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Recent advances in biomarkers, artificial intelligence, and targeted therapeutic strategies in breast cancer: Current
Divya Sharma1, Peeyush Bhardwaj1, Kaushalendra Kumar Mishra1
1Institute of Pharmacy, Bundelkhand University, Jhansi, India.
Abstract:
Breast cancer is a complex and heterogeneous disease that remains a major global health challenge. Recent progress in molecular biology, artificial intelligence (AI), and precision medicine has transformed its diagnosis and treatment. Conventional biomarkers such as ER, PR, HER2, and BRCA mutations continue to guide therapeutic decisions, while emerging biomarkers including TP53, PTEN, and STK11 offer new insights into tumor behavior and drug resistance. AI-based technologies, including machine learning and deep learning, have improved early detection and diagnostic accuracy through advanced medical imaging and multimodal analysis. Current treatment strategies extend beyond conventional chemotherapy and surgery to include targeted therapy, endocrine therapy, immunotherapy, antibody-drug conjugates, and gene-based approaches. Novel therapeutics such as CDK4/6 inhibitors, PARP inhibitors, PI3K inhibitors, and selective estrogen receptor degraders have demonstrated promising clinical outcomes in advanced breast cancer. Additionally, emerging technologies such as CRISPR/Cas9 gene editing and nanotechnology-based drug delivery systems show significant potential for personalized cancer therapy. This review summarizes recent advancements in breast cancer biomarkers, AI-assisted diagnostics, and modern therapeutic strategies aimed at improving precision medicine and patient outcomes.
Insights
Recent advancements in breast cancer research include novel biomarkers and artificial intelligence (AI) for improved diagnostics. New therapies like targeted drugs and gene editing offer personalized treatment strategies for better patient outcomes.
Area of Science:
- Oncology
- Biotechnology
- Medical Imaging
Background:
- Breast cancer is a complex, heterogeneous disease posing a significant global health challenge.
- Conventional biomarkers (ER, PR, HER2, BRCA) guide treatment, while emerging ones (TP53, PTEN, STK11) reveal tumor behavior and resistance.
- Progress in molecular biology, AI, and precision medicine is revolutionizing breast cancer diagnosis and treatment.
Purpose of the Study:
- To review recent advancements in breast cancer biomarkers.
- To explore the role of artificial intelligence (AI) in breast cancer diagnostics.
- To summarize modern therapeutic strategies for precision medicine in breast cancer.
Main Methods:
- Review of current literature on breast cancer biomarkers, AI applications, and therapeutic strategies.
- Analysis of conventional and emerging biomarkers.
- Evaluation of AI-driven diagnostic tools (machine learning, deep learning) in medical imaging and multimodal analysis.
- Summary of established and novel treatment modalities.
Main Results:
- AI technologies enhance early detection and diagnostic accuracy.
- Emerging biomarkers provide deeper insights into tumor characteristics and treatment response.
- Novel therapeutics, including CDK4/6, PARP, and PI3K inhibitors, show promise in advanced breast cancer.
- CRISPR/Cas9 gene editing and nanotechnology offer potential for personalized therapy.
Conclusions:
- Integration of advanced biomarkers and AI is crucial for precise breast cancer diagnosis and treatment.
- Novel therapeutic agents and technologies are expanding personalized treatment options.
- Continued research in these areas aims to improve patient outcomes and survival rates in breast cancer.
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