Cancer Genetics and Epigenetics 2024, Vol.12, No.3, 144-156 http://medscipublisher.com/index.php/cge 153 discussed the importance of standardizing data to optimize treatment strategies and delay resistance evolution. They proposed a population dynamics model incorporating both pre-existing and acquired resistance, demonstrating the need for standardized, high-quality data for accurate predictions (Mathur et al., 2020). Figure 5 illustrates the importance of tumor matrix protein (TMP) in preserving primary tumor features. The diversity of data types and sources was fully reflected in this study, and the graph shows different types of experimental data, including protein abundance, cell proliferation, tumor area, and morphological changes. These data come from various sources, such as scanning electron microscopy (SEM) images, immunofluorescence (IF) staining, hematoxylin eosin (H&E) staining, and Ki-67 staining. 6.2 Technical and computational challenges Analyzing high-dimensional data is a critical challenge in precision oncology. The vast amount of data generated from high-throughput sequencing and other omics technologies requires sophisticated computational tools for effective analysis. Fan et al. (2020) highlighted the computational challenges associated with single-cell transcriptomics, which provides detailed insights into tumor heterogeneity. Their review emphasized the need for advanced algorithms to handle the complexity of high-dimensional data, including trajectory and RNA velocity analysis to delineate tumoral evolution (Fan et al., 2020). The computational resources required for analyzing large-scale datasets are substantial. Deep learning and other advanced machine learning techniques necessitate high computational power and memory. Sakellaropoulos et al. (2019) demonstrated that deep neural networks outperformed traditional machine learning frameworks in predicting drug responses, but also noted the significant computational demands of these methods. Efficient use of computational resources is essential for developing scalable models that can be applied in clinical settings (Sakellaropoulos et al., 2019). 6.3 Ethical and privacy concerns Ensuring the privacy and security of patient data is paramount in cancer research. The integration of diverse datasets, including genomic, clinical, and imaging data, increases the risk of breaches in patient confidentiality. Researchers must implement stringent data protection protocols to safeguard patient information. Levitin et al. (2018) discussed the ethical considerations in single-cell transcriptomic analysis, highlighting the importance of maintaining patient privacy while leveraging detailed molecular data for treatment predictions (Levitin et al., 2018). Ethical considerations extend beyond privacy to include issues of consent and data ownership. Patients must be fully informed about how their data will be used and the potential implications of data integration. Ensuring ethical standards in data handling and usage is critical to maintaining public trust and advancing precision oncology. Berlow et al. (2018) emphasized the ethical dimensions of integrating patient-derived data in personalized treatment models, advocating for transparent and ethical research practices to enhance the applicability of computational predictions (Berlow et al., 2018). 7 Future Directions 7.1 Advancements in technology Emerging technologies in molecular profiling and imaging are driving advancements in predicting treatment responses for cancer. Tools like high-throughput sequencing and RNA sequencing have greatly improved the ability to detect predictive and prognostic molecular alterations in tumors. Gambardella et al. (2020) highlighted that these advancements enable precision medicine by identifying specific molecular changes in cancer cells, which guide personalized treatment strategies (Gambardella et al., 2020). Advances in computational power and algorithms are crucial for managing large-scale biological data. Deep learning and other AI technologies require substantial computational resources for complex data analysis. Sakellaropoulos et al. (2019) demonstrated the superiority of deep neural networks in predicting drug responses, although these methods demand significant computational power. These improvements are essential for developing scalable models applicable in clinical settings (Sakellaropoulos et al., 2019).
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