Abstract Cancer remains a significant global health challenge, accounting for nearly one in six deaths worldwide and imposing a heavy burden on society and healthcare systems. Although conventional approaches, such as surgery, radiotherapy, chemotherapy, and targeted therapy, have made significant progress in cancer prevention and treatment, the high heterogeneity of tumors at the genetic, transcriptional, phenotypic, and immune microenvironmental levels significantly reduces the effectiveness of these treatment strategies. This heterogeneity is a core reason for treatment resistance, disease recurrence, and poor prognosis in patients. The emergence of high-throughput sequencing (HTS) technology has profoundly transformed research and clinical practice in oncology. By integrating genomic, transcriptomic, epigenomic, and spatial location information, HTS can systematically depict the heterogeneity of tumor cells and precisely characterize the composition, state, and spatial distribution of immune cells, as well as their dynamic interactions with tumor cells. The incorporation of HTS data into clinical workflows has facilitated the discovery of predictive biomarkers and the development of precision immunotherapy strategies. Deep sequencing of patient tumor samples can identify driver mutations, immune microenvironment characteristics, and potential resistance mechanisms, thereby guiding individualized treatment. This article reviews the latest progress of HTS in analyzing tumor heterogeneity, advancing precision oncology, and improving immune efficacy. It also assesses the main challenges in clinical application, including insufficient data standardization, highly complex analysis procedures, and relatively high economic costs, while discussing future perspectives for clinical translation.
Liu et al. (2026) studied this question.