BIOTECHNOLOGY
Figure 2: Overview of oncology research approaches and
applications
WES, WGS, RNA-SEQ, WTS WES targets the protein-coding regions of a genome (the exome), which contain up to 85% of disease-associated variants [4], making it a cost-effective approach. However, WES can miss relevant variants located outside of the exome in non-coding genomic regions. If this is a concern, WGS can address that limitation as it generates sequence data for the entire genome. Relative to WES, WGS is more expensive and has the potential to miss lower allele frequency variants due to its lower sequencing depth. However, WES is more likely to miss large genomic changes. It is also important to note that sequencing costs have regularly decreased overtime. Beyond DNA, having information
from the transcriptome via RNA-seq provides oncology researchers with a variety of unique insights. RNA-seq captures information concerning splice variants, fusion transcripts, and other transcriptional biomarkers associated with cancer development. RNA-seq results can be heavily impacted by the quality of samples and requires genomic data to obtain meaningful insights. This multi-disciplinary approach is called whole genome and transcriptome sequencing (WGTS) [5]. All these approaches offer broad
sequencing data, ensuring that when new biomarkers are discovered, there is no need to re-design or change the sequencing workflow. Though the increase in sequencing data obtained by these approaches reduces the risk of missing important mutations, it also increases the complexity of data obtained. This requires sophisticated
analysis pipelines, and the less-targeted scope of sequencing data could lead to the identification of incidental findings –unrelated to the cancer mutations being targeted.
APPROACHES FOR A TRULY COMPLETE BIOMARKER PROFILE The decision to use any of these approaches to identify actionable mutations in a sample depends on factors such as sample quality, institutional and country guidelines, and resource availability (Figure 2). While some methods like CGP assays can stand alone to characterise cancer samples, others are applied in tandem to support applications like tumour- informed minimal residual disease (MRD) research. Here, a tumour tissue sample is sequenced via WES then the variant detection data is used to design a custom gene panel to deeply sequence liquid biopsy samples for traces of those variants in circulating tumour DNA (ctDNA) shed from the solid tumour. For tumour-informed MRD solutions, IDT provides researchers with multiple components for an optimal workflow, including the xGen™ cfDNA & FFPE DNA Library Prep Kit, xGen Exome v2 Hyb Panel, xGen MRD Hybridization Panel, and custom enrichment panels and design services. For solid tumour CGP solutions, paired Archer™ panels, such as the VARIANTPlex™ Complete Solid Tumour combined with the FUSIONPlex™ Pan Solid Tumour v2, provides a content- flexible comprehensive biomarker profile. These assays are designed to analyse DNA (VARIANTPlex) and RNA (FUSIONPlex) to identify relevant
SNVs, indels, CNVs, ITDs, MSI, and TMB. Additionally, all Archer research assays use the Archer™ Analysis software, allowing researchers to analyse data rapidly and at scale. Ultimately, precision medicine relies on all these techniques (Table 1). IDT is dedicated to supporting researchers by providing expert support throughout the continuum of these methodologies. From small to large gene panels like Archer Research Assays to components for library prep like the xGen cfDNA & FFPE Library Prep Kit and WES enrichment panels like the xGen Exome v2 Hyb Panel–IDT is prepared to help. n
REFERENCES 1. Bayle A, Bonastre J, Chaltiel D,
et al. ESMO study on the availability and accessibility of biomolecular technologies in oncology in Europe. Ann Oncol. 2023;34(10):934-945. 2. Miki Y, Swensen J, Shattuck-
Eidens D, et al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science. 1994;266(5182):66-71. 3. Wooster R, Bignell G, Lancaster J,
et al. Identification of the breast cancer susceptibility gene BRCA2. Nature. 1995;378(6559):789-792. 4. Ng SB, Turner EH, Robertson PD,
et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461(7261):272-276. 5. Nakagawa H, Fujita M. Whole
genome sequencing analysis for cancer genomics and precision medicine. Cancer Sci. 2018;109(3):513-522.
. . n For more information visit
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www.scientistlive.com 45
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