In the field of immuno-oncology (where researchers are looking to leverage a cancer patient’s own immune system to fight the cancer), there is a need to identify potential diagnostic biomarkers, potential therapeutic targets, and biomarkers that may predict whether a patient will respond to an immunotherapy.
The continual development of higher-throughput genetic analysis tools has accelerated research in genomics, transcriptomics and gene-expression profiling, and these tools are now making an impact in immuno-oncology research by facilitating the identification and investigation of relevant molecular pathways for biomarker discovery. Every cancer is different and there is an unmet need to identify new gene signatures that will enable the development of effective, non-toxic immunotherapies . Both immune response and tumor biology are exceedingly complex, making the use of multiple genetic analysis technologies necessary for biomarker discovery and verification.
What technologies are used for genetic analysis in immuno-oncology?
Microarrays and next-generation sequencing (NGS) are useful tools for identifying novel RNA- and DNA-based biomarkers. NGS can be used to study gene expression, an application that can be also evaluated effectively with reverse transcription quantitative PCR (RT-qPCR). Sanger sequencing is consistently used for sequence-based biomarkers in the research community.
NGS has proven to be a disruptive technology, furthering our scientific knowledge and opening research opportunities faster than anyone could have envisioned even just 10 years ago. While whole-genome NGS has helped to advanced discovery and human health, some regions of the genome are difficult to analyze using this approach, which results in sequencing bias. In contrast, a targeted NGS approach utilizes molecular biology methods to enrich for specific genetic sequences, allowing researchers to focus their studies on individual genes or genomic regions. Obtaining sequence coverage of challenging genomic regions is now possible, including regions from difficult sample types such as samples with degraded DNA or RNA, or circulating cell-free nucleic acids (cfNA) from blood. By only sequencing what is needed, the cost benefits go beyond more efficient computational processing and informatics: Focusing on specific regions of interest allows researchers to sequence at a much higher depth of coverage for rare variant discovery. Many more samples can also be processed simultaneously in a single sequencing run, offering faster time-to-results from individual and cohort samples.
The Oncomine TCR Beta-SR Assays are robust, targeted NGS solutions designed to study the complementarity determining region 3 (CDR3) of the T cell receptor (TCR) beta chain using either RNA or DNA. The CDR3 region is unique to each T cell clone and codes for the part of the TCR beta chain involved in antigen recognition. With low sample input and a comprehensive analysis suite, this short-read assay enables accurate T cell repertoire analysis from FFPE and peripheral blood samples. The assays can accurately identify and measure clonal expansion, as well as characterize immune status.
For longer sequencing read capabilities, the Oncomine TCR Beta-LR Assay efficiently captures all three complementarity-determining regions of the TCR beta chain (CDR1, CDR2, CDR3) with high precision to enable predictive or prognostic biomarker discovery, T cell characterization, and identification of variable gene polymorphisms. As little as 10 ng of RNA input for the assay allows sequencing of productive and relevant variable (V), diversity (D) and joining (J) rearrangements. This enables improved identification of rare and abundant clones from whole blood, fresh-frozen tissue or FACS-sorted cells. With the low substitution error rate of Ion Torrent sequencing technology, TCR convergence can be evaluated accurately. TCR convergence may arise due to chronic antigen stimulation from emerging cancer. Research has shown that TCR convergence may have potential as an emerging and non-invasive biomarker for immunotherapies. The same targeted sequencing approach is also available with the Oncomine BCR IGH SR and Oncomine BCR IGH LR assays to study the B cell receptor (BCR) immunoglobulin heavy chain (IgH). The long-read (LR) assay, in particular, enables quantification of somatic hypermutations and can be used to assess isotype switching and abundance to study clonal evolution.
For analysis of smaller sets of genes, Applied Biosystems™ TaqMan® Gene Expression Assays for RT-qPCR contain over 400 biomarkers related to immuno-oncology research, allowing detailed analysis of targets such as cytokines, chemokines, immune regulators, apoptosis markers and more. TaqMan Assays are commonly used to confirm NGS expression profiles. To demonstrate the usefulness of TaqMan Assays for this purpose, the concordance in the expression of 22 genes (CD2, CD28, CD52, CDKN3, CTLA4, CXCL9, DDX58, FOXP3, GUSB, GZMA, ID2, IFNG, IL6, IL7R, KLRG1, LCN2, MLANA, PMEL, TBP, TFRC, TNF and TNFRSF14) was evaluated using both the Oncomine Immune Response Research Assay and the Applied Biosystems™ TaqMan® qPCR assay. See the data in the white paper titled “Performance of the Oncomine Immune Response Research Assay — a highly sensitive and robust tool for immune response research.”
For targeted gene signature panels, try the Applied Biosystems™ TaqMan® Array Human Immune Panel for quantitative gene expression analysis. The array is preformatted and inventoried, cost-effective, and easy to use. The microfluidic technology enables consistent, reproducible results across samples, studies and labs.
Similar tools can be used to understand how immune cells select their targets and how to alter them to fight cancer more effectively. For example, Sanger sequencing is a useful tool for assessing T cell repertoire diversity by sequencing the gene for the T cell receptor (TCR), and for analysis of sequences up to 700 bp. Tumor neoantigens identified by NGS techniques can also be verified by Sanger sequencing, since it can detect low-level somatic mutations in difficult samples such as FFPE tissues. Explore the SeqStudio genetic analyzer for Sanger sequencing by capillary electrophoresis.
Microarrays can be used to reveal differences between tumors and healthy tissue, and changes in the tumor over time as mutations accumulate. A more mature technology than NGS, microarrays allow RNA expression biomarkers to be located quickly and relatively inexpensively.
Applied Biosystems™ Clariom™ D and S Pico Assays offer either simple, gene-level analysis (Clariom S Assay) or deep transcriptome-level analysis (Clariom D) of coding and noncoding RNA and alternative splicing events, enabling the investigation of thousands of transcripts. As an example, Dr. Kristin G. Anderson from the Fred Hutchinson Cancer Research Center used the Clariom D assay and single-cell RNA sequencing to elucidate the molecular mechanisms driving T cell dysfunction in ovarian cancer and develop a mouse model on which to test adoptive T cell therapy. Read the paper here.
Whether you’re looking to discover and verify new immuno-oncology biomarkers or understand more about the genetic components underlying important immuno-oncology pathways, Thermo Fisher Scientific has a wide portfolio of genetic analysis solutions to meet your needs. As an industry leading, worldwide trusted partner with scientific expertise and full service and support capabilities, we can help guide you to the right genetic analysis solution for your research.
Explore more of our genetic analysis solutions for cancer genomics research using the links above.
- Masucci, G. V. et al. (2016). Validation of biomarkers to predict response to immunotherapy in cancer: Volume I – pre-analytical and analytical validation. J. Immunother. Cancer 4, 76.
- Nixon, A. B. et al. (2019). Peripheral immune-based biomarkers in cancer immunotherapy: can we realize their predictive potential? J. Immunother. Cancer 7.
- George, A. P., Kuzel, T. M., Zhang, Y. & Zhang, B. (2019). The discovery of biomarkers in cancer immunotherapy. Comput. Struct. Biotechnol. J. 17, 484–497.
For research use only. Not for use in diagnostic procedures.