Cancer Genetics and Epigenetics 2024, Vol.12, No.1, 55-65 http://medscipublisher.com/index.php/cge 57 Therefore, a deep understanding of the nature and sources of tumor heterogeneity is crucial for the development of more effective diagnostic and therapeutic strategies. Through the study of tumor heterogeneity, it is expected to reveal the molecular mechanisms of tumor occurrence and development, discover new therapeutic targets, and provide scientific basis for precise tumor treatment. 1.2 Overview of traditional research methods In the process of deeply exploring tumor heterogeneity, traditional research methods play an important role, although they have certain limitations. These methods mainly rely on macroscopic and microscopic analysis of tumor tissue samples, as well as the application of molecular biology techniques. Dagogo-Jack and Shaw (2018) discuss the nature of cancer as a dynamic disease, which often becomes more heterogeneous as the disease progresses. Because of this heterogeneity, the entire tumor may contain a diverse collection of cells carrying different molecular signatures that vary in sensitivity to treatment. This heterogeneity may result in non-uniform distribution of genetically distinct tumor cell subpopulations within and outside the disease site (spatial heterogeneity) or temporal changes in the molecular makeup of cancer cells (temporal heterogeneity). At the histological level, traditional methods include morphological observation under a light microscope, such as hematoxylin-eosin staining (H&E staining) to evaluate the morphology, arrangement, and differentiation of tumor cells. Immunohistochemistry (IHC) further uses specific antibodies to detect protein expression in tumor cells, thereby revealing the existence of different cell subpopulations and their molecular characteristics. These methods can provide important information on the spatial distribution and cellular composition of tumor tissues (Luecken and Theis, 2019). At the molecular biology level, traditional research methods mainly focus on expression analysis of genes and proteins. For example, gene expression profiling detects the expression levels of thousands of genes in tumor samples using microarray or quantitative PCR techniques to identify gene groups associated with specific cell subpopulations or biological processes. In addition, mutation screening and genome sequencing are also widely used to identify genetic variations in tumor cells that may be important drivers of tumor heterogeneity. However, these traditional approaches face some common challenges when dealing with tumor heterogeneity. Cassidy et al. (2015) explored how patient-derived tumor xenograft models (PDX) can maintain the molecular heterogeneity of their original samples. Although PDX models can largely reproduce the multigene structure of human tumors, they cannot fully Explaining heterogeneity in the tumor microenvironment. 1.3 Limitations of traditional methods Although traditional research methods are widely used in oncology, they have shown obvious limitations in exploring tumor heterogeneity. These limitations mainly stem from the limitations of the technology itself and the complexity of tumor heterogeneity. Jamal-Hanjan et al. (2015) pointed out that although genomic studies have revealed a complex and heterogeneous clonal landscape of different tumor origins and treatment responses, cancer progression, and risk of disease recurrence, the significance of subclonal mutations, especially in driver genes The mutations in, as well as their evolution over time and response to cancer treatments, have yet to be determined. Traditional methods often require population analysis of large numbers of cells, thus failing to accurately capture the heterogeneity between individual cells. Because tumors are complex tissues composed of many different cell types, these cells can differ significantly in gene expression, metabolic activity, and response to treatment. However, traditional methods often only provide averaged results and fail to reveal these subtle cell-to-cell differences, leading to information loss and misinterpretation.
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