Impact of a Pre-Operative Exercise Intervention on Breast Cancer Proliferation and Gene Expression: Results from the Pre-Operative Health and Body (PreHAB) Study
Bibliographic record
Abstract
Abstract Purpose: Exercise after breast cancer diagnosis is associated with lower cancer-specific mortality, but the biological mechanisms through which exercise impacts breast cancer are not fully understood. The Pre-Operative Health and Body (PreHAB) Study was a randomized window-of-opportunity trial designed to test the impact of exercise on Ki-67, gene expression, and other biomarkers in women with breast cancer. Experimental Design: Inactive women with newly diagnosed breast cancer were randomized to an exercise intervention or mind–body control group, and participated in the study between enrollment and surgery (mean 29.3 days). Tumor and serum were collected at baseline and surgery. Results: Forty-nine women were randomized (27 exercise, 22 control). At baseline, mean age was 52.6, body mass index was 30.2 kg/m2, and exercise was 49 minutes/week. Exercise participants significantly increased exercise versus controls (203 vs. 23 minutes/week, P < 0.0001). There were no differences in changes of expression of Ki-67, insulin receptor, and cleaved caspase-3 in exercise participants versus controls. KEGG pathway analysis demonstrated significant upregulation of 18 unique pathways between the baseline biopsy and surgical excision in exercise participants and none in control participants (q < 0.1). Top-ranked pathways included several implicated in immunity and inflammation. Exploratory analysis of tumor immune infiltrates demonstrated a trend toward a decrease in FOXP3+ cells in exercise versus control participants over the intervention period (P = 0.08). Conclusions: A window-of-opportunity exercise intervention did not impact proliferation but led to alterations in gene expression in breast tumors, suggesting that exercise may have a direct effect on breast cancer. See related commentary by Koelwyn and Jones, p. 5179
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".