dataesr/scientific-paragraphs-categorization huggingface dataset:
The representation of the rows (samples) and columns (CpG-probes) of each table are obtained by projection onto the reference system (intrastructure). These analyses and graphics were performed using the R package ade4  [12]. Differential gene expression was considered as significant when the absolute fold change was superior or equal to log2 (1. 2) and the fdr-adjusted p value inferior or equal to 0. 1. Association between PFS and CpG-probes were assessed using the Cox proportional-hazards regression model and the log-likelihood ratio test (LRT). The additive models included CpG-probes, treatment or treatment and 1p/19q codeletion status as covariables, and contained the interaction term between CpG-probes and treatment. Time was taken from the date of surgery (not randomization as in the clinical trial) and an event (treatment failure/progression or death) after randomization to RT or TMZ.
The IDH mutation status was not available for all tumor samples in any of the four datasets (available for 512/757; see 'Molecular subtype' in Methods), therefore, we used the G-CIMP status as proxy for the IDH mutation status. Where both were available, G-CIMP and IDH1/2 mutation information agree almost perfectly, 100% for TCGA-1 and TCGA-2, 97. 56% for EORTC 22033, and 97. 42% for AGlioma. Throughout the manuscript we will refer to 'IDH status'. Table  1  summarizes the proportions of the molecular WHO subtypes and basic clinical characteristics of the patients in the four datasets (EORTC 22033, TCGA1 and 2, AGlioma) used in the subsequent analyses.
The DDR methylome of glioma We aimed at identifying DDR genes down-regulated by aberrant promoter methylation that could either serve as biomarkers for outcome prediction or inform on inherent DNA repair vulnerabilities amenable to treatment. To this end the split TCGA LGG datasets (which includes both grade II and III glioma) were used for the selection of functionally methylated CpGs located in the promoters of DDR genes as outlined in supplementary Fig. S2  (Online Resource 1). Functional methylation was defined under the postulate that DNA methylation of the promoter down-regulates expression. The HM-450K chip annotates 3749 CpGs as located in the promoter regions of 410 DDR genes. Thereof 62 CpGs satisfied the criteria of functional methylation in both datasets, identifying 24 DDR genes (supplementary Table  S2 , Online Resource 2).
The functionally methylated probes are displayed for the EORTC 22033 dataset in a heatmap in Fig. 1 , annotated with key clinical and molecular information. The heatmap visualizes the high concordance of CpG methylation within the individual genes. This subset of methylation probes largely separated the LGG according to the molecular subtypes. For the subsequent data analyses, we only considered IDHmt LGG. IDH wild-type (IDHwt) astrocytoma were rare in our cohort (n = 12); furthermore, they are considered a heterogeneous group that may belong to other tumor entities  [43]. First, we aimed at demonstrating the robustness of the methylation patterns of interest across the four datasets as a prerequisite for the validity of the following analyses. The two TCGA datasets (TCGA1 and 2) were highly similar in regard to the functional methylation of the selected 62 functional CpGs in DDR genes, as determined by comparing their Pearson correlation between gene methylation and gene expression [RV-coefficient = 0.
92 (max. achievable value is 1), p value = 0. 001 for 999 permutations; supplementary Fig. S3 , Online Resource 1]. Next, a common DNA methylation pattern among the four datasets was confirmed for the 62 CpGs (Fig. 2a , STA-TIS interstructure, global permutation test, simulation p value < 0. 001 for 999 permutations). In concordance, pairwise comparison of the methylation patterns in the four datasets (TCGA-1 and 2, AGlioma and EORTC 22033) showed high similarity with all RV coefficients > 0. 8 (STATIS analysis) (Fig. 2a ). An overview of the correlations between CpGs of all four datasets is visualized in Fig. 2b  showing the consensus representation of the functional CpG probes (first two axes). Genes featuring multiple functionally methylated CpGs in their promoter, such as UBB, MLH3, MGMT, POLE4 and BCAS2, highlight the high concordance of methylation among their CpGs, confirming the robustness of DNA methylation patterns at the gene scale and across datasets (Fig.
2b ). This consensus gene organization indicates a strong DNA methylation gradient implying a higher extent of methylation in IDHmt codeleted tumors, as compared to IDHmt non-codeleted tumors (Fig. 2b ). Accordingly, differences between the IDHmt codeleted and non-codeleted subpopulations were observed for all four datasets (Fig. 2d-g ). The separation of the IDHmt codeleted and non-codeleted subgroups across all four datasets is also demonstrated by cluster analysis (Fig. 2c ). The functional probeset included MGMT that is of known relevance for DNA repair associated with TMZ treatmentinduced lesions, and the putative mismatch repair (MMR) gene MLH3. Other functionally methylated DDR genes identified have been associated with nuclear excision repair (NER), e. g. POLE4, or homologous recombination (HR), e.
Predictive markers for progression-free survival in IDHmt LGG of EORTC 22033 The EORTC 22033 dataset allows for correlation of gene methylation with outcome and influence of treatment   S4 , Online Resource 4). The two CpGs selected for MGMT were identical to those we previously selected for the logistic regression model to calculate the MGMT methylation score used by the MGMT-STP27 classifier  [4, 5]. The interaction term with treatment was also significantly different from zero for this score [Table  2 , HR = 0. 755, 95% CI (0. 5786, 0. 9772), p value = 0. 033, Fig. 4a ], independently whether the codeletion status was added to the model (Table  2 ). As example, Kaplan-Meier representations are given for two cut-offs (first and third quartiles of MGMT score distribution) that illustrate the interaction effect between high MGMT methylation and treatment with TMZ in EORTC 22033 (Fig.
4b, c ). Of note, the codeletion status was not significantly Fig. 3  DDR networks based on pathways as defined in Pearl et al. [40]. DDR genes identified as functionally methylated are indicated and marked in green (FP). Functional DDR genes that are differen-tially methylated between IDHmt codel and IDHmt non-codel LGG are marked in red (FDMP) associated with PFS among IDHmt patients in a univariable model (p = 0. 16, log rank test). (a) (b) (c) (d) (e) (f) (g) The MGMT methylation score was significantly higher in the codeleted than in the non-codel IDHmt glioma in all four datasets (Fig. 5 ). A scatter plot of the MGMT methylation score and MGMT expression visualizing the negative correlation is displayed for the three molecular LGG subtypes of the TCGA dataset, for which RNA-seq expression data are available (supplementary Fig.
S7 , Online Resource 1). It has been reported that tumor purity, "contaminated" by microglia, non-tumoral brain tissue, and tumor infiltrating cells, differs between histologic subtypes of LGG  [1]  and may therefore be a confounding factor for estimating subtype dependent differences of DNA methylation. In order to exclude that the higher MGMT methylation score in the codeleted tumors is driven by higher purity, we estimated sample purity based on the methylation data (see methods). Significant differences between the codeleted and noncodeleted IDHmt tumors (p value < 0. 01, t test, supplementary Fig. S8 , Online Resource 1) were observed in all four datasets (EORTC 22033, TCGA1 and 2, AGlioma). These results confirm that codeleted IDH-mutant tumors appear more pure. A significant but only weak association between the MGMT score and tumor sample purity was detected for EORTC 22033 (p value = 0.
0013, Wald test based on robust estimation). The purity index explained 7. 1% of the variance. Adjusting the MGMT score for the purity effect using a linear model had only a minor effect on the association of the MGMT score with the molecular subtype (supplementary Fig. S9 , Online Resource 1) and led only to minor modifications in the Cox proportional-hazards models and the significance tests in EORTC 22033 [Table  2 , HR = 0. 7451, 95% CI (0. 5655, 0. 9817), p value = 0. 037]. Similarly, a weak association between the MGMT score and purity was observed for the TCGA-1 (p value < 0. 0001, R 2 = 7. 1%) and TCGA-2 (p value < 0. 0001, R 2 = 6. 7%) datasets, whereas the explained variance was higher in the AGlioma dataset (R 2 = 22. 6%, p value < 0. 0001).
Discussion Here we set out to investigate whether epigenetic inactivation of DDR genes affected the patients' benefit (PFS, time lapse from initial surgery to treatment failure upon randomization to RT or TMZ therapy) from genotoxic treatment with either RT or TMZ in EORTC 22033. Furthermore, we aimed at uncovering potential DDR pathway vulnerabilities as potential drug targets. Through multidimensional analyses of the molecular LGG dataset of TCGA, we identified 24 candidate DDR genes that are functionally downregulated by aberrant promoter methylation. Importantly, promoter methylation of four of these DDR genes was predictive for benefit (PFS) from either alkylating agent chemotherapy (TMZ) or RT in EORTC 22033. The most prominent functionally methylated gene was MGMT that is known as predictive factor for benefit from TMZ in GBM  [22].
SMC4 is part of the condensin complexes I and II that are essential for chromosome assembly and segregation, and is involved DSB repair  [34]. Genes encoding members of the cohesion complex are targeted by deletion or mutation in 16% of LGG/GBM, suggestive of a glioma genesis relevant role of the pathway  [11]. The search for biomarkers identifying potential vulnerabilities in repair pathways yielded a number of candidates. Twenty-four functionally methylated DDR genes were confirmed in three independent LGG datasets, and some may open novel treatment options. Most interestingly, recent encouraging results from clinical trials have raised the interest in targeting DNA repair vulnerabilities in cancer. Responses to the PARP inhibitor olaparib have been observed in metastatic prostate cancer with defects of repair genes (mutations, deletions)  [37].
In breast cancer inactivation of BRCA1 and/or 2 by promoter methylation is currently considered for treatment with the same PARP inhibitor in a trial (clinicaltrial. gov, NCT03205761). Veliparib is currently undergoing phase 3 evaluation in GBM in a randomized Alliance trial (NCT02152982). Along the same lines, PARP inhibitors may be useful for LGG patients with promoter methylation of MLH3, or XRCC1 that when defective have been reported to render cells sensitive to PARP inhibition, and are therefore discussed as being potentially amenable to PARP inhibitor treatments  [37, 38]. Of interest, XRCC1 is located on CHR 19q, hence haploinsufficient in the IDHmt codeleted tumors. Consequently, detection of promoter methylation informs on the silencing of the remaining allele. Furthermore, based on predictions for druggability of DDR genes (see recent reviews by Pearl et al.
[41]  and Stover et al. [47] ), five of our functionally methylated DDR genes (RPA2 SMARCA5, SMC4, XRCC1, mTOR) may warrant further investigations. Targeting of mTOR is currently under investigation in a trial for LGG with or without TMZ treatment (NCT02023905). The current efforts to promote the HM-450K methylation platform (or the more recent version, EPIC) as a diagnostic tool for classification of brain tumors  [10, 17] , will make the here identified markers potentially evaluable in routine diagnostics and may allow validation of the presented results. This diagnostic tool integrates the MGMT methylation score as part of the MGMT-STP27 classifier  [10]. However, most quantitative assays for MGMT methylation analysis, such as methylation-specific pyrosequencing should be amenable to determine the extent of methylation.
[2]ine Resource 1; supplementary Table S2, Online Resource 2); one-third of them belong to 4 functionally methylated DDR genes (10/36 CpGs: 2 CpGs for MGMT, 5 CpGs for POLE4, 1 CpG for MVP, and 2 CpGs for SMC1B; Fig. 3, supplementary Table S2, Online Resource 2). Gene expression analyses in the TCGA-1 and 2 datasets identified 74 of 410 DDR genes as differentially expressed (DE): 39 genes down-regulated in the codeleted group, and 35 up-regulated (supplementary TableS3, Online Resource 3 and supplementary Fig. S5, Online Resource 1). The top up-regulated DDR gene in IDHmt codeleted tumors was hTERT, whose activating promoter mutations are known to be correlated with overexpression and 1p/19q codeletion (supplementary TableS3, Online Resource 3)[2]. Three downregulated DDR genes (MGMT, POLE4 and SMC1B) were differentially methylated, 22 genes are located on either chromosome 1p or 19q, and downregulation may be.
Acknowledgements The authors would like to thank the staff at the participating centers of  EORTC 22033 , the  EORTC Headquarter  and the  German Glioma Network  for their great support as well as the participating patients and their relatives. The results published here are in part based upon data generated by The  Cancer TCGA Genome Atlas  pilot project established by the  NCI  and  NHGRI. Information about TCGA and the investigators and institutions constituting the TCGA research network can be found at "http://cance rgeno me. nih. gov". This work was supported by the  Swiss Bridge  Award 2011, the  Swiss Cancer League  ( KFS-29-02-2012  to MEH) and the  Swiss National Science Foundation  ( 3100A-163297 , to MEH and MD). Funding This work was supported by the  Swiss Bridge  Award 2011, the  Swiss Cancer League  ( KFS-29-02-2012  to MEH) and the  Swiss National Science Foundation  ( 3100A-163297 , to MEH and MD).
[2]ine Resource 1; supplementary Table S2, Online Resource 2); one-third of them belong to 4 functionally methylated DDR genes (10/36 CpGs: 2 CpGs for MGMT, 5 CpGs for POLE4, 1 CpG for MVP, and 2 CpGs for SMC1B; Fig. 3, supplementary Table S2, Online Resource 2). Gene expression analyses in the TCGA-1 and 2 datasets identified 74 of 410 DDR genes as differentially expressed (DE): 39 genes down-regulated in the codeleted group, and 35 up-regulated (supplementary TableS3, Online Resource 3 and supplementary Fig. S5, Online Resource 1). The top up-regulated DDR gene in IDHmt codeleted tumors was hTERT, whose activating promoter mutations are known to be correlated with overexpression and 1p/19q codeletion (supplementary TableS3, Online Resource 3)[2]. Three downregulated DDR genes (MGMT, POLE4 and SMC1B) were differentially methylated, 22 genes are located on either chromosome 1p or 19q, and downregulation may be 4 19 3 8 19 1 19 2 2 2 10 10 11 1 1 1 1 1 1 2 2 2 2 10 10 10 10 10 22 22 10 11 22 22 17 17 17 3 3 12 16 8 13 12 12 11 11 12 12 14 14 14 14 14 14 17 17 17 17 17 17 17 chr arm DMP.
For each identified protein, we derived a peptide intensity-based score that we called LTOP2 -"L", because it includes a log 2 transformation, and "TOP2" for the minimum number of required peptides (see Materials and Methods). The LTOP2 score of proteins identified in the total protein extract was well correlated with protein abundance estimates  (Ho et al, 2018) , with a Pearson determination coefficient of 0. 82  (Fig 1D). Upf1 had the highest LTOP2 score in the Upf1-TAP purification, as expected, validating both the use of this MS score for protein quantitation and the efficiency of the affinity purification. Most of the other proteins with high LTOP2 scores in the purification, such identification of specific partners in affinity-purified samples  (Fig 1F and Dataset EV1). To simultaneously visualize both the amount of protein found in a purified sample and its enrichment score, we combined LTOP2 and enrichment in a graphical representation that is similar to the intensity versus fold change "MA" plots used for the analysis of differential gene expression  (Fig 1G).
None of the proteins found enriched with Upf1 by a factor of 8 or higher were detected in the control purification (Dataset EV1). These results indicated that Upf1 has more protein partners than previously thought and validated quantitative mass spectrometry of purified complexes as a powerful strategy to characterize yeast NMD complexes. Upf1-associated proteins are grouped in two mutually exclusive complexes Since our data indicated that Upf1 is associated with a relatively large number of factors, and since Upf1 directly binds RNA  (Weng et al, 1998)  in large polysomal complexes  (Atkin et al, 1997) , some identified interactions could be mediated by RNA. To establish which interactions were RNA dependent, we repeated the Upf1-TAP purification, in a protocol including two RNase A and RNase T1 treatments.
Ribosome constituents and RNA binding proteins were lost following this harsh RNase treatment (P-values associated with GO term enrichment analysis between 10 À7 and 10 À30 for the corresponding terms, Appendix Table  S1 ). RNase treatment was efficient, suggesting that the proteins that remained specifically enriched were bound to the Upf1 complex through direct protein-protein interactions. Among these proteins, we identified the decapping enzyme Dcp2 and its co-factors Dcp1 and Edc3, Nmd4, Ebs1 and the protein kinase Hrr25, a homologue of mammalian casein kinase delta, CSNK1D (Fig  2A ). Upf2 and Upf3 interactions with Upf1-TAP were sensitive to RNase. In view of these results, and to obtain a global view of NMD complexes in yeast, we affinity-purified TAP-tagged versions of the components of Upf1-associated complexes identified here with or without RNase treatment: Upf2, Upf3, Nmd4, Ebs1, Dcp1 and Hrr25 (Dataset EV1).
TAP-Upf1-FL was expressed to levels 10-fold to 50-fold higher than the C-terminal tagged Upf1 expressed from the chromosomal locus, as estimated by immunoblots on total protein cell extracts. The various fragments were also stably expressed to high levels at the expected size  (Fig EV2A). Enrichment of the various proteins in the purification done with overexpressed TAP-Upf1-FL was well correlated with the enrichment of the same proteins in the previous purifications done with the protein tagged at its C-terminus and expressed from the endogenous UPF1 locus  (Fig EV2B and Dataset EV1) , confirming that neither overexpression nor tag position had major effects on Upf1-23 and Upf1-decapping composition (see  Fig 2A compared with Fig 3B). The only marked difference between the results with the overexpressed N-terminal Upf1 and the C-terminal tagged Upf1 was the persistence of Upf2 and Upf3 among the enriched proteins after RNase treatment  (Fig 3B, orange bars).
The purification of Upf1-FL and Upf1-HD-Cter coenriched Nmd4-HA, while the Upf1-CH region alone did not. Unexpectedly, overexpression of Upf1-FL or Upf1-HD-Cter also led to increased levels of Nmd4-HA in the total extracts  (Fig 3F) , suggesting that Nmd4-HA protein stability can be affected by its interaction with Upf1. To test the importance of the observed interaction between Nmd4 and Upf1 for the association with other factors of the Upf1-decapping complex, we purified Upf1-TAP in the absence of NMD4. We observed no major effects on the various associated proteins  (Fig 4A, Appendix Fig S2 and Dataset EV1). In the reciprocal experiment, in which we purified the Nmd4-associated complexes in the absence of UPF1, all the specific components of the Upf1-decapping complex were lost  (Fig 4B).
and UPF3 (Fig  6D ). The absence of Upf1 had no impact on the formation of the Upf2-Upf3 complex, independent of which of the two proteins was tagged  (Fig 6E and Dataset EV1). Altogether, these results suggest that Upf2 and Upf3 form a heterodimer independent of Upf1 and that both proteins are required for a stable association with Upf1 in the Upf1-23 complex.
Fast affinity purification preserves transient interactions  (Oeffinger et al, 2007) , and the use of surface-coated magnetic beads gives access to the large RNA-protein complexes in which NMD takes place  (Zhang et al, 1997)  without the bias induced by porous chromatography media  (Halbeisen et al, 2009). To get rid of abundant contaminants, we used the relative enrichment of proteins, based on mass spectrometry quantitations (see Materials and Methods), which efficiently highlighted specific interactions. These robust quantitative data identified novel interactions, including two protein kinases associated with Upf1 (Hrr25; Figs  1F  and G , and 2A, Dataset EV1) and Upf2 (Casein Kinase 2 complex; Fig  2B  and Dataset EV1 ). Besides the ability to show the presence of interactions, quantitative mass spectrometry provided a way to define lost or absent proteins in a given complex.
Altogether, our enrichment approach and the combination of RNase treated and native complexes analyses drastically reduced the number of NMD-associated candidate proteins to a short list that only contains highly specific top hits and is devoid of factors that are not related to NMD  (Fig 2I and Dataset EV1). The extensive measurements of the dynamics of NMD complexes in different mutant conditions bound or not to RNA support a new hierarchy of the molecular events required for yeast NMD (Fig  7G ).
Plasmids construction for in vitro interaction studies Coding sequences of yeast full-length NMD4, yeast UPF1 helicase domain and human UPF1 helicase domains (Uniprot accession codes Q12129, P30771 and Q92900-2, respectively) were cloned between NheI/NotI, NheI/XhoI and NdeI/XhoI in variants of pET28a (Novagen) for the in vitro interaction tests.
Mass spectrometry, sample preparation, acquisition and data analysis Protein samples were treated with Endoprotease Lys-C (Wako) and Trypsin (Trypsin Gold Mass Spec Grade; Promega). Peptide samples were desalted using OMIX C18 pipette tips (Agilent Technologies). The peptides mixtures were analysed by nano-LC-MS/MS using an Ultimate 3000 system (Thermo Fisher Scientific) coupled to an LTQ-Orbitrap Velos mass spectrometer or LTQ-Orbitrap XL (Thermo Fisher Scientific). Peptides were desalted on-line using a trap column (C18 Pepmap100, 5 lm, 300 lm × 5 mm, Thermo Scientific) and then separated using 120 min RP gradient (5-45% acetonitrile/0. 1% formic acid) on an Acclaim PepMap100 analytical column (C18, 3 lm, 100 A ˚, 75 lm id × 150 mm, Thermo Scientific) with a flow rate of 0. 340 ll min À1.
cerevisiae proteome database (downloaded 12/02/2016; total 6,721 entries) completed with the 22 virus protein sequences from yeast, and a list of commonly observed contaminants supplied by MaxQuant. Searches allowed either trypsin specificity with two missed cleavages. Cysteine carbamidomethylation was selected as fixed modification, and oxidation of methionine and N-terminal acetylation was searched as variable modifications. Peptide identification was performed using 6 ppm as mass tolerance at the MS level for FT mass analyser and 0. 5 Da at the MS/MS level for IT analyser. MaxQuant/Andromeda used seven amino acid minimum for peptide length, with 0. 01 false discovery rate for both protein and peptide identification. The protein identification was considered valid only when at least one unique or "razor" peptide was present.
Reverse transcription and quantitative PCR To measure the enrichment of NMD substrates such as the premessenger RPL28 or the DAL7 mRNA in different experiments, we performed reverse transcription followed by qPCR. After RNA extraction and DNase treatment, 500 ng of RNA was reverse-transcribed using Superscript III (Invitrogen) and a mix of reverse qPCR oligonucleotides, CS888 (RPL28-premature), CS889 (RPL28), CS1077 (RIM1), CS1430 (DAL7); for RNA immuno-precipitation, the purified and input samples were used for reverse transcription with transcript-specific primers. Eightfold dilutions of cDNA were tested by quantitative PCR. The amplification was done in a Stratagene MX3005P and corresponding software (MXpro quantitative PCR system), with step 1 (95°C for 5 min) and step 2 (40 cycles of 95°C for 20 s and 60°C for 1 min).