Current Landscape of Targeted Therapy in Hormone Receptor-Positive and HER2-Negative Breast Cancer
Bibliographic record
Abstract
Background: Hormone receptor-positive and HER2-negative breast cancer (HR + BC) is the most prevalent breast cancer. Endocrine therapy is the mainstay of treatment, however, due to the heterogeneous nature of the disease, resistance to endocrine therapy is not uncommon. Over the past decades, the emergence of novel targeted therapy in combination with endocrine therapy has shown improvement in outcomes of HR + BC. This paper reviews available data of targeted therapy and the results of pivotal clinical trials in the management of HR + BC. Methods: A literature search in PubMed and Google Scholar was performed using keywords related to HR + BC and targeted therapy. Major relevant studies that were presented in international cancer research conferences were also included. Results: Endocrine therapy with tamoxifen and aromatase inhibitors are backbone treatments for women with early-stage HR + BC leading to a significant reduction in mortality. They can also be used for primary prevention in women with a high risk of breast cancer. Preliminary data has shown the efficacy of adjuvant cyclin-dependent kinase (CDK) 4/6 inhibitor, abemaciclib, in high-risk disease in combination with aromatase inhibitors. For most women with advanced HR + BC, endocrine therapy is the primary treatment. Recent evidence has shown that the use of CKD 4/6 inhibitors, mTOR inhibitors, and PI3K inhibitors in combination with endocrine therapy has been associated with better outcomes and delays initiation of chemotherapy. Several novel agents are under study for HR + BC. Discussion: Targeted treatment options for HR + BC have evolved. The future of overcoming resistance to targeted therapy, novel compounds, and predictive markers are key to improving HR + BC outcomes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".