In vivo models for numerous diseases and conditions have endpoints that have involved animals being gravely ill or dying. As researchers have sought to utilize animal models in more humane and practical ways, surrogate endpoints have been developed that prevent animals from suffering and provide critical research data.
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In vivo models for numerous diseases and conditions have endpoints that have involved animals being gravely ill or dying. As researchers have sought to utilize animal models in more humane and practical ways, surrogate endpoints have been developed that prevent animals from suffering and provide critical research data. Flow cytometry has been instrumental to these advances. Consider these aspects of preclinical flow cytometry endpoint analysis as you develop new protocols.
Flow cytometry provides the most useful data when the cell subsets of interest are well-defined and robust. You may need to analyze existing research literature or do pilot studies to define the immune cell subsets of interest for a particular disease state, be it changes in regulatory T cells in the tumor microenvironment, or the proliferation of plasmablasts in different leukemias. You must identify which profound changes in different cell populations are most closely correlated with morbidity and mortality in your animal model.
Preclinical studies with surrogate endpoints are valuable for screening potential therapeutic candidates. These drugs or biologics may have undesirable off target effects as well. In designing a flow cytometry assay for alternative endpoints, it is critical to identify the changes in immune cell subsets that reflect therapeutic improvements or indicate potential toxicity or off target effects.
Flow cytometry endpoint analysis not only advances the humane use of animal models but can be translated into informative clinical protocols that are critical for the evaluation of potential therapies.
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.
