Abstract
Early detection of cancer saves lives, but an effective detection strategy in public health settings requires a delicate balance - periodic screening should neither miss rapidly progressing disease nor fail to detect rare tumors at unusual locations; on the other hand, even a modest false positive rate carries risks of over-diagnosis and over-treatment of relatively indolent non-malignant disease. Genomic profiling of cell-free DNA from liquid biopsy using massively parallel sequencing is emerging as an attractive, non-invasive screening platform for sensitive detection of multiple types of cancer in a single assay. Genomic data from cell-free DNA can not only identify oncogenic mutation status, but also additional molecular signatures related to potential tissue of origin, the extent of clonal growth, and malignant disease states. Utilization of the full potential of the molecular signatures from cfDNA sequencing data can guide clinical management strategies for targeted follow-ups using imaging or molecular marker-based diagnostic platforms and treatment options.
Highlights
Cancer diagnosis significantly reduces cancer mortality and improves the chances of a favorable response to treatment (Campbell et al, 2016)
An accurate, ideally non-invasive, pan-cancer screening strategy has been a longstanding interest in the clinical cancer research community, and can fundamentally benefit clinical management strategies for early detection of cancer
Next-generation sequencing-based molecular profiling of cell-free DNA in blood has emerged as a promising, non-invasive method for advanced, high precision assessment of multi-cancer risk in a single assay (Crowley et al, 2013; Wan et al, 2017)
Summary
Cancer diagnosis significantly reduces cancer mortality and improves the chances of a favorable response to treatment (Campbell et al, 2016). Next-generation sequencing-based molecular profiling of cell-free DNA (cfDNA) in blood has emerged as a promising, non-invasive method for advanced, high precision assessment of multi-cancer risk in a single assay (Crowley et al, 2013; Wan et al, 2017).
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