dataesr/scientific-paragraphs-categorization huggingface dataset:
97bLE)  51  on the regions from the bed files provided by Agilent (SeqCap_EZ_Exome_v2_probe-covered. bed) and QIAGEN, respectively, for the WES and targeted sequencing data. Consistency between sex reported in the clinical data and WES data was assessed by computing the total coverage on X and Y chromosomes (Supplementary Fig. 28A ). Variant calling and filtering on DNA. WES data: We re-performed variant calling for all typical and atypical carcinoid WES, including already published data, in order to remove the possible cofounding effect of variant calling in the subsequent molecular characterisation of carcinoids. Software Needlestack v1. 1 (https://github. com/IARCbioinfo/needlestack)  52  was used to call variants. Needlestack is an ultrasensitive multi-sample variant caller that uses the joint information from multiple samples to disentangle true variants from sequencing errors.
We performed two separate multi-sample variant callings to avoid technical batch effects: (1) The 16 WES atypical carcinoids newly sequenced in this study were analysed together with 64 additional WES samples sequenced using the same protocol from another study in order to increase the accuracy of Needlestack to estimate the sequencing error rate; (2) The 15 WES LNET (ten typical and five atypical carcinoids) previously analysed (Fernandez-Cuesta et al. )  11  were reanalysed with their matched-normal. For both variant callings, we used default software parameters except for the minimum median coverage to consider a site for calling, the minimum mapping quality, and the SNV and INDEL strand bias 13 threshold (they were set to 20, 13, 4, and 10, respectively). Annotation of resulting variant calling format (VCF) files was then performed with ANNOVAR (2018Aprl16)  53  using the PopFreqAll (maximum frequency over all populations in ESP6500, 1000G, and ExAC germline databases), COSMIC v84, MCAP, REVEL, SIFT, and Polyphen (dbnsfp30a) databases.
We performed the same variant filtering after each of the two variant callings, based on several stringent criteria. First, we only retained variants that have never been observed in germline databases or present at low frequency (≤ 0. 001) but already reported as somatic in the COSMIC database. Second, we only retained variants that were in coding regions and that had an impact on expressed proteins: we filtered out silent, non-damaging single nucleotide variants (based on MCAP, REVEL, SIFT, or Polyphen2 databases) and variants present in non-expressed genes (mean and median FPKM < 0. 1 over all carcinoid tumours). Additionally, for calling (2), we re-assessed the somatic status of variants reported by Needlestack in light of possible contamination errors. Indeed, Needlestack is a very sensitive caller and will sometimes detect low allelic fraction variants in normal tissue that actually come from contamination by tumour cells.
In such cases the variant is found in both matched samples and is reported as germline, but we still considered a variant as somatic if its allelic fraction in the normal tissue was at least five times lower than the allelic fraction observed in the tumour. Targeted sequencing data: Software Needlestack was also used to call variants on targeted sequencing data from 16 atypical carcinoids and their matched-normal tissue. We performed the calling with default parameters except for the phredscaled q-value and minimum median coverage to consider a site (20 and 10, respectively). These parameters were decreased compared to WES variants calling because we wanted a larger sensitivity in the validation set than in the discovery set. The annotation procedure was the same as for WES data. No other filters were used.
Validation: For both previously published data and data generated in this study, we only report somatic mutations that were validated using a different technique: targeted sequencing, RNA sequencing (see below for variant calling in RNA-seq data), or Sanger sequencing. Results are presented Supplementary Data 4. Structural variant calling. Somatic copy number variations (CNVs) were called from WGS data using an in-house pipeline (software WGinR, available at https:// github. com/aviari/wginr) that consists of three main steps. First, the dependency between GC content and raw read count is modelled using a generalised additive smoothing model with two nested windows in order to catch short and long distance dependencies. The model is computed on a subset of human genome mappable regions defined by a narrow band around the mode of binned raw counts distribution.
This limits the incorporation of true biological signal (losses and gains) by selecting only regions with (supposedly) the same ploidy. In a second step, we collect heterozygous positions in the matched-normal sample and GCcorrected read counts (RC) and alleles frequencies (AF) at these positions are used to estimate the mean tumour ploidy and its contamination by normal tissue. This ploidy model is then used to infer the theoretical absolute copy number levels in the tumour sample. In the third step, a simultaneous segmentation of RC and AF signals (computed on all mappable regions) is performed using a bivariate Hidden Markov Model to generate an absolute copy number and a genotype estimate for each segment. Somatic structural variants (SV) were identified using an in-house tool (crisscross, available at https://github.
Results are presented as Supplementary Data 8 and one sample is highlighted in Fig. 3c. Gene-set enrichment analysis of somatic mutations. Gene-set enrichment for somatic mutations was assessed independently for each set of Hallmark of cancer genes 18 using Fisher's exact test. We built the contingency tables used as input of the test taking into account genes with multiple mutations and used the fisher. test R function (stats package version 3. 4. 4). We also included validated mutations (we removed silent and intron/exon mutations) reported in SCLC  13. In each group the p-values given by Fisher's exact test performed for all Hallmarks were adjusted for multiple testing. Supplementary Data 5 lists the altered hallmarks, including the mutated genes and the associated q-value for each group, as well as the mutated genes for each hallmarks present in each supra-carcinoid, cluster LNET, LCNEC, and SCLC samples.
075; Supplementary Data 3). Similarly, we observed that for several SCLC samples, once the sample was removed, the deregulating cellular energetics and inducing angiogenesis hallmarks became significant at the 0. 05 false discovery rate threshold (Supplementary Data 5). For supra-carcinoids samples, we performed GSE for each sample individually. The code used for the gene set enrichment analyses on somatic mutations (Hallmarks_of_cancer_GSEA. R) is available in the explaining the variation on PC2, we investigated the range of samples from each provider on PC2 (Supplementary Fig. 30B ). We can see that samples from Provider 1 and provider 2 span a similar range on PC2 (from values less than -20 to values greater than 40). Restricting the analysis to atypical carcinoids, we can further see that AC samples from provider 2 have a range included in that of provider 1, which is expected given their differing sample sizes (five from provider 2 compared to 20 from provider 1).
For each differential expression (DE) analysis, among the 57,822 genes from the raw read count tables, genes that were expressed in less than 2 samples were removed from the analysis, using a threshold of 1 fragment per million reads aligned. We also removed samples with missing data in the variables of interest (either histopathological types, LNET clusters, or LNEN clusters) or in any of the clinical covariables included in the statistical model (sex and age). This resulted in excluding two samples with missing age data from the three analyses (samples S01093, S02236), and further excluding three samples with no clear histopathological type (classified as carcinoids in Supplementary Data 1) from analysis (i) (samples S00076, S02126, S02154). For each analysis, we then identified DE genes from the raw read counts using R package DESeq2 (v.
B), DE genes that differentiate A from C (A vs. C) and take the intersection of these gene sets [(A vs. B)∩(A vs. C)]. The code used for the DE analyses (RNAseq_supervised. R) is available at https://github. com/ IARCbioinfo/RNAseq_analysis_scripts. Results of analysis (i) are reported in Supplementary Data 15 and Supplementary Fig. 31 ; results of analysis (ii) are reported in Supplementary Data 10 and Fig. 5a ; results of analysis (iii) are reported in Supplementary Data 12. See section Multi-omics integration for comparisons between the analyses based on histopathological types [analysis (i)] from all 'omics perspectives. Note that an alternative method for finding DE genes would be to compare a focal group to all the other samples together. For example, comparing group A to both groups B and C simultaneously [denoted A vs.
the rest. Immune contexture deconvolution from expression data. We quantified the proportion of cells that belong to each of ten immune cell types (B cells, macrophages M1, macrophages M2, monocytes, neutrophils, NK cells, CD4+ T cells, CD8+ T cells, CD4+ regulatory T cells, and dendritic cells) from the RNA-seq data using software quanTIseq (downloaded 23 March 2018)  62. quanTIseq uses a rigorous RNA-seq processing pipeline to quantify the gene expression of each sample, and performs supervised expression deconvolution in a set of genes identified as informative on immune cell types, using the least squares with equality/inequality constrains (LSEI) algorithm with a reference data set containing expected expression levels for the ten immune cell types. Importantly, quanTIseq also provides estimates of the total proportion of cells in the bulk sequencing that do and do not belong to immune cells.
We tested whether immune composition differed between histopathological types, LNET clusters, LNEN clusters, and supra-carcinoids using linear permutation tests (R package lmperm, v. 2. 1. 0). Permutations tests are exact statistical tests that do not rely on approximations and assumptions regarding the data distribution, and are thus well-fitted to test whether a few samples come from the same distribution as a larger group of samples. As such, they were well-fitted to handle the tests involving supra-carcinoids, for which only three samples had RNA-seq data. For each of the three analyses (histopathology, LNET clusters, and LNEN clusters), and for each pair of groups, we fitted one model per immune cell type, with the proportion of this cell type in each sample as explained variable and the cluster membership as explanatory variable.
We adjusted the p-values for multiple testing. The code used for these three analyses is available on https://icbi. imed. ac. at/software/quantiseq/doc/index. html and the associated results are presented Figs. 2f, 4b , and Supplementary Figs. 15, 19, and 32. EPIC 850k methylation array. Epigenome analysis was performed on 33 typical carcinoids, 23 atypical carcinoids, and 20 LCNEC, plus 19 technical replicates. Epigenomic studies were performed at the International Agency for Research on Cancer (IARC) with the Infinium EPIC DNA methylation beadchip platform (Illumina) used for the interrogation of over 850,000 CpG sites (dinucleotides that are the main target for methylation). Each chip encompasses eight samples, so 12 chips were needed for the 95 samples. We used stratified randomisation to mitigate the batch effects, ensuring that the three histopathological types were present on every chip, while also controlling for potential confounders (the sample provider, sex, smoking status, and age of the patient); replicates were placed on different chips.
For each sample, 600 ng of purified DNA were bisulfite converted using the EZ-96 DNA Methylation-Gold TM kit (Zymo Research Corp. , CA, USA) following the manufacturer's recommendations for Infinium assays. Three replicates included half the amount (300 ng). Then, 200 ng of bisulfite-converted DNA was used for hybridisation on Infinium Methylation EPIC beadarrays, following the manufacturer's protocol (Illumina Inc. ). This array shares the Infinium HD chemistry (Illumina Inc. ) and a similar laboratory protocol used to interrogate the cytosine markers with HumanMethylation450 beadchip. Chips were scanned using Illumina iScan to produce two-colour raw data files (IDAT format). Methylation data processing. The resulting IDAT raw data files were preprocessed using R packages minfi (v. 1. 24.
0)  63 and ENmix (v. 1. 14. 0)  64. We first removed unwanted technical variation in-between arrays using functional normalisation of the raw two-colour intensities, and computed the β-values for the 866,238 probes and 96 samples. Then, we filtered four types of probes that could confound the analyses. (i) We removed probes on the X and Y chromosomes, because we were interested in variation between tumours and treated sex as a confounder. (ii) We removed known cross-reactive probes-i. e. , probes that cohybridise to other chromosomes and thus cannot be reliably investigated. (iii) We removed probes that had failed in at least one sample, using a detection p-value threshold of 0. 01, where p-values were computed with the detection P function from R package minfi, that compares the total signal (methylated + unmethylated) at each probe with the background signal level from non-negative control probes.
(iv) We removed probes associated with common SNPs-that reflect underlying polymorphisms rather than methylation profiles-using a threshold minor allele frequency of 5% in database dbSNP build 137 (function dropLociWithSnps from minfi). (v) We removed probes putatively associated with rare SNPs by detecting and removing probes with multimodal β-value distributions (function nmode. mc from R package ENmix). Next, we removed duplicated samples, randomly choosing one sample per pair so as to minimise potential discrepancies, and we removed one sample that came from a metastatic tumour rather than a primary tumour. The final data set contained the β-values of 767,781 CpGs for 76 samples. We performed quality controls of the raw data. Two-colour intensity data of internal control probes were inspected to check the quality of successive sample preparation steps (bisulfite conversion, hybridisation).
We did not find outliers when comparing the methylated/unmethylated channel intensities of all samples, nor did we find samples with overall low detection p-values (the sample with the lowest mean p-value had a value of 0. 001). Concordance between the sex reported in the clinical data and the methylation data was assessed using a predictor based on the median total intensity on sex-chromosomes, with a cutoff of -2 log 2 estimated copy number (function getSex from minfi). Consistently with the WES and RNA-seq data, we found one sample with a mismatch between reported and inferred sex (see results in Supplementary Fig. 28C ). We investigated batch effects at the raw data level using surrogate variable analysis. We used function ctrlsva from package ENmix to compute a principal component analysis of the intensity data from non-negative control probes.
The code used for the clustering analyses (integration_unsupervised. R) is available at https://github. com/IARCbioinfo/ integration_analysis_scripts. GSEA on multi-omic latent factors. We performed gene set enrichment analysis (GSEA) on the latent factors identified by MOFA using the built-in function FeatureSetEnrichmentAnalysis  15. This tests for each latent factor whether the distribution of the loadings of features (genes or CpGs) from a focal set are significantly different from the global distribution of loadings from features outside the set. We performed the analysis using two reference databases of gene sets: GO and KEGG. To retrieve the appropriate databases, for all genes from the mutiomics integration analysis, we downloaded GO terms using R package biomaRt  68  , and we retrieved KEGG pathways using R package KEGGgraph (v.
1. 38. 0)  69. Results are presented in Supplementary Data 6. Expression and methylation correlation analysis. We performed correlation tests in two analyses: (i) between LNET clusters (clusters A1, A2, and B), and (ii) between LNEN clusters (clusters A, B, and LCNEC). We selected for each gene, the set of CpGs in the region -2000 to +2000 from the transcription start site (TSS) using function getnearestTSS from R package FDb. InfiniumMethylation. hg19 version 2. 2. 0 based on the IlluminaHumanMethylationEPICanno. ilm10b2. hg19 annotation (get Annotation function from R package minfi version 1. 24. 0)  63. We performed correlation test analyses (function cor. test from R package stats version 3. 5. 1) using the core genes lists (Supplementary Data 10 and 12) to find associations between expression and methylation data for each CpG, using Pearson's correlation coefficient.
We fixed the elastic net mixing parameter α to 0. 5 and used leave-one-out crossvalidation to determine the regularisation parameter λ (cv. glmnet function from glmnet package). To be more stringent, the optimal regularisation parameter chosen was the one associated with the most regularised model with crossvalidation error within one standard deviation of the minimum. In order to identify the genes associated with the poor survival of the cluster Carcinoid B, we included in the model only the expression of the core genes of this cluster defined in the MOFA considering only the LNET samples (see section Multi-omics integration). We used the normalised read counts, and centred and scaled them using R package caret (v6. 0-80). The genes with non-zero estimated coefficients are listed in Supplementary Data 13.
For the methylation data, the M values were computed using the R package minfi (v1. 28. 3); the features of the training set were mean-centred and scaled to unit variance, then the test sample features were centred and scaled using the values learned from the training set. For each fold of the leave-one out, the training set was used for the feature selection. Based on the training set, we selected the most variable features, representing 50% and 5% of the total variation in expression and methylation data, respectively. The code used for the machine learning analyses (ML_functions. r) is available in the Supplementary Software 1 and the associated results are reported in Supplementary Data 1. Defining an Unclassified category: The random forest algorithm provides for each predicted sample the class probabilities.
We represented the Kaplan-Meier curves of the predictions groups by selecting the groups with >10 samples and gathering the unclassified samples in the same group. Using Cox's proportional hazard model and using the logrank test statistic (R package survival v2. 42-3) we compared the overall survival of LCNEC, atypical and typical samples based on the histopathological classification and based on the ML predictions (Supplementary Fig. 11A ). Forest plots were drawn using R package survminer (v0. 4. 3). The same survival analysis was performed using the ML predictions based on MKI67 expression only (Supplementary Fig. 11C ). Comparison between the supervised analyses of typical and atypical carcinoids. We contrasted the results of the different supervised analyses between typical and atypical carcinoids based on clinical data, specific markers (Ki67), machine learning, differential expression, and differential methylation (Supplementary Fig.
Acknowledgements We thank the patients donating their tumour specimens. We also thank  Prof. Roman K. Thomas ,  Dr. Martin Peifer ,  Dr. Julie George ,  Dr. Paul Brennan , and  Dr. Ghislaine Scelo  for their help with logistics. We also thank  Dr. Ricard Argelaguet  for his advice in using MOFA. This study is part of the lungNENomics project and the  Rare Cancers Genomics initiative  (www. rarecancersgenomics. com). This work has been funded by the  US National Institutes of Health  ( NIH R03CA195253  to L. F. C. and J. D. M. ), the  French National Cancer Institute (INCa ,  PRT-K-17-047  to L. F. C. and TABAC  17-022  to J. D. M. ), the  Ligue Nationale contre le Cancer  ( LNCC 2016  to L. F. C. ),  France Genomique  (to J. D. M. ), and the  Italian Association for Cancer Research (AIRC)  ( IG 19238  to M.
Data availability The exome sequencing data, RNA-seq data, and methylation data have been deposited in the European Genome-phenome Archive (EGA) database, which is hosted at the EBI and the CRG, under accession number EGAS00001003699. Other data sets referenced during the study are available from the EGA website under accession numbers EGAS00001000650 (pulmonary carcinoids)  11  , EGAS00001000708 (LCNEC)  12  , and EGAS00001000925 (SCLC)  13, 14. All the other data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding author upon reasonable request. A reporting summary for this article is available as a Supplementary Information file.
However, more longitudinal studies are needed to establish thresholds for biomarkers of diet quality in terms of cognitive health. Although each of these nutrients have been or are currently undergoing trial testing  [18]  (clinicaltrials. gov: NCT03613116), their combination may ultimately prove most beneficial through a variety of interactive mechanisms. For instance, the n-3 PUFAs have known anti-inflammatory and synaptogenic properties, and the HCy-lowering B vitamins may improve blood-brain barrier integrity  [19, 20] , one carbon  [21, 22]  and tau metabolism  [21] , whereas D may promote neurogenesis by maintaining day-to-day clearance of soluble amyloid-beta (Ab) oligomers from the central nervous system (CNS) into the periphery  [23] [24] [25] [26] [27] [28]. Nutrient biomarkers that include blood-based nutrients, subsequent metabolites, or metabolic indicators thereof can allow the objective assessment of diet quality and nutrient interaction in relation to cognitive decline and dementia risk.
8 m/s or. 5 seconds to walk 4 m. Participants with a Mini-Mental State Examination score below 24, dementia, difficulty in basic activities of daily living, and those taking n-3 PUFA supplements were excluded. Blood draw and banking of biospecimens yielded 729 volunteers with baseline blood samples available for nutrient biomarker analysis, of which, 712 had complete data to carry out hypothesis testing. The demographic and clinical characteristics of our analytic sample (n 5 712) and that of the MAPT participants missing baseline biospecimens (n 5 968) were similar in age (mean age, 75. 6 vs. 75. 1, P 5. 17 via t test), cognitive composite Z scores (20. 028 vs. 20. 011, P 5. 14 via t test), and education (no diploma: 23. 6% vs. 21. 8%; secondary education: 34. 3% vs. 33. 2%; high-school diploma: 14.
NRI and 3-year age-related cognitive trajectories in older adults In adjusted mixed-effects models, participants without nutritional risk (NRI-0) had superior baseline cognitive performance (Fig. 1A ; Table  3 ). The annual rate of decline on the cognitive composite Z score across all participants was 20. 008 standard units (SU)/y. Fig. 1   2 ). NRI-1 [D] was the only nutritional risk factor that independently associated with cognitive decline (b 5 20. 06 SU/y) (Fig. 1B ; Table  4 ). However, any two nutritional risk factors combined was associated with acceleration in rates of cognitive decline ranging from 20. 07 SU/y (NRI-2 [O3 1 HCy]) to 20. 09 SU/y (NRI-2 p[HCy 1 D]) (Fig. 1C ; Table  4 ). Further adjustment for baseline CDR (0 vs. 0. 5), APOE ε4 carrier versus not and their interaction with time produced no material changes to the estimated effects (data not shown).