dataesr/scientific-paragraphs-categorization huggingface dataset:
2 0. 4 Spearman correlation 0. 6 0. 8 Ann MT MT dry qua Isothermality T seasonality T ann rge M NHx M NHx g T seasonality MT dry qua y MT dry qua Ann MT T seasonality T ann rge Isothermality M NHx MT col qua Min T col mon M diurnal rge MT war qua MaxT war mon Ppt war qua Ppt seasonality Ppt col qua MT wet qua Ppt wet mon Ppt wet qua Ppt dry mon Ppt dry qua Ann ppt Interprotein Function p-value a Freq b Freq_all c IPR001810 F-box domain *** 7 67 IPR005225 Small GTP-binding protein domain *** 3 10 IPR001806 Small GTPases ** 2 4 IPR003578 Small GTPase Rho ** 2 4 IPR003579 Ras small GTPase ** 2 4 IPR020849 Small GTPase superfamily ** 2 4 IPR000008 C2 domain ** 2 7 IPR000629 ATP-dependent RNA helicase ** 2 7 DEAD-box, conserved site IPR009000 Translation protein, beta-barrel * 2 11 domain superfamily IPR014014 RNA helicase, DEAD-box type, Q * 2 12 motif IPR027417 P-loop containing nucleoside * 8 182 triphosphate hydrolase IPR001650 Helicase, C-terminal * 3 41 IPR014001 Helicase superfamily 1/2, ATP- * 3 41 binding domain a ***p-value <.
1 ). During their rst (Baseline) visit, subjects underwent hybrid FDG-PET/fMRI imaging. This provided 18 mins of resting-state BOLD data which was used for FC de nition of stimulation sites, as well as a Baseline measure of whole-brain glucose metabolism. As FC with the sgACC has been linked to the antidepressant e cacy of TMS  [30] [31] [32] [33]  , we selected two dlPFC targets that were functionally coupled to the sgACC in each individual (see De nition of TMS targets in Materials and Methods). Connectivity with the sgACC was determined using a prede ned map of sgACC FC computed by Fox and colleagues  31. One target was selected as the region showing maximal positive FC with this sgACC FC map, while the other target showed maximal negative FC with this map. Through this procedure, our stimulation targeted two anticorrelated regions that belonged to anticorrelated distributed networks  34.
TMS-induced changes in functional connectivity Changes in FC following SAL-TMS We next asked whether stimulation affects functional connectivity differently when applied to different networks. Fig. 5  shows FC changes in fMRI data as a result of SAL-TMS. Quantitatively, we estimated FC between individual regions covering the whole brain using a region-by-region analysis. Pair-wise correlations of the mean signal within all regions from the Yeo et al. 2011 parcellation (n = 51 regions) were calculated. The matrices in Fig. 5A  show these correlation values immediately before (pre-TMS) and after (post-TMS) SAL stimulation, as well as their direct comparison. Stimulation of the salience network did not result in signi cant within-network changes. However, a cluster of default network regions did show modest FC increases (i.
Sessions The overall experimental design is depicted in Fig. 1. All experimental procedures were conducted at the Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital in the MR-PET suite. Subjects participated in three experimental visits on separate days: a "Baseline" visit and two "experimental" visits. During the Baseline visit, subjects underwent a combined FDG-PET/fMRI imaging session. This Baseline data was used to functionally determine, on an individual-subject basis, the locations of the DN and SAL targets (see section on De nition of TMS targets below). The two separate experimental visits occurred on separate days at least 6 days apart (mean=32 days, median=23 days, range=6-236 days). During each experimental visit, subjects rst underwent an fMRI imaging session, then received 20-Hz repetitive TMS at one of the pre-de ned target sites (SAL or DN), then immediately underwent a combined FDG-PET/fMRI imaging session.
The order of stimulated targets (SAL vs. DN) was counterbalanced across subjects, such that half of subjects received SAL stimulation in their rst experimental visit, while the other half received DN stimulation rst. This paradigm recapitulates our previous experimental designs  17, 18. MRI acquisition MRI data were acquired using a 3. 0-T whole-body scanner (Siemens™), equipped for echo-planar imaging with a 12-channel 3-axis gradient head coil. Head movements were restricted using foam cushions. For each fMRI scanning session, one structural scan (~8 minutes) and three consecutive fMRI BOLD (blood oxygenation level dependent) resting-state runs (6 minutes each, 18 minutes per fMRI session) were performed. In the experimental visits, to minimize the time between TMS and FDG-PET/fMRI and to capture the effects of neuromodulation, the three BOLD runs were acquired rst, immediately after the localizer.
FDG-PET acquisition FDG-PET images were acquired during fMRI scanning on a BrainPET prototype (Siemens Healthineersä, Erlangen, Germany). FDG-PET procedures were identical across the subjects' three FDG-PET/fMRI scans (Baseline and two experimental sessions). Following their pre-TMS fMRI scan, subjects were injected with 18 Fluorodeoxyglucose: average 188 MBq (range: 164-212); average 5. 1 mCi (range 4. 4-5. 7). TMS was started approximately 7 minutes after injection (mean ± SD: 462 ± 252 s for SAL-TMS, and 390 ± 102 s for DN-TMS) to allow time for the radioligand to circulate and best capture the effects of TMS on FDG uptake. Approximately 34 minutes after injection (mean in Baseline visit = 33. 0 minutes, SAL-TMS visit = 34. 3 minutes, and DN-TMS visit = 34. 3 minutes), PET data was collected for a duration of approximately 36 minutes (up to a timepoint of 70 minutes post-injection).
De nition of TMS targets Baseline fMRI data was used to de ne, based on functional connectivity estimates, two dlPFC targets in each individual. To identify the two targets, we used a procedure developed by Fox and colleagues  31. Speci cally, we used a group-averaged seed-based functional connectivity map of the sgACC (sgACC FC map) as a seed for FC in all subjects. This map was derived by Fox et al. 2013 by rst de ning a 10mm spherical ROI in the right sgACC (MNI coordinates: 6, 16, -10), with the location of this based on prior studies. This ROI was then used by Fox et al. 2013 as a seed to generate a seed-based functional connectivity map in a cohort of 98 individuals (a subset of the 1000 individuals in Yeo et al. 2011). So as not to bias dlPFC target selection, the dlPFC was removed from this map.
Region of interest (ROI) selection In order to estimate FDG-PET and FC changes, 51 regions-of-interest (ROIs) from the Yeo et al. 7-network parcellation  22  , and covering the entire cortical mantle, were used for data analysis. The majority of analyses focused on ROIs within the default and salience networks, as these were the two networks stimulated. This included 11 ROIs within the salience and 10 ROIs within the default network. In addition, we used larger ROIs covering the entire default and salience networks as whole-network ROIs, as well as an sgACC seed de ned by Fox et al. 31. Functional connectivity (FC) analysis BOLD data were rst preprocessed using spatial normalization directly to a standard MNI 152 template brain, as well as motion and slice timing corrected. Registration was performed with linear regression with the Statistical Parametric Mapping (SPM) software package (Wellcome Department of Cognitive Neurology, London, UK).
Functional connectivity maps were projected to the surface for visualization using the Connectome Workbench (v1. 4) toolbox (www. humanconnectome. org/software/connectome-workbench). FDG-PET analysis PET images were reconstructed using an ordered-subsets expectation maximization (OSEM) algorithm using 6 iterations and 16 subsets, and correcting for random coincidences, dead time, isotope decay, detector sensitivity, photon attenuation and scatter. Attenuation correction was provided via a validated and highly reproducible SPM-based method  75, 76  (see also FDG-PET repeatability measures section). Static images were reconstructed 45 to 65 min post-injection in order to best capture the glucose metabolism changes induced by TMS stimulation. The reconstructed PET volume consisted of a 256 × 256 × 153 matrix of 1.
Quality control of BOLD data As head motion has a signi cant effect on FC  77  , we employed stringent quality control of the BOLD data. Each run was evaluated for slice-based temporal SNR (tSNR), mean and maximum relative motion, mean and maximum absolute motion, and relative motion greater than 0. 1 mm and 0. 5 mm. A given run was excluded from the analysis if the tSNR for that run was lower than 2 SD below the group mean of all runs, or if there were more than 5 movements greater than 0. 5 mm in that run. By these criteria, only one subject was affected, with 3 of their 9 BOLD runs being unusable, leading to that subject's exclusion from further analysis on the basis of head motion.
FDG-PET repeatability measures Intrascanner reproducibility for a subset of this PET dataset (13 out of 20 subjects) has been published elsewhere  75. Brie y, we assessed relative changes, intra-class correlation coe cient, reproducibility coe cient and Bland-Altman limits of agreement to assess repeatability across scans. This revealed minimal, insigni cant relative changes across the three PET acquisitions (p = 0. 90). Target site locations are shown in Fig. 2. Subjects then returned for two experimental visits on separate days. During the experimental visits, subjects rst underwent an fMRI scan, after which they were administered an FDG injection a few minutes before high-frequency (20 Hz) repetitive TMS to one of the target sites, in a cross-over design. Following about 22. 5 minutes of repetitive TMS, subjects were immediately scanned using simultaneous FDG-PET/fMRI.
Geographic study setting The Ajaccio area conurbation in Corsica Patients were recruited via their community-based general practitioner (GP), gynaecologist or urologist as well as via hospital-based physicians. Recruitment was subsequently extended to include patients attending medical laboratories (where information describing the study was delivered through leaflets posted in waiting rooms), pharmacists and via social networks. A snowball effect was produced as recruited patients had contacts with women with similar complaints. ] [19]  Indeed, data saturation is considered to be reached when there is enough information to replicate the study, when the ability to obtain additional new information has been attained and when further coding is no longer feasible. 20.
Data analysis Each recorded interview was transcribed verbatim by a single trained secretary, with as many details as possible, both verbal and non-verbal. A debriefing procedure by the researcher (LB) took place immediately following each interview to record the overall impression and identify the main ideas put forward by the patients and was shared with coauthors (PT, VM). This allowed to identify the point when theoretical data saturation was reached after discussion and agreement from all researchers, that is, no new ideas arose, and discontinue recruitment. 18 19 nductive thematic analysis  18  was performed using NVivo software. This included six stages: getting acquainted with the content of the interview (familiarisation), followed by initial coding where codes were approved by all researchers after discussion, identification of overarching themes, grouping of themes or categories of ideas, exploration of links and interaction between themes, description and supporting quotations, according to an iterative procedure.
Acknowledgements We wish to thank the patients who accepted to participate in this study and the members of the Réso-Infectio-PACA-Est (https://www. resoinfectio. fr/) who contributed to the development of the patient information leaflet. Contributors VM and PT were responsible for the study design. LB conducted the interviews. LB and PT analysed the results. PT drafted the manuscript. VM is acting as guarantor. Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
RAS-mutant cancers are among the most refractory to treatment. Apart from new G12C genotype targeted therapies, strategies to kill RAS-mutant cells by directly targeting RAS or its downstream effectors have been mostly unsuccessful, mainly due to pathway redundancy and heterogeneities in RAS-induced phenotypes. Here we identified a RAS-phenotype that can be targeted by the histone deacetylase inhibitor (HDACi) romidepsin. We showed that the hyperacetylation induced by romidepsin depleted acetyl-CoA, the cell donor substrate for acetylation, and led to metabolic stress and death in KRAS-activated cells. Elastic net analysis on transcriptomics from a 608-cell panel confirmed that HDACi sensitivity was linked to a difference in profiles in two pathways involved in acetyl-CoA metabolism. The analysis of a clinical dataset confirmed that perturbation of the two acetyl-CoA pathways were correlated with HDACi sensitivity in patients treated with belinostat.
For example, in pancreatic cancer, KRAS promotes a shift in the glutamine pathway to support redox maintenance, as well as an increase in glycolysis to divert nutrients into the glycosylation and pentose phosphate pathways. KRAS-driven cancers also recycle metabolites through increased autophagy or macropinocytosis to meet their increased demand for metabolites. Identification of these KRAS-tumor-specific and potentially targetable metabolic pathways led to new clinical trials involving pyruvate dehydrogenase inhibition in patients with KRAS-mutated pancreatic cancers, and glutaminase inhibition in lung cancer (NCT03699319, NCT02071862). Importantly, these KRAS-linked metabolic reprograming features differ according to KRAS copy number, tumor types, and genetic context  (10, 11). In accordance, scientists pinpointed the development of biomarkers as critical to allow selection among patients bearing oncogene-driven tumors of those more likely to benefit from metabolic-targeting drugs  (12).
Transcriptomic analysis with R Gene expression basal level for NCI-60 and CCLE panel were downloaded via the cBio Cancer Genomics Portal  (18). Gene expression for acute myeloid leukemia samples was downloaded from Gene Expression Omnibus (dataset GSE39363). Response to eight HDACi for CCLE panel were obtained from The Genomics of Drug Sensitivity in Cancer Project public database  (19). Gene expression for romidepsin-treated cells was obtained as follows. Cells were harvested after treatment and RNA extraction was performed using RNeasy Mini Kit (QIAGEN, Hilden, Germany), following manufacturer's instruction. Samples in triplicate were sent to the Frederick Cancer Research Core (NIH, NCI), where mRNA levels were determined by using Affymetrix GeneChip® Human Genome U133 Plus 2. 0 Array. Microarray data have been deposited in NCBI Gene Expression Omnibus (accession number GSE133120).
Flow cytometry Cell death was measured by flow cytometer after annexin V-FITC/ propidium iodide staining, according to manufacturer's instruction (eBioscience, Inc. , San Diego, CA). Cell lines with high expression of the multidrug efflux transporter P-glycoprotein (P-gp) according to cellMiner database  (20)  were co-treated with a Pgp inhibitor to avoid the bias of drug efflux during the assessment of toxicity. Reactive Oxygen Species (ROS) quantification was performed with 10µM H2DCF-DA (Abcam, Cambridge, UK) or 1µM MitoSOX™ Red (Invitrogen, Carlsbad, CA). 1µM tariquidar and 50µM MK571 (Selleckchem) were added to medium to inhibit fluorescent compound efflux by ABC transporters. Mitochondrial membrane potential was measured after incubation of cells in phenol red-free medium with 5µM JC1 (Invitrogen) and 1µM tariquidar.
e. Athymic mice were injected with KRAS mutation bearing A549 cells forming tumor, then treated with romidepsin and/or MEKi for 28 days. Tumor size (median+/-IQR) and mice weight (Supplementary Figure  2c ) were measured over time, and tumor weight (median+/-IQR) was measured after mice sacrifice and tumor dissection. Statistical difference between control and treated groups was determined using Welch's t-test (unequal variance t-test). were insensitive to AKTi or MEKi alone. These results indicate that RAS downstream inhibitors did not trigger cell death, but amplified romidepsin-induced cell death. We queried mutations in well-known key cancer genes carried by these cell lines from the Cosmic Cancer Database. Eleven relevant genes were grouped in four categories: TKR/RAS/BRAF pathway (EGFR, Erb2, KRAS, NRAS, HRAS and BRAF), PI3K pathway (PTEN, PI3CA and PI3R1), CDKN2A, and p53 (Figure  1a ).
Noticeably, the cluster of cells that did not respond to treatment correlated with absence of mutation in TKR/RAS/BRAF pathway, with a 0. 47 correlation (p-value=0. 002). Cell doubling time, mutations in the PI3K pathway, CDKN2A or p53 did not correlate with treatment efficacy (Supplementary Figure  1a-b ). Basal expression levels for the 65-cell panel  (18)  were analyzed by a Significance Analysis of Microarrays (SAM) to identify significant genes in romidepsin nonsensitive and sensitive clusters. The significant genes were analyzed by Gene Set Enrichment Analysis (GSEA) software using the hallmark gene sets. Confirming the mutation analysis, "KRAS signaling up" gene signature was significantly enriched (nominal p-value<0. 01 and FDR q-value<0. 05) in the two romidepsin-sensitive cell clusters (Figure  1c ).
Romidepsin affects acetyl-CoA availability To understand the disturbances in β-oxidation and glutamine metabolism induced by romidepsin, a metabolomic analysis was performed in A549 (KRAS-mutant) and ACHN (WT) cell lines. Cells were treated 12h or 18h with romidepsin using clinical dosing, and 629 metabolites were quantified. Romidepsin-altered pathways were determined by Metabolync pathway enrichment analysis. Fatty acid metabolism and TCA cycle pathways were significantly modified in both cell lines at 12h and/or 18h (Figures 4a and Supplementary 4a), as well as valine/leucine/isoleucine metabolism, another source of the TCA cycle-fueling metabolite acetyl-coA, confirming OCR/ECAR data (Figure  3 ). Metabolites in the TCA cycle and in its two fueling pathways (glutaminolysis and βoxidation) are reported in Figure  4b , and metabolites from valine/leucine/isoleucine degradation pathway and glycolysis in Supplementary Figure  4b-c.
To validate the metabolomics analysis, we quantified cellular acetyl-CoA in four cell lines by mass spectrometry. Romidepsin induced a decrease in acetyl-CoA in the two KRAS-mutant cell lines as well as in the two KRAS-WT ones (Figure  4c ). Knowing that the rate of flow through TCA is limited by the availability of its substrates oxaloacetate and acetyl-CoA, these data suggest that acetyl-CoA depletion caused a block in TCA cycle metabolite flux in KRAS-mutant cells, consequently causing an accumulation of glutamine downstream metabolites that were feeding into the cycle in the KRAS cells only. To explore this hypothesis, we returned to the Seahorse experiments. Cells were placed for 1h in a glucose/glutamine-deprived medium, then glutamine, DM-glutamate (cell permeable glutamate), DM-glutarate (cell permeable αKG) or pyruvate were added while OCR was measured (Figure  4d ).
Acetyl-CoA precursors replenish acetyl-CoA stock and rescue KRAS-mutant cells from metabolic stress Acetyl-CoA is a central metabolite that links cell energy status and transcription regulation. It is a TCA cycle limiting factor, as well as the only substrate for histone acetylation, the epigenetic mechanism regulating transcription  (22). To gain further insight into romidepsin-modified acetyl-CoA metabolism, microarray analysis was performed on A549, Miapaca2 and ACHN cell lines after 6h or 18h romidepsin treatment (clinical dosing), and pathway enrichment analysis using the KEGG signature database to cover more thoroughly modifications in metabolic pathways. Pathway mining highlighted modifications in fatty acid metabolism, valine/leucine/isoleucine degradation, and steroid biosynthesis in the top10-modified pathways for the three tested cell lines (Supplementary Figure  5a ).
These results showed that the decrease in acetyl-CoA levels was a leading cause of romidepsin-induced cell death in KRAS-mutant cells. Romidepsin-induced acetyl-CoA decrease caused a reduction in TCA cycle flux and an increase in cytoplasmic ROS production that triggered cell death in KRAS-mutant cells only. Notably, romidepsin-induced damage was reversed by acetate + 5mM citrate followed by addition of 40µM etomoxir then 5µM oligomycin A (left). Basal OCR level for romidepsintreated and untreated cells was calculated and plotted according to presence or not of acetyl-CoA precursors in medium (right). d. ROS production was measured by flow cytometry (DCF-DA) in cells treated or not for 24h with romidepsin, in presence or not of 20mM acetate + 5mM citrate. For all graphs, data are plotted as mean +/-SD, and statistical difference was assessed using unpaired two-tailed Student's t-test.
Two acetyl-CoA metabolism pathways are markers of HDAC inhibitor sensitivity Because romidepsin-induced acetyl-CoA decrease was observed in all tested cell lines, whereas only KRAS-mutant cell lines showed a decrease in acetyl-CoA-linked cell death after HDAC inhibition, we hypothesized that KRAS-mutant cells were more acetyl-CoA-dependent than KRAS-WT cell lines. To determine whether HDACi-sensitive cells had an acetyl-CoA altered metabolism at baseline, we decided to derive an HDACi-sensitivity metabolic signature from transcriptomic datasets. We used the Cancer Cell Line Encyclopedia panel (CCLE, 608 cell lines) with known response to eight HDACis to gather enough samples to gain a sufficient statistical power  (19). A machine learning approach was taken to identify key metabolic pathways related to HDACi sensitivity; the pipeline and results are illustrated in Supplementary Figure  7.
These differences between in vivo and in vitro experiments could be a barrier to a translation into clinical therapy. Nevertheless, romidepsin-induced tumor growth inhibition in our mouse model, confirming the activity of romidepsin in a KRAS-mutated tumor model in whom glucose is preferentially used over glutamine. Moreover, gene expression analysis from romidepsin-treated cutaneous T-cell lymphoma patients also revealed acetyl-CoA metabolism perturbation (Supplementary Figure  5c ). Recently, pancreatic cancer has been shown to undergo reprogramming in lipid-related and acetyl-CoA metabolism pathways  (37) (38) (39) , extending our finding about the importance of acetyl-CoA metabolism in romidepsin sensitivity in KRAS-activated cell lines. Accordingly, a phase II study in patients with KRAS-mutant NSCLC is ongoing with the fatty acid synthase inhibitor TVB-2640 (NCT03808558).
ACKNOWLEDGMENT This work was supported by the  Intramural Research Program (IRP) of the National Cancer Institute  ( AB/CS/RWR/WML/SEB ), the  IRP of the National Institute of Diabetes and Digestive and Kidney Diseases (PCV/ML) , and the  IRP of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (DLS). AB received a fellowship from  NIH Intramural Visiting Fellow Program , and funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 841313. Dr. Bates ' work is supported by a  Translational Research grant  from the  Pancreatic Cancer Action Network  and by the  Pancreas Center, Columbia University Irving Medical Center. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.