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On the Horizon

Leading news, knowledge, and industry trends in genetic research

The clinical success rate of new oncology drugs is only 3.4% compared to 20.9% in other disease types (Wong et al, 2018). One contributing factor to this issue is the testing systems used, with two-dimensional (2D) monolayer assay formats as the traditional mainstay of high throughput screening. Although 2D monolayer assays have identified many successful drugs, it is increasingly recognised that they do not accurately model key aspects of the three-dimensional (3D) tumour environment. Therefore, the adoption of high throughput screening approaches using 3D assays to complement 2D approaches could substantially improve prediction of clinical outcomes and reduce the high failure rate of cancer drugs in clinical trials.

Precision medicine is defined by the Precision Medicine Initiative as, "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person". It allows doctors to prescribe treatment tailored to the individual patient and allows drug developers to create treatments that are more targeted to those individuals.

After all the hard work of editing your cell line, you want to have confidence in your new research model. So, how do you verify your cell line is what you expect it to be? Could a heterogeneous cell population be obscuring your editing effects? Is observed phenotype being caused by the targeted gene edit, or unintended off-target effects? Here we discus ways to add supporting data to validate your gene-engineering projects.