Cancer Genetics and Epigenetics 2024, Vol.12, No.3, 144-156 http://medscipublisher.com/index.php/cge 145 and real-world applicability (Bulen et al., 2023). Overcoming these challenges is essential for the successful implementation of integrative predictive approaches in clinical oncology. The aim of this study is to comprehensively summarize the current integrated methods for predicting treatment response in advanced solid tumors. By studying the latest advances in genome sequencing, proteomic analysis, imaging techniques, and clinical data integration, this study will highlight the advantages and disadvantages of these methods. We will also explore the application of these methods in clinical settings and their impact on treatment decision-making. In addition to summarizing the current status of integration methods, this study will also determine future research and development directions. This includes exploring emerging technologies, computing power, and algorithm improvements in the fields of molecular analysis and imaging, as well as the potential for collaborative research and data sharing. This study will discuss the impact of these advances on personalized healthcare and how to utilize them to improve the prognosis of patients with advanced solid tumors. By identifying key areas for future research, a roadmap is provided for researchers and clinicians to improve the accuracy and clinical practicality of integrated method predictions. 2 Current State of the Art 2.1 Molecular and genetic profiling The rapid development of integrative genomic approaches has significantly advanced personalized cancer therapy. Uzilov et al. (2016) demonstrated an approach that combines whole-exome sequencing (WES) and single-nucleotide polymorphism (SNP) microarray genotyping to identify somatic mutations, copy number alterations, gene expression changes, and germline variants in tumors. Their study found that 91% of patients had actionable genetic alterations, significantly impacting treatment decisions and outcomes. This integrative method provides comprehensive genomic information, forming the basis for personalized treatment plans (Uzilov et al., 2016). Sailer et al. (2019) extended this approach by combining whole-exome sequencing and transcriptome analysis in a cohort of patients with advanced and metastatic cancers. This integrative analysis identified clinically relevant genetic alterations in 39% of cases, with an additional 16% identified when RNA sequencing was added. This suggests that combining genomic sequencing with transcriptomic data provides a more comprehensive understanding of tumor molecular characteristics, improving the specificity and efficacy of treatments (Sailer et al., 2019). Wheeler et al. (2020) conducted multi-platform analyses of cancer patients, including genetic and epigenetic abnormalities and tumor microenvironment assessments, to identify mechanisms behind exceptional treatment responses. Their research found that some cancer patients exhibited extraordinary responses to specific treatments, which were associated with synthetic lethal interactions and rare genetic lesions. These findings offer valuable insights for developing new therapeutic strategies and highlight the importance of comprehensive molecular analyses (Wheeler et al., 2020). Genomic sequencing is not only used to identify mutations in tumors but also to discover new biomarkers. Biomarkers play a crucial role in predicting treatment responses and monitoring disease progression. For example, Liu et al. (2023) demonstrated that integrating multiple data sources and using machine learning algorithms significantly improved the accuracy of drug response predictions. This indicates the importance of discovering and applying biomarkers in personalized cancer treatment (Liu et al., 2023). 2.2 Imaging techniques Radiomics is a technique that extracts quantitative features from medical images, providing crucial information for the diagnosis and treatment of tumors. Sun et al. (2018) developed a radiomic signature to assess tumor-infiltrating CD8 cells in patients undergoing immunotherapy. Their study showed that this radiomic biomarker was validated across multiple independent cohorts and demonstrated potential in predicting clinical outcomes. Specifically, they found that the radiomic signature accurately predicted the levels of tumor-infiltrating CD8 cells, supporting personalized immunotherapy (Sun et al., 2018).
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