Personalized medicine for metastatic breast cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: With recent advances in DNA sequencing technology, recurrent genomic alterations can be identified in tumor samples from patients with metastatic breast cancer (MBC) to enrich clinical trials testing targeted therapies. This review provides an overview of clinically relevant genomic alterations in MBC and summarizes the recent clinical data from early phase trials of novel targeted treatments. RECENT FINDINGS: The clinical development of personalized treatment includes targeted agents directed against PI3K/mTOR, fibroblast growth factor receptor (FGFR), human epidermal growth factor receptor 2 (HER2), DNA repair, and cell cycle pathways. PI3K/mTOR pathway drugs are active in endocrine and trastuzumab-resistant disease. Drugs targeted at PI3K/mTOR, FGFR, and poly(ADP-ribose) polymerase show early signs of efficacy in MBC subpopulations enriched with relevant pathway aberrancies. Regimens combining targeted agents with either endocrine, anti-HER2, or chemotherapy treatments are also being studied in hormone receptor-defined and HER2-defined or pathway-enriched subgroups. SUMMARY: A new approach to personalized medicine for MBC that involves molecular screening for clinically relevant genomic alterations and genotype-targeted treatments is emerging. Clinical trials are needed to determine whether rare subpopulations of MBC benefit from genotype-targeted treatments.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".