BIOTECHNOLOGY
range of cancer-associated genes, providing insights into the genetic profi le 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 off er consolidated workfl ows 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-specifi c 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 profi ling (CGP) is a tumour-agnostic, NGS
Description Benefi ts
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 profi ling specifi c pathologies, there is less risk of incidental identifi cation 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.
Table 1. Summary of approaches used by oncology precision medicine Approach
Single gene testing Small gene panels Large gene panels
Molecular approach that targets known, oncogenic genes to identify mutations
NGS approach that targets < 50 cancer-associated genes
NGS approach that targets > 50 cancer- associated genes
Well established protocols; simple, streamlined analyses
Cost-eff ective for relevant data, simple, streamlined analyses, high-throughput
Cost-eff ective for relevant data, relatively simple, streamlined analyses, identifi cation of many biomarkers in parallel, high-throughput
CGP
Assay that provides insights into many major, known cancer biomarkers
Time and resource saving for comprehensive profi ling data, reduces the need for sequential biomarker assays
WES, WGS, RNA- seq, WGTS
NGS approaches that either target protein coding regions of a genome (WES), the entire genome (WGS), the transcriptome (RNA-seq), or both the whole genome and transcriptome (WGTS)
Comprehensive, identifi cation of cancer biomarkers, high- throughput, no re-design needed when new markers are discovered
Limitiations
Potential to miss relevant variants outside of target
Potential to miss relevant variants outside of targets
Potential to miss relevant variants outside of targets
Less complete profi ling of cancer sample relative to WGS/WES, workfl ows and analysis can be complex to establish, typical fi xed content results in reduced fl exibility with analysis
Currently high costs (expected to decrease with time) and heavy computing/analysis demands, potential ethical risks with unintended identifi cation of non-cancer disease biomarkers
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