Dr. Aisner on the Limitations of Molecular Profiling

Dara L. Aisner, MD, PhD
Published: Thursday, Aug 09, 2018



Dara L. Aisner, MD, PhD, associate professor in the department of Pathology at the University of Colorado School of Medicine, discusses the limitations of molecular profiling.

There are limitations to molecular testing, says Aisner, as no test is perfect. Interpreting the results of molecular testing requires technical expertise, and if you feel you lack the technical expertise, it is very easy to seek it out either on a case-by-case basis or in a more systemic way, explains Aisner. More importantly is understanding what to do following the results of a test that comes back negative. It is important to consider what was tested, what questions are being asked and, what questions are not being asked.

Technologies are evolving very rapidly, explains Aisner. What is exciting and interesting today is not going to be at the forefront of oncology in a few years. There is a balance to be had between using a test because it is new and “hot,” versus going for data that are well established. There is a middle ground in between those 2 extremes, states Aisner.
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Dara L. Aisner, MD, PhD, associate professor in the department of Pathology at the University of Colorado School of Medicine, discusses the limitations of molecular profiling.

There are limitations to molecular testing, says Aisner, as no test is perfect. Interpreting the results of molecular testing requires technical expertise, and if you feel you lack the technical expertise, it is very easy to seek it out either on a case-by-case basis or in a more systemic way, explains Aisner. More importantly is understanding what to do following the results of a test that comes back negative. It is important to consider what was tested, what questions are being asked and, what questions are not being asked.

Technologies are evolving very rapidly, explains Aisner. What is exciting and interesting today is not going to be at the forefront of oncology in a few years. There is a balance to be had between using a test because it is new and “hot,” versus going for data that are well established. There is a middle ground in between those 2 extremes, states Aisner.

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