dataesr/scientific-paragraphs-categorization huggingface dataset:
The classification was validated in the remaining 123 samples plus an independent set of 1,058 CC samples, including eight public datasets. Furthermore, prognosis was analyzed in the subset of stage II-III CC samples. The subtypes C4 and C6, but not the subtypes C1, C2, C3, and C5, were independently associated with shorter relapse-free survival, even after adjusting for age, sex, stage, and the emerging prognostic classifier Oncotype DX Colon Cancer Assay recurrence score (hazard ratio 1. 5, 95% CI 1. 1-2. 1, p = 0. 0097). However, a limitation of this study is that information on tumor grade and number of nodes examined was not available.
Patients The French national Cartes d'Identitedes Tumeurs (CIT) program involves a multicenter cohort of 750 patients with stage I to IV CC who underwent surgery between 1987 and 2007 in seven centers. Fresh-frozen primary tumor tissue samples were retrospectively collected at the Institut Gustave Roussy (Villejuif), the Hopital Saint Antoine (Paris), the Hopital Europeen Georges Pompidou (Paris), the Hopital de Hautepierre (Strasbourg), the Hopital Purpan (Toulouse), and the Institut Paoli-Calmettes (Marseille), and prospectively collected at the Centre Antoine Lacassagne (Nice). Patients who received preoperative chemotherapy and/or radiation therapy and those with primary rectal cancer were excluded from this study. Clinical and pathologic data were extracted from the medical records and centrally reviewed for the purpose of this study.
Patients were staged according to the American Joint Committee on Cancer tumor node metastasis (TNM) staging system  [2]  and monitored for relapse (distant and/ or locoregional recurrence; median follow-up of 51. 5 mo). Patient and tumor characteristics are summarized in Table  1  and detailed in Table  S1. Of the 750 tumor samples of the CIT cohort, 566 fulfilled RNA quality requirements for GEP analysis (Figure  S1 ). The 566 samples were split into a discovery set (n = 443) and a validation set (n = 123), well balanced for the main anatomoclinical characteristics (Table  1 ). The validation set also included 906 CC samples available from seven public datasets (GSE13067, GSE13294, GSE14333, GSE17536/17537, GSE18088, GSE26682, and GSE33113). These datasets corresponded to all available public datasets fulfilling the following criteria: available GEP data obtained using a similar chip platform (Affymetrix U133 Plus 2.
0 chips) with raw data CEL files, and tumor location and either common DNA alteration (n = 457) and/or patient outcome (n = 449) data available. Within the discovery (n = 443) and the validation (n = 1,029) sets, 359 and 416 patients with stage II-III CC and documented relapse-free survival (RFS) were available for survival analysis, respectively (Figure  S1 ). The dataset from The Cancer Genome Atlas (TCGA)  [13] , although obtained using a non-Affymetrix platform and therefore analyzed separately, was added to the validation set because of the extensive DNA alteration annotations provided for 152 CC samples.
Gene Expression Analysis The GEP of 566 primary CC samples were determined on Affymetrix U133 Plus 2. 0 chips. For 19 patients, adjacent nontumor tissue (normal tissue  [NT] ) was also available and was tested. The methods used for RNA purification, quality control, fluorescent probe production, hybridization, and raw data processing were as previously described  [18]. Each dataset was normalized independently in batches using the robust multi-array average method implemented in the R package affy  [19]. For the CIT dataset, residual technical batch effects were corrected using the ComBat method implemented in the SVA R package  [20]. Data are available via the NCBI Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/; accession number GSE39582).
Array-Based Comparative Genomic Hybridization Analysis A total of 464 of the 750 primary CC samples from the CIT cohort could be analyzed for array-based comparative genomic hybridization (CGH) on a BAC array containing 4,434 bacterial artificial chromosome clones with a median resolution of 0. 6 Mb. DNA labeling, hybridization, and data processing were as previously described  [21]. CIN was defined from CGH profiles: samples with at least 20% gain or loss of whole chromosomes or fractions of chromosomes were scored as CIN+ (see Text S1 for details).
Unsupervised Subtype Discovery Based on Gene Expression Analysis Unsupervised classification of the discovery set was performed using hierarchical clustering (Ward linkage and 1 2 Pearson correlation coefficient distance used) on the most variant class of probe sets (n = 1,459). To obtain a robust classification, we used a consensus unsupervised approach  [22]  implemented in the R package ConsensusClusterPlus. The consensus clusters were obtained from 1,000 resampling iterations of the hierarchical clustering, by randomly selecting a fraction of the samples and of the most variant probe sets (90%). The optimal number of clusters was selected according to the approach criteria detailed in Text S1.
Validation Set Subtype Assignment Validation datasets were independently assigned to GEP subtypes according to a standard distance-to-centroid approach  [23]. A centroid-based predictor was built by a 10-fold crossvalidation approach, resulting in the selection of the five top upregulated and five top down-regulated genes specific to each subtype, yielding 57 genes (three genes were shared by two subtypes). The approach was implemented in the R package citccmst, and is detailed in Text S1.
Molecular Subtype Characterization The Chi-squared test and logistic regression were used to study associations between anatomoclinical features, common DNA alterations, and subtypes. Each molecular subtype was further characterized according to (i) GEP of NT counterparts from our dataset; (ii) previously published supervised signatures based on intestinal stem cell phenotype  [24, 25] , BRAF mutation  [26] , and serrated CRC phenotype  [27] , as described in Text S1; (iii) cancer-relevant signaling pathways retrieved from the Kyoto Encyclopedia of Genes and Genomes (see Text S1); and (iv) CGH alteration frequencies.
Clinical and Molecular Relevance of Colon Cancer Subtypes Associations with anatomoclinical and DNA alterations data are shown in Figures  1C  and S3A  and in Table  S4. Tumors classified as C1, C5, and C6 were more frequently CIN+, CIMP2, TP53mutant, and distal (p,0. 001), without any other molecular or clinicopathological features able to discriminate these three clusters clearly. Tumors classified as C2, C4, and C3 were more frequently CIMP+ (59%, 34%, and 18%, respectively, versus ,5% in other clusters) and proximal. C2 was enriched for dMMR (68%) and BRAF-mutant tumors (40%). C3 was enriched for KRAS-mutant tumors (87%). No association between clusters and TNM stage was found, except enrichment for metastatic (31%) tumors in C4. The analyses of CGH arrays revealed that CIN+ samples shared a typical DNA copy alteration pattern including +7, 28p, +8q, +13q, 217p, 218, +20q.
Signaling Pathways Associated with Colon Cancer Subtypes We analyzed cancer-related signaling pathways from the Kyoto Encyclopedia of Genes and Genomes database for specific deregulation in each subtype signature (Figure  2 ). As expected, up-regulated immune system and cell growth pathways were found in C2, the subtype enriched for dMMR tumors. C4 and C6 both showed down-regulation of cell growth and death pathways and up-regulation of the epithelial-mesenchymal transition/ motility pathways. Most signaling pathways were down-regulated in C1 and C3. In C5, cell communication, Wnt, and metabolism pathways were up-regulated. In C1, cell communication and immune pathways were down-regulated.
Exploratory Analysis of Cell and Precursor Origins of the Subtypes These six molecular subtypes were further investigated using GEP data from NT and previously published supervised signatures based on DNA alterations and cellular phenotypes to explore the subtype origins. Based on the growing amount of data suggesting that cancer is closely linked to stem cells, a mouse-derived intestinal stem cell signature  [24]  and a human colon top and bottom crypt signature were selected and applied to our GEP data  [25]. C4 appeared highly enriched for tumors displaying ''stem cell phenotype-like'' GEPs (91%) and up-regulating of the bottom crypt signature (96%). (Figure  S3A ). This finding was consistent with the pathways specifically deregulated in C4 (cell cycle pathway down-regulated and cell communication pathway upregulated).
Validation of the Subtypes across Nine Colon Cancer Datasets To validate our six-subtype classification, a 57-gene centroid classifier was built from the discovery set by a 10-fold crossvalidation approach (,5% misclassification; Figure  S5 ; Table  S5 ). We applied this signature to the Affymetrix validation set of 1,029 samples (Table  1 ). All subtypes were found in the same proportions as in the discovery set, and the main associations between the different clusters and anatomoclinical/DNA/GEP characteristics described above were confirmed (Figures  S2B  and S3B ), except for the enrichment of C4 with BRAF-mutant and stage IV tumors. When applied to the Agilent TCGA dataset (n = 152)  [13] , the molecular and clinical characteristics of the subtypes were all confirmed (Figure  S3C ). To further validate the six-subtype classification in the validation dataset, we performed the same consensus clustering approach with the whole validation set; the subtypes generated were highly concordant with the six assigned subtypes (Chi-squared test, p,10 216 ).
0003) (Figure  4C ). To compare the prognostic value of our classification to other prognostic covariates, we recoded our classification by combining C4 and C6 into a single high-risk group, versus all other subtypes as the lowrisk group. This binary classification led to an even stronger association of the high-risk group versus the low-risk group with RFS (hazard ratio [HR] 1. 7, 95% CI 1. 1-2. 6, p = 0. 014, in the discovery set; HR 2. 3, 95% CI 1. 5-3. 5, p = 0. 00012, in the validation set; HR 2, 95% CI 1. 5-2. 7, p = 7. 1610 26 , in the overall dataset) (Figures  4D  and S6 ) and remained an independent prognostic factor, together with TNM stage, in the multivariate analysis (discovery and validation sets analyzed separately and merged) (Tables  2  and S6 ). The binary classification also remained an independent prognostic factor (p,0.
Prognostic Classifiers within Subtypes The Oncotype DX recurrence score  [8]  is an emerging prognostic classifier, and we attempted to assess its prognostic value with our data. This score had prognostic value in both the discovery and validation sets, and in the overall dataset (p = 3. 4610 26 ; Figure  S6 ). In particular, 97% of the C4 samples were classified as high risk by the Oncotype DX score. However, this score was not prognostic for all of the subtypes (Figure  S7 ). In a multivariate stepwise analysis, both our recoded classification and the Oncotype DX score remained independently prognostic, together with TNM stage (Table  2 ). We also attempted an exploratory analysis of the signature described by Salazar et al. [7]  by investigating 17 of the 18 probe sets available on the Affymetrix U133 Plus 2.
Discussion Using a large comprehensively characterized multicenter cohort of CC patients, we identified six robust molecular subtypes of CC individualized by distinct clinicobiological characteristics. Importantly, this six-subtype classification was validated in nine independent datasets. Furthermore, classification into high-and low-risk subtypes was of prognostic value. Although retrospective, our cohort was very representative of the clinicopathological characteristics and common DNA alteration frequencies observed in the population of patients with CC. Our findings clearly demonstrate that anatomoclinical factors and common DNA alterations alone are helpful for highlighting subtype characteristics, but they are not sufficient to define boundaries between subtypes and to describe the molecular heterogeneity of CC.
Altogether, these observations supported these two clusters being representative of two distinct molecular entities. The biological relevance of our subtypes was highlighted by significant differences in prognosis. In our unsupervised hierarchical clustering, patients whose tumors were classified as C4 or C6 had poorer RFS than the other patients. Thus, our study, like others  [7, 31] , supports the idea that the unsupervised analysis of transcripts in primary tumors yields information of prognostic value. The prognostic value of our signature was statistically significant in the validation and the overall datasets, independently of TNM stage, with a worse prognosis confirmed for C4 and C6 subtypes. Subtype C4 was enriched in CIMP+ BRAF-mutant tumors and may correspond to the poor prognostic cluster reported by Salazar et al.
containing the same proportion of BRAF-mutant tumors  [7]. Prognostic analyses based solely on common DNA alterations can distinguish between risk groups, but are still inadequate, as most CCs are pMMR CIMP2 BRAFwt (75% in our series; data not shown). Indeed, the markers BRAF-mutant, CIMP+, and dMMR may be useful for classifying a small proportion of cases, but are uninformative for a large number of patients. This was illustrated in the study by Salazar et al. in which BRAF mutation was found in both good and poor outcome clusters, but was rare in the intermediate prognosis cluster used to build the ColoPrint prognostic classifier  [7]. The ColoPrint and Oncotype DX prognostic classifiers were developed recently to improve risk prediction in early-stage CRC  [7, 8]. ColoPrint was validated in three independent datasets of stage II-IIIA CC, and the robustness of the signature is currently being evaluated prospectively  [33, 34].
The corresponding 17 probe sets available on Affymetrix chips did not identify risk groups in our series (data not shown). Oncotype DX has been validated as a prognostic score in the QUASAR and CALGB9581 trials, and more recently in an independent cohort of patients with   stage III CC  [35] [36] [37]. Although not identified by a genome-wide gene expression approach, the Oncotype DX score's prognostic value was confirmed in our overall stage II-III CC dataset but not in every subtype: it had prognostic value for the C3, C4, and C6 subtypes, and marginally in C5; it did not have prognostic value in C1 and C2, which represent 44% of our overall dataset. Our classification added prognostic information that remained significant in the multivariate analysis adjusted for TNM and Oncotype DX score.
Supporting Information Figure  S1  Discovery and validation sets used in the study. The data used in this study were collected from the CIT program cohort (a French multicenter cohort) and from publicly available datasets. There were 750 CC samples from the CIT program suitable for common DNA alteration characterization, and 566 of these provided tumor RNA samples satisfying stringent quality control criteria. These RNA were hybridized on an Affymetrix chip (asterisk) and used for molecular subtype determinations. The discovery set was composed of 443 tumors from the CIT cohort. The validation set was composed of the remaining CIT cohort CC samples, CC samples from seven Affymetrix publicly available datasets (indicated with their NCBI GEO accession number), and CC samples from the non-Affymetrix TCGA program (performed on an Agilent platform).
For survival analyses, only stage II and III patients were considered, stage I and IV patients not being informative as almost all survive or die, respectively; there were thus 359 cases in the CIT discovery set and 416 in the CIT validation set and three public datasets included in this analysis. pbs, probe sets. (PDF) Figure  S2  Heatmaps of subtype-discriminant probe set expression profiles in the discovery set and in the Affymetrix validation set. (A) Heatmap of the discovery set samples ordered according to gene hierarchical clustering (1 2 Pearson metric, Ward linkage) and by subtypes. (B) Heatmap of Affymetrix validation set samples ordered as in (A). For each subtype, discriminant probe sets were selected from the discovery set using a moderated t-test, comparing the given subtype to the other subtypes, with an adjusted p,10 25 and a |log fold change|.
0. 5, yielding 1,108 discriminant probe sets. (PDF) Figure  S3  Associations between molecular subtypes and anatomoclinical characteristics, DNA alterations, and supervised signature annotations in the discovery and validation sets. Associations were assessed in (A) the Affymetrix discovery set, (B) the Affymetrix validation set, and (C) the TCGA, non-Affymetrix, validation set. For each subtype and variable, the proportion of each modality is represented (dark grey: ''positive/ true/yes'' proportion; white: ''negative/false/no'' proportion; grey: ''data not available'' proportion), and the percent of the main feature (dark grey) within each subtype is indicated. The Chi-squared test p-values are indicated in red. (PDF)    S3  List of the 1,108 subtype-discriminant probe sets. For each subtype, discriminant probe sets were selected from the discovery set using a moderated t-test, comparing a given subtype to the other subtypes (adjusted p,10 25 and a |log fold change|.
What Did the Researchers Do and Find? The researchers used genetic information from a cohort of 750 patients with stage I to IV colorectal cancer who underwent surgery between 1987 and 2007 in seven centers in France. The researchers identified relevant clinical and pathological staging information for each patient from the medical records and calculated recurrence-free survival (the time from surgery to the first recurrence) for patients with stage II or III disease. In the genetic analysis, 566 tumor samples were suitable-443 were used in a discovery set, to create the classification, and the remainder were used in a validation set, to test the classification. The researchers also used information from eight public datasets to validate their findings. Using these methods, the researchers classified the colon cancer samples into six molecular subtypes (based on gene expression data) and, on further analysis and validation, were able to distinguish the main biological characteristics and deregulated pathways associated with each subtype.
For all samples, regions having an alteration frequency higher than 20% are displayed. (PDF) Figure S5 Determination of subtype prediction centroids. (A) Percentage of misclassification of the discovery set as a function of the number of top up-and down-regulated gene pairs used in the centroids. Misclassification is computed for the validation set by a 10-fold cross-validation procedure and is plotted by subtype (top) and averaged (bottom). (B) Heatmap of the 57-gene centroids used to assign a new dataset. (PDF) Figure S6 Prognostic value of the recoded CIT classification and of the Oncotype DX-like and Coloprint-like prognostic classifiers in the discovery and validation sets for patients with TNM stage II or III CC. RFS according to the recoded molecular subtype classification (C4/C6 subtypes versus other subtypes) in each TNM stage category (left, TNM II; middle, TNM III; right, TNM II-III), RFS of high-and low-risk patients as predicted by the Oncotype DX-like classifier, and RFS of patients belonging to cluster 1 and cluster 2 of the ColoPrint 17gene expression signature in the discovery set (top), the validation set (middle), and the both datasets combined (bottom).
Acknowledgments We thank  Prof. Jacqueline Godet  and  Dr. Jacqueline Me  ´tral for their constant support during the course of this work. The expertise of the personnel serving the  RNA extraction and qualification (Saint-Louis Hospital, Paris), Affymetrix expression array (Institut de Genetique et de Biologie Moleculaire et Cellulaire, Strasbourg) , and array-based CGH CIT platforms and of the  CIT  bioinformatics team is gratefully acknowledged. We also thank all the pathologists, biologists, and clinicians of each center who have participated to the collection of samples and clinical data. Some of the results reported herein were presented in part at the annual meeting of the  American Association for Cancer Research, Chicago ,  Illinois , March 31-April 4, 2012 (abstract 5065).
A new version of the TomoRebuild data reduction software package is presented, for the reconstruction of scanning transmission ion microscopy tomography (STIMT) and particle induced X-ray emission tomography (PIXET) images. First, we present a state of the art of the reconstruction codes available for ion beam microtomography. The algorithm proposed here brings several advantages. It is a portable, multi-platform code, designed in C++ with well-separated classes for easier use and evolution. Data reduction is separated in different steps and the intermediate results may be checked if necessary. Although no additional graphic library or numerical tool is required to run the program as a command line, a user friendly interface was designed in Java, as an ImageJ plugin. All experimental and reconstruction parameters may be entered either through this plugin or directly in text format files.
A simple standard format is proposed for the input of experimental data. Optional graphic applications using the ROOT interface may be used separately to display and fit energy spectra. Regarding the reconstruction process, the filtered backprojection (FBP) algorithm, already present in the previous version of the code, was optimized so that it is about 10 times as fast. In addition, Maximum Likelihood Expectation Maximization (MLEM) and its accelerated version Ordered Subsets Expectation Maximization (OSEM) algorithms were implemented. A detailed user guide in English is available. A reconstruction example of experimental data from a biological sample is given. It shows the capability of the code to reduce noise in the sinograms and to deal with incomplete data, which puts a new perspective on tomography using low number of projections or limited angle.
Over the years, the comparison of these approaches led the authors to prefer the FBP code to reconstruct STIMT data, for its simplicity, reliability and speed. Two main aspects justify this choice: (i) the STIMT projections, measured by transmission, usually have very little noise and (ii) the calculation of physical processes can usually be implemented following an analytical method. In this way, quantitative data can be obtained for thin samples  [8]. The situation is more difficult for PIXET: the quantitative reconstruction of emission data requires an iterative algorithm, less sensitive to noise than FBP, and moreover able to take into account the attenuation of the emitted X-rays from their emission point to the detector. Pioneering studies were carried out by Schofield and Lefevre  [3]  and Antolak and Bench  [9]  using least squares methods derived from SPECT medical imaging (both from the Donner library).
The only code so far able to take into account the large solid angle required in real PIXET experiments was initially developed by Sakellariou et al. [10]. The DISRA program has remained the most complete code for the processing of STIMT/PIX-ET data, and has spread over the years in several research groups: Surrey  [11] , Leipzig  [12] , Bordeaux  [13]. However, some restrictions inherent to the reconstruction method employed led some of them to explore other ways -although no complete solution has been reached so far. We would like here to summarize the main aspects of the reconstruction procedure in DISRA, to make clearer the origin of these limitations and how we could try to go beyond.