Next-generation sequencing (NGS) technology has transformed the biomedical research landscape. Only a few years ago, high resolution genome or exome sequencing would be cumbersome and cost-restrictive, but current NGS technology platforms now allow for basic and clinical researchers to include these approaches for routine DNA and RNA sequencing needs. What are the different NGS sequencing approaches and how are they applied to oncology research?
Next-generation sequencing (NGS) technology has transformed the biomedical research landscape. Only a few years ago, high resolution genome or exome sequencing would be cumbersome and cost-restrictive, but current NGS technology platforms now allow for basic and clinical researchers to include these approaches for routine DNA and RNA sequencing needs. What are the different NGS sequencing approaches and how are they applied to oncology research?
1. Whole Genome Sequencing (WGS): NGS technology can be used for WGS of human genomes and tumor-specific genomes, as well as animal model and microbial genomes. WGS produces high resolution genomic sequences of expressed genomic regions as well as unexpressed regulatory regions. For preclinical oncology research, WGS is critical for characterizing genomic profiles associated with tumor progression or potential responsiveness to targeted drug therapies. WGS can detect single nucleotide variants, copy number variants, and insertions/deletions in tumor cells1. The comprehensive scope of WGS makes it well-suited for detecting mutations in both coding and non-coding regions2. WGS is also useful for population level oncology studies that evaluate genetic susceptibility to specific cancers and potential heritability3.
2. Whole Exome Sequencing (WES): WES techniques focus on sequencing the exome, which are comprised of protein expressing regions, or exons, within the genome. WES is an appropriate method for identifying genetic mutations that alter protein sequences, and WES data can be used toward measuring the tumor mutational burden (TMB) and predicting treatment efficacy4. WES data can also be used to identify potential new drug targets or mechanisms of drug resistance5.
3. Targeted Sequencing: This method focuses on defined gene regions and is typically used in diagnostic applications or for validation of WGS or WES results. Targeted sequencing works well for screening tumor samples for well characterized mutations, such as those associated with BCL2, BRCA-1/2, BRAF, and EGFR, and can be used for identifying appropriated targeted therapies6.
4. RNA sequencing: RNA sequencing is now emerging as powerful tool that complements NGS DNA methods because the transcriptional profile of a single cell can be measured and used to bridge genomic data with cellular phenotypes. Single-cell RNA sequencing (scRNA-seq) has specifically emerged as a powerful method for understanding the heterogeneity of cell populations within a tumor. Together with histological data, scRNA-seq data can be used to distinguish between neoplastic cells, immune cells, and healthy cells from the surrounding tissue, and it can also be used to evaluate how experimental treatments alter the tumor microenvironment7.
NGS methods are transforming both basic oncology research and clinical care, from identifying novel mutations to pinpointing personalized cancer therapies. Each method is suited to specific applications, so working with experts in NGS technology is critical to method selection and data analysis.
1 Bewicke-Copley F et al. Applications and Analysis of Targeted Genomic Sequencing in Cancer Studies. Comput. Struct. Biotechnol. 2019;17: 1348-1359.
2 Nakagawa H, Fujita M. Whole Genome Sequencing Analysis for Cancer Genomics and Precision Medicine. Cancer Sci. 2018;109(3):513-522.
3 Rotunno M et al. A Systematic Literature Review of Whole Exome and Genome Sequencing Population Studies of Genetic Susceptibility to Cancer. Cancer Epidemiology and Prevention Biomarkers. 2020;29(8):1519-34.
4 Klempner SJ et al. Tumor Mutational Burden as a Predictive Biomarker for Response to Immune Checkpoint Inhibitors: A Review of Current Evidence. Oncologist. 2020 Jan;25(1): e147-e159.
5 Beltran H et al. Whole-Exome Sequencing of Metastatic Cancer and Biomarkers of Treatment Response. JAMA Oncol. 2015;1(4):466-474.
6 Vestergaard LK et al. Next Generation Sequencing Technology in the Clinic and Its Challenges. Cancers (Basel). 2021;13(8):1751.
7 Fan J et al. Single-Cell Transcriptomics in Cancer: Computational Challenges and Opportunities. Exp Mol Med. 2020; (52)1452–1465.
Advances in preclinical oncology research are dependent on gaining insights into tumor biology and applying these insights to the development of novel diagnostics or therapeutics. Next-generation sequencing (NGS) technology has been instrumental in bridging basic immuno-oncology findings and preclinical applications. Here we provide an overview of NGS applications that are transforming preclinical oncology research.
Solid tumors are now understood to have dynamic and unique tumor microenvironments (TMEs) that influence the immune response as well as tumor progression. Combined with flow cytometry and immunohistochemistry, single-cell NGS methods such as scRNA sequencing (scRNA-seq) and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) allow scientists to measure and understand which cells are present in the TME. These findings may characterize how different stromal cells are present and contributing to tumorigenesis[1] and how the TME can create an immunosuppressive environment[2].
Liquid biopsies have gained popularity as a method to measure circulating tumor cells (CTCs) related to either solid tumor metastasis or hematological malignancies. 
NGS methods have greatly improved insights gained from this sampling method[3]. Previous bulk sampling methods for quantifying CTCs were hampered by sample contamination with leukocytes and the extremely low frequency of CTCs. Single-cell NGS methods, including methods to measure single nucleotide variations as well as large-scale mutations such as copy number variations or chromosomal rearrangements, have provided unprecedented information about CTC specimens in different anatomical locations or over time. These methods have also driven the development of liquid biopsy methods that can be used to track treatment efficacy and the emergence of resistance.
Tumor mutation burden (TMB) is a measurement of mutations within a tumor’s genomic sequence and has emerged as a potential biomarker associated with immune checkpoint inhibitor (ICI) efficacy. Numerous studies have now applied NGS methods to measure TMB in relation to ICI efficacy[4], and robust methods are now being validated for use clinically[5]. These findings have consistently found that high TMB correlates with ICI efficacy, and other NGS-based studies have been used to compare TMB with other genomic aberrations like microsatellite instability[6].
NGS data will continue to provide critical insights into preclinical oncology research and is certain to drive the development of better diagnostics and therapeutics.
[1] Lambrechts D, Wauters E, Boeckx B, et al. (2018). Phenotype molding of stromal cells in the lung tumor microenvironment. Nature Medicine, 24(8), 1277-1289.
[2] Devalaraja S, To TK, Folkert IW, et al. (2020). Tumor-derived retinoic acid regulates intratumoral monocyte differentiation to promote immune suppression. Cell.180(6):1098-114.
[3] Lim SB, Di Lee W, Vasudevan J, et al. (2019). Liquid biopsy: one cell at a time. NPJ Precision Oncology. 3(1):1-9.
[4] Goodman AM, Kato S, Bazhenova L, et al. (2017). Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers. Mol Cancer Ther. 16(11):2598-2608.
[5] Fenizia F, Alborelli I, Costa JL, et al. (2021). Validation of a Targeted Next-Generation Sequencing Panel for Tumor Mutation Burden Analysis: Results from the Onconetwork Immuno-Oncology Consortium. J. Mol. Diagn. S1525-1578(21)00117-3.
[6] Goodman AM, Sokol ES, Frampton GM, et al. Microsatellite-Stable Tumors with High Mutational Burden Benefit from Immunotherapy. (2019). Cancer Immunol. Res. 10:1570-1573.
Next-generation sequencing (NGS) technology has transformed the biomedical research landscape. Only a few years ago, high resolution genome or exome sequencing would be cumbersome and cost-restrictive, but current NGS technology platforms now allow for basic and clinical researchers to include these approaches for routine DNA and RNA sequencing needs. What are the different NGS sequencing approaches and how are they applied to oncology research?
1. Whole Genome Sequencing (WGS): NGS technology can be used for WGS of human genomes and tumor-specific genomes, as well as animal model and microbial genomes. WGS produces high resolution genomic sequences of expressed genomic regions as well as unexpressed regulatory regions. For preclinical oncology research, WGS is critical for characterizing genomic profiles associated with tumor progression or potential responsiveness to targeted drug therapies. WGS can detect single nucleotide variants, copy number variants, and insertions/deletions in tumor cells[1]. The comprehensive scope of WGS makes it well suited for detecting mutations in both coding and non-coding regions[2]. WGS is also useful for population level oncology studies that evaluate genetic susceptibility to specific cancers and potential heritability[3].
2. Whole Exome Sequencing (WES): WES techniques focus on sequencing the exome, which are comprised of protein expressing regions, or exons, within the genome. WES is an appropriate method for identifying genetic mutations that alter protein sequences, and WES data can be used toward measuring the tumor mutational burden (TMB) and predicting treatment efficacy[4]. WES data can also be used to identify potential new drug targets or mechanisms of drug resistance[5].
3. Targeted Sequencing: This method focuses on defined gene regions and is typically used in diagnostic applications or for validation of WGS or WES results. Targeted sequencing works well for screening tumor samples for well characterized mutations, such as those associated with BCL2, BRCA-1/2, BRAF, and EGFR, and can be used for identifying appropriate targeted therapies[6].
4. RNA sequencing: RNA sequencing is now emerging as a powerful tool that complements NGS DNA methods because the transcriptional profile of a single cell can be measured and used to bridge genomic data with cellular phenotypes. Single-cell RNA sequencing (scRNA-seq) has specifically emerged as a powerful method for understanding the heterogeneity of cell populations within a tumor. Together with histological data, scRNA-seq data can be used to distinguish between neoplastic cells, immune cells, and healthy cells from the surrounding tissue, and it can also be used to evaluate how experimental treatments alter the tumor microenvironment[7].
NGS methods are transforming both basic oncology research and clinical care, from identifying novel mutations to pinpointing personalized cancer therapies. Each method is suited to specific applications, so working with experts in NGS technology is critical to method selection and data analysis.
1 Bewicke-Copley F et al. Applications and Analysis of Targeted Genomic Sequencing in Cancer Studies. Comput. Struct. Biotechnol. 2019;17: 1348-1359.
2 Nakagawa H, Fujita M. Whole Genome Sequencing Analysis for Cancer Genomics and Precision Medicine. Cancer Sci. 2018;109(3):513-522.
3 Rotunno M et al. A Systematic Literature Review of Whole Exome and Genome Sequencing Population Studies of Genetic Susceptibility to Cancer. Cancer Epidemiology and Prevention Biomarkers. 2020;29(8):1519-34.
4 Klempner SJ et al. Tumor Mutational Burden as a Predictive Biomarker for Response to Immune Checkpoint Inhibitors: A Review of Current Evidence. Oncologist. 2020 Jan;25(1): e147-e159.
5 Beltran H et al. Whole-Exome Sequencing of Metastatic Cancer and Biomarkers of Treatment Response. JAMA Oncol. 2015;1(4):466-474.
6 Vestergaard LK et al. Next Generation Sequencing Technology in the Clinic and Its Challenges. Cancers (Basel). 2021;13(8):1751.
7 Fan J et al. Single-Cell Transcriptomics in Cancer: Computational Challenges and Opportunities. Exp Mol Med. 2020; (52)1452–1465.
