Abstract P1-05-01: The epithelial to mesenchymal transition: Identifying a signature of recurrence in ductal carcinoma in situ
Notice bibliographique
Résumé
Abstract Background: The epithelial to mesenchymal transition (EMT) plays a critical role in the progression from non-invasive to invasive breast carcinomas (IBC). It is characterized by alterations in gene expression, changes in cellular polarity, the disruption of tight junctions; production of metalloproteinases, transforming growth factor-β (TGFβ) induction, expression of cancer stem cell markers, hypoxia, decrease in e-cadherin expression, along with other molecular biological events. Several transcription factors including ZEB1/2, TWIST1, SNAIL1/2, FOX family, GATA4/6 are involved in the process. There is a need to identify the molecular events driving the progression of ductal carcinoma in situ (DCIS); and to derive a signature that differentiates DCIS lesions that have the potential to recur as a subsequent DCIS, an IBC, or to not recur. To catalog the changes associated with EMT that may reveal a clinically relevant signature of progression from DCIS to DCIS or IBC recurrences using a panel of 200 genes related to EMT. Methodology: RNA was extracted from formalin-fixed paraffin embedded (FFPE) sections of pure primary DCIS lesions representing three categories of outcome: those that did not recur; those that recurred with a subsequent DCIS; and those that recurred with invasive cancer. RNA abundance profiling was performed using Nanostring platform and data processing using an R statistical environment. Levels of mRNA abundance were modelled as a function of recurrence status. Coefficients were fit to terms representing the effect and the standard errors of the coefficient were adjusted with an empirical Bayes moderation. Model-based t-tests were then used to test if the coefficients were significantly different from zero. Results: Using a technical control sample, pairwise comparisons across three replicates showed high correlation (ρ=0.99, Pρ<2.2x10-16 for all 3 comparisons), suggesting the robustness of the assay. In our preliminary survey of 45 patients across the three groups, we have identified a number of genes that showed differential mRNA abundance levels between patients who recurred (either DCIS or invasive recurrence) vs. those who did not recur. Using Random Forest analysis in a leave-one-out cross-validation approach, we were able to obtain a classifier with a sensitivity of 82% and specificity of 58%. Based on these initial findings, an additional 200 samples have been processed to support these initial findings. Conclusion: The current literature provided increasing evidence that transcriptomic patterns reflecting the EMT may reveal novel biomarkers and elucidate molecular mechanisms leading to improved prognosis. Among breast carcinomas, differential expression of the EMT genes has been associated with a worse outcome, among estrogen receptor-negative and basal-like carcinomas. However, the understanding of the role of EMT genes in DCIS is limited; therefore, to elucidate whether the EMT plays a role in the progression of DCIS, we have designed an EMT gene panel that also includes genes that are significant prognosticators for IBC, including ER, PgR, Ki67 and HER2. In an exploratory analysis of cases trained based on clinical outcome, the sensitivity for predicting recurrence (whether DCIS or invasive) was 82%. Citation Format: Felipe Lima J, Yao CQ, Yan F, Dion D, Quintayo MA, Lungu I, Nofech-Mozes S, Pruneri G, Viale G, Boutros PC, Bartlett JMS, Bayani J. The epithelial to mesenchymal transition: Identifying a signature of recurrence in ductal carcinoma in situ. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P1-05-01.
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 | 0,001 |
| 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 | 0,001 | 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) | 0,005 | 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 ».