Abstract P5-13-26: The future of HER2-positive breast cancer patients might be written in miRNAs: An exploratory analysis from the NeoALTTO study
Notice bibliographique
Résumé
Abstract Importance. Neoadjuvant therapy with dual HER2 blockade improved pathological complete response (pCR) rate. Nevertheless, it would be desirable to identify patients exquisitely responsive to single agent trastuzumab to minimize or avoid overtreatment. Objective. To evaluate the predictive and prognostic value of basal primary tumor miRNA expression profile within the trastuzumab arm of the NeoALTTO study.Design, Setting and Participants. RNA samples from baseline biopsies were randomized into training (n =45) and testing (n =47) sets. After normalization, miRNAs associated with Event-free survival (EFS) and pCR were identified by univariate analysis. Multivariate models were implemented to generate specific signatures which were first confirmed, and then analyzed according to other clinical and pathological variables.Main outcomes and measures. Association between miRNA expression and pCR and/or EFS.Results. We identified a prognostic signature including hsa-miR-153-3p (HR 1.831, 95%CI: 1.34-2.50) and hsa-miR-219a-5p (HR 0.629, 95%CI: 0.50 - 0.78). For two additional miRNAs (miR-215-5p and miR-30c-2-3p), we found a statistically significant interaction term with pCR (p.interaction: 0.017 and 0.038, respectively). Besides, a two-miRNA signature was predictive of pCR (hsa-miR-31-3p, OR 0.70, 95%CI: 0.53 - 0.92, and hsa-miR-382-3p, OR: 1.39, 95%CI: 1.01 -1.91). Notably, the performance of this predictive miRNA signature resembled that of the genomic classifiers PAM50 and TRAR, and did not improve when the extended models were fitted.Conclusions and relevance. Analysis of primary tumor tissue miRNAs holds the potential of a parsimonious tool to identify patients with differential clinical outcomes after trastuzumab based neoadjuvant therapy. Trial registration. ClinicalTrials.gov Identifier: NCT00553358. Table 1.MiRNAs associated with EFS. Results of the univariate Cox regression model- training set.miRNAsHR95% CIhsa-miR-12000.671(0.512; 0.879)ahsa-miR-1238-3p0.669(0.543; 0.826)ahsa-miR-12650.839(0.705; 0.997)ahsa-miR-129-5p0.785(0.628; 0.980)ahsa-miR-153-3p1.412(1.026; 1.943)ahsa-miR-15390.826(0.697; 0.979)ahsa-miR-1908-5p0.796(0.648; 0.978)ahsa-miR-205-5p0.737(0.553; 0.982)hsa-miR-219a-5p0.787(0.627; 0.989)ahsa-miR-25-5p0.566(0.391; 0.819)ahsa-miR-3000.581(0.399; 0.845)ahsa-miR-382-3p0.791(0.636; 0.985)ahsa-miR-4920.774(0.635; 0.944)ahsa-miR-519a-3p0.834(0.696; 0.998)ahsa-miR-551b-5p0.666(0.452; 0.981)ahsa-miR-5830.727(0.549; 0.963)ahsa-miR-6001.373(1.03; 1.829)ahsa-miR-6260.819(0.673; 0.998)ahsa-miR-651-5p0.611(0.382; 0.977)hsa-miR-7610.845(0.721; 0.991)ahsa-miR-891b0.658(0.479; 0.904)ahsa-miR-92a-2-5p0.787(0.637; 0.971)ahsa-miR-937-3p0.748(0.578; 0.967)aHR, Hazard Ratio; CI, Confidence Intervala microRNAs retained its statistically significance (at 10%) also when normalized for the overall mean. Table 2.MiRNAs associated with pCR. Results of the univariate logistic model - training set.miRNAsOR95% CIhsa-miR-132-3p0.410(0.169; 0.991)ahsa-miR-23b-5p0.520(0.316; 0.856)ahsa-miR-31-3p0.566(0.351; 0.912)ahsa-miR-31-5p0.508(0.291; 0.887)ahsa-miR-330-3p0.501(0.251; 0.999)hsa-miR-34b-3p0.673(0.474; 0.956)ahsa-miR-382-3p1.392(1.006; 1.925)ahsa-miR-548j-5p1.702(1.090; 2.658)aOR, Odds Ratio; CI, Confidence Interval. a microRNAs retained its statistically significance (at 10%) also when normalized for the overall mean. 2 Citation Format: Marilena Valeria Iorio, Sara Pizzamiglio, Giulia Cosentino, Chiara M Ciniselli, Loris De Cecco, Alessandra Cataldo, Ilaria Plantamura, Tiziana Triulzi, Sarra El-abed, Yingbo Wang, Mohammed Bajji, Paolo Nuciforo, Jens Huober, Susan Ellard, David Rimm, Andrea Gombos, Mariagrazia Daidone, Paolo Verderio, Elda Tagliabue, Serena Di Cosimo. The future of HER2-positive breast cancer patients might be written in miRNAs: An exploratory analysis from the NeoALTTO study [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-13-26.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,003 | 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 ».