Pharmacogenomics has become integral to personalised medicine in breast cancer, utilising genetic insights to customize treatment strategies and enhance patient outcomes. Understanding how genetic variations influence drug metabolism, response, and toxicity is crucial for guiding treatment selection and dosing regimens. Genetic polymorphisms in drug-metabolizing enzymes and transporters significantly impact pharmacokinetic variability, influencing the efficacy and safety of chemotherapy agents and targeted therapies. Biomarkers associated with the hormone receptor status of breast cancer and mutations serve as key determinants of treatment response, aiding in the selection of therapies. Despite substantial progress in understanding the pharmacogenomic landscape of breast cancer, efforts to identify novel genetic markers and refine treatment optimisation strategies are required. Genome-wide association studies and advanced sequencing technologies hold promise for uncovering genetic determinants of drug response variability and elucidating complex pharmacogenomic interactions. The future of pharmacogenomics in breast cancer lies in real-time treatment monitoring, the discovery of additional predictive markers, and the seamless integration of pharmacogenomic data into clinical decision-making processes. However, translating pharmacogenomic discoveries into routine clinical practice requires collaborative efforts among stakeholders to address implementation challenges and ensure equitable access to genetic testing. By embracing pharmacogenomics, clinicians can tailor treatment approaches to individual patients, maximizing therapeutic benefits while minimizing adverse effects. This review discusses the integration of pharmacogenomics in breast cancer treatment, highlighting the significance of understanding genetic influences on treatment response and toxicity, and the potential of advanced technologies in refining treatment strategies.
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http://dx.doi.org/10.1016/j.cca.2024.119893 | DOI Listing |
Breast Cancer Res
January 2025
Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Road, Hangzhou, Zhejiang, China.
Background: Neoadjuvant chemotherapy (NACT) is the standard-of-care treatment for patients with locally advanced breast cancer (LABC), providing crucial benefits in tumor downstaging. Clinical parameters, such as molecular subtypes, influence the therapeutic impact of NACT. Moreover, severe adverse events delay the treatment process and reduce the effectiveness of therapy.
View Article and Find Full Text PDFBMC Cancer
January 2025
Division of Clinical Research and Technological Development, Brazilian National Cancer Institute, 37 Andre Cavalcanti Street, 5th floor, Annex Building, 20231050, Rio de Janeiro, Brazil.
Background: Breast cancer (BC) has exhibited varied epidemiological trends based on distinct age categories. This research aimed to explore the incidence and mortality rates of BC within pre-defined age groups in the Brazilian population.
Methods: BC incidence trends were assessed from 2010 to 2015 using Brazilian Population-Based Cancer Registries, employing age-standardized ratios and annual average percentage change (AAPC).
BMC Med Imaging
January 2025
Electronics and Communications, Arab Academy for Science, Heliopolis, Cairo, 2033, Egypt.
Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P. R. China.
Background: This study aims to explore the value of habitat-based magnetic resonance imaging (MRI) radiomics for predicting the origin of brain metastasis (BM).
Purpose: To investigate whether habitat-based radiomics can identify the metastatic tumor type of BM and whether an imaging-based model that integrates the volume of peritumoral edema (VPE) can enhance predictive performance.
Methods: A primary cohort was developed with 384 patients from two centers, which comprises 734 BM lesions.
Breast Cancer Res Treat
January 2025
University of Pittsburgh School of Medicine (Center for Clinical Genetics and Genomics), Pittsburgh, PA, USA.
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