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The advent of next-generation sequencing (NGS) represents the onset of a new era in personalised therapy. The treatment landscape for cancer is shifting from the conventional ‘one-size-fits-all’ approach of chemotherapy and radiotherapy to a more personalised strategy, in which therapeutic drugs are selected based on the comprehensive genomic profile of cancer. Although NGS has revolutionised personalised treatment, it has also introduced the complex challenge of managing a deluge of genetic variants, requiring standardised interpretation and consistent clinical application of these variants.1
Interpreting a single somatic variant across different cancer types requires careful consideration of the latest treatment guidelines from the National Comprehensive Cancer Network, American Society of Clinical Oncology and European Society for Medical Oncology. Additionally, the actionability of these variants must be verified through multiple databases (DBs), including OncoKB (Memorial Sloan Kettering Cancer Center), Knowledge Base for Precision Oncology (The University of Texas MD Anderson Cancer Center) and Clinical Interpretation of Variants in Cancer (Washington University in St. Louis). This process also involves reviewing Food and Drug Administration (FDA)-approved drug lists, clinical trial statuses and geographical applicability.2
The question then arises: how much time is required to interpret the multiple variants identified in a patient? Decision support platforms (DSPs) can play a crucial role in addressing this issue.
In this study, we aimed to evaluate the DSPs currently available for comprehensive molecular reporting due to the increasing reliance on these platforms. DSPs use extensive literature and advanced machine-learning algorithms to sift through variant call format (VCF) or binary alignment map files, identifying significant actionable variants and assigning personalised combination treatment recommendations.3 We focused on parameters such as …
Footnotes
Handling editor Vikram Deshpande.
Contributors HK contributed to the design, data acquisition, data interpretation and drafted; contributed to the data analysis and critically revised the manuscript; KUP contributed to the conception, design, data acquisition and data interpretation and critically revised the manuscript. All authors gave final approval and agreed to be accountable for all aspects of the work.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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