Cancer Genetics and Epigenetics 2024, Vol.12, No.5, 279-293 http://medscipublisher.com/index.php/cge 286 in research, clinical trials, and regulatory frameworks to establish liquid biopsy as a robust and reliable tool in cancer care. 7 Genetic Diagnosis and Personalized Medicine 7.1 How genetic testing guides personalized screening programs for breast cancer Genetic testing plays a crucial role in guiding personalized screening programs for breast cancer by identifying individuals at higher risk due to genetic predispositions. For instance, multigene assays (MGAs) are utilized to assess the risk of early-stage breast cancer, aiding in the selection of appropriate adjuvant therapies and predicting patient prognosis (Yang et al., 2023). These assays help in stratifying patients based on their genetic risk, allowing for more tailored screening schedules and methods. Personalized screening programs, such as those being tested in the WISDOM, MyPEBS, and TBST trials, aim to replace the traditional population-based approach with risk-adapted strategies that consider individual genetic profiles (Allweis et al., 2021). Moreover, genetic testing can identify specific gene mutations that predispose individuals to breast cancer, such as BRCA1 and BRCA2 mutations. This information is critical for implementing more frequent and intensive screening protocols for high-risk individuals, potentially leading to earlier detection and better outcomes. The ENVISION consensus highlights the importance of developing breast cancer subtype-specific risk assessment tools that are applicable to women of all ancestries, further enhancing the precision of personalized screening programs (Pashayan et al., 2020). 7.2 The role of genetic diagnosis in personalized treatment Genetic diagnosis significantly influences personalized treatment plans for breast cancer by providing detailed insights into the molecular characteristics of the tumor. For example, genetic testing can identify specific mutations and gene expression profiles that predict the tumor's response to various treatments. This allows clinicians to select the most effective antitumor agents for metastatic breast cancer patients based on their genetic makeup (Yang et al., 2023). Additionally, tissue-based mRNA tests are routinely used in clinical practice to assess the recurrence risk and guide adjuvant endocrine therapy and chemotherapy, ensuring that patients receive treatments tailored to their genetic profiles (Čelešnik and Potočnik, 2023). Furthermore, advanced molecular diagnostic tools, such as next-generation sequencing (NGS), enable the detection of genetic heterogeneity within breast cancer tumors. This information is crucial for developing personalized treatment plans that target the unique genetic alterations present in each patient's cancer. The integration of genetic data with clinical and pathological information through multigene assays helps in predicting treatment outcomes and avoiding unnecessary treatments for low-risk patients, thereby minimizing treatment-associated morbidity (Zubair et al., 2021). 7.3 The contribution of genetic data and big data analysis to the early diagnosis of breast cancer The integration of genetic data with big data analysis has revolutionized the early diagnosis of breast cancer. By analyzing large datasets of genetic information, researchers can identify patterns and biomarkers associated with early-stage breast cancer. For instance, blood-based mRNA tests have emerged as promising diagnostic tools that offer minimal invasiveness and cost-efficiency. These tests can detect small tumors and provide valuable information for early cancer detection well before conventional diagnostic approaches (Čelešnik and Potočnik, 2023). Big data analysis also facilitates the development of advanced diagnostic methods, such as biosensors and microwave imaging techniques, which can rapidly and accurately detect breast cancer biomarkers. These technologies promise to improve the sensitivity and specificity of early-stage breast cancer detection, addressing the limitations of traditional screening methods (Wang, 2017). Additionally, the ENVISION consensus emphasizes the need for hybrid effectiveness-implementation research combined with modeling studies to evaluate the long-term population outcomes of risk-based early detection strategies, further highlighting the importance of big data in advancing personalized medicine (Pashayan et al., 2020).
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