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Subspecialties Oncology, Omics, Genetics and epigenetics, Precision medicine

Seeking the Perfect Fit

sponsored by Thermo Fisher Scientific

Clinical laboratories worldwide are implementing next-generation sequencing (NGS) to identify cancer genomic variants and ultimately improve patient outcomes. But different sizes of NGS panels have different advantages and drawbacks for tumor molecular profiling – and their clinical applicability Also varies. Here, we explore how a variety of panel sizes address key aspects of clinical testing...

Clinical laboratories worldwide are implementing next-generation sequencing (NGS) to identify cancer genomic variants and ultimately improve patient outcomes. But different sizes of NGS panels have different advantages and drawbacks for tumor molecular profiling – and their clinical applicability Also varies. Here, we explore how a variety of panel sizes address key aspects of clinical testing...

Diagnostic yield and clinical utility
 

The diagnostic yield is an important selection criterion for determining the performance of any assay. It is primarily defined as “the likelihood that a test will provide the required information for a genetic diagnosis.” In a study aimed at understanding the appropriate size of a solid tumor sequencing panel to identify clinically actionable variants (27), researchers directly compared the results of a large gene panel (315 genes) with those of a medium-sized panel (161 genes) and a small hotspot panel (50 genes). Although the larger panel detected more variants, the additional variants beyond those included in the medium panel had no impact on patient management. Even more remarkably, 88.5 percent of those variants would have been identified by the 50-gene panel. Overall, these results indicate that small and medium-sized optimized gene panels are as informative as larger panels when the primary goal is to identify clinically actionable mutations.

Turnaround time and cost-effectiveness
 

One of the most critical components of clinical testing for rapid decision- making is the turnaround time of the test. Larger gene panels are more time- consuming because they often require a more complex data analysis workflow. In contrast, small hotspot panels (<50 genes) or medium-sized panels are best- suited to obtaining faster results because they are less “sequencing-intensive” and their analysis is based on a limited number of clinically valuable targets (16).

Another important component of diagnostic testing is cost, which is directly influenced by several components, including the size of the genome targeted and the labor and equipment required for data generation and analysis (19). The cost of library preparation and overall sequencing also depends on sample batching. Sequencing solutions are now available that allow cost compression to combine small- to medium-sized targeted panels with optimized sample batching capabilities (31).

Sample quality and quantity requirements
 

Regardless of the NGS approach and methodology used, the feasibility of molecular profiling depends on the quality and quantity of the sample to be tested. The use of NGS to detect low-allele-frequency somatic variants in nucleic acids extracted from formalin- fixed paraffin-embedded tumor tissue is challenging for clinical molecular diagnostic laboratories because these types of samples often yield low quantities of degraded, poor-quality genetic material (10,11). It is estimated that molecular profiling fails in 5–30 percent of tested patients due to insufficient material or poor sample quality (12,13), with the hybridization capture method being the most affected by this issue (5). Additionally, many patients with cancer are only diagnosed at advanced stages, when the available sample material is often limited (16). This hampers the use of large gene panels, which may require a large amount of sample material to provide reliable results. The desired limit of detection must also be considered to determine the minimum amount of DNA or RNA a test requires and the lowest frequency of mutant alleles it can detect (18).

Tier I. Ready for routine use

Alteration-drug match is associated with improved outcome in clinical trial

Tier II. Investigational

Alteration-drug match is associated with anti-tumor activity, but magnitude of benefit is unknown

Tiers III and IV. Hypothetical target

III. Alteration-drug match is suspected to improve outcome based on clinical trial data in other tumor types; IV. Pre-clinical evidence of actionability

Tier V. Combination development

Alteration-drug match is associated with objective response, but without clinical benefit

Tier X. Lack of evidence

Lack of evidence for actionability 

Bioinformatics and variant interpretation
 

The large amount of raw data NGS-based assays generate requires a bioinformatics pipeline capable of conver ting nucleotide sequences into meaningful biological and clinically actionable results. In addition, such an analysis must meet several analytical requirements and ensure the accuracy and reproducibility of the results. A typical pipeline for analyzing NGS data can be divided into four main operations: base calling, read alignment, variant identification, and variant annotation (20). The larger the region of the sequenced genome, the greater the likelihood of encountering rare or novel variants that require complex interpretation.

Careful cost-effectiveness analysis is needed because testing all patients with late-stage cancer using large panels is not affordable for most healthcare systems.

Another challenge is deciding which genes to test in a given clinical scenario. Although there are guidelines that define the most common mutations or genes of interest (tests that are usually reimbursed), the literature and clinician interest may propose other genes (tests that are usually not reimbursed) that may be medically useful (19,23). To standardize the reporting and interpretation of clinically relevant genomic data in the management of patients with cancer, the European Society for Medical Oncology (ESMO), led by the ESMO Translational Research and Precision Medicine Working Group, developed the Scale for Clinical Actionability of Molecular Targets (ESCAT) ranking system (24).

In summary, choosing the best panel size for clinical practice has sparked intense debate among researchers and clinicians. For routine patient testing, diagnostic yield and clinical utility – along with technical aspects such as sample availability and turnaround time – should be the guiding principles in making decisions regarding the most appropriate sequencing method and panel size. Careful cost-effectiveness analysis is needed, because testing all patients with late-stage cancer using large panels is not affordable for most healthcare systems, nor does it currently provide substantial clinical benefit for all patients. The need to advance our understanding of cancer biology and provide patients with the opportunity to participate in clinical trials while keeping the financial burden reasonable requires thorough consideration of what panel size will best serve the target patient population.

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