Assessing the Differential Expression of Molecular Biomarkers in Breast Cancer.
Notice bibliographique
Résumé
Abstract Background: Molecular profiling of breast cancer has identified multiple biomarkers with potential to better predict clinical outcomes and response to treatment than existing clinicopathological indices. We focused on 8 biomarkers of particular relevance as suggested by the literature: CD44, methyl guanine methyltransferase (MGMT), epidermal growth factor receptor (EGFR), cyclooxygenase-2 (COX2), cytokeratin-5 (CK5), estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). This study aims to clarify the prognostic value of these breast cancer biomarkers as well as to define their relationships with each other.Methods: Formalin fixed paraffin embedded (FFPE) breast tumor samples from 140 patients diagnosed with breast cancer from Jan 2001 to Dec 2005 at St. Michael's Hospital in Toronto, Canada were examined retrospectively using tissue microarray analysis. Samples were stained for CD44, COX2, MGMT, EGFR, CK5, ER, PR, and HER2 by immunohistochemistry. Biomarkers including histological features and staining patterns were then evaluated. A manual chart review documenting relevant clinical and pathological features was also conducted. Subsequent statistical analysis utilized Kaplan-Meier survival curves to identify biomarker-survival associations and simple linear regression analysis to determine biomarker cluster groups.Results: Median patient age was 56 (range 31-86). Median follow-up time was 62 months (range 5-102). Tumors were of various pathological types and stages (I – IV). No significant associations between biomarkers and disease-free survival (DFS) were found. DFS (median = 53.5 months) did correlate with tumor stage, nuclear grade, and lymphovascular invasion (LVI) as expected. However, only LVI was found to correlate with DFS in triple negative patients (n=24). Regression analysis yielded two cluster groups of biomarkers; Cluster group 1 includes ER, PR, Cox2, CD44; and Cluster group 2 includes HER2, EGFR, CK5. Expression of biomarkers within one cluster group relate directly with those in the same group, but inversely with biomarkers from the other cluster group. In addition, biomarkers in Cluster group 1 relate inversely to the mitotic count, tumor size, and nuclear grade, while biomarkers from Cluster group 2 relate directly with these indices. For example, CK5 positive tumors with HER2 and EGFR overexpression tend to have a high mitotic count, large tumor size, and high nuclear grade. MGMT did not show association with any histological feature or DFS.Conclusions: This retrospective observational study of 140 breast cancer patients focused on the prognostic implications and relationships of 8 biomarkers: CD44, COX2, MGMT, EGFR, CK5, ER, PR, and HER2. Two cluster groups of biomarkers have been identified. Cluster group 1 (ER, PR, COX2, CD44) correlates with a less aggressive tumor morphology, while Cluster group 2 (HER2, EGFR, CK5) correlates with a more aggressive one. No associations have yet been found between biomarkers and DFS. This study has been extended to include 373 patients with further analysis underway. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 6041.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».