dataesr/scientific-paragraphs-categorization huggingface dataset:
The 4-NQO model derived TGS discriminates OSCC from normal mucosa and identifies OSCC with specific genomic alterations In order to test the relevance of the 4-NQO model in human disease, we first determined whether the TGS was able to discriminate cancer vs. normal tissues in three datasets downloaded from public repositories, using arraybased or RNAseq expression profiles (Supplementary Table  7 ). A PCA and unsupervised hierarchical cluster analysis was performed using the TGS in two independent human datasets: GSE9844  [21]  and GSE30784  [22]. Two main clusters were observed in GSE30784 (Figure  2A ): the first one included no normal samples (0/45), 89% (151/169) of tumor samples and 59% (10/17) of dysplastic samples, whereas the second one included all normal samples (45/45), 11% (18/169) of tumor samples and 41% (7/17) of dysplastic samples (Fisher's exact test P<0.
0001). Similar results were obtained in GSE9844 (Figure  2B ) with one cluster including 92% (24/26) of OSCC of the tongue and no normal mucosa, while the other one included 8% (2/26) of OSCC of the tongue and all the normal samples (Fisher's exact test P<0. 0001). We then computed the enrichment score (ES) of each sample for the TGS using ssGSEA and consistently found higher ES in cancer vs. normal mucosa samples in GSE30784 (Figure  2C ) and GSE9844 (Figure  2D ). Interestingly, dysplasia had intermediate scores, some of them overlapping with tumors, while others overlapped with normal mucosa (Figure  2C ). These results were validated in a TCGA set  [23]  of 221 OSCC and 24 normal mucosa samples (Figure  2E ). 4-NQO is a DNA adductforming agent that serves as a surrogate of tobacco exposure.
Interestingly, we evidenced that TGS ES scores were higher in smokers and drinkers (SD) compared to never-smokers and never drinkers (NSND) (P=0. 0400) (Supplementary Figure  2 ). We then tested whether the 4-NQO model was representing a subgroup of OSCC. For this purpose, we performed an analysis integrating gene expression, mutation and copy number (CN) data available for 152 patients with OSCC in the TCGA. Although TGS ES was not associated with the percent of malignant cells, OSCC tumors were ranked according to the TGS ES (Figure  3 ). Tumors enriched for TGS were associated with copy number gains in 11q13. 3, a region that includes in particular FADD, MIR548K and PPFIA13. A significant correlation was found between the TGS ES and FADD absolute CN (r=0. 27, P=0. 0007) (data not shown).
Most of them were inhibitors of the MAPK/ERK pathway and of the cell cycle (Figure  4A ). The PGS ES was correlated with sensitivity to a variety of drugs. Those included an mTOR inhibitor (AMPK agonist) and an EGFR inhibitor (gefitinib), two targets that have been previously reported to prevent oral cancer development in the 4-NQO model  [26, 27]. Interestingly, all four MEK1/2 inhibitors included in the panel of 98 drugs were significantly correlated with the ES for EGS, IGS and PGS subsets (Figure  4A ). Consistent with the results obtained in the TCGA OSCC dataset (Figure  3A ), and using drug sensitivity and CNA data from CCLE  [28] , we observed a trend for association of copy number gain in 11q13. 3 with increased sensitivity to the two MEK1/2 inhibitors (PD-0325901 and AZD6244) (Figure  4B ) tested in 37 head and neck, esophagus and lung cell lines (Supplementary Table  8 ).
The 4-NQO derived gene sets in human OPL and their association with outcome Finally, we tested whether patients with OPL (oral premalignant lesions) enriched for 4-NQO derived gene subsets were at higher risk of developing OSCC. Gene expression profiles of 86 OPL collected in a prospective cohort of 86 patients were used (GSE26549)  [11]. In this study, 35 patients developed an OSCC as the median follow-up was 7 years (95% CI  [5. 6-8. 6] ). Enrichment scores of these 86 OPL were computed for TGS, EGS, IGS, LGS, and PGS. For each gene subset, 3 groups of patients were defined by dividing the ES into tertiles (low, intermediate, high score). The Kaplan Meier analysis revealed a tendency of association between a poor OCFS and higher ES for the EGS (Log-rank test p=0. 0529) and PGS (Log-rank test p= 0.
Overview of the results obtained across datasets In the 4-NQO model, enrichment of the NFKB pathway was observed at early stages, and the activation of MAPK/ERK signaling pathway, was enriched at both early (significant enrichment of the NTHI pathway) and late (significant enrichment of the TNFR1 pathway) stages (Figure  6A ). Consistently, we found that in vitro (Figure  6B ), sensitivity to inhibitors of the MAPK/ERK or NFKB pathways (IKK) were correlated to the ES of 4-NQOderived gene subsets. Similarly, IL4 pathway, that involves activation of the cell cycle, was found to be enriched in the 4-NQO model, consistent with the efficacy of CDK4/6, CDK1, and AURKB/C inhibitors cell cycle inhibitors in vitro. A trend toward association of EGS and PGS with a poor OCFS is in favour of the clinical relevance of association of EGS and PGS with these biological pathways (Figure  6C ).
DISCUSSION By analyzing the dynamics of gene expression changes in the 4-NQO model of oral tumorigenesis and by projecting those changes in various human in vitro and in vivo datasets, we have shown that the 4-NQO is a relevant mouse model for human OSCC with frequent CASP8 mutations and amplifications at 11q13. 3, that may be sensitive to MEK inhibition. This work is in line with a recent observation of cross-species genomic conservation between preclinical models of HNSCC and HNSCC in humans  [29]. We also illustrate the value of studying preclinical models of early tumorigenesis to help defining new strategies for prevention and therapy of HNSCC. CASP8 mutations have been reported in 8-10% HNSCC  [30] [31] [32]. They are considered as either lossof-function through suppression of apoptosis initiated by this apoptosis related-gene  [30] , or gain-of-function mutations conferring resistance to death receptor, or enhancing migration, invasion and tumor growth in vitro C.
Later, the concept of field cancerization was supported by the identification of similar genetic alterations in matched Figure  5 : Patients with oral preneoplasia enriched for the mouse early (EGS) or progressive (PGS) gene sets and oral cancer free survival (OCFS). Enrichment scores were computed as detailed in in 86 patients with oral preneoplasia included in a chemoprevention trial (GSE26549)  [11]  (see Material and Methods section). Higher enrichment scores for EGS (P=0. 0529) and PGS (P=0. 0495) were associated with poor oral cancer-free survival. Statistical significance was given by the log-rank test. dysplastic and malignant lesions from the oral cavity  [4]. The use of molecular markers has validated the concept of field cancerization. Within the context of the recently proposed paradigm termed "reverse migration", we tested the association between the enrichment of SCC cell lines for mouse-derived gene sets with drug sensitivity in order to identify new candidate drugs that may also benefit patients in the context of prevention  [38, 39].
Human datasets downloaded from public repositories for in silico analysis Raw data of 3 independent datasets were downloaded from GEO. These included a set of 26 microdissected OSCC of the mobile tongue and 12 matching normal mucosa (GSE9844)  [21] ; a set of 167 OSCC, 17 unpaired oral dysplasia, and 45 oral cavity or oropharynx normal mucosa collected in patients who underwent a surgery for diseases other than cancer (GSE30784)  [22] ; and a set of 86 expression profiles from OPL biopsies collected prospectively in a chemoprevention trial, with a median follow-up of 7 years (95% CI  [5. 6-8. 6] ) and available outcome (GSE26549)  [11]. A detailed description of these datasets is provided in Supplementary Table  7. The Cancer Genome Atlas (TCGA) was queried  [23]  (last query in July 2014).
Clinical and pathological information from 221 HPV negative OSCC and 24 matching normal oral mucosa were downloaded. Level 3 RNAseqv2 data were downloaded, corresponding to gene read counts that were generated from the IlluminaHiSeq platform and normalized using MapSplice  [45]  to do the alignment and RSEM  [46]  to perform the quantitation. Then a log2 transformation was performed. Mutation and copy number (CN) data were downloaded from cBioPortal  [47]. Among genes found to be significantly mutated by MutSig, we selected those mutated in at least 5% of HNSCC. The putative copy number (-2, -1, 0, 1 or 2) and the log 2 CN given by GISTIC for 310 genes with recurrent CN alterations (CNAs) in HNSCC were downloaded. Combined mutation and CN data was available for 152 patients with OSCC. Gene expression raw data for 51 cell lines as well as drug sensitivity data (half maximal inhibitory concentration, IC 50 ) for 144 drugs tested in those cell lines were downloaded as well from "The Genomics of Drug Sensitivity in Cancer" (GDSC) project  [24, 25].
Bioinformatics analysis Data analysis was performed using the Array Studio software (Omicsoft Corporation) and the Bioconductor packages in the R language (http://www. bioconductor. org)  [48]. Raw data from microarrays were processed using quantile normalization and the robust multi-array average (RMA) algorithm and were log 2 transformed  [49]. Hierarchical cluster analysis using the Pearson correlation and Ward linkage method and principal component analysis (PCA) using the first 2 components were performed to provide an overview of the data and visualize the distribution of various samples according to the similarities between their expression profiles. Both analyses used either genome-wide expression profiles or specific gene sets as defined above. Gene Set Enrichment Analysis (GSEA) was performed using the "pre-ranked" tool  [19].
GSEA is a robust computational method that determines whether an a priori defined set of genes shows statistically significant differences between 2 biological states (in our case tumor vs. normal). GSEA aims to interpret large-scale expression data by identifying pathways and process. The input data for GSEA procedure were the following: i-a complete table of genes ranked according to the log 2 transformed FC between tumor and normal mucosa, ii-a mapping file for identifying transcripts in the corresponding platform (GeneChip Mouse Gene ST 2. 0 Array), and iii-a catalogue of functional gene sets from Molecular signature Database (MSigDB database v4. 0 updated May 31, 2013, GSEA/ MSigDB web site v4. 02 released  Jan 18, 2014). A total of 4722 curated gene sets (canonical pathway, chemical and genetic perturbations, BioCarta, GenMAPP, and KEGG gene sets) and 189 oncogenic signatures were available.
Default parameters were used. Inclusion gene set size was set between 15 and 500 and the phenotype was permutated 1,000 times. The single-sample GSEA (ssGSEA) projection tool from GenePattern  [50]  was used to compute separate enrichment scores (ES) for each sample of a given dataset using defined gene sets i. e. TGS, IGS, LGS, PGS subsets and BioCarta pathways from the Molecular Signature Database (MSigDB database v4. 0 updated May 31, 2013; v4. 02 released  Jan 18, 2014, ref 20). The gene expression values for a given sample are rank-normalized, and an ES is produced using the empirical cumulative distribution functions of the genes in the gene set and the remaining genes  [19, 20]. When UP and DN versions of a gene set are available, a combined score was computed. A rate of ES was computed for early (EGS), intermediate (IGS), late (LGS) and progressive (PGS) gene sets at each step of oral tumorigenesis: from "normal to hyperplasia" (H->N), from "hyperplasia to dysplasia" (D->H), and from "dysplasia to tumor" (T->D), using the following formula:.
Statistical analysis Unpaired two-sided Student's t-test or nonparametrical Mann-Whitney test were performed to compare continuous data between two groups and oneway ANOVA or Kruskal-Wallis test if more than 2 groups. Pearson's χ2 test or Fisher's exact test were used to analyze qualitative data. The Pearson Correlation Coefficient (r) was also estimated to measure the strength of a linear association between two continuous variables. The r coefficient can take values from +1 (positive association) to -1 (negative association). A value of 0 indicates no association. About the analysis of the 86 samples from GSE26549 dataset, the median follow-up was estimated with the inversed Kaplan Meier method. Oral Cancer Free Survival (OCFS) distributions were estimated using the Kaplan-Meier method and compared with the Log-Rank test between groups of patients defined by tertiles of gene set enrichment scores (low, intermediate and high score).
OCFS was defined as time from first biopsy to oral cancer or to the date of last follow-up (for censored patients). Univariate and multivariate Cox proportional hazard model, including age and histology, were built to investigate the potential association between the ES for TGS, EGS, IGS, LGS and PGS and oral cancer-free survival. All statistical tests were two-sided, and P-values <= 0. 05 were considered to be statistically significant. The statistical analysis was performed using GraphPad Prism version 6. 00 (San Diego, SA) and SAS software version 9. 3 (SAS Institute Inc, Cary, NC, USA).
Histomorphometric measurements Histomorphometric measurements were completed using Image J (National Institutes of Health, Bethesda, MD, USA). To evaluate osseointegration, bone-implant contact (BIC) was measured on histologic sections (enlargement of ×25). The total length of bone in contact with the implant was measured, and the percentage of the threaded portion of the implant in contact with the bone was calculated. The bone height was calculated by measuring the distance between the buccal or alveolar wall crest and the marginal level of the implant using Image J software on histology sections compared with the measures obtained before the operations.
Studies regarding French adult population are very scarce and did not include representative samples. Thus at least 1 decayed tooth needing treatment was found in 33% of the 35-44 years old adults attending a dental consultation in medical centers of the national health insurance between 1999 and 2004  [16]. In another study, at least one untreated decayed tooth was found in 40% of the in the 30-50 years old adults attending a dental consultation in the farmers insurance dental centers (farmers, response rate to dental visit invitation = 22%)  [17]. Therefore representative epidemiological data on the adult population in France are missing, as stressed also by the French National Authority for Health (Haute Autorité de Santé, HAS) in its 2010 data review and recommendations  [18]. In this study we evaluated the prevalence of self-reported need for dental care in a representative sample of the adult population of the Paris region.
Data sources The SIRS (French acronym for "Health, Inequalities and Social Ruptures") cohort study aims at studying the individual and contextual determinants of health  [19]. The cohort population is representative of French-speaking adults living in Paris and its suburbs. In 2005, 3,000 people were randomly selected using a three-stage stratified sampling method (50 districts, 60 housing units per district and one adult per housing unit using the birthday method) (refusal rate of 22%). In 2010, participants were contacted again to carry out a new survey. Among the adults enrolled in 2005, 47% could be interviewed again. Indeed, 2. 6% had died, 1. 8% were too sick to participate, 13. 9% had moved out of the Paris area, 2. 7% were absent during the survey, 18. 4% refused to answer and 13. 4% could not be contacted.
Statistical analysis: We estimated the prevalence of the self-reported dental care need relative to each of the variables described above. For each variable, we tested the association with the need for dental care and calculated the level of significance. We then applied a logistic regression model to the unweighted data (adjusted for age and sex) in order to identify variables associated with the need for dental care. To identify the most strongly associated variables, we constructed an initial model that included all variables with a significance level lower than 0. 20 in the previous prevalence analysis. The final model retained the most strongly associated variables after a step-by-step backward selection procedure. We set the type I error to 0. 05. We performed all statistical analyses with StataCorp version 11.
Data Sources and Literature Search A systematic review with meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines  (Moher et al. , 2015). Relevant articles published in the English language were identified up to December 2015, from MEDLINE, EMBASE, and Cochrane Library databases. Gray literature was also explored by searching non-published randomized controlled trials (RCTs) in ICTRP (WHO), OpenSIGLE, and ClinicalTrials. gov registers. Finally, electronic searches limited to abstracts were conducted in the main dental and periodontal journals (i. e. , Journal of Dental Research, Journal of Periodontology, Annals of Periodontology, Clinical Advances in Periodontics, Journal of Clinical Periodontology, Journal of Periodontal Research, and Oral Microbiology and Immunology).
Study Selection The selection included RCTs with (i) patients receiving systemic administration of AMX + MTZ as an adjunct to NSPT (test group) vs. patients receiving either NSPT alone or with a placebo (control group); (ii) microbiological technologies aiming to identify P. gingivalis and/or A. actinomycetemcomitans from subgingival plaque samples; (iii) a follow-up of at least 3 months; (iv) dichotomous data indicating the presence/absence of P. gingivalis and A. actinomycetemcomitans. Exclusion criteria were the following: (i) studies published in non-English language, (ii) non-randomized trials, (iii) trials that included surgical periodontal therapy.
Study Characteristics The following data were extracted from each study in blinded conditions by two independent investigators (AD and AB): first author, year of publication, country, definition of cases, sample size with loss of follow-up, mean age, sampling strategy, microbiological technology, follow-up duration, type of treatment, antibiotic regimens, maintenance regimen, and main results. The quality assessment of studies was evaluated in blinded conditions by two independent investigators (AD and AB) using the Cochrane Collaboration tool for assessment of the risk of bias. This tool assesses seven main criteria (sequence generation, allocation concealment, blinding of participants and staff, blinding of outcome examiners, incomplete outcome data, selective outcome reporting, and other sources of bias), all recorded as adequate, unclear, or inadequate.
Literature Search After removal of the duplicate articles, 142 studies were identified from the electronic search. Reading titles and abstracts reduced the number to a total of 32 eligible studies. One eligible nonpublished randomized clinical trial was identified in the gray literature (ICTRP database, Trial ID: ISRCTN17605083). The corresponding author was contacted but data were not obtained. After reading of the full articles, 15 studies were excluded. Reasons for exclusion are described in Supplemental Table  3. Finally, 13 studies were included in the meta-analysis: data of interest were found directly in seven studies  (Winkel et al. , 2001; Rooney et al. , 2002; Ehmke et al. , 2005; Cionca et al. , 2010; Mestnik et al. , 2010; Aimetti et al. , 2012; Guerrero et al. , 2014). Unpublished data were obtained from the authors in six studies  (Xajigeorgiou et al.
Also, a strict study selection was performed. Studies with less than 3-month follow-up were excluded because previous observations have found that most clinical and microbiological improvements were observed 3 months after NSPT and antimicrobial therapy  (Badersten et al. , 1984; Berglundh et al. , 1998; Winkel et al. , 2001; Haffajee et al. , 2006; Cionca et al. , 2010). Moreover, unpublished data were collected and gray literature was explored. Therefore, the sample size of the meta-analysis resulted relatively large (536 subjects at baseline; 13 RCTs). In addition, the 12-month follow-up strengthened the outcomes of RCTs, showing a tendency of bacterial recolonization over time. However, the present study has also some limitations. Moderate to high heterogeneity was found, probably due to the differences between the included studies.
ACKNOWLEDGMENTS The authors would like to thank  Professors George Hajishengallis  and  Flavia Teles  for their invaluable advices. The authors also thank  Professors Eduardo Feres-Filho  ( Silva-Senem  et al. , 2013),  Poliana Mendes Duarte  (Miranda et al. , 2014),  Magda Feres  (Matarazzo et al. , 2008;Silva et al. , 2011;Soares et al. , 2014), and  Dimitra Sakellari  (Xajigeorgiou et al. , 2006) for providing unpublished data.
Methods: The incidence data for oral cavity and oropharynx cancers for South America were obtained from Cancer Incidence in Five Continents/International Agency for Research on Cancer for the period 1998-2007. Mortality rates in South America were obtained from the World Health Organization/IARC database for the period 1999-2012. The number of cases for Brazil was obtained from the National Cancer Institute/missing stage for the period 2000-2010, whereas the Sa ˜o Paulo cancer registry was used to collect data from the most populated state in Brazil for the period 2000-2008. Results: The incidence of oral and oropharynx cancers in South America varied, with the highest rates observed in Brazil among males. The mortality data in selected South American countries ranged from 0. 72 to 6. 04/100,000 and the proportion of illdefined deaths in South America varied from 5.
] [11]  Although the Brazilian National Institute of Cancer estimated 11,280 new cases in men and 4010 in women to be diagnosed in 2014,  12  determining the real incidence and trends of these cancers in Brazil and the other South America countries is still a challenge due to major gaps in the available public databases or the complete absence of such databases in some regions. Therefore, the objective of this study is to describe the incidence and mortality rates of oral and oropharynx cancers in South America using available public databases, not only describing their epidemiology but also discussing the main pitfalls for acquiring reliable data.
Material and methods The incidence data of oral cavity (International Classification of Diseases (ICD)-O3:C02-C06) and oropharynx (ICD-O3: C01, C09 and C10)  13   Data on mortality for lip, oral cavity, and other pharynx cancers for South America were obtained from the World Health Organization (WHO/IARC) database in the period 1999-2012 for the following South American countries: Argentina, Brazil, Bolivia, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay and Venezuela (accessed 20 February 2016). Mortality trends for these neoplasms were modelled using the linear regression analysis software Joinpoint (version 4. 2. 0. 2) (INCA, 2015). 12.
The majority of oral cavity and oropharynx cancers registered in the INCA database (Integrador RHC/INCA) is in southeast Brazil, with a M:F ratio of 3. 58:1 for oral cavity cancers and 6. 20:1 for oropharynx cancers (Table  2 ). From HCR in Brazil (in 2013, there were 245 HCRs registered), we identified 61,840 cases of oral cavity and oropharynx cancers in the period 2000-2010 (75% and 84% of the oral cavity and oropharynx cancers occurred in males, respectively). Regarding the clinical stage of the registered cases, we observed that advanced tumours (stage III/IV) accounted for the majority, whereas missing stage information represented about 29% of all oral cavity and oropharynx cancers (Table  3 ). According to the HCRs available in the FOSP files for oral cavity and oropharynx cancers for the period 2000-2008, there were 13,025 cases (8056 cases affecting the oral cavity (61.