TABLE 5.
Novel platforms and technologies for Next-Generation Sequencing in Oncology.
| Platform/Technology | Key aspects |
|---|---|
| Single-Cell Sequencing (Ishida et al., 2024) | Single-cell sequencing allows analyses of genetic material from individual cells within a tumor. This approach uncovers the heterogeneity within tumors, providing insights into the mechanisms of cancer evolution, metastasis, and resistance to therapy. By understanding the genetic diversity within tumors at the single-cell level, more targeted and effective therapies can be developed |
| Long-Read Sequencing Technologies (Yahya et al., 2023) | While traditional NGS technologies generate short reads that can be challenging to assemble in highly repetitive or complex regions of the genome, long-read sequencing technologies, such as those offered by Pacific Biosciences and Oxford Nanopore, produce much longer reads. This ability enhances the detection of structural variants, fusion genes, and complex rearrangements that play critical roles in cancer development and progression, improving the accuracy of genomic analysis |
| Integrated Multi-omics Platforms (Aldea et al., 2023; Volpe et al., 2023) | Emerging NGS platforms are increasingly integrating genomic sequencing with other ‘omics’ analyses, such as transcriptomics, proteomics, and metabolomics. This integrated approach provides a more comprehensive view of the molecular drivers of cancer, enabling the identification of novel therapeutic targets and biomarkers for treatment response and resistance |
| CRISPR-Cas9 Based Targeted Sequencing (Malekshoar et al., 2023) | The integration of CRISPR-Cas9 genome editing technology with NGS allows for targeted sequencing of specific genomic regions of interest, enhancing the efficiency and specificity of sequencing cancer-related genes, and enabling the identification of mutations and alterations with greater precision. It holds promise for the development of highly targeted diagnostic tests and the discovery of new therapeutic targets |
| Artificial Intelligence and Machine Learning-Enhanced Analysis (Thirunavukkarasu et al., 2024) | The application AI and machine learning algorithms to NGS data is transforming the analysis and interpretation of complex genomic datasets. These technologies can identify patterns and predictive markers within large-scale genomic data that may not be apparent to human analysts, developing predictive models for cancer prognosis, treatment response, and the identification of novel therapeutic targets |
| Portable and Real-time Sequencing Devices (Glowienka-Stodolak et al., 2024) | The development of portable NGS devices, such as the MinION from Oxford Nanopore, enables real-time genomic sequencing in clinical settings, research laboratories, and even in field conditions. This accessibility could revolutionize cancer diagnostics and monitoring, allowing for immediate genomic analysis and decision-making regarding treatment strategies |
| Digital Spatial Profiling (Glyn et al., 2024; Su et al., 2024) | Digital spatial profiling is an innovative approach that combines NGS with in situ analysis of protein and RNA biomarkers within the tumor microenvironment. This technology provides spatial context to genomic data, enabling the understanding of the tumor architecture and the interaction between cancer cells and the immune system, which is vital for the development of effective immunotherapies |
NGS: Next-Generation Sequencing. AI: artificial intelligence.