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Most CMS4 tumours are characterized by very high levels of all stromal scores, in particular of the C score Fig. The absence of overlaps with CRIS classes corroborates the notion that assignment to CMS4 mostly depends on stromal transcript contribution rather than intrinsic cancer-cell features. A similar lack of overlap with CRIS classification and asymmetric distribution of stromal scores across subtypes was also observed when previously published transcriptional CRC classifiers were applied to the TCGA data set Supplementary Fig.

The sensitivity of CRIS for more granular detection of functionally relevant cancer-cell intrinsic traits is particularly evident for CMS2. This subgroup, the vastest of the consensus, includes CIN tumours that show overall upregulation of WNT targets, and displays consistently low stromal scores. Collectively, these findings further attest to the independent value and higher resolution of CRIS taxonomy. We and others have identified a number of genetic alterations that associate with resistance to cetuximab 12 , 36 , 37 , 38 , In principle, this could suggest that the higher rate of cetuximab-sensitive CRIS-C tumours is due to depletion in cases harbouring resistance-conferring alterations.

The fraction of sensitive or resistant cases specifically in the PDX subpopulation that does not harbour resistance biomarkers is shown in the bars. The analysis includes PDXs that do not harbour known genetic markers of resistance. Complementary to genetic markers of resistance, the expression of a set of genes indicative of EGFR pathway activity has been found to correlate with cetuximab sensitivity 35 , 36 , This suggests that, although partially overlapping, these layers of information convey integrative knowledge, which could be combined to obtain more accurate prediction of cetuximab sensitivity.

This suggests that CRIS-based stratification could be exploited in combination with clinical and pathological parameters for a superior prognostic assessment of CRC. This indicates that the negative prognostic value of CRIS-B is not biased by chemotherapy sensitivity. These results suggest that CRIS-B membership and high CAF infiltration identify alternative means to acquire analogous traits of cancer aggressiveness, whose negative prognostic impact is not further exacerbated by the coexistence of the two Supplementary Fig.

As previously reported 8 , the contribution of the CAF score was negligible for treated patients Supplementary Fig. Importantly, the association between CRIS-B and poor prognosis was confirmed in an independent cohort of 1, samples, which was assembled by combining data from five independent data sets Supplementary Fig. As an initial attempt to translate the CRIS taxonomy into a diagnostic tool amenable to clinical applications, we developed a single-sample classifier based on the top scoring pair algorithm TSP 41 and its multiclass extension k-TSP 42 , 43 , A TSP is a binary predictor based on the relative ranking of two measurements for example, the expression of a pair of transcripts , which switch order between two subclasses of samples.

This approach can be extended to multiclass problems by identifying the TSPs associated with each pair-wise subclass comparison and then aggregating the votes across all gene pairs. To ensure cross-platform portability of the classifier, candidate TSP genes were challenged against a training data set of gene expression profiles from both PDXs and original tumours, obtained using multiple technological platforms Supplementary Data This process resulted in the selection of 40 gene pairs Methods; Supplementary Data Altogether, these data show that reducing the size of the CRIS classifier to 80 genes preserves most of its classifying capability across different gene expression platforms, and indicate the feasibility of deploying such a reduced gene set for a single-sample classification based on a TSP approach.

Gene expression analysis based on total RNA of bulk cancer tissues provides an aggregate portrait of the main components that make up the whole tumour ecosystem, including cancer cells, vessels, fibroblasts and immune cells. Although global differences in gene expression patterns have proved useful to distinguish cancer subtypes for effective disease stratification 45 , separating the molecular signatures of tissue compartments from measurements of total tumour samples is expected to provide higher resolution of biologically and clinically pertinent parameters 8 , The contribution of individual tumour constituents to better capturing some cancer characteristics has been mainly documented for stromal cells in several tumour types.

For example, a signature reflecting response of human fibroblasts to serum, suggestive of active wounds, was found in a subgroup of cases at early stages, persisted during treatment, and predicted increased risk of metastasis and death in breast, lung and gastric carcinomas In pancreatic ductal adenocarcinomas, the integration of tumour- and stroma-specific gene expression profiles resulted in improved prognostic power over traditional signatures In CRC, stromal traits have been shown to critically impact cancer prognosis and response to therapy 8 , 9.

On the contrary, how cancer-cell intrinsic gene expression patterns influence subtype classification remains elusive, likely because the proportion of normal tissue lineages present in whole tumour transcriptomes acts as a dominant source of variation that obscures biologically relevant transcriptional features inherently displayed by cancer cells. To attempt unambiguous exploration of cancer-cell gene expression attributes we took advantage of a large collection of PDXs, in which transcripts of manifest cancer-cell origin could be extracted by the deployment of human-specific probes.

The ensuing transcriptional profiles were then leveraged for a class discovery effort. Of note, CRIS subclasses only barely overlap with the reported CRC transcriptional classification systems, which empowers a higher dimension of analytical resolution and refines biological insight into CRC heterogeneity. In particular, removal of stromal signals in the class discovery process resulted in remarkable orthogonality between CRIS and the recently published CMS signatures, with lack of classification for CMS subtypes enriched for mesenchymal phenotypes CMS1 and CMS4 and detection of genetic and functional peculiarities with a potential to instruct novel diagnostic and therapeutic approaches.

Although further studies based on preclinical experimentation and prospective trials in patients are needed to support this assumption, CRIS-A might pinpoint tumour subgroups potentially responsive to anti-metabolic therapies The positive predictive value of CRIS-C is of particular importance because it proved to be independent of all known genetic biomarkers of response or resistance. The finding that high IGF2 levels attenuate dependency on the EGFR pathway underscores the functional relevance of this alteration, which is also a candidate target for alternative treatment protocols High WNT pathway activity was more generally observed in the CRIS-C—D—E subfamily, thus defining a subset of tumours for which pharmacologic inhibitors of this pathway 48 , 49 may have therapeutic potential.

As a further layer of relevant information for translational purposes, not only does CRIS introduce a new partitioning of known molecular traits, but it also puts forward a number of autocrine signalling loops that are selectively enriched in distinct classes. If validated through functional studies, these signals could constitute an entirely new population of candidate druggable targets for specific CRC subtypes.

Although the analysis of cancer-cell intrinsic traits can provide relevant information for CRC management, the contribution of the stromal compartment should not be overlooked. We and others have reported that the extent of stromal infiltration predicts poor outcome, resistance to radiotherapy and—possibly—sensitivity to chemotherapy 8 , 9. Here we show that the capture of cancer-cell intrinsic traits by CRIS can be efficiently integrated with stromal signatures to obtain even superior prognostic and predictive power.

In particular, high CAF score and assignment to CRIS-B independently predict poor prognosis for almost one third of tumours whose clinical and pathological features would not dictate adjuvant treatment. These results call for prospective validation in larger cohorts for drawing definitive conclusions and, if confirmed, could have major clinical implications.

The translation of the CRIS taxonomy into a clinically useful companion diagnostic would require the development of a tool for effective classification of individual patients. Here we show that a set of 40 gene pairs amenable to TSP-based single-sample classification retains the classification power of the original gene classifier. However, the performance of CRIS-TSP was negatively affected by retrospective application to existing data sets, likely because of the diversity of procedures adopted and technological platforms used for data generation.

Therefore, whenever the goal is to classify already available gene expression data sets obtained by diverse technological platforms hybridization-based or sequencing-based , NTP-based CRIS categorization remains the option of choice. At the same time, we found that the TSP genes perform well when rechallenged for classification using the NTP approach. This suggests that implementation of this signature into a clinically applicable TSP-based single-sample classifier is feasible for prospective classification of new samples, for which dedicated and standardized data-generation procedures can be adopted.

One potential limitation of our study is that some CRIS features could in fact emerge as a consequence of tumour xenotransplantation. In principle, the PDX approach might exert a number of distortive effects on the transcriptome of cancer cells, including selection drifts related to engraftment and propagation, limited cross-species reactivity between human and mouse cytokines with consequent perturbation of paracrine signals, and lack of proper immune components in recipient animals.

However, the likelihood of a strong impact of such biases on the CRIS taxonomy is reduced by the observation that CRIS efficiently classified several data sets from bulk CRC patient tumours, regardless of their source of origin primary or metastatic.

A way to conclusively cope with this issue would be to exploit alternative methods to gather pure cancer cell transcriptional profiles from patient tumours, and test whether the key CRIS features remain valid. Such kind of approaches—mainly based on cell sorting of dissociated tumours or microdissection of histological specimens—have been already applied in small-scale efforts 50 , 51 , 52 , 53 , but need to be broadened to larger data sets for reliable validation Similar findings are beginning to emerge also in other tumour types, using different methodologies This suggests that the same basic concepts introduced here for CRC can be generalized, with wide impact on cancer diagnosis and treatment.

A total of tumour samples and matched normal samples were obtained from patients who had undergone surgical resection of liver metastases at the Candiolo Cancer Institute, the Mauriziano Umberto I Hospital and the San Giovanni Battista Hospital Torino, Italy. Each collected sample was fragmented and either frozen or prepared for implantation subcutis as previously described 12 , At passage two, multiple samples were subjected to gene expression profiling: two samples for tumours, three samples for 13 tumours and four samples for 10 tumours.

Genetic data and annotation of sensitivity to cetuximab were obtained as described previously 12 , In vivo experiments and related biobanking data were stored in the Laboratory Assistant Suite, a web-based, in-house developed data management system for automated data tracking All animal procedures were approved by the Animal Care Committee of the Candiolo Cancer Institute, in accordance with Italian legislation on animal experimentation. Hybridized arrays were stained and scanned in a Beadstation Illumina.

To minimize the noise due to cross-species hybridization of transcripts deriving from murine infiltrates in PDX tissues, two pure murine samples were hybridized on human arrays 8 in a pilot experiment, and all probes that generated detectable signals in this assay were removed from further analyses. For each of such genes, only the probe with the highest variance of signal was selected. The panel included a total of samples corresponding to unique patients.

The mutational load was calculated based on exome sequencing Illumina data available from the TCGA data portal. All the somatic mutations were included in the calculation and normalized assuming an approximate exome size of 30 megabases. The analysis was carried out with the SomaticSignatures R package Only data unambiguously referred to unique tumour samples were considered.

For each sample, all the regions with an absolute segmented value greater than 0. This threshold was chosen based on previous work, in which standard methods for calling a copy number alteration for a segment in GISTIC analysis of a single sample were defined The copy number load was calculated as the number of nucleotides included in such altered regions, relative to the sum of all nucleotides in all the segments identified in the genome of the patient under consideration.

For genes with multiple probe sets, those with the highest average levels were selected. The identification of cancer-cell intrinsic subtypes was performed by applying unsupervised clustering analysis, following consolidated methods 1. All the available transcriptional profiles from PDXs were exploited for class discovery. To take into account tumour heterogeneity in the process of subtype identification, PDXs derived from the same original tumour were treated as independent samples.

To maximize the portability of the results across multiple platforms, we restricted our analyses only to transcripts that were also explored in the RNAseq data set available from the TCGA data portal. We applied consensus-based NMF 20 to the 1, most variable genes. In accordance with Sadanandam et al. NMF was performed with the predetermined number of clusters K varying from 2 to 6.

By applying significance analysis of microarrays 21 on the remaining samples, 1, genes differentially expressed across subtypes were identified. This was obtained by applying an FDR threshold of 0. Through PAM, we then generated the shrunken centroids of each class by selecting the configuration that minimized the overall error rate in leave-one-out cross-validation analyses.

This led to further prioritization of the discriminating transcripts to a total of genes, with an overall error rate of 0. For implementation of an NTP-based classifier, we selected genes positively and specifically associated to each of the subtypes. Indeed, the PAM score represents the extent and sign of association of each gene to each class.

Starting from the genes selected as specified above, genes did not have a positive PAM score for any of the classes as a consequence of the centroid shrinkage procedure and could not be used for NTP. Then, we deployed our published methodology 8 to remove from the classifier those genes for which the major component of signal was defined as having stromal origin.

To do so, we calculated the fraction of stromal mouse transcripts contributing to the overall signal of each gene using RNAseq data from CRC PDXs, in which mouse stroma substitutes the human stroma 8. The NTP algorithm does not allow redundancy between the signatures used to assign membership to different classes. Thus, all genes featuring a positive PAM score for more than one class had to be non-redundantly assigned to one class only. To do so, we used our previously published procedure 8 and assigned genes that were positively associated to more than one class to the best PAM scoring class only when the second highest value for assignment to another PAM class was at least 0.

In all other cases corresponding to transcripts , the genes were excluded from the analysis. The whole analytical pipeline, from class discovery to the gene NTP classifier, is shown in Supplementary Fig. The threshold chosen for significant classification of a sample was Benjamini—Hochberg-corrected false discovery rate BH.

When referring to published classifications that is, Fig. To develop a simplified classification system for CRIS, we used the TSP approach, a rank-based, parameter-free binary predictor relying on the relative ordering of two features for example, the order of expression of two genes , and its extension, the k-TSP classifier, which aggregates the votes of multiple TSPs and can be used for multiclass problems 41 , 42 , 43 , 64 as detailed below.

To this end we first identified candidate genes for classifier development starting from CRIS genes out of in common across three distinct data sets obtained from different platforms: PDXs analysed on Illumina microarrays, RNA-seq samples from TCGA and samples analysed on Affymetrix microarrays available from the public domain gse, gse and gse , for a total of samples Supplementary Data None of the samples used to develop our k-TSP-based classifier was included in subsequent analyses investigating the clinical relevance of the CRIS classification.

The TSP algorithm assigns a sample to a specific phenotype if gene A is larger than gene B, or to the other phenotype otherwise. There are , possible TSPs that can be formed using all combinations of genes. To avoid over-fitting, however, we limited the search space in the training phase by filtering out all genes that proved to be irreproducible across the three analytical platforms considered Illumina, RNA-seq, and Affymetrix. To this end, we used the MergeMaid R-package to calculate a gene reproducibility index called ICOR 65 , 66 , which allows to identify genes that are reproducible across distinct data sets without relying on any phenotypic information.

We calculated within each separate study, and for each pair of genes, the correlation coefficient of expression value ranks across subjects, and then retained only the genes for which such correlations agreed across studies. Supplementary Figure 21a shows the histograms, the observed and the null distributions as obtained from 1, permutations for the three pairwise integrative correlations across the three data sets.

To select the most reproducible genes we analysed the total integrative correlation obtained by averaging the pairwise integrative correlations using the expectation-maximization EM algorithm This approach allowed us to dichotomize the ICOR values and classify the intrinsic genes based on their reproducibility across platforms. Supplementary Figure 21b shows the distribution of the total ICOR along with the thresholds identified by the expectation-maximization algorithm.

There are still 35, possible TSPs that can be formed using all combinations of the most reproducible genes. To select disjoint TSPs for each class comparison, the genes used to form pairs were omitted from the search in subsequent comparisons. In selecting the most discriminative TSPs we started from the comparisons between CRIS-B and the other classes, since this class showed prognostic value in our previous analyses.

We then proceeded with the remaining class comparisons according to the total number of available genes to form the pairs, in increasing order. For each of the ten pair-wise comparisons we selected from 1 to 5 TSPs, for a total of 10, 20, 30, 40 and 50 disjoint TSPs, using a total of 20, 40, 60, 80 and non-overlapping genes, respectively. Hence, we developed our kTSP classifier using 10, 20, 30, 40 and 50 non-overlapping TSPs for a total of 20, 40, 60, 80 and genes, respectively. Genes with high variance 0.

The significance of enrichment was estimated using default settings and 1, gene permutations 28 , For SSEA of curated functional signatures, a score was calculated for each signature and each sample using median-centered Log2 ratios of gene expression values, as follows:.

We then evaluated the enrichment in class assignment for each CRIS class by performing GSEA preranked analysis using as ranked lists the samples ordered by the score of interest, and as sets the lists of sample membership to the different CRIS subtypes. Calculations were done with 1, permutations.

Then, each set of genes encoding the receptor and its ligands was used to rank samples, based on median-centered Log2 ratios of gene expression values, as follows:. In case of multiple testing, the results were considered significant when the Benjamini—Hochberg FDR was below 0.

Contact claudio. Gene expression microarray data generated in the course of this study have been deposited in the GEO database with accession number GSE PDX data, profiles from patients and GSE liver metastases data, profiles from patients. How to cite this article: Isella, C. Selective analysis of cancer-cell intrinsic transcriptional traits defines novel clinically relevant subtypes of colorectal cancer.

Sadanandam, A. A colorectal cancer classification system that associates cellular phenotype and responses to therapy. De Sousa E Melo, F. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Marisa, L. Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value.

PLoS Med. Roepman, P. Colorectal cancer intrinsic subtypes predict chemotherapy benefit, deficient mismatch repair and epithelial-to-mesenchymal transition. Cancer , — Budinska, E. Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer.

Schlicker, A. Subtypes of primary colorectal tumors correlate with response to targeted treatment in colorectal cell lines. BMC Med. Genomics 5 , 66 Perez-Villamil, B. Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior.

BMC Cancer 12 , Isella, C. Stromal contribution to the colorectal cancer transcriptome. Calon, A. Stromal gene expression defines poor-prognosis subtypes in colorectal cancer. Reconciliation of classification systems defining molecular subtypes of colorectal cancer: interrelationships and clinical implications.

Cell Cycle 13 , — Guinney, J. The consensus molecular subtypes of colorectal cancer. Bertotti, A. Cancer Discov. Chou, J. Phenotypic and transcriptional fidelity of patient-derived colon cancer xenografts in immune-deficient mice. Julien, S. Characterization of a large panel of patient-derived tumor xenografts representing the clinical heterogeneity of human colorectal cancer.

Cancer Res. Network, C. Comprehensive molecular characterization of human colon and rectal cancer. Nature , — Hoshida, Y. Nearest template prediction: a single-sample-based flexible class prediction with confidence assessment. Gao, H. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Kang, N. Hepatic stellate cells: partners in crime for liver metastases? Hepatology 54 , — The role of fibroblasts in tumor behavior.

Cancer Metastasis Rev. Brunet, J. Metagenes and molecular pattern discovery using matrix factorization. Natl Acad. USA , — Tusher, V. Significance analysis of microarrays applied to the ionizing radiation response. USA 98 , — Tibshirani, R. Diagnosis of multiple cancer types by shrunken centroids of gene expression. USA 99 , — Subclass mapping: identifying common subtypes in independent disease data sets.

Jorissen, R. Metastasis-associated gene expression changes predict poor outcomes in patients with dukes stage B and C colorectal cancer. Haddad, R. Microsatellite instability as a prognostic factor in resected colorectal cancer liver metastases. Medico, E. The molecular landscape of colorectal cancer cell lines unveils clinically actionable kinase targets. Betting articles Poker articles e-Sports articles. Automatic login:. Login via facebook. This site uses cookies. When you browse the site you are consenting to its use.

Know more. Chievo Reggina betting prediction. Chievo vs Reggina. Open an account and win Academy pts. You can also win. Register now! Odds may vary. Game file Stats Preview Tips Odds. Betting suggestion: The probable scenario for this match will be for Chievo Verona to get the three points.

Playing at home, the local club must dominate and have the best chances to score, taking advantage of the defensive weaknesses of the opponent. On the other hand, the visiting team enters this journey with the objective of continuing the result obtained in the last game, where they sealed a negative series of results. That said, and taking these factors into account, risking Chievo Verona's victory is a value bet. Analysis Chievo After 4 wins, 2 draws and 3 losses, the home team is in the 9 th position, havinf won 14 points so far.

Interestingly enough, this is a team that has had better results in away matches than at home, since they have won 8 points in away matches and only 6 at their stadium. In the last 4 home league matches Chievo has a record of 2 wins and 2 losses, so they have won 6 points out of 12 possible.

Their offense has scored frequently, since they have scored goals in 8 of the last 9 matches for this competition. Chievo Verona arrives in this match with a defeat, by , in the trip to Frosinone, in game of the tenth round of the second Italian league. Luigi Canotto and Francesco Margiotta scored the visiting team's goals.

With this result, the home team added the third consecutive game without winning in the championship, where they occupy the eighth place with fourteen points won. The group led by Alfredo Aglietti must present themselves in a formation, privileging ball possession and positional attacks. The two most advanced players, responsible to put the opposing defensive structure in alert are Francesco Margiotta and Filip Dordevic.

For this game, the coach of the home team has all the players available. Analysis Reggina The away team is currently in the 11 th position of the league, with 10 points won, after 2 wins, 4 draws and 4 losses. This is a team that usually makes good use of the home advantage, since they have won 3 points in away matches and 7 points at their stadium, with 6 goals scored and 5 goals conceded at home, against 4 goals scored and 8 conceded in away matches.

In the last 5 away league matches Reggina has a record of 3 draws and 2 losses, so they have won 3 points out of 15 possible. Reggina comes confidently into this game after a win against Brescia, thus putting an end to a series of seven straight games without knowing the taste of victory for this competition where they continue in the thirteenth in the leaderboard with ten points.

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Gives a result. Actions about the calculator or the tape. Number formating actions fractions and scientific notation. All calculations are saved on the tape. Click on any number or operator on the tape and change it at any time. Hit equals and the new result will appear.

You can use the print button to print out the tape. Clear button clears the last input. All clear button clears the calculator, tape, and resets any functions. Memory clear button clears the memory. Memory recall button retrieves the number you have in memory and places it in the display field. Memory plus button adds the number displayed to the contents of the memory. Memory minus button subtracts the number displayed from the contents of the memory. Note: The percent function will also work if you enter the number first and then the percentage you want i.

Click a number and then click fraction bar, then click another number. Enter a number, then click fraction space, click another number and then click on the fraction bar button, lastly enter another number. When you choose the one the other is switched off. And the number represents the total return , not just the profit like American and fractional odds. The Packers would be 1. The Vikings would be 2. Fractional Odds are used primarily in the UK and Ireland.

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Odds correlate to the probability of a team winning, which is the implied probability. A favorite has about a To calculate implied probability, use the following formulas:. As a responsible bettor, it is important to understand proper bankroll management. Your payout includes your potential winnings, plus whatever you bet originally.

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Betting e Sports Trading - Futuro e Q&A| Francesco Bellomo · Francesco Bellomo YOU NEED TO USE THIS GREYHOUND BETTING CALCULATOR NOW. (i) Column chart showing the distribution of CRIS subtypes based on NTP CRIS-A/B against all other samples, P<5 × 10−10, odds ratio , Claudio Isella, Sara E. Bellomo, Francesco Galimi, Consalvo Petti. Francesco Bellomo, Paola D'Antonio. ______ It is evident that in order to calculate the growing Therefore, the grape harvester gives decidedly bet-.