Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".