BIOM-27. PREDICTIVE BIOMARKERS OF BRAIN METASTASIS IN TRIPLE-NEGATIVE BREAST CANCER: CLINICOPATHOLOGICAL CORRELATES AND EMERGING EVIDENCE
Bibliographic record
Abstract
Abstract INTRODUCTION Of all central nervous system (CNS) malignancies, only 15% are primary, while 85% are metastatic. Of the metastases, the highest incidences are in lung (35%) and breast (30%) cancer, with triple-negative breast cancer (TNBC) being the most common subtype to metastasize to the CNS. TNBC is highly invasive and aggressive, with half of all primary TNBC patients remaining susceptible to CNS metastasis even after treatment, thereby contributing to its dismal prognosis. Identifying clinicopathological markers associated with TNBC brain metastasis (TNBCBM) is crucial in developing risk-stratification tools and new therapeutic targets. Objective: To identify the most promising biomarkers that are predictive of TNBCBM and key mechanisms involved in the transformation and neurologic dissemination of TNBC. METHODS We extracted publications that identified TNBCBM molecular pathways from PubMed, Scopus, Embase, Cochrane, and Web of Science. RESULTS Of the publications reviewed, 33.3% identified proteins or protein-coding genes, 33.3% miRNAs, 26.7% transcription factors (TFs), 20% molecular pathways or regulatory axes, and 6.7% circRNAs as predictive biomarkers of TNBCBM. Among these, EN1, miR-211, and STAT3 were discussed most, with higher levels associated with higher rates of BM, enhanced BM, and facilitation of BM, respectively. A subset of articles explore subtype switching, specifically to estrogen and progesterone receptor positive metastatic lesions. CONCLUSION While TNBC aggressiveness has been linked to several biomarkers, few are specific to brain metastases. EN1, miR-211, and STAT3 particularly offer potential as predictive indicators of TNBCBM. Investigating markers offers insight into the mechanisms by which TNBC cells breach the blood-brain barrier and colonize the CNS. Furthermore, receptor positivity in metastasis allows for the development of targeted therapies which could not previously be applied to TNBC. These findings can enhance clinical trial design, drive biomarker-driven therapies, and improve outcomes for patients facing one of the most lethal complications of TNBC.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".