Contributed Commentary By Jarret Glasscock
October 14, 2020 | It has been said that the most dangerous phrase in business is, “We’ve always done it this way”.
Believing that the status quo is sufficient and assuming past success will sustain future growth can be an easy trap to fall into. Medicine and healthcare are no strangers to this phenomenon. In an environment where regulatory oversight is high, taking on the risk of innovation requires significant activation energy and investment, so it can seem easier to keep things as they are and avoid change. As a result, while we see advancements in medicine daily, there are still major obstacles holding us back from achieving true precision medicine. One of these barriers is the precision medicine gap, created by missing or outdated diagnostic technologies which have proved inadequate at providing a bridge between the numerous treatment options, and the subset of patients who will respond to them.
In oncology, the historic drive to innovate has been most publicly visible in the area of drug development. We see thousands of new therapies in clinical trials, and (for those that succeed) in FDA approval announcements. Certainly these efforts have yielded enhanced patient care. We’ve moved from chemotherapy (kill all the cells), to targeted therapies (kill the cancer cells), and now into immunotherapies (use our immune system to fight cancer). However, even as we enhance our understanding of cancer biology, and develop new modalities, we’ve plateaued. Plateaued in the sense that we are held back by expecting medicines of the 21st century to be broadly effective across a patient population. At the heart, it is our broad definitions of a “patient population” that we have had to rethink.
Knowing this, we must recognize that fully realizing precision medicine relies on the use of molecular diagnostics to play the appropriate matchmaker between the therapy options and the diverse patient population. This interaction between the three components (medicines, diagnostics, and diverse patient population) explains why, while the explosion of treatments should be celebrated, it has also created a gap. One easy way to characterize this gap is to look at the response rates of some of the most innovative therapies we have today: immunotherapies. Tumor response to immune checkpoint inhibitors, for example, is, on average, less than 25%. This means that if physicians treat all patients with these therapies (which is common, especially in recurrent and metastatic settings), then 75% of patients do not respond. And this gap continues to grow because, unfortunately, predictive diagnostics have not evolved at the same rate as therapeutics. One key example is the lack-of-confidence cited for the predictive diagnostic used for immunotherapy response prediction. PD-L1 immunohistochemistry (IHC) is described in the Biomarker Fact Sheet provided by the European Society for Medical Oncology:
“PD-L1 IHC positivity is an imperfect biomarker of response and currently not suitable as a definite biomarker for selection for therapy with PD-1/PD-L1 inhibitors. It is likely that a more complex, multicomponent predictive biomarker system will be required to refine appropriate patient selection for PD-1/PD-L1 blockade.”
This precision medicine gap is the biggest threat to healthcare because if we keep doing things the same way as we’ve done in the past, we will be doing a disservice to patients, payers, and even drug developers. There are two major drivers for innovation in healthcare: improving patient care and lowering healthcare costs. In fact, these two can be addressed at the same time by investing in predictive diagnostics. As described previously, predictive diagnostic technologies specifically come into play once a patient has been diagnosed with cancer, and a clinician is evaluating possible treatment plans. By enabling more accurate therapy selection, these predictive diagnostics not only do right by patients, but also save the healthcare system money by avoiding unnecessary prescriptions and adverse events.
Thankfully, we’re seeing advancements in predictive diagnostics in this direction (ie multicomponent predictive biomarkers). Veracyte (Afirma, Percepta, and Prosigna), Agendia (Mammaprint, Blueprint) and Genomic Health (OncotypeDX) have all demonstrated the value of enhancing predictive diagnostics by taking a multidimensional approach to measuring biomarkers within the tumor. Moving a level of detail deeper, a patient’s immunological profile can greatly impact their response to therapy, especially those therapies that harness and modulate the immune system (i.e. immunotherapies). Therefore, using a technology that primarily evaluates the tumor, and not the tumor microenvironment, can limit our ability to predict response. So, in order to close the precision medicine gap, particularly in this age of immunotherapies, we must combine measurement of immune response with a multidimensional approach. This approach, coined Predictive Immune Modeling, has been shown to improve our ability to predict tumor response over PD-L1 IHC in recurrent and metastatic squamous cell carcinoma of the head and neck. This predictive power leads to putting patients on the correct treatment sooner, saving precious time. And, in the spirit of improving health care costs, there is an average savings of $28,000 for every patient who is put on the correct treatment path in this indication. When we consider nearly 50% of yearly diagnosed cancer patients are eligible for these treatments, Predictive Immune Modeling has the promise to improve care and costs for over 1 million patients annually.
Maybe not surprisingly, the theme of the work highlighted here is that precision medicine is being powered by not only a new generation of more precise medicines and therapies, but also a new class of diagnostics (predictive diagnostics). Predictive diagnostics are fulfilling the need created by the thousands of clinical trials and the abundance of new therapies that have resulted in a precision medicine gap. This precision medicine gap requires a new class of tools that is equally as innovative as the therapies we are developing. Interestingly, different groups working independently have identified that the key to becoming more precise in our ability to identify the appropriate patient populations requires technologies capable of building multidimensional biomarkers and diagnostics. It is a brave new world we’re embracing while we all work together to define the next era of precision medicine.
Jarret Glasscock, PhD is the founder and CEO of Cofactor Genomics. He began his career as a geneticist and computational biology faculty in the Department of Genetics and The Genome Institute at Washington University in St. Louis. After contributing to the Human Genome Project, he founded Cofactor Genomics where he leverages the power of RNA to achieve true precision medicine. He can be reached at firstname.lastname@example.org.