Abstract ED03-03: Impact of exercise on breast cancer outcomes
Notice bibliographique
Résumé
Abstract Background: Exercise may improve breast cancer outcomes; however, current research is limited in its ability to inform clinical exercise trials and clinical oncology practice. The purpose of this presentation is to summarize the current research on exercise and breast cancer outcomes using a framework proposed to improve the clinical utility of this research. The Exercise as Cancer Treatment (EXACT) Framework organizes and characterizes the critical aspects of a clinical oncology setting to allow for a more systematic and clinically informative approach to the study of exercise as a cancer treatment. The EXACT Framework proposes that two key clinical oncology variables—tumor/disease status (i.e., primary tumor present, primary tumor removed, metastatic disease present) and treatment status (i.e., treatment naïve, actively being treated, previously treated)—are critical for understanding the effects of exercise on cancer outcomes, but few studies have addressed these variables. Application of the EXACT Framework to existing studies may improve their interpretation and identify directions for future research. Results: Presently, there are no adequately powered randomized controlled trials that have examined the effects of exercise on breast cancer outcomes in any clinical oncology scenario, although secondary analysis of smaller phase II trials have been reported. Preclinical research using rodent models has generally shown that exercise slows the growth and spread of treatment naïve breast tumors through various mechanisms including immune response, tumor metabolism, apoptosis, and DNA synthesis and repair. More recently, these preclinical studies have enhanced their clinical utility by including an existing cancer treatment in their design and have generally shown that exercise enhances the delivery and/or efficacy of concurrent cancer treatments through mechanisms related to improved tumor vasculature and perfusion. Most human evidence on exercise and breast cancer outcomes has been gleaned from secondary analysis of observational studies involving healthy cohorts or breast cancer survivor cohorts. These studies have generally included early-stage breast cancer patients who received heterogeneous cancer treatments. Physical activity data was typically collected either well before diagnosis and/or well after treatments. Overall, these studies have generally shown that higher prediagnosis and postdiagnosis physical activity are associated with a lower risk of breast cancer recurrence and mortality. Discussion: There is a disconnect between the clinical oncology scenarios addressed by animal and human studies of exercise and breast cancer outcomes. Moreover, most studies do not adequately address treatment combinations and sequencing. Future observational studies of exercise and breast cancer outcomes may improve their clinical utility by: (a) recruiting breast cancer patients with the same tumor/disease status receiving the same or similar first-line treatment protocols, (b) collecting detailed data on all planned and unplanned cancer treatments, (c) assessing exercise in relation to those cancer treatments (i.e., before, during, between, after) rather than in relation to the cancer diagnosis (i.e., various time periods before and after diagnosis), (d) collecting data on cancer-specific outcomes (e.g., disease response, progression, recurrence) in addition to mortality, and (e) conducting subgroup analyses based on cancer treatments received. Conclusions: Preclinical and observational studies may contribute important knowledge regarding the role of exercise as a cancer treatment; however, modifications to study design and analysis are necessary if they are to inform clinical research and practice. The ultimate goal of precision exercise oncology is to provide the right exercise prescription to the right breast cancer patient at the right time postdiagnosis. Citation Format: K. Courneya. Impact of exercise on breast cancer outcomes [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr ED03-03.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 tête enseignante, 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 ».