Cancer Genetics and Epigenetics 2024, Vol.12, No.3, 157-165 http://medscipublisher.com/index.php/cge 161 Single-cell sequencing helps understand embryonic development and organ formation processes, as well as the regulatory mechanisms of cell differentiation, playing a significant role in developmental biology. Additionally, it can analyze patient samples to understand individual disease differences and guide personalized treatment strategies. 2.2 Bioinformatics tools and databases In cancer research and mutation analysis, bioinformatics tools and databases play a crucial role. These databases and tools provide important resources and support for mutation analysis, helping to uncover the biological and clinical significance of cancer mutations and guide the development of personalized treatment strategies. 2.2.1 Important mutation databases The Cancer Genome Atlas (TCGA) is a database that includes multiple cancer types and provides extensive genomic data, including mutations, copy number variations, RNA expression, and clinical information. Researchers can find mutation information for various cancers here. The Catalogue of Somatic Mutations in Cancer (COSMIC) is a database containing mutation information from various cancers. It provides detailed mutation annotations and classifications, helping to study the function and impact of mutations. ClinVar aggregates clinical information related to genetic variations, including the association of mutations with diseases. It is very useful for understanding the clinical significance of mutations. dbSNP is a database that contains information on various genetic variations, including single nucleotide polymorphisms (SNPs). Although it primarily focuses on genetic polymorphisms, it also includes mutation information. The Clinical Interpretations of Variants in Cancer (CIViC) is a database dedicated to organizing clinical interpretation information for cancer mutations, helping to understand the relationship between mutations and treatment responses. 2.2.2 Data analysis methods To analyze whole genome or transcriptome data, researchers can use a series of bioinformatics tools and workflows, such as GATK (Genome Analysis Toolkit), Mutect, SAMtools, and others. Machine learning algorithms can be used to predict the functional impact, clinical relevance, and patient classification of mutations. Commonly used machine learning libraries include scikit-learn and XGBoost. Mutation annotation tools, such as ANNOVAR, Variant Effect Predictor (VEP), and SnpEff, are used to annotate mutations and help determine their functional impacts (Dhanda et al., 2023). Variant frequency analysis is used to determine the frequency of mutations in populations, such as Exome Aggregation Consortium (ExAC) and Genome Aggregation Database (gnomAD). Mutation mapping tools, such as cBioPortal and Integrative Genomics Viewer (IGV), can visualize the distribution of mutations in the genome and the co-occurrence of mutations. 3 Clinical Applications of Cancer Mutation Analysis 3.1 Personalized treatment In the field of cancer treatment, personalized treatment is a groundbreaking clinical strategy designed to tailor the most effective treatment plan based on each patient's unique genetic characteristics and tumor mutation status. Cancer mutation analysis plays a crucial role in personalized treatment, helping doctors select the most appropriate treatment methods, primarily including targeted therapy and immunotherapy. 3.1.1 Targeted therapy Targeted therapy is a treatment approach based on specific molecular targets that intervenes in key signaling pathways essential for tumor cell growth, proliferation, or survival, achieving a more precise therapeutic effect. Cancer mutation analysis helps doctors identify specific target mutations present in the patient's tumor, enabling them to choose the most suitable targeted drugs. For example, in patients with EGFR mutations in non-small cell lung cancer, drugs such as Gefitinib and Erlotinib have been successfully used for targeted therapy, improving survival rates and quality of life. For melanoma patients with BRAF V600E mutations, BRAF inhibitors can be selected (Aleksakhina and Imyanitov, 2021). This personalized treatment strategy can enhance therapeutic efficacy, reduce unnecessary drug toxicity, and extend patient survival.
RkJQdWJsaXNoZXIy MjQ4ODYzNQ==