TMB & MSI are predictive biomarkers that can be used in cancer research to provide more precise and comprehensive data for determining the potential efficacy of immunotherapies.
Tumor mutation burden (TMB) is the total number of mutations identified in the genome of a tumor . It can be used as a biomarker for predicting the effectiveness of immunotherapy on some types of cancer. Tumors with high TMB (TMB-H) have been shown to have more ‘neo-antigens’ which coat the tumor cell that leads to the identification of tumor cells by the immune system. Therefore, it is thought that some TMB-H tumors are more responsive to immunotherapy though many factors, such as ethnicity, can influence response .
Another predictive cancer biomarker is microsatellite instability (MSI). MSI occurs when a short sequence of DNA that is repeated (a microsatellite or short tandem repeat) undergoes a mutation when it is copied . MSI is the result of a malfunction in the DNA mismatch repair (MMR) system, which generates mutations in microsatellites. MSI is associated with colon, gastric, ovarian, skin, and a variety of other types of cancers . MSI is classified into three categories: high, low, and stable (MSI-H, MSI-L, MSS, respectively). The frequency of MSI, can also be analyzed to predict response to immunotherapies .
Even though TMB and MSI are both distinct biomarkers used in cancer research, studies have shown that using both can provide more precise and comprehensive data for determining the efficacy of immunotherapies (e.g., immune checkpoint inhibitors (ICI)) .
Classic approaches for identifying MSI in samples consistent of methods such as PCR, capillary electrophoresis, and immunohistochemistry. These methods are well established and can provide key insights into the levels of MSI in samples, however relative to next generation sequencing (NGS) these approaches are very low throughput . The ability to screen a large number of samples in parallel has encouraged researchers to incorporate NGS techniques into studies of TMB and MSI.
More specifically, researchers employ targeted next generation sequencing techniques for TMB and MSI biomarker research. Targeted NGS largely falls into two categories – amplicon sequencing and hybridization capture. Amplicon sequencing relies on specially designed primers and PCR to amplify genomic regions of interest, while hybridization capture uses biotinylated oligonucleotide probes.
One common type of hybridization capture for cancer research is exome sequencing, where the area of interest is the protein-coding regions of the human genome (the exome). Exome sequencing can be very informative for determining TMB and MSI levels as it allows researchers to quantify multiple biomarkers and mutations in a single sequencing run [6,7]. Custom hybridization gene panels are another popular option used for quantifying TMB and MSI.
A new three-part series that provides an overview of what whole exome sequencing (WES) is, why it is important, and how it is furthering discoveries in oncology research.
Once sequencing is complete, researchers can use bioinformatic approaches to calculate the TMB and MSI levels in their data. However, despite having numerous benefits, NGS approaches can be technical challenging.
When starting from FFPE samples, choosing a library preparation kit is an important step, as the DNA in these samples tends to be low-quality and degraded, potentially making the generation of a high-quality sequencing library complicated. Further, tumor heterogeneity and insufficient read depth can make identifying mutations difficult and can impact research reproducibility [8,9].
Researchers have found that analyzing cell-free DNA (cfDNA) from liquid biopsy samples (e.g., blood) can reduce the impact of tumor heterogeneity. CfDNA can be acquired less invasively and can be used to track biomarkers longitudinally. However, it’s currently not clear how the results from liquid biopsy samples compare to those obtained from tissue samples when determining TMB and MSI , and relying on cfDNA means researchers have to work with relatively low input quantities for library preparation.
IDT offers a number of products to help cancer researchers overcome these hurdles, such as the xGen cfDNA & FFPE DNA Library Prep Kit, which has been designed for highly complex variant identification from degraded research samples (e.g., FFPE).
Research in the discovery and identification of new, targetable biomarkers is driven by comprehensive tumor profiling using NGS. However, converting tissue samples into NGS libraries is often challenging due to the low quantity and quality of DNA in such samples. Download this application note to explore how low-frequency variants have been identified in this application.
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