The Significance of Thyroid Hormone Receptors in Breast Cancer: A Hypothesis-Generating Narrative Review
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC) is frequently diagnosed among Canadian women. While targeted therapies are available for most BC patients; treatment resistance is common and novel therapeutic targets are of interest. Thyroid hormones (TH) bound to thyroid hormone receptors (THR) influence cell proliferation and differentiation; they are also involved in the growth and development of normal breast tissue. Evidence suggests that THRβ is a tumor suppressor in various solid tumors. PURPOSE: This narrative review discusses retrospective studies regarding the clinical relevance of THRβ as a potential prognostic biomarker and therapeutic target in BC. METHODS: We consulted with an information specialist to develop a search strategy to find all literature related to THRα expression as a potential prognostic and therapeutic biomarker in breast cancer. The primary search was developed for Medline and translated to Embase. The searches were conducted on the Ovid platform on 18 August 2023. RESULTS: Across seven retrospective studies identified, several have shown an association between higher THRβ1 expression with a lower risk of BC recurrence and with longer overall survival. CONCLUSIONS: Some evidence suggests that THRβ expression is associated with a lower risk of BC recurrence and death. Validation of THRβ as an independent prognostic biomarker and possible predictive biomarker of response to endocrine therapy and/or chemotherapy is of interest. Given that THRβ is upstream of the AKT/PI3K pathway, its potential as a predictive biomarker of response to AKT inhibitors and/or PI3K inhibitors may also be of value. Finally, the potential re-purposing of THRβ agonists as anti-cancer agents warrants investigation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".