Cancer Genetics and Epigenetics 2024, Vol.12, No.3, 125-136 http://medscipublisher.com/index.php/cge 131 populations revealed diverse viral statuses, including non-HBV and non-HCV cases, across different ethnic groups. The histological comparisons between LIMORE models and original cancers demonstrated consistency, reinforcing the panel's reliability. Copy number alteration (CNA) frequencies showed strong correlations between LIMORE and TCGA HCC datasets, confirming the genetic relevance of the models. The integration of HBV in LIMORE models closely mirrored that in primary liver cancers, with significant variability in integration sites and numbers. Principal component analysis (PCA) of gene expression data further validated the LIMORE panel's representativeness, as a substantial proportion of TCGA HCCs correlated highly with LIMORE models, emphasizing their utility in liver cancer research. 5.3 Lessons learned The implementation of precision treatment for liver cancer has provided several valuable lessons. One key lesson is the importance of comprehensive molecular profiling. Studies have shown that a higher matching score, which indicates a greater alignment between identified molecular alterations and targeted therapies, correlates with better clinical outcomes (Rodón et al., 2019). This underscores the need for extensive genomic and transcriptomic analyses to guide treatment decisions. Another lesson is the necessity of a multidisciplinary approach. The involvement of molecular tumor boards, as seen in the study at the Second Hospital of Tianjin Medical University, has proven effective in improving the success rate of genomic-guided therapies. This approach ensures that treatment recommendations are based on a thorough evaluation of molecular data and clinical expertise. Furthermore, the heterogeneity of liver cancer poses a significant challenge. Precision treatment must account for the varied genetic susceptibilities, morphological diversity, and microenvironmental discrepancies of liver cancer. This requires ongoing research and the development of personalized treatment strategies that can adapt to the dynamic nature of tumor biology (Fu and Wang, 2018). In conclusion, while precision treatment for liver cancer holds great promise, its success depends on comprehensive molecular profiling, a multidisciplinary approach, and the ability to address the heterogeneity of the disease. Ongoing clinical trials and research efforts continue to refine these strategies, aiming to improve patient outcomes and expand the applicability of precision oncology. 6 Current Challenges in Precision Treatment 6.1 Tumor heterogeneity Tumor heterogeneity remains a significant challenge in the precision treatment of liver cancer. The complexity and diversity of genetic alterations within and between tumors complicate the development of effective targeted therapies. For instance, liver cancers exhibit marked heterogeneity, which is difficult to model accurately in preclinical studies. This heterogeneity includes variations in immune microenvironment, metabolic reprogramming, and expression of drug targets, which can influence treatment outcomes (Zugazagoitia et al., 2016). Additionally, the existence of multiple subclones within a single tumor can lead to differential responses to therapy, further complicating treatment strategies (Sullivan et al., 2018). 6.2 Drug resistance Drug resistance is another critical obstacle in the precision treatment of liver cancer (Kato et al., 2020). Many patients initially respond to targeted therapies but eventually develop resistance, leading to treatment failure. This resistance can arise from various mechanisms, including secondary mutations, activation of alternative signaling pathways, and phenotypic changes in cancer cells. For example, the underwhelming success of mutation-driven therapies in liver cancer is often due to a lack of functional insight into the genomic alterations that drive resistance. Moreover, the dynamic nature of tumor biology necessitates continuous monitoring and adaptation of treatment strategies to overcome resistance (Tsimberidou et al., 2021). 6.3 Data interpretation The interpretation of large-scale genomic data poses a significant challenge in precision oncology. Next-generation sequencing (NGS) technologies have revolutionized the field by enabling comprehensive molecular profiling of tumors. However, the clinical utility of these data is often limited by the complexity of interpreting the results and translating them into actionable treatment plans. The integration of various molecular characterization strategies, such as transcriptomics, immunophenotyping, and single-cell analyses, can provide a
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