Exploring the revolution in precision medicine

Courtney Thomas PhD reports on the advances in oncology research.

Precision medicine has created a paradigm shift in oncology research, allowing scientists to identify individualised cancer-associated biomarkers. In this space, next generation sequencing (NGS) has emerged as a transformative tool, offering a range of insights from single nucleotide variants (SNVs) to comprehensive profiles of cancer genomes. This knowledge can be further used to streamline laboratory workflows and resources, optimising assay content and protocols for efficient and accurate genomic profiling of tumours. The integration of NGS data with real-time monitoring approaches fosters a dynamic approach to understanding genetic variations associated with cancer and can contribute to the discovery of new cancer targets biomarkers.

Despite this, there are several factors, e.g., cost and accessibility, that have slowed progress in this field [1]. Investing in equipment for NGS can be prohibitive. Additionally, the large amount of data generated with NGS often requires advanced bioinformatic analyses and interpretation. Accessibility to the latest oncology research NGS approaches varies drastically by country. In some, like Germany, NGS for oncology precision medicine has been adopted as standard alongside a commitment to making whole genome sequencing a widely accessible tool. Other countries are working towards integrating small, targeted gene panels into their standard practices [1]. There is a clear need to improve availability of NGS tools globally and to standardise implementation of these approaches.     

IDT has long recognised the power of NGS and made a commitment to empowering oncology research labs in their pursuit of important insights in precision medicine. 

“Over its more than 35-year-history, IDT has been innovating alongside its customers to equip them with the right tools when they need them,” said Steven Henck, PhD, Vice President, R&D at Integrated DNA Technologies. “Our NGS portfolio, which is comprised of stand-alone library preparation, target enrichment, and normalisation chemistries, as well as connected NGS solutions with secondary analysis software support, have been foundational to some of the world’s greatest genomic discoveries. We’re proud to make these solutions widely available to the research community, which depends on IDT to deliver the critical tools they need to continue advancing cancer breakthroughs.”

This article discusses the spectrum of approaches used in oncology research and highlights the benefits and the limitations of each technique.

Single gene testing

Single gene techniques (also referred to as whole gene testing) are among the most widely available methods.

Examples include fluorescence and chromogenic in situ hybridization (FISH), PCR, and micro-satellite instability (MSI) tests [1]. They are well established, allow for streamlined analyses and are also cost-effective, making them widely accessible. For certain types of cancers, PCR and other single gene targeting approaches focus on known mutations that drive oncogenesis –e.g., BRCA1/2 in breast cancer [2,3]. These approaches can fail to capture the full picture of mutations driving tumourigenesis since some cancers are the result of an accumulation of mutations across multiple genes.

Small to large panel testing

The limitations presented by single gene targeting approaches can be largely overcome using small to large gene panel testing. Panels rely on NGS to obtain information about specific genes consolidating relevant targets into a multi gene panel. Small panels (less than 50 genes), and large panels (more than 50 genes) [1] provide more information per sample than a single gene approach.

Small gene panels target a select set of genes that were previously identified as having relevant mutations in specific cancer types. This focused sequencing of a limited number of genes helps streamline data analyses and interpretation, while also providing more accessibility with less cost than large gene panels [1]. Small gene panels can be employed before samples are analysed using approaches that target more genes like large gene panels or comprehensive genomic profiling (CGP).

Large gene panels target a wider range of cancer-associated genes, providing insights into the genetic profile of a tumour tissue or liquid biopsy sample. Increasing the scope of sequencing via large gene panels further reduces the risk of missing key mutations. Relative to the depth of information generated, large gene panels offer consolidated workflows and relatively short turnaround times. Additionally, some gene panels (small and large) are designed to be tumour agnostic, which can be a time saving advantage allowing researchers to cast a wide net to capture relevant biomarkers rather than relying on multiple tumour-specific assays.

With increased information generated by small to large gene panels sequencing costs increase along with a decrease in accessibility. This is true across the continuum of approaches, where there is currently a trade-off between accessibility, cost, and information gathered (Figure 1).

While the scope of small or large gene panels is wider than that of single gene techniques, there is still a risk of missing relevant gene alterations with this approach. Approaches in oncology research that provide an even greater understanding of the genomic signature in cancer include CGP, whole exome sequencing (WES), whole genome sequencing (WGS), RNA sequencing (RNA-seq), and whole genome and whole transcriptome sequencing (WGTS).

Comprehensive genomic profiling

Comprehensive genomic profiling (CGP) is a tumour-agnostic, NGS method that can provide a more comprehensive view of the cancer biomarker landscape. CGP assays are designed to detect many cancer-associated genomic alterations such as SNVs, indels, copy number variants, fusions, splice variants, as well as other key cancer genomic signatures like tumour mutational burden (TMB) and MSI.

Obtaining this information in a multiplexed, targeted approach saves vital time, and reduces costs as well as the need for sequential biomarker testing. Additionally, because CGP assays are more focused on profiling specific pathologies, there is less risk of incidental identification of unrelated biomarkers–an issue faced by NGS approaches like WES and WGS. However, like other large content approaches mentioned here, the accessibility to CGP is limited.


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.

Courtney Thomas PhD is with Integrated DNA Technologies


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.


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