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Enregistrement W2765719474 · doi:10.1158/0008-5472.can-17-1584

Cross-Cancer Analysis Reveals Novel Pleiotropic Associations—Response

2017· letter· en· W2765719474 sur OpenAlexaff
Gordon Fehringer, Graham Casey, Stephen B. Gruber, Ulrike Peters, Ellen L. Goode, Thomas A. Sellers, Christopher A. Haiman, David J. Hunter, Peter Kraft, Christopher I. Amos, Matthew L. Freedman, Michael D. Wilson

Notice bibliographique

RevueCancer Research · 2017
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGenomics and Chromatin Dynamics
Établissements canadiensHospital for Sick ChildrenLunenfeld-Tanenbaum Research InstitutePublic Health OntarioUniversity of Toronto
Organismes subventionnairesNational Cancer Institute
Mots-clésSyntenyBiologyGeneticsGenomeCancerGeneCarcinogenesisCentimorganGenome-wide association studyCongenicComputational biologyGene mappingSingle-nucleotide polymorphismChromosomeGenotype

Résumé

récupéré en direct d'OpenAlex

Genomic research based on mouse models has led to major advances in our understanding of disease biology in humans, and it is one of the most widely used mammalian model organisms, due in part to their genetic tractability and the high number of human orthologous genes partitioned into regions of conserved synteny (1, 2). We are pleased to see that our cross-cancer genome-wide analysis based on data of five cancers in human (3) has inspired further investigation using existing experimental data based on mouse models (4). Using recombinant congenic strains and microsatellite markers, Quan and colleagues investigated lung and colon cancer susceptibility loci in the mouse genome. They identified significant colocalization of lung and colon cancer susceptibility loci in 27 gene clusters, defined as loci mapped within 10 centiMorgan (cM), while our study did not identify pleiotropic loci for lung and colorectal cancers. We found the work of Quan and colleagues very valuable, and the fact that some of the lung–colon gene clusters identified in Quan and colleagues fall in human–mouse syntenic regions is intriguing, as it may suggest that there are more pleiotropy regions related to carcinogenesis in mammals to be identified. The differences between our results may be due to several fundamental differences in our respective approaches, and the organismal differences between humans and mice such as tissue-specific gene regulatory networks, which we outline in the following paragraphs.Our previous cross-cancer analysis was based on a two-stage genome-wide association approach, analyzing approximately 9.9 million common germline sequence variants across human genome for the risk of lung, ovary, breast, prostate, and colorectal cancer based on 61,851 cancer patients and 61,820 controls in the discovery set. Those that showed pleiotropic signals were then further replicated in independent studies of 55,789 patients and 330,490 controls (3). In this analysis, we aimed to detect biological pleiotropic effects; therefore, we were primarily focused on signals from the same genomic regions (within the same gene, or the same region of high linkage disequilibrium in intergenic regions; ref. 5). This is different from the definition of clusters used by Quan and colleagues, which considered the overlapping regions of lung and colon susceptibility loci within 10 centiMorgan (10 cM). Although there is no fixed ratio between cM and basepairs, 1 cM on average would correspond to crudely 1 to 1.2 million basepairs (Mb) in human genome, with the ratio varying by sex, chromosome position, and other factors (6, 7). The majority of the susceptibility loci that were investigated in our analysis were less than 3 Mb in sizes, with the exception of the MHC region. Therefore, colocalization at 10 Mb would not be detected in our analysis.Moreover, even though mouse and human models shared similarities in the control networks of gene activities, the two species in different mammalian orders have significantly diverged at the sequence level, and only approximately 40% of the human nucleotides can be mapped to the mouse genome (1, 2). In addition, the expression profiles of many mouse genes are different from their human orthologs, and similar genes may be engaged in different biological pathways in two different species (2). Transcription factor binding between human and mouse is rapidly evolving (8) and that transcriptional remodeling of regulatory regions themselves is pervasive (9), which in principle could alter the tissue-specific expression of susceptibility loci. These biological differences between mouse and human may explain at least part of differences in our findings.Finally, given the large amount of statistical testing one needed to perform for the common sequence variation, our interpretation of the statistical significance was penalized by the multiple comparisons. It is possible that there are colon–lung cancer pleiotropy loci that were not identified in our analysis given the stringent statistical threshold applied. For example, we did observe several loci with nominal associations with both lung and colorectal cancers in the GAME-ON/GECCO discovery set, such as variants in 6p21.33 and 13q13.1, but not in the replication stage, which could be due to reduced power of the colorectal cancer study in the latter. This was acknowledged in our discussion, and the full list of loci with potential pleiotropic effects was shown in Supplementary Table S1 to facilitate further investigations.Overall, the mouse model and the genome-wide association approaches are highly complementary, and each has its own strengths and limitations. Mouse models are essential for studying human cancers, but it is clear from the considerable efforts being taken to humanize mice, the development of patient-derived xenografts, as well as the need for different types of animal models in cancer research, that not all aspects of human cancer susceptibility and biology can be recapitulated in mice (10, 11). Combining both approaches could potentially provide greater insights into cancer etiology and tumorigenesis mechanism.See the original Letter to the Editor, p. 6042No potential conflicts of interest were disclosed.We thank Prof. James R. Woodgett for the insightful comments and suggestions.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,019

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,061
Tête enseignante GPT0,423
Écart entre enseignants0,362 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2017
Routes d'admission1
Résumé présentoui

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