Advancements in Targeted Therapies and Pharmacogenomics for Personalized Breast Cancer Treatment: The Role of Gene SNPs in Treatment Resistance.
Bibliographic record
Abstract
Breast cancer remains a prevalent and diverse disease, significantly contributing to cancer- related deaths among women worldwide. Recent advancements in molecular biology have paved the way for targeted therapies and pharmacogenomics, which are crucial for developing personalized treatment strategies. This literature review synthesizes findings from recent studies on these approaches, emphasizing clinical trials, genomic profiling, and personalized medicine. It aims to focus on studies examining targeted treatments, such as human epidermal growth factor receptor- 2 (HER2) inhibitors and CDK4/6 inhibitors, alongside pharmacogenomic data that influence drug metabolism, efficacy, and toxicity. Additionally, it examines the role of gene SNPs (Single Nucleotide Polymorphisms) correlated with treatment resistance, which have emerged as key biomarkers affecting therapeutic outcomes in breast cancer. These SNPs, found in genes involved in drug metabolism and tumor progression, contribute to variability in treatment responses and resistance in specific subtypes. They encompass various breast cancer subtypes, including hormone receptorpositive (HR+), HER2-positive, and triple-negative breast cancer (TNBC). The targeted therapies, particularly HER2 inhibitors, have markedly improved outcomes for specific subtypes. Furthermore, pharmacogenomics personalizes treatment by identifying genetic variations that affect drug response, optimizing therapy selection, and minimizing adverse effects. Despite these advancements, drug resistance remains a significant challenge, highlighting the necessity for ongoing research in molecular diagnostics and innovative therapeutic combinations. The literature suggests that precision medicine, driven by genomic profiling, pharmacogenomic data, and single nucleotide polymorphisms (SNPs) analysis, is enhancing treatment efficacy for breast cancer patients. HER2- positive and HR+ patients have especially benefitted from these targeted therapies while emerging treatments are addressing the complexities of TNBC. Additionally, genetic testing, such as BRCA1/2 mutation screening, is vital for guiding treatment decisions. Targeted therapies and pharmacogenomics have revolutionized breast cancer treatment, providing more personalized and effective care. Nevertheless, overcoming drug resistance and expanding access to genomic testing are essential for future advancements in this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".