Abstract 5548: Clinical management and decision making in early ER-positive breast cancers through improved prognosis and pathway directed molecular profiling
Notice bibliographique
Résumé
Abstract Hormone receptor positive (HR+ve) breast cancer (BCa) comprises over 80% of all newly diagnosed BCas. While there is an initial good response to anti-hormone therapies, many patients will experience a recurrence. Validated prognostic tests are used to guide chemotherapy decisions, but the goal of precision medicine has yet to be achieved. We developed and validated a 95-gene prognostic signature (Bayani et al 2017) from the TEAM trial (van de Velde et al, 2011), demonstrating this risk classifier performed as well as the 21-gene, 50-gene and 70-gene tests and can be used in HER2+/-ve cases including only nodal status. RNA profiling has improved decisions regarding adjuvant chemotherapy but is insufficient for stratification to targeted therapies increasingly available in the early setting. The genomic landscape of BCas has identified recurrent patterns of mutation and copy-number changes. Except for HER2, there are few genes for whom mutational or gene dosage are reliable for stratification to targeted therapies. It is increasingly evident that a multi-omic approach to precision medicine is needed to encompass the biological complexity of cancer. Here we present the findings from the RNA profiling of patients from the TEAM trial using a custom diagnostic-grade NGS panel of the 95-gene risk classifier and DNA sequencing using a large (500 gene) comprehensive genomic profiling panel (OCAPlus, Thermo Fisher Scientific). 95-gene prognostic results of 1,182 patients showed prognostic utility using the custom panel with 265 (22%) low-risk patients experiencing >90% relapse free survival (DRFS)(HR=4.47 (95% CI 2.46-8.02, p=5.54e-07)) at 10 years. In 857 cases profiled with OCAPlus, the genes most frequently mutated included PIK3CA (53%), MAPK31 (25%), TP53(17%), CDH1 (17%) and GATA3 (10%). Frequent copy number changes were identified in CCND1 (18%), FGFR1 (12%), and MDM2 (5%). To investigate the potential for stratification to targeted therapies, a pathway approach was taken to identify aberrations in targetable signaling pathways. Among the 788 cases with both OCAPlus and the 95-gene results, the most frequently impacted pathways were PI3K/AKT (67%), HHR Pathway (55%), Chromatin regulation (50%), RAS/RAF/MEK/ERK (40%) and Cell Cycle (37%). To address the clinical need for those patients deemed at risk for recurrence, the consequence of aberrations in those pathways were investigated. Among 95-gene high-risk patients (n=604), those with mutations in genes of the Cell Cycle pathway experienced poorer DRFS (HR=1.95 (95%, CI 1.29-2.95, p= 0.0159), suggesting these patients might benefit cell cycle-targeting therapies. With no reliable biomarkers to predict response, and with associated side effects/toxicities of these agents, this offers a rational pathway-directed method of decision making for those identified as high-risk of recurrence. Citation Format: John M. Bartlett, Cheryl Crozier, Vinay K. Mittal, Dan Dion, Angela De Luca, Adam E. Sundby, Elizabeth Woroszchuk, Bradley d’Souza, Louis Gasparini, Mary Anne Quintayo, Mehar Chahal, Anna Y. Lee, Mathieu Larivière, Kyusung S. Park, Anupma Sharma, Jeffrey M. Smith, Seth Sadis, Daniel W. Rea, Melanie Spears, Jane Bayani. Clinical management and decision making in early ER-positive breast cancers through improved prognosis and pathway directed molecular profiling. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5548.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».