Framework Ranks 'Actionability' of Genetic Mutations for Precision Medicine

David J. Kerr, CBE, MD, DSc, FRCP, FMedSci


July 18, 2019

This transcript has been edited for clarity.

Hello. I'm David Kerr, professor of cancer medicine at the University of Oxford.

I'd like to discuss actionable mutations. One of the great promises of precision medicine is that we can use next-generation sequencing of our patients' tumors to detect potentially actionable mutations that could lead us to treat patients in a unique way with a particular drug based on their genotype. So far, though, one would have to say that the results have been rather lukewarm. Rather than unlocking the key to some very specific treatments, there has been a degree of confusion. The results of the small, randomized trials have been disappointing.

There is a nice paper by Dr Condorelli and colleagues[1] in a recent issue of Annals of Oncology in which they introduce us to a new framework for assessing whether a mutation is actionable or not, and they apply this to breast cancer. This is work that's being performed under the auspices of the European Society for Medical Oncology (ESMO). The name of this scale is ESCAT, the ESMO Scale for Clinical Actionability of molecular Targets.

The scale provides a framework to assign DNA alterations into tiers, according to the level of evidence supporting the link of a genetic mutation to a specific drug. For example, the highest level of evidence, tier IA, would be if there were prospective, randomized trials indicating that in patients with that particular actionable mutation, that treatment with drug X would lead to survival benefits. Again, tier IA is the highest level of evidence, and it goes down to tier IV, which is no evidence whatsoever. [Editor's note: According to the ESCAT grading system, tier X is lack of evidence for actionability.] It's good to be able to categorize things in that way. When we're presented with sequencing data, it comes as a large jumble. There may be an attached reference saying that there's some evidence to suggest that this mutation might be associated, but nothing more predetermined than that.

Condorelli and colleagues trolled through all of the public databases and determined that there are about 40 recognized potential driver mutations for breast cancer. They then performed a huge literature review in which they attempted to place their 40 so-called driver mutations within the ESCAT framework. In terms of those 40, [there were only three ranked tier IA], including ERBB2 amplification, germline BRCA1/2 mutations, and PIK3CA mutations. The remaining driver mutations were categorized into different levels of activity: tier II, nonrandomized studies; tier III, preclinical links only if there was strong clinical evidence; and tier IV, no evidence at all. [Editor's note: According to the ESCAT grading system, tier IV is preclinical evidence of actionability, and tier X is lack of evidence for actionability.]

For those of us who are involved and are keen to establish multidisciplinary teams who work with molecular data, this evidential framework would be really helpful [for clarifying] the link between next-generation sequencing data and drugs. I'm going to try to apply the ESCAT framework to colorectal cancer, the disease in which I'm most interested, so that we can have a framework. Then, when interesting mutational data do spring up, we can go to our framework and decide whether it is logical to try drugs X, Y, or Z. It's really worth the attempt to help us make better clinical sense of the sometimes overwhelming amount of data that come from next-generation sequencing.

This paper is well worth the read. I'd be interested to hear what you think about it and whether you think you can apply this framework to your own clinical practice.

As always, Medscapers, thanks for listening. For the time being, over and out.

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