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Enregistrement W2020697195 · doi:10.1186/bcr1827

Are triple-negative tumours and basal-like breast cancer synonymous?

2007· letter· en· W2020697195 sur OpenAlexaff
Emad A. Rakha, David S.P. Tan, William D. Foulkes, Ian O. Ellis, Andrew Tutt, Torsten O. Nielsen, Jorge S. Reis‐Filho

Notice bibliographique

RevueBreast Cancer Research · 2007
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueBreast Cancer Treatment Studies
Établissements canadiensVancouver Coastal Health Research InstituteUniversity of British ColumbiaMcGill University
Organismes subventionnairesnon disponible
Mots-clésBreast cancerTriple-negative breast cancerBasal (medicine)Breast tumoursCancerImmunohistochemistryTissue microarrayMicroarrayPathologyBiologyMedicineGeneOncologyCancer researchInternal medicineGene expressionGenetics

Résumé

récupéré en direct d'OpenAlex

Kreike and colleagues [1] examined the gene expression and pathological characteristics of a retrospectively accrued cohort of 97 triple-negative phenotype (TNP) (oestrogen receptor [ER]-negative, progesterone receptor-negative, and HER2-negative) invasive breast cancers. TNP tumours were profiled with oligonucleotide microarrays and compared with a control group of 102 non-TNP tumours, which were obtained from an unrelated project. The authors then investigated whether the TNP would accurately identify basal-like cancers, by assessing the correlation coefficient between the gene expression profiles of each TNP cancer and the centroids of the molecular subgroups as defined by Hu and colleagues [2]. As expected, the majority (91%) of TNP tumours were classified as 'basal-like' tumours. However, 9% of tumours either showed a normal-like phenotype or were unclassifiable [1]. The authors presented a hierarchical clustering analysis of both TNP and control cases, based on a partial 'intrinsic gene list' and a different reference RNA when compared with those reported by Hu and colleagues [2], and observed that all of the TNP group and 18.6% of the control non-TNP group clustered together [1]. Based on the above results, the authors drew the provocative conclusions that 'basal-like tumours can be reliably defined by triple-negative immunohistochemistry' and that 'triple-negative tumours are synonymous with basal-like tumours'. We believe that equating TNP tumours with basal-like breast cancer is misleading [3] and, in fact, is not supported by the data the authors themselves present. Given that only 91% of TNP tumours displayed a significant association with the basal-like centroid and that 18.6% of non-TNP tumours clustered together with TNP tumours in the 'basal-like' cluster, a more reasonable conclusion is that the majority of, but not all, TNP tumours have a basal-like phenotype and that the majority of, but not all, basal-like tumours have a TNP phenotype. Therefore, it is also reasonable to conclude that the above findings do not demonstrate that TNP tumours are synonymous with basal-like tumours. One could argue that the results of the study conducted by Kreike and colleagues [1] are, in fact, in almost complete agreement with those of previous studies that demonstrate that a TNP immunohistochemical (IHC) phenotype is not an ideal surrogate for the identification of microarray-defined basal-like breast cancers [4-9]. Based on previous expression profiling/hierarchical clustering analysis, not only basal-like cancers but also normal breast-like cancers harbour a TNP phenotype at the mRNA level [8,10,11]. Importantly, normal breast-like tumours have been shown to have a distinct response to neoadjuvant chemotherapy [8] and prognosis when compared with basal-like breast tumours. The only IHC signature of basal-like breast tumours which has been validated by expression profiling demonstrated that a panel composed of ER, HER2, Ck 5/6, and epidermal growth factor receptor (EGFR) can identify these tumours with 100% specificity and 76% sensitivity [4]. In that study [4], if basal markers (that is, Ck 5/6 and EGFR) were not included, the specificity of the signature (that is, solely composed of ER and HER2) would be significantly reduced with a marginal increase in the sensitivity [4]. Furthermore, apart from the remarkably low prevalence of EGFR expression in TNP tumours, the results of Kreike and colleagues [1] are in agreement with those of several previously published comparisons on the IHC features of basal-like tumours as defined by expression arrays, which clearly demonstrate that most, but certainly not all, have a TNP phenotype [8,11,12]. In fact, ER IHC expression and HER2 3+ or gene amplification are reported to be found in 5% to 45% and 5% to 15% of basal-like tumours as defined by expression arrays, respectively [3,8,9,11,12]. In addition, Harris and colleagues [13] recently reported on a subgroup of HER2-amplified breast cancers that harbour a basal-like transcriptomic profile. Previous studies have shown that the expression of 'basal markers' (that is, Ck 5/6, Ck 14, Ck 17, and/or EGFR) is associated with a poor prognosis [5-7,14], regardless of hormone receptor expression. The expression of basal markers (basal cytokeratins and EGFR) in TNP tumours (core basal phenotype) also correlates with a worse prognosis and identifies a clinically distinct subgroup within the TNP group [5,7]. Moreover, it should be noted that identification of a subgroup of tumours solely based on the lack of expression of immunohistochemistry (for example, TNP) risks mis-assignment based on technical artifacts [3,4]. From a technical perspective, a word of caution should be voiced concerning the design of the study of Kreike and colleagues [1]: slide batch spotting biases and differences in the reference RNA used have been reported to have a significant effect on microarray data analysis [15]. When these biases are not corrected by additional processing and rescaling of the data [15-17] and cases are subjected to hierarchical clustering analysis, cluster assignment is typically biased by the noise inherent in each slide batch and/or reference. Hence, it is not clear to what extent the 97 TNP samples in this study clustered together owing purely to the similarities of their gene expression biology as opposed to the contributions to the expression profiles induced by distinct slide batches and/or references used for the analysis of TNP and control groups (so-called 'batch effect') [15-18]. Molecular profiling undoubtedly has had a dramatic impact on our understanding of breast cancer [4,10,11]. Given the difficulties in applying expression array analysis to identify the molecular subgroups in clinical practice (in particular, when formalin-fixed paraffin-embedded samples are used), the identification of simple IHC panels that reliably identify these subgroups, as described by Kreike and colleagues [1], is undeniably a meritorious effort. However, caution should be exercised in the translation of results obtained with mRNA-based expression analysis to IHC markers. Two of the most pressing challenges of breast cancer research are (a) to unravel the complexity of TNP tumours and basal-like breast carcinomas and (b) to identify novel therapeutic targets for these tumours. Blurring the boundaries of these two subgroups of breast tumours by using surrogate markers derived from microarray-based studies that are not optimally designed may lead to misleading conclusions and serve only to further confound the study of already enigmatic and clinically challenging entities.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,501
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,042
Tête enseignante GPT0,362
Écart entre enseignants0,320 · 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 tête enseignante, pas un consensus.

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

Citations114
Publié2007
Routes d'admission1
Résumé présentoui

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