Predictive Diagnostics are Unlocking Precision Medicine

The term “personalized medicine” was introduced more than 20 years ago in a brief article published in The Wall Street Journal titled, “New Era of Personalized Medicine: Targeting Drugs for Each Unique Genetic Profile”. The same article was reprinted in The Oncologist shortly after, and the way we talk about and practice medicine forever changed.

Not surprisingly, the term was quickly interpreted to mean the future of medicine would be tailored to us as individuals, with all of the unique genetic and biological heterogeneity that’s implied. The field of pharmacogenomics was born, and the promise of genomic sequencing enabling the prescription of unique treatments matched to a patient’s DNA was the goal.

A short 10 years later, the field recognized that while individually tailored treatments remained a moonshot goal, there was an intermediate goal within reach that required a clearer definition and focus. Not only was this goal more achievable, it also represented a major step forward for healthcare. In 2011, the National Research Council with the National Academies published a report titled, “Toward Precision Medicine,” describing the goals of precision medicine. While similar to personalized medicine, the authors noted that precision medicine is not thwarted by the tens of thousands of genomic differences between humans, but rather leverages this variability to identify biological patterns.

As large data sets are generated, shared, and analyzed, key signals emerge. In the field of oncology, predictive biomarkers that are founded on these signals have become central to the new vernacular of describing disease. In fact, there are many of us scientists working on harnessing these signals to close what’s known as the precision medicine gap, which is the gap that exists in matching the patients to the therapies that will benefit them.

Biomarkers are being used to identify patient populations; for example, those who will be responders or nonresponders to treatments, which is particularly important for transformative therapies that only work in a subset of the disease population. This has provided clinicians much needed guidance on treatment decisions and therapy prioritization, resulting in cancer patients receiving the right treatment sooner.

This saves precious time, which is highly valued by patients. Identifying the appropriate treatment may also avoid painful side effects of antiquated treatments like chemotherapy, or adverse events experienced by patients given the wrong treatment. And finally, the health economics savings of better matching patients with treatments that work are quantifiable, and justify the cost of developing and implementing these diagnostic tools.

While today we still may hear the terms “personalized medicine” and “precision medicine” used interchangeably, it is clear that the term precision medicine” is truly what is being implemented as we define disease through different molecular biomarkers present in subsets of the disease population (rather than at the individual patient level). This approach is not only helping us to more accurately describe disease, but with the development of predictive diagnostics is also matching new drugs and therapies to the specific subset of patients that will benefit.

Moving these signals from biomarker to diagnostic test and into clinical practice is essential to deliver on the promise of precision medicine: the right treatment for the right patient given at the right time. The pursuit of personalized medicine will likely continue to drive research and development, while today predictive diagnostics fill the most pressing need in the practice of medicine, and will enable us to close the precision medicine gap.

Jarret Glasscock, Ph.D., is the co-founder and CEO of Cofactor Genomics, an RNA diagnostics company.