Analysis of genetic variants in myeloproliferative neoplasms using a 22-gene next-generation sequencing panel.

BACKGROUND: The Philadelphia (Ph)-negative myeloproliferative neoplasms (MPNs), namely essential thrombocythaemia (ET), polycythaemia vera (PV) and primary myelofibrosis (PMF), are a group of chronic clonal haematopoietic disorders that have the propensity to advance into bone marrow failure or acute myeloid leukaemia; often resulting in fatality. Although driver mutations have been identified in these MPNs, subtype-specific markers of the disease have yet to be discovered. Next-generation sequencing (NGS) technology can potentially improve the clinical management of MPNs by allowing for the simultaneous screening of many disease-associated genes. METHODS: The performance of a custom, in-house designed 22-gene NGS panel was technically validated using reference standards across two independent replicate runs. The panel was subsequently used to screen a total of 10 clinical MPN samples (ET n = 3, PV n = 3, PMF n = 4). The resulting NGS data was then analysed via a bioinformatics pipeline. RESULTS: The custom NGS panel had a detection limit of 1% variant allele frequency (VAF). A total of 20 unique variants with VAFs above 5% (4 of which were putatively novel variants with potential biological significance) and one pathogenic variant with a VAF of between 1 and 5% were identified across all of the clinical MPN samples. All single nucleotide variants with VAFs ≥ 15% were confirmed via Sanger sequencing. CONCLUSIONS: The high fidelity of the NGS analysis and the identification of known and novel variants in this study cohort support its potential clinical utility in the management of MPNs. However, further optimisation is needed to avoid false negatives in regions with low sequencing coverage, especially for the detection of driver mutations in MPL.

Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics.

There is a growing attention toward personalized medicine. This is led by a fundamental shift from the 'one size fits all' paradigm for treatment of patients with conditions or predisposition to diseases, to one that embraces novel approaches, such as tailored target therapies, to achieve the best possible outcomes. Driven by these, several national and international genome projects have been initiated to reap the benefits of personalized medicine. Exome and targeted sequencing provide a balance between cost and benefit, in contrast to whole genome sequencing (WGS). Whole exome sequencing (WES) targets approximately 3% of the whole genome, which is the basis for protein-coding genes. Nonetheless, it has the characteristics of big data in large deployment. Herein, the application of WES and its relevance in advancing personalized medicine is reviewed. WES is mapped to Big Data "10 Vs" and the resulting challenges discussed. Application of existing biological databases and bioinformatics tools to address the bottleneck in data processing and analysis are presented, including the need for new generation big data analytics for the multi-omics challenges of personalized medicine. This includes the incorporation of artificial intelligence (AI) in the clinical utility landscape of genomic information, and future consideration to create a new frontier toward advancing the field of personalized medicine.