dataesr/scientific-paragraphs-categorization huggingface dataset:
Fecal CRC markers reflect enrichments in tumor biopsies In order to make use of functional data extracted from the metagenome, fecal samples would need to reflect, at least partially, the microbial composition in the tumor environment. However, profiling colonic tissue samples with shotgun metagenomic sequencing is still ineffective due to excessive contamination with human DNA  (Castellarin et al, 2012; Kostic et al, 2012). As an alternative, targeted sequencing of prokaryotic 16S rRNA gene (16S) fragments from tumor biopsies allows for taxonomic abundance estimation and identification of enriched microbes compared to nearby intact mucosa. For this, we newly sequenced 48 tumor-normal tissue pairs (13 from patients that were also part of study population G) and reanalyzed 79 such deeply sequenced pairs from a published study with US American, Vietnamese, and Spanish patients  (Kostic et al, 2012)  (see Supplementary Dataset S2 and Materials and Methods).
To be able to distinguish relevant differences between the microbial communities at the tumor site and in stool from technical disparities due to different sample processing methods, we generated a third dataset of 116 fecal samples (part of study population F, see Supplementary Dataset S2) that were subjected to 16S amplicon sequencing. To profile the taxonomic composition of 16S samples, we constructed operational taxonomic units (OTUs, 98% sequence identity) from comprehensive databases of published 16S sequences (see Materials and Methods) and mapped 16S reads against these. To facilitate comparisons across datasets, OTUs were further matched to the marker species from the fecal metagenomic signature of study population F (using a best-hit approach based on the respective 16S fragments, see Materials and Methods).
When comparing relative abundances between datasets (Fig 3), both fecal CRC marker species from the Fusobacterium genus showed a consistent enrichment at the tumor site, as was expected from previous studies  (Castellarin et al, 2012; Kostic et al, 2012; Warren et al, 2013 ). Higher detection rates were observed in the tumor microenvironment compared to feces (Fig  3 ), suggesting a dilution effect that ◀ Figure  1. Signature of CRC-associated gut microbial species. A Relative abundances of 22 gut microbial species, collectively associated with CRC, are displayed as heatmap in the left panel as fold change over the median relative abundance observed in controls (indicated to the right); the control group included neoplasia-free and small adenoma patients. The mean contribution of each marker species to the classification is shown to the right (bars correspond to log-odds ratio in logistic regression; numbers indicate percentage of absolute total weight, see Materials and Methods).
Different cancer stages are color-coded below the heatmap (see Table  1 , Supplementary Table  S1  and Supplementary Dataset S1 for patient data). Below, the classification score of the microbial signature (from cross-validation) is shown as gray scale (see key) with the decision boundary and resulting false positives and true positives indicated in red (using colonoscopy results as a ground truth). Displayed alongside are the results of the standard Hemoccult FOBT routinely applied for CRC screening and an experimental CRC screening test based on methylation of the wif-1 gene, a Wnt pathway member  (Lee et al, 2009; Mansour & Sobhani, 2009)  (see main text for details). B Test accuracy of the metagenomic classifier is depicted as ROC curve summarizing mean test predictions made in ten times resampled tenfold cross-validation on study population F (N = 141, 95% confidence intervals of true-positive rate are shaded, see Materials and Methods and Table  1 ).
Additionally, the accuracy of the wif-1 methylation test  (Lee et al, 2009; Mansour & Sobhani, 2009)  as well as of the FOBT is shown (as assessed for the same patients). A combination test, in which the FOBT results and microbial abundance profiles were jointly used as predictors, resulted in significantly enhanced accuracy over both the metagenomic classifier and the FOBT alone, compared to which the relative gain in sensitivity is > 45% at the same specificity (*denotes one-sided bootstrapping P-values < 0. 05 of TPR improvement over FOBT and of difference in the whole ROC curve to the metagenomic test, respectively, see Materials and Methods). All screening tests are evaluated relative to colonoscopy findings (see key and main text for details; see also Supplementary Figs S4, S5 and S6 for additional details on the classifier, and Table  1 , Supplementary Table  S1  and Supplementary Dataset S1 for patient data).
C When applied to the larger study populations G and H (335 metagenomes from several countries including 38 from German CRC patients) for external validation, the metagenomic classifier achieved very similar accuracy as in cross-validation, as measured by the area under the ROC curve (AUC) of mean test prediction scores (ROC curve and confidence intervals as in (A); see also Supplementary Figs  S5  and S6  and Table 1 , Supplementary Table  S1  and Supplementary Dataset S1). D Sensitivity (TPR) of the metagenomic classifier for carcinomas in early stages (AJCC stages 0, I, and II) was similar as for late-stage, metastasizing CRC (AJCC stages III and IV) in both study populations F and G highlighting its potential utility for early detection (see also Despite this, most of the abundance differences in feces between CRC patients and tumor-free controls were at least as significant as between tumor and normal tissue in the datasets compared here.
We further observed increased abundance of the third CRC marker species, Peptostreptococcus stomatis, in CRC consistent with biopsies, but this trend was only significant in the published dataset  (Kostic et al, 2012). Where comparability could be established across sequencing technologies, most metagenomic marker species with significantly decreased abundance in CRC patients from study population F also showed similar abundance changes in normal tissue compared to tumor, as it was the case for Eubacterium spp. and Streptococcus salivarius (Fig 3  and Supplementary Fig S9A). These results indicate that similar trends for the relative abundances of marker species between fecal and biopsy samples from CRC patients are detectable despite the apparent differences in patient nationality, sample origin, experimental techniques, and analysis methodology.
Significance was assessed by unpaired and paired Wilcoxon tests for fecal and biopsy datasets, respectively (*nominal P-value < 0. 05, **P-value < 0. 005, ***P-value < 0. 0005). Note that for the majority of the species shown, the significance for distinguishing CRC patients from controls is higher (lower P-value) in metagenomic than 16S readouts. Vertical bars display the prevalence (prev. ) of CRC marker species per patient/sample group (percentage of individuals in which these species/OTUs were detected with a relative abundance exceeding 1E-5). ª   (Warren et al, 2013 ): Previously, it was noted that several CRC-associated bacteria, for example, Fusobacterium spp. , were first described as oral pathogens, and it has been hypothesized that their invasion of the gut microbiome might cause or contribute to tumorigenesis  (Warren et al, 2013).
Our results indicate functional and taxonomic associations with CRC and conceptually establish the possibility of CRC detection from fecal microbial markers. Their future application in mass screening will depend on the development of cost-effective assays. Toward this goal, we assessed whether 16S sequencing of fecal samples would be a suitable alternative. A 16S-based classifier for CRC detection, cross-validated on the subset of study population F for which we had also generated 16S data (N = 116), achieved almost as good an accuracy (AUC 0. 82; Supplementary Fig S10 ) as the models based on metagenomic community profiles (AUC 0. 84-0. 87; Supplementary Fig  S10 ). While our work was under review, another study  (Zackular et al, 2014)  independently arrived at the conclusion that 16S sequencing of fecal samples would allow for accurate CRC screening based on classification models that showed some overlap with our CRC markers at higher taxonomic ranks.
But because these were trained on a smaller dataset and were neither cross-validated nor validated in external datasets, it is questionable whether the reported accuracy would be maintainable in other study populations. While our results of 16S-based CRC detection underline the potential of microbiota for cancer screening, for future development of economic and robust PCR-based assays, it remains to be seen how the 16S gene compares to metagenomics-derived marker genes  (Mende et al, 2013). Moreover, additional metagenomic data will enable more detailed investigation of cancer-associated differences in gene function, gene content, and genomic variation  (Schloissnig et al, 2013) , serving as a starting point for proposing testable mechanistic hypotheses about the roles of microbiota in cancer onset and progression.
Metagenomic sequencing Whole-genome shotgun sequencing of fecal samples collected in France and Germany was carried out on the Illumina HiSeq 2000/ 2500 (Illumina, San Diego, USA) platform. All samples were pairedend sequenced with 100-bp read length at the Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, to a targeted sequencing depth of 5 Gbp (see Supplementary Dataset S1 for sequencing results). 16S rRNA gene sequencing DNA from 48 tissue sample pairs (tumor and healthy mucosa) and 129 fecal samples (which are a subset of study population F) was amplified using primers targeting the V4 region of the 16S rRNA gene (F515 5 0 -GTGCCAGCMGCCGCGGTAA-3 0 , R806 5 0 -GGACTACHVGGGTWTCTAAT-3 0 )  (Caporaso et al, 2011). PCR was carried out according to the manufacturer's instructions of the Q5 high-fidelity polymerase (New England BioLabs, Ipswich, USA) using bar-coded primers (NEXTflex TM 16S V4 Amplicon-Seq Kit, Bioo Scientific, Austin, Texas, USA) at final concentrations of 0.
Data analysis Taxonomic profiling of fecal samples Using MOCAT (option screen with alignment length cutoff 45 and minimum 97% sequence identity), taxonomic relative abundance profiles were generated by mapping screened high-quality reads (see below for details) from each metagenome to a database consisting of 10 universal single-copy marker genes extracted from 3,496 NCBI reference genomes  (Mende et al, 2013; Sunagawa et al, 2013). Quantification proceeded in two steps, by first estimating taxonomic abundances from all sequenced DNA fragments (nucleotide counts; each read contributing number of counts equal to its length) that mapped uniquely, and in a second step, nucleotide counts from reads mapping to multiple taxa with the same alignment score were distributed among them proportionally to nucleotide counts originating from reads uniquely mapped to these taxa.
Finally, base counts were gene length-normalized (option profiling). Abundance estimates at species level were made based on a recently proposed consistent species-level clustering  (Mende et al, 2013) , while abundance summarization at higher taxonomic levels was based on the NCBI Taxonomy (Supplementary Figs  S2  and S3 ). Taxonomic profiling of tissue and fecal samples using 16S rRNA gene sequences Raw sequencing data were quality-controlled as described below for the generation of the metagenomic gene catalog (minimum read length = 45 bp; minimum base quality score = 20). Paired-end reads were merged using the SeqPrep software (https://github. com/ jstjohn/SeqPrep) requiring perfect overlap between the high-quality paired-end reads. After excluding reads shorter than 200 nt or with more than five ambiguous bases, we aligned them to the SILVA 16S reference database of bacterial and archaeal 16S rRNA sequences.
These alignments were cropped to only retain the region spanning V3 to V4 using mothur, version 1. 30. 2  (Schloss et al, 2009). With the same screening and alignment routines, we reanalyzed 16S rRNA sequencing reads from  Kostic et al (2012)  to facilitate comparisons across datasets. For further analysis, we only retained sample pairs with at least 1,000 reads aligned in the tumor as well as the normal tissue sample (a requirement met by 79 sample pairs from  Kostic et al (2012) ). The boundaries of this core alignment were adjusted to accommodate both the pyrosequencing reads from  (Kostic et al, 2012)  and ours from the MiSeq platform, by first separately determining the region spanned by 90% of the reads from each sequencing platform and second by interval merging to cover both of these regions.
For high-resolution taxonomic profiling, we built OTUs from large collections of published 16S rRNA genes. We included 436,028 sequences from the SILVA database (version 115  (Pruesse et al, 2007) ) and 5,224 extracted from the prokaryotic genome sequences used for taxonomic profiling of metagenomic reads (see above). These were aligned with the same protocol as the 16S reads and cropped to the core alignment region using mothur. Subsequently, we removed redundancy by dereplication (mothur's unique. seqs) and clustering at 98% sequence identity using UCLUST (version 6. 0. 307 with the option -maxrejects 1000 for more accurate matching)  (Edgar, 2010). As an initial quantification and ordering for the heuristic UCLUST algorithm, we mapped 10,000 reads from each sample against the reference sequences and sorted them according to the number of mapped reads using USEARCH routines (98% ID, best-hit).
Functional annotation of the metagenomic gene catalog The metagenomic gene catalog was annotated by aligning the translated amino acid sequence of each gene to the KEGG  (Kanehisa et al, 2008)  (version 62) ortholog database using BLAST  (Altschul et al, 1990 ) (version 2. 2. 24) (max. e-value 0. 01) and then annotating the genes using SmashCommunity  (Arumugam et al, 2010 ) (version 1. 6). CAZy  (Cantarel et al, 2009)  annotations were made using the dbCAN pipeline  (Yin et al, 2012)  with the recommended default settings; additional substrate information for CAZy families was obtained from CAZypedia (http://www. cazypedia. org/index. php? title=Carbohydrate-binding_modules&oldid=9411, assessed 28 October 2013) and from  Cantarel et al (2012).
Relative abundance transformations and abundance filtering For all subsequent analyses, read counts were transformed into relative abundances (by dividing through the total number of reads per sample, including high-quality reads that could not be mapped to reference databases or annotated). For display purposes and fold change calculations, we applied a logarithmic transformation to relative abundances using the function log 10 (x + x 0 ), where x are the original relative abundances and x 0 a small constant (1E-6 for taxonomic and 1E-8 for functional features). As an unsupervised feature reduction technique (that is independent of any participant metadata), we applied a low-abundance filter, which discarded functional and taxonomic features whose relative abundance did not exceed 0. 0001 and 0.
001, respectively, in any sample (for combined taxonomic and functional features, we used an abundance cutoff of 0. 0001). Analysis of diversity and community structure Species abundances (using the species delineation from  Mende et al (2013) ) were used to calculate Shannon diversity index and species richness for each sample in study population F using the diversity and specnumber functions, respectively, of the vegan R package (http://cran. r-project. org/web/packages/vegan/index. html). Differences between tumor-free and CRC patients were assessed by the Kruskal-Wallis test (Supplementary Fig  S1D  and E ). Gene richness (the number of genes from the metagenomic gene catalog with nonzero abundance) was calculated for each sample from study population F after rarefying to 3 million reads per sample; differences were evaluated using the Kruskal-Wallis test  (Supplementary Fig S1F).
, to the nearest cluster center). Differences in enterotype composition between CRC patients (all stages) and tumor-free controls (some with adenomas) of study population F were assessed using  the Fisher test (Supplementary Fig S1B). Additionally, we subjected study population F to a PCoA independently of other datasets and investigated the separation of CRC cases from controls (neoplasia-free participants and patients with small adenomas) along principal coordinates; significance was assessed using the Wilcoxon test (Supplementary Fig S1G  -J ). To assess whether differences in such high-level descriptors of microbial community structure are useful for CRC detection, we built a logistic regression model with the ten first principal coordinates (from Supplementary Fig S1G ) and the Bacteroidetes to Firmicutes ratio  (Supplementary Fig S1C)  as predictors.
3 Partitioning data for tenfold stratified cross-validation (we resampled dataset partitions ten times to obtain more stable accuracy estimates). 4 Fitting a LASSO model on the training data of each cross-validation fold: The LASSO hyperparameter was optimized for each model in a nested fivefold cross-validation on the training subset using the area under the precision-recall curve as model selection criterion and also enforcing at least five nonzero coefficients. To assess the effect of taxonomic resolution on CRC detection accuracy in study population F, we reran the described classification pipeline for taxonomic abundance profiles summarized at the genus and phylum level  (Supplementary Fig S6D). Similarly, the predictivity of functional metagenomic features (relative abundances of KEGG modules and CAZy gene families for study population F) was assessed using the same modeling pipeline  (Supplementary Fig S6E).
Finally, we also built and cross-validated such a model based on a combination of species, KEGG modules and CAZy families as input features on study population F (Supplementary Fig  S6E  and G ). To explore the effect of sequencing method on the ability to detect CRC, we also applied the classification pipeline to 16S OTU abundance profiles from the subset of study population F for which we had obtained fecal 16S sequencing data (41 CRC cases and 75 controls with small adenomas or without any neoplasia; Supplementary Fig S10 ). External model validation, model comparison and confounder analysis Independent (holdout/external) validation consisted of two steps: (1) application of the feature filtering and normalization using the same parameters as for cross-validation data (i. e. , discarding features according to the cross-validation low-abundance filter and applying the log-standardization with mean and standard deviation values as estimated in cross-validation, see above) and (2) application of the trained LASSO models to make predictions on the validation data.
To more globally assess similarities between microbiota at the tumor relative to nearby normal tissue and fecal taxonomic profiles of CRC patients relative to those of tumor-free controls, we compared microbial composition at the genus level. For 16S samples, the genus identity of each OTU was inferred using the RDP classifier  (Wang et al, 2007) , while for metagenomic stool samples, a reference-based approach was taken (see above)  (Kultima et al, 2012). Genera (after low-abundance filtering, see above) with significant differential abundance between tumor and normal tissue or CRC patients and controls (FDR-corrected P < 0. 1 and abundance fold change > 5) were determined separately for each datasets (141 fecal metagenomics samples from study population F, 116 fecal 16S samples, which are part of study population F, 48 16S biopsy sample pairs from this study, and 79 16S biopsy sample pairs from  Kostic et al (2012) ; see Table  1 , Supplementary Table  S1  and Supplementary Dataset S1) using unpaired and paired Wilcoxon tests for fecal samples and tumor-normal tissue pairs, respectively.
Based on the log-transformed relative abundances of the union set of differentially abundant genera from any of the datasets, we conducted a joint PCA of all samples (Supplementary Fig  S9B ). Although sample origin and technical differences between 16S and shotgun metagenomic sequencing were detectable in this PCA, it revealed a separation between tumor and control samples along the first principal component that was common to all the datasets analyzed  (Supplementary Fig S9B).
Targeted analysis of cancer-related gene functions and toxins To explore whether genes encoding known bacterial toxins might be enriched in the metagenomes from CRC patients, we collected protein sequences of toxins that have previously been implicated in intestinal diseases, mainly colorectal cancer, from the literature  (Fasano, 2002; Dutilh et al, 2013). With these, we performed BLAST  (Altschul et al, 1990)  searches against NCBI nr as well as an inhouse database of 3,496 high-quality bacterial reference genome sequences and manually selected additional bona fide members for each toxin gene family. Subsequently, we aligned the sequences from each toxin family using Clustal Omega  (Sievers et al, 2011)  and built HMM sequence profiles from these alignments using HMMer 3. 0  (Eddy, 2011 ).
Patient recruitment at the University Hospital Heidelberg (Germany) 38 colorectal cancer patients without a medical history of inflammatory disease were selected from the ColoCare Study (study population G) (see Supplementary TableS1and Supplementary Dataset S1 for patient metadata). ColoCare is an international prospective cohort study recruiting newly diagnosed colorectal cancer patients prior to surgery in Germany and the USA. Recruitment sites in Heidelberg are the Department of Surgery at the University Hospital Heidelberg and the affiliated Hospital Salem. Written informed consent was obtained from all study participants. The study protocol was approved by the ethics committee of the Medical Faculty at the University of Heidelberg and by the respective institutional review board (EMBL Bioethics Internal Advisory Board) and is in agreement with the WMA Declaration of Helsinki and the Department of Health and Human Services Belmont Report.
This work has received funding through the  CancerBiome project (European Research Council  project reference  268985 ), the  METACAR-DIS  project ( FP7-HEALTH-2012-INNOVATION-I-305312 ), the  International Human Microbiome Standards project  ( HEALTH-F4-2010-261376 ), a French LNCC 2004 grant, ACD grants, and in part through the  PHRC 2009 Vatnimad grant.
Data availability The shotgun metagenomic sequencing data and the 16S rRNA amplicon sequencing data from this study are available from the European Nucleotide Archive (ENA) database (http://www. ebi. ac. uk/ena): accession number ERP005534. Taxonomic abundance profiles derived from metagenomics data are provided as Supplementary Dataset S3. Published metagenomics datasets analyzed here are available from ENA: accession number ERA000116  (Qin et al, 2010).
Data collection for 16S rRNA gene sequencing from colonic tissue Patient recruitment at the University Hospital Heidelberg (Germany) Colonic tissue was collected from 48 patients undergoing colorectal cancer surgery at the Department of Surgery, University Hospital Heidelberg after obtaining informed consent. Written consent procedure and the study protocol were approved by the ethics committee of the Medical Faculty at the University of Heidelberg (see Supplementary Dataset S2 for participant metadata). Georg Zeller et al Colorectal cancer detection from fecal microbiota Molecular Systems Biology 2011). Whole fresh stool was collected in sterile boxes, and 10 g only lead to a slight (conservative) overestimation of false-positive was frozen at À20°C within 4 h and deposited at the Henri Mondor CRC predictions in study population H.