dataesr/scientific-paragraphs-categorization huggingface dataset:
The STAR alignment algorithm  (Dobin et al. , 2013)  was used to obtain the number of reads associated to each gene in the human reference genome GRCh37 by the Gencode release 31 annotation (restricted to protein-coding genes, antisense and long intervening noncoding RNAs). Raw counts for each sample were imported into R statistical software. Extracted count matrix was normalized for library size and coding length of genes to compute FPKM expression levels. We also included RNA-seq data from human acute leukemia cell lines for validation analysis. The gene expression data, cell lines annotations and KMT2A fusion information were retrieved from the Cancer Cell Line Encyclopedia (CCLE) database (https://sites. broadinstitute. org/ccle/) at the Dependency Map (DepMap) portal (https://depmap. org/portal/download/; CCLE 2019 release).
Machine Learning Analysis The workflow used in this step is described in Supplementary Figure  S1. For the identification of predictive markers of KMT2A-r using machine learning approaches, we obtained a unique gene expression data with the FPKM normalized values of 14,287 genes. Ensembl IDs were converted to gene IDs using the  Bioconductor R package "biomaRt" version 2. 44. 4 (Durinck et al. , 2005; Durinck et al. , 2009) , and duplicate genes were removed. We also added a batch effect correction step in order to remove the variability from the different datasets using the removeBatchEffect function of the  Bioconductor R package "limma" version 3. 44. 3 (Ritchie et al. , 2015). Then, the continuous variables were standardized using z-score. Variables with a correlation greater than 0. 90 or low variance (zero or near zero) were excluded.
Then, a wrapper feature selection step was performed to reduce the dimensionality of this dataset using BorutaPy version 0. 3  (Kursa and Rudnicki, 2010). The acute leukemia subtype and age group information-pediatric (<22 years-old), younger adults (≥22 years-old and <60 yearsold), and older adults (≥60 years-old)-were also evaluated for the construction of the model. Variables with more than two categories were represented by a set of dummy variables, with one variable for each category. The Random Forest and LightGBM models were trained with 70% of the data (training set), and tested in the remaining 30% (testing set). The 30-folds cross validation was used to adjust hyperparameters with the GridSearchCV function. Due to class imbalance, we resampled our training dataset using the SMOTEENN method, which is an interesting technique that combines both undersampling [using Edited Nearest Neighbor (ENN)] and oversampling (SMOTE).
Afterwards, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were analyzed to assess the performance of our model. The combination of these parameters with the value of the area under the receiver operating characteristic (ROC) curve (AUC) was used to select the best model. Besides that, we calculated the Shapley values of each feature according to the predictive model to understand their respective contributions using SHAP (SHapley Additive exPlanations) version 0. 36. 0  (Lundberg et al. , 2017). All previous analyzes were performed using the Python programming language with the scikit-learn library  (Pedregosa et al. , 2011). Heatmaps were constructed with the CRAN R package "pheatmap" version 1. 0. 12 (Kolde, 2019) using log 2 (FPKM + 1) values, z-scaled across samples.
Pathway Enrichment Analysis The gene list obtained from machine learning step was evaluated for known functional processes by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using the overrepresentation analysis (ORA) methodology in the WEB-based GEne SeT AnaLysis Toolkit  (Liao et al. , 2019). We used genome protein-coding as a reference Set. p values were controlled for false discovery rate (FDR) using the Benjamini-Hochberg method, and we considered FDR ≤ 0. 05 as significant results.
Selection of Candidate Drugs for KMT2A-r The half-maximal inhibitory concentration (IC50) z-score values of the drugs with data available in the Genomics of Drug Sensitivity in Cancer (GDSC) repository  (Yang et al. , 2013)  were downloaded for acute leukemia cell lines from GDSC1 dataset (https://www. cancerrxgene. org/downloads/bulk_ download). We also downloaded IC50 values of some smallmolecule inhibitors from the Supplementary Table  S8  of Beat AML dataset  (Tyner et al. , 2018). The data were obtained from ex vivo functional drug screening analyses using freshly isolated mononuclear cells from AML samples. To search for drug-gene interactions, for which KMT2A-r cell lines demonstrated sensitivity was used as an input in the web resource Drug-Gene Interaction database (DGIdb; https://www.
Statistical Analysis The ROC curves and AUC values were generated by the CRAN R package "pROC" version 1. 17. 0. 1  (Robin et al. , 2011). Association analyses were represented by box plots using the  CRAN R package "ggplot2" version 3. 3. 3 (Wickham, 2016). Expression and IC50 data were compared between two or more groups with the unpaired two-samples Wilcoxon test and the Kruskal-Wallis test, respectively. p-values < 0. 05 were considered statistically significant. All analyses presented in this study, with exception of the machine learning step, were performed in the R statistical environment version 4. 0. 5.
Dataset Description A total of 1,659 acute leukemia samples were evaluated in this study, consisting of 899 AML, 415 B-ALL, 273 T-ALL and 72 ALAL from six different datasets. These samples were mostly obtained at the time of diagnosis (90. 3%) and refer to pediatric patients (61. 4%) (Table  1 ). The frequency of cytogenetic/ molecular alterations in each acute leukemia subtype is shown in Supplementary Figure  S2. We included 121 KMT2A-r cases (7. 3%) and classified the other molecular subgroups as KMT2A-WT (n = 1,538; 92. 7%). As expected, KMT2A-r were more frequently in the pediatric age group, and less frequently in the older adults, when compared to KMT2A-WT (76. 0% vs. 60. 3%, and 5. 0% vs. 20. 4%, respectively; p = 0. 0001).
A Machine Learning Model for KMT2A-r Prediction For the algorithm development, we selected the data from four of the six datasets: TARGET, Beat AML, ALL-Ekert, and AML-Griffioen. We used gene expression data of 14,287 genes from 1,332 acute leukemia samples, including 100 KMT2A-r and 1,232 KMT2A-WT cases. First, we performed a feature selection analysis to reduce the dimensionality of the dataset. As a result, 247 genes were selected due to their potential to predict KMT2A-r (Supplementary File S1), such as CSPG4, MLLT10, MEIS1 and several genes of HOX family (HOXA3, HOXA4, HOXA5, HOXA6, HOXA7, HOXA9 and HOXA10). Among them, 176 genes had increased expression in KMT2Ar acute leukemias (Supplementary Figure  S3A ). The enrichment analysis revealed a significant association of 13 genes (ETV1, FCGR1A, HOXA10 HOXA9, MEF2C, MEIS1, PBX3, PROM1, RUNX2, SMAD1, SPINT1, SUPT3H and ZEB1) with the "Transcriptional misregulation in cancer" pathway (FDR < 0.
001; Supplementary Figure  S3B ). It is noteworthy that, based on KEGG Gene Set (hsa05202), HOXA9, HOXA10 and  MEIS1 have association with differentiation resistance in KMT2A-MLLT1 (or MLL-ENL) T-ALL, while this same biological process has the involvement of PBX3, RUNX2, SMAD1, MEF2C, HOXA9 and HOXA10 in KMT2A-AFF1 (or MLL-AF4) B-ALL. The SUPT3H gene is a chromatin regulator and PROM1 is a signaling mediator, both are also associated with KMT2A-AFF1 (or MLL-AF4) in B-ALL. With a final dataset composed by 247 genes and 1,332 samples, we randomly split our cohort into 70% training (932 samples, 79 KMT2A-r and 853 KMT2A-WT) and 30% testing (400 samples, 21 KMT2A-r and 379 KMT2A-WT) data subsets (Supplementary Tables  S2, S3 ). First, we used the Random Forest and LightGBM algorithms to build a better model based on the 247 selected features and some well-known features associated with KMT2A-r, such as age group and acute leukemia subtypes.
After the KMT2A-r prediction in the testing dataset, we compared the performance measures of each model. In general, we had similar performances between the different models (Supplementary Table  S4a ). However, we chose the model built with the LightGBM algorithm and 247 genes without clinical variables due to the best combination of AUC, accuracy, sensitivity and specificity (0. 988, 0. 973, 0. 905, 0. 976, respectively). In other words, given the testing dataset, our model was able to predict 19 of 21 KMT2A-r patients and 370 of 379 KMT2A-WT patients (Figure  1A ). In order to reduce the gene set list, we selected the top 20 genes with the greatest contributions to the prediction of KMT2A-r in the LightGBM algorithm model, as indicated by the Shapley values (Figure  1B ). Most genes are upregulated in KMT2A-r patients in comparison to KMT2A-WT patients (Supplementary Figure  S3C ).
Therefore, we created a good predictor for KMT2A-r samples (AUC = 0. 868), with higher accuracy, sensitivity, and specificity (0. 974, 0. 750, and 0. 986, respectively; Figure  2C ). We also analyzed one French dataset, which included 176 pediatric patients with acute leukemia (173 B-ALL, 2 T-ALL, and 1 ALAL), consisting of 163 KMT2A-WT and 13 KMT2A-r. Of notice, KMT2A in-frame fusions were associated with USP2 (n = 3), MLLT10 (n = 3), MLLT3/AF9 (n = 3), AFF1/AF4 (n = 1), and CBL (n = 1). Two patients had out-of-frame fusions with MGMT (n = 1) or within the 7q22. 3 region (n = 1). Therefore, we evaluated whether the other genes (11 of 20 genes) would still be able to cluster KMT2A-r patients. As expected, the two samples with out-of-frame did not present a similar expression profile of the other KMT2A fusions, as well as one sample with KMT2A-USP2, which had higher expression of NEDD4 and lower expression of SKIDA1, HOXA9 and MEIS1 (Figure  2D ).
Therapeutic Options for KMT2A-r Acute Leukemia Since KMT2A-r are often associated with chemo-refractory acute leukemia, several studies aimed to search for new possibilities of  either new targets or drugs for the treatment of this leukemia subtype. Here, we evaluated drug screening for 33 acute leukemia cell lines (20 AML and 13 ALL) using the IC50s of 345 drugs available in the GDSC database (Supplementary Table  S8 ). After comparing KMT2A-r (n = 6) with KMT2A-WT (n = 27), we observed that KMT2A-r cell lines were more sensitive to 5-Fluorouracil (5FU) and Gemcitabine (both antimetabolite chemotherapy drugs; p = 0. 01 and p = 0. 04, respectively), WHI-P97 (JAK-3 inhibitor; p = 0. 03), Foretinib (MET/VEGFR inhibitor; p = 0. 01), SNX-2112 (Hsp90 inhibitor; p = 0. 02), AZD6482 (PI3Kβ inhibitor; p = 0.
V592A; MOLM-13, ITD). When we evaluated the IC50 values in the Beat AML data, we observed that samples with FLT3 activating mutations were significantly more sensitive compared with FLT3-WT samples (Figure  4C ). In order to identify other potential drug-gene interactions, we uploaded a list with these eight drugs, whose KMT2A-r cell lines demonstrated sensitivity, in the DGIdb database. As result, 127 potential targets were found for three drugs (Fluorouracil, Foretinib, Pevonedistat) (Supplementary Table  S9 ). Next, we evaluated if these targets were upregulated in KMT2A-r acute leukemia samples. We found ALDH3A1, PIK3R2, and TYMP expression levels increased in KMT2A-r samples when compared with KMT2A-WT (Figure  4D ).
Based on that, we verified that patient's cells harboring FLT3 mutations, a frequent secondary event in KMT2A-r leukemia, showed higher sensitivity to Foretinib, suggesting that this drug might turn into a therapeutic option for these subgroups. Foretinib has already been used in several cancer treatment trials, including Hepatocellular Carcinoma (NCT00920192), Breast Cancer (NCT01147484, NCT01138384), Non-Small-Cell Lung Cancer (NCT02034097, NCT01068587), Solid Tumours (NCT00742131, NCT00742261, NCT00743067), Head and Neck (NCT00725764), Papillary Renal Cell Carcinoma (NCT00726323), and Gastric Carcinoma (NCT00725712). In leukemia, this drug induces mitotic catastrophe in chronic myelogenous leukemia (CML) cells via JNK-dependent inhibition of Plk1 expression and triggers apoptosis by a caspase 2-mediated mechanism  (Dufies et al.
ACKNOWLEDGMENTS The results published here are in part based upon data generated by the  Therapeutically Applicable Research  to Generate Effective Treatments (TARGET; https://ocg. cancer. gov/programs/target)initiative, and by the  TCGA Research Network  (https://www. cancer. gov/tcga). The authors would like to thank  Paul Ekert  ( ALL-Ekert ) and  Marieke Griffioen  ( AML-Griffioen ) for accepting our request for access to their respective data. The authors would also like to thank the  Bioinformatics Core Facility (INCA-RJ)  for their support.
FUNDING BL, CP, and CT were supported by the  Brazilian Ministry of Health. BL received a return fellowship from the  Alexander von Humboldt Foundation. ME is supported by  Brazilian National Counsel of Technological and Scientific Development-CNPq  ( PQ-311220/2020-7 ) and  Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro-FAPERJ  ( E_26/203. 214/2017 ; E-26-010. 101072-2018 ;and  E-26/ 010. 002187//2019 ) research grants.
DATA AVAILABILITY STATEMENT Publicly available datasets were analyzed in this study. This data can be found here: https://github. com/bioinformatics-inca/ KMT2Ar-prediction (codes for machine learning analysis); https://portal. gdc. cancer. gov/projects (TARGET and TCGA datasets: accession TARGET-AML, TARGET-ALL-P2, TARGET-ALL-P3, and TCGA-LAML).
The Cryogenic Underground Observatory for Rare Events (CUORE) is the first cryogenic experiment searching for neutrinoless double-beta (0) decay that has been able to reach the one-ton scale. The detector, located at the Laboratori Nazionali del Gran Sasso in Italy, consists of an array of 988 TeO 2 crystals arranged in a compact cylindrical structure of 19 towers. Following the completion of the detector construction in August 2016, CUORE began its first physics data run in 2017 at a base temperature of about 10 mK. Following multiple optimization campaigns in 2018, CUORE is currently in stable operating mode. In 2019, CUORE released its 2 nd result of the search for 0 with a TeO 2 exposure of 372. 5 kg•yr and a median exclusion sensitivity to a 130 Te 0 decay half-life of 1. 7 • 10 25 yr.
Search for neutrinoless double beta decay CUORE has collected 7 datasets since April 2017. Each dataset includes one-day-long runs for a total of about one month of Physics data, and is started and ended with a calibration measurement. The number of active channels is 984. However, during the analysis, some of these channels were removed for different reasons (e. g. high noise, failure during one or more analysis steps, insufficient statistics collected during calibration, etc). In the end, the analysis was performed with a number of channels that changed dataset by dataset (900 min -950 max). The average energy resolution at the  Q-value, mediated over all the active channels, was (7. 0 ± 0. 4) keV, with an observed improvement during the data collection thanks to the optimization campaign.
Sonographic demonstration of compromised hepatic blood flow can support the diagnosis, but is frequently normal. In case of SOS in a patient in the experimental TEAM arm, that patient is taken off 6TG and treated according to the 6MP/MTX control arm. Any case of SOS should be registered in the ALLTogether1 online database within 24 h. Nodular regenerative hepatotoxicity (NRH): This is a non-cirrhotic portal hypertension, which can be induced by thiopurine therapy. There are typically symptoms of portal hypertension (weakness, ascites, splenomegaly, esophageal varices) in a patient with little evidence of chronic liver disease. The diagnosis is confirmed by liver biopsy, showing absence of significant fibrosis and presence of nodularity usually best defined by reticulin staining. On superficial review, the liver biopsy may appear normal.
NRH is usually accompanied by unexplained thrombocytopenia. Serum enzymes are generally normal or minimally elevated, although acute elevations in serum enzymes accompanied by SOS may precede the development of NRH. Jaundice is rare. Ultimately, NRH may lead to liver failure and need for liver transplantation. Among ALL patients, NRH has mostly been described with 6TG therapy, but it is very rare with 6MP therapy or low dose 6TG therapy (< 12. 5 mg/m 2 ). In case of NRH in a patient in the experimental TEAM arm, that patient is taken off 6TG and then treated according to the 6MP/MTX control arm. Any case of NRH should be registered in the ALLTogether1 online database within 24 h. Skin rashes: Skin rashes as an adverse drug reaction can be seen during maintenance therapy, but is far more common 2-6 weeks after cessation of maintenance therapy.
g. , organ toxicity or infections) or if it should raise suspicion of poor treatment adherence, which can then be discussed with the patient/family. DNA-TG > 1500 fmol/μg is rarely seen during 6MP/ MTX maintenance therapy. If DNA-TG for a patient in the TEAM experimental arm is > 1500 fmol/μg DNA at two consecutive measurements during a 3-month period (but at least 2 weeks apart), the 6TG dose should be reduced by 2. 5 mg/m 2 /day. Sample storage: Blood samples will be analyzed shortly after arrival at Rigshospitalet, Copenhagen (> 95% within 1 month after arrival) and stored until analysis. The storage will be submitted for approval by the Danish Data Protection Agency, since this is required if research samples are stored for more than 8 days. The samples will be discarded or returned to their local (or national) biobanking facility when all analyses have been performed.
Randomization and treatment allocation Screening is only allowed after written informed consent and can start 1-2 weeks after start of the last highdose MTX in consolidation III. Screening after 2 weeks is preferred to prevent screening failures because of increased ALT, bilirubin or creatinine after high-dose MTX. 6MP will be stopped 2 weeks after start of HD-MTX. After screening, patients can be randomized as soon as they meet the inclusion criteria and do not meet any exclusion criteria. Randomization will be using minimization stratified by: country, sex, age, WBC, immunophenotype, MRD and genetic risk (high-risk genetic changes, TEL/ AML1, high hyperdiploidy, B-other). During the study (i. e. , until all randomized patients are off therapy), the randomization code will be broken to the study statistician to provide the annual progress report to the Data and Safety Monitoring Board (DSMB).
Data safety monitoring board (DSMB) The DSMB for the TEAM study will be the same as for the ALLTogether1 protocol. The DSMB periodically (at least annually) reviews safety and study data and makes recommendations based on their review along with assessing the performance of overall study operations and any other relevant issues, as necessary. The basis for the review is a report provided by the study statistician without the knowledge or influence by the sponsor, PI or steering committee. The DSMB will receive annual progress reports on patient recruitment, rate of events (relapse, DCR1 and SMN), occurrences of SOS, NRH, AESI, SAEs, SUSARs and levels of DNA-TG in the randomization arms. The DSMB can recommend extension of the study to ensure adequate enrollment. The advice of the DSMB will be sent to the sponsor of the study to be shared with the steering committee.
Sample size calculation Historic data was pooled from four of the study-groups (UKALL, DCOG, COALL and NOPHO) of patients with positive EOI MRD-values consistent with the IR-group. This group was, by way of threshold analysis, sub-divided into an IR-Low and an IR-High risk group. The IR-high risk group was considered to have an unacceptably low 5-year DFS due to relapses (0. 844), after consideration of events before the protocol time for the TEAM randomization (9 months).
All consent givers will be informed of their right to bring a lay representative and this is stated in the written information material. If any of the individuals giving the informed consent are not comfortable with the national language, an interpreter fluent in their native language must be provided. The primary investigator will be the direct contact person for the patients and their parents in case of enquiries regarding the project and relevant contact information is stated in the informed consent form. The PI or the data manager at the participating site will hereafter enter the required data in the online platform used in ALLTogether and randomization will then immediately take place. Data collection will commence at the time of study inclusion, where diagnostic and demographic data will be registered in the CRF in addition to a baseline physical examination.
The physician will confirm in the CRF that the patient meets all inclusion criteria, and that all exclusion criteria are absent. All adverse event data will be continuously registered in the CRF. Furthermore, the CRF is used to keep account of significant changes in therapy due to occurrence of events. At the end of therapy, blood counts and liver parameters as well as drug doses are registered. Patients will be followed for disease status and SMNs until at least 7 years from end of induction therapy.
Public disclosure and publication policy This trial will be registered at EudraCT before any patients will be included. All publications will refer to this registration number. All publications from the ALL-Together1 consortium will comply with the guidelines provided by ICMJA (International Committee of Medical Journal Editors) concerning the definition of authorship and ethical rules regarding scientific publications. The full TEAM protocol is made public through open access publication. Upon study termination, deidentified participant-level data and statistical code will be made available and shared to the extent possible based on the ALLTogether data sharing policy, which is currently under review and will be published on Clini calTr ials. gov before any data are published.
Ethical approval This study conformed to the standards set by the Declaration of Helsinki, except for registration in a database. Participants included in this study were part of the TGFβ-PCOS trial (ACTRN12618000155291) and the iHIT-PCOS trial (ACTRN12615000242527): https: //www. anzctr. org. au/ (Australian New Zealand Clinical Trials Registry). Ethical approval was obtained from the Victoria University Human Research Ethics Committee (Reference HRE17-232), and all participants provided written informed consent prior to participation in the study.
Bioinformatic analysis of RNA sequencing data RNA-seq reads (n ≈ 36. 8 M) from FASTQ files were aligned using STAR (v2. 7. 2b) aligner  (Dobin et al. , 2013)  with Ensembl  (Cunningham et al. , 2019)  human annotation (GRCh38, release 98) resulting in 28 M reads on average (Supporting information, Fig. S1 ). One library (sample: P_c_EPS_A30_26) was excluded from downstream analysis due to low read number (n = 137). Gene coverages were counted using featureCounts  (Liao et al. , 2014)  (n ≈ 23. 7) from the subread (v1. 6. 2) package. Genes with low expression were removed using the filterByExpr function from the edgeR package  (Robinson et al. , 2009) (v3. 28. 1) , and thereby 19,231 genes were considered for differential expression analysis. A generalised linear model (GLM), 0 + group (group is defined by merging experimental conditions 'Disease' , 'EPS-treatment' and 'hormone-treatment'), was fitted by edgeR's (v3.
Pathway enrichment analysis Gene set enrichment analysis (GSEA) was performed by using clusterProfiler  (Yu et al. , 2012) (v3. 14. 3 ) and Reactome database (ReactomePA R package  (Yu & He, 2016) , v1. 30. 10) with log 2-fold changes (logFC) calculated by the differential expression analysis. The REACTOME ontology terms with less than 10 and more than 500 genes were removed from the analysis. REACTOME ontology terms with q-value <0. 05 were considered as enriched. The ontology terms with the genes were plotted as a network, CNET plots, with selected REACTOME ontology terms shown using the cnetplot function from the clusterProfiler package.