analysis of the Zhao cohort suggests that TME High tumour-bearing patients trend towards improved OS following anti-PD1 therapy. In a very limited subset J o u r n a l P r e -p r o o f of matched samples collected pre-and post-ICI therapy (N=3 patients), no significant alterations were detected in MCP scores before or after treatment (Supplementary Figure  17 ).
As mentioned above, conclusions with respect to subtype switching may not be drawn from such a limited number of samples. Thus, further studies in expanded patient cohorts are now warranted. Interestingly, a relationship between TME subtype assigned at time of primary tumour resection and response to anti-PD1 was observed regardless of standard of care treatment regimen prior to anti-PD1 therapy. This observation also requires further validation. Thirdly, we investigated whether TME-subtypes were predictive of survival within the small PSVRIPO dataset (NCT01491893 J o u r n a l P r e -p r o o f   GBM tumours are complex ecosystems composed of diverse malignant (e. g. stem) and non-malignant (e. g. glial, microglia, immune cells, vascular cells, reactive astrocytes) cell populations which exist in several niches, interact with heterogeneous tumour cells 3 and J o u r n a l P r e -p r o o f exhibit a dynamic heterogeneity and plasticity.
GLIOTRAIN Discovery cohort Informed consent for use of multi-omics data and associated clinical annotation was obtained via appropriate institutional channels. The GLIOTRAIN cohort was comprised of 123 retrospectively collected fresh frozen (FF) GBM samples, acquired at time of surgery, with corresponding clinical follow-up data. Patient samples were collected based on the GLIOTRAIN biobank inclusion criteria (Table  1 ). FF tumour samples from three participating institutions were collected (Table  2 ) and clinical data associated with GLIOTRAIN samples are described in Table  3.
Cohorts Transcriptomic and clinical data from the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) data collection was downloaded from the National Cancer Institute Genomic Data Commons (GDC) data portal (TCGA RNA-seq cohort)  15. mRNAseq_693 (batch 1) dataset and clinical data was downloaded from the Chinese Glioma Atlas database (http://www. cgga. org. cn)(CGGA RNA-seq cohort)  16.
Immunotherapy Trial Cohorts Transcriptomic data for GSE121810 were provided upon request (Cloughesy cohort  13  ). Transcriptomic data was downloaded from SRAPRJNA482620 (Zhao cohort  18  ). RNAseq bam files for the PVSRIPO clinical trial were downloaded from Genotypes and Phenotypes (dbGaP) database (PVSRIPO cohort). Clinical annotation for all datasets was also provided. 19 (Table  5  and 6 ). Discovery, validation, GLASS longitudinal and immunotherapy clinical trial cohorts were filtered as outlined (Supplementary figure  1 ). For detailed descriptions of next generation sequencing methods, MCP-counter modification, interrogation of TME composition and validation of novel TME subtypes, Wang subtype classification, neoantigen prediction, multiplexed immunohistochemistry methods, IvyGAP dataset analysis, gene ontology analysis and statistical methods, see supplementary materials, at Annals of Oncology online.
Identification of novel TME-subtypes Partition around medoids (PAM) clustering, based on patient GBM-MCP-counter scores, was performed on the GLIOTRAIN cohort. Clustering identified 3 distinct, novel subtypes with significantly different TME compositions (silhouette statistic methods and principal component analysis (PCA) (Supplementary Figure  3A-D ). These subgroups were defined as TME Low , 'Immune-Low', (24%), TME Med , 'heterogeneous immune populations', (46%) and TME High , 'Immune-High' (30%) (Figure  1A ). These findings were reproduced in TCGA, CGGA and DUKE datasets (Figure  1B -D, Supplementary Figure  4 , Supplementary Figure  5 ). A representative cohort (N=26), from /span>GLIOTRAIN (GLIOTRAIN-IHC cohort) was assigned to TME-subtypes (Supplementary Figure  6A ) and quantitative IHC data orthogonally validated each TME-subtype (Supplementary Figure  6B ,C)  14.
Notably, B7 homolog 3 protein (B7-H3/CD276  22  ) expression was significantly downregulated in TCGA cohort TME High patients (P=0. 012; Figure  S7A ). No significant difference in B7-H3 expression was observed across novel TME subgroups in other cohorts (Figure  2A , Supplementary Figure  8B, C ). Interestingly, several previous studies assessing B7-H3 expression in GBM have observed similar diverse expression patterns  [23] [24] [25]. We further interrogated TME-subtype mutational landscape within the TCGA RNA-seq cohort, where matching WES data were available. As expected, tumour mutational burden (TMB) was low (median: 48 mutations)[Data not shown]. Moreover, mutational analysis revealed no difference in neo-antigen prediction or mutation count across TMEsubtypes (Kruskal-Wallis, P=0. 14 and P=0.