Cancer Genetics and Epigenetics 2024, Vol.12, No.4, 166-181 http://medscipublisher.com/index.php/cge 177 can lead to therapeutic resistance and variable patient outcomes. Addressing this challenge requires a deeper understanding of the molecular mechanisms underlying tumor heterogeneity and the development of strategies to overcome resistance (Eccles et al., 2013; Baliu-Piqué et al., 2020). Another challenge is the need for more comprehensive and clinically relevant models to study breast cancer. Enhanced resources to support in vitro and in vivo tumor models, as well as improved access to annotated clinical samples, are critical for advancing translational research. Additionally, there is a need for validated biomarkers to predict therapeutic response and guide treatment decisions. Developing and standardizing these biomarkers will be essential for the successful implementation of personalized medicine in breast cancer care (Eccles et al., 2013). Opportunities for advancing genetic research in breast cancer include the integration of new technologies and methodologies. For example, the use of CRISPR-Cas9 for gene editing and the application of single-cell sequencing techniques can provide new insights into the genetic and epigenetic landscape of breast cancer. These technologies have the potential to uncover novel therapeutic targets and improve our understanding of tumor biology (Lima et al., 2019; Waarts et al., 2022). 9.3 The role of AI and big data Artificial intelligence (AI) and big data are poised to revolutionize genetic research in breast cancer by enabling the analysis of large and complex datasets. AI algorithms can identify patterns and correlations in genetic data that may be missed by traditional analytical methods, leading to the discovery of new genetic targets and biomarkers. For instance, machine learning techniques can be used to predict patient outcomes based on genetic profiles, helping to tailor treatment strategies to individual patients (Waarts et al., 2022). Big data initiatives, such as The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project, provide valuable resources for genetic research. These databases contain vast amounts of genomic, transcriptomic, and clinical data that can be leveraged to gain insights into the genetic basis of breast cancer (Guo et al., 2018). By integrating data from multiple sources, researchers can develop more comprehensive models of breast cancer pathogenesis and identify new opportunities for intervention. Moreover, AI can facilitate the development of predictive models for breast cancer risk and progression. By analyzing genetic and clinical data, AI algorithms can identify individuals at high risk for developing breast cancer and recommend preventive measures. This approach has the potential to improve early detection and reduce the burden of breast cancer on healthcare systems (Koldehoff et al., 2021). In conclusion, the future of genetic research in breast cancer lies in the identification of new genetic targets, addressing current research gaps, and leveraging AI and big data to enhance our understanding and treatment of the disease. Continued investment in these areas will be essential for developing more effective and personalized therapies, ultimately improving outcomes for breast cancer patients. 10 Concluding Remarks The genetic landscape of breast cancer is complex and multifaceted, involving a variety of high, moderate, and low-penetrance genes. Pathogenic variants in BRCA1 and BRCA2 are strongly associated with a high risk of breast cancer, with odds ratios of 7.62 and 5.23, respectively. Other significant genes include PALB2, BARD1, RAD51C, RAD51D, ATM, CDH1, and CHEK2, each contributing to varying degrees of risk depending on the breast cancer subtype. Genome-wide association studies (GWAS) have identified over 150 common genetic loci for breast cancer risk, yet the target genes and mechanisms remain largely unknown. The prevalence of pathogenic variants and variants of unknown significance (VUS) is notably high, emphasizing the need for better classification and understanding of these variants. The identification of pathogenic variants in breast cancer susceptibility genes has significant implications for clinical practice. Genetic testing can inform personalized risk assessment and management strategies, enabling targeted screening and preventive measures for high-risk individuals. The use of gene panels in clinical settings has become more common, but the high prevalence of VUS poses challenges for clinical decision-making.
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