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Guest Commentary: The right way to use genetic information in clinical trials

When differences in genetic variants are accounted for in clinical trial design, interventional trials can become much more successful for particular patient groups
Written byJill Johnston & Karmen Trzupek
| 6 min read

When a patient receives a drug or therapy, the outcomes can vary widely from good to poor, or even result in an adverse event. These outcomes may appear to occur randomly, but most variability in treatment response can be attributed to personal underlying differences. For many diseases, genetic susceptibility factors account for much of this variability.

Take the case of PARP inhibitors for breast cancer, for example. Early studies of PARP inhibitors for late-stage, triple-negative breast cancer failed to show a clear benefit. These were large, well-designed randomized studies. Later clinical trials of PARP inhibitors, in patients with BRCA-positive breast cancer, were highly successful. The difference? The early studies failed to stratify trial patients by their underlying genetic cause of disease.

As this example shows, when differences in genetic variants are accounted for in clinical trial design, interventional trials can become much more successful for particular patient groups. To the extent possible, patients can receive therapy tailored to their own individual biology. This is known as personalized medicine, and it is taking hold for many diseases with strong genetic predispositions, such as breast cancer and heart disease.

In reality, almost every disease can be better treated through some degree of personalized medicine. Today, genetic risk factors are known for most common diseases. With personalized medicine, drugs and therapies can be targeted toward the genetic makeup of an individual rather than the disease as a whole. With targeted treatments, each of the groups may benefit.

Personalized medicine is proving to be extremely effective, but the clinical trial protocol for incorporating and utilizing this genetic data is complex. Traditional clinical trial models do not factor in the complications that arise with the incorporation of genetic testing, processes or handling of the data itself.

It’s complicated

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