In a recent post, we discussed key considerations for designing a robust next-generation sequencing (NGS)-based lung cancer assay. Putting those plans into action in the development phase brings forth a new set of challenges. Through our experience developing NGS reference materials and the relationships we’ve built with assay developers of all stripes, we’ve identified those important factors and ways to navigate them. But before you begin designing and optimizing your assay, you should become very familiar with binomial and Poisson distributions and their use because the outcome of many analytical steps can be modeled and explained with them.
An important goal in cancer disease management is early detection. When detected early, disease progression can be significantly mitigated with a plethora of options (targeted therapy, chemotherapy, surgery, etc.) available to medical practitioners, to afford progression free survival and a higher quality of life. A great promise of liquid biopsies is the possibility of early detection of cancer long before clear evidence of lesions and tumor growth observable by imaging or other techniques.1 As proxy for solid tissue biopsies, plasma-based liquid biopsy application is rapidly gaining traction in cancer disease diagnosis, progression, monitoring, and in predicting resistance to treatment options.2
Next-generation sequencing (NGS) allows deeper insights than ever before into the human genome and a host of diseases and conditions. So it makes sense that there is a worldwide movement to employ NGS in a growing number of applications. But as the saying goes, with great power comes great responsibility.
Simply described, copy number variations (CNVs) are DNA segments present at a variable copy number in comparison to a normal genome. It was originally thought that a CNV consisted of a region of greater than 1 kilobases, however advances in technology have allowed for identification of CNVs as small as 50 basepairs1.