Abstract P6-07-17: Proteomic screening of FFPE tissue identifies FKBP4 as an independent prognostic factor in hormone receptor positive breast cancers
Notice bibliographique
Résumé
Abstract Background: Adjuvant endocrine therapy reduces the risk of recurrence and death in hormone receptor positive breast cancer patients; however, 40–50% of estrogen receptor (ER) positive tumors are resistant to endocrine therapy. Identifying prognostic and predictive biomarkers to detect these non-responsive breast cancers at time of diagnosis may allow for improved clinical outcome for these patients. FKBP4 (FKBP52) is a co-chaperone protein that has been shown to regulate progesterone but not estrogen receptor activity, and was recently found to be highly expressed in early stage breast cancer compared to benign breast tissue. This evidence suggests that FKBP4 may play an important role in endocrine-responsive breast cancers. Methods: Global proteomic screening for biomarkers of aggressive breast cancer was performed on tissue samples from 24 patients with lymph node negative (LN−) disease and 24 patients with lymph node positive (LN+) disease randomly selected from the Calgary Tamoxifen Cohort, a retrospective cohort of breast cancer patients treated with adjuvant tamoxifen [n=511] from 1985–2000 at the Tom Baker Cancer Centre (Calgary, Canada). Liquid tissue lysates from FFPE tissue were analyzed by mass spectrometry and differentially expressed proteins were identified by the semi-quantitative spectral count method. FKBP4 was identified as an upregulated target in LN+ patients. The mRNA expression database of Kao et al. (BMC Cancer, 2011) was used to assess the prognostic potential of targets identified in the proteomic screen. FKBP4 protein expression was evaluated in tissue microarrays built from FFPE samples from the Calgary Tamoxifen Cohort using quantitative fluorescence immunohistochemistry and HistoRx AQUA analysis. Ten-year overall survival (OS) or five-year disease free survival (DFS) were the primary outcomes. Continuous variable FKBP4 data was dichotomized at the top quartile for both mRNA and protein expression analysis. Results: Univariate analysis demonstrated a significant association between high levels of FKBP4 mRNA and worse OS in ER+HER2− patients [n=182, HR=2.118 (1.070–4.192), p = 0.031] within the Kao et al database. Similarly, univariate analysis demonstrated that high levels of FKBP4 protein expression was associated with significantly worse DFS in ER+ HER2− patients [n=358, HR=1.632 (1.001–2.659), p = 0.049] in the Calgary Tamoxifen Cohort. FKBP4 was found to be an independent prognostic factor in both mRNA and protein expression cohorts using multivariate analysis adjusted for age, T stage, and lymph node status [OS: HR=2.786 (1.394–5.570), p = 0.004], or age, tumor size, tumor grade, and lymph node status [DFS: HR=1.875 (1.021–3.442), p = 0.043], respectively. Conclusions: Proteomic screening from FFPE tissue can identify new breast cancer biomarker candidates. Using this technique we have identified FKBP4 as an independent prognostic factor in ER+HER2− breast cancers, measured either by mRNA expression analysis or by quantitative protein expression analysis. Further studies are required to determine if FKBP4 may also be a predictive biomarker for tamoxifen response in ER+HER2− patients. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P6-07-17.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,003 | 0,001 |
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.
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 ».