Jarret Glasscock, PhD, CEO of Cofactor Genomics explains how diagnostics are emerging as the key to ensuring the right patients get matched to the right therapy, at the right time.
Precision medicine promises a paradigm shift to confidently match the right patients to the right treatment at the right time. In pursuit of this, the number of new therapeutics in development has boomed. In the cancer space alone over the last five years, clinical trials have increased 500% for new drugs and new drug combinations. New classes of therapies have added to this growth, including immune checkpoint inhibitors and genome targeted therapies. Inherent to therapies developed during this era of precision medicine is the notion that many of these new therapies only work in a subset of patients. Identifying the population subsets who will benefit is essential to the success of these therapies in both the clinic and in the clinical trials that will bring these drugs to market.
Before drugs make it to market they have to pass through a gauntlet of stages involved in clinical trials. As such, patient selection for clinical trials has become an increasingly critical consideration for drug trial success. Selecting patients with the strongest potential for response can accelerate trial completion and reduce the necessary cohort size while still helping manufacturers reach key endpoints and achieve statistical significance. Yet, even though a patient may be eligible for a clinical trial, successful results are not guaranteed. Finding the subset of patients who will benefit from the treatment not only helps the trial by increasing the success rate, it also helps more patients receive beneficial treatment and avoid unnecessary or potentially toxic therapy. Patients who enroll in clinical trials are subsequently disqualified for any new trials, making it imperative that they are matched to the most appropriate trials at the onset for both the benefit of the clinical trials, as well as the benefit of the patient.
Where do we fulfill this need in matching patients to trials, and ultimately to treatments? Through testing and diagnostics. In many ways, our definition of disease is proving inadequate, as we have learned in recent years. For example, defining disease as breast cancer, lung cancer, or colorectal cancer is no longer enough. There is an ever-evolving landscape of biomarkers to define subsets of disease populations within these disease states. These markers have utility when they serve to identify a patient as a better fit for a treatment path either in the clinic or at the clinical trial stage. Therefore, it is not surprising that our definition of diagnostics has also changed over recent years. No longer is a diagnostic defined narrowly in meaning and practice as a test to diagnose a patient with disease after onset of symptoms. The landscape of diagnostics has expanded to include early detection (before symptoms appear), predictive diagnostics (the matchmaker between patient and therapy), and disease recurrence (non-invasive monitoring for the purpose of catching recurrence or relapse). Predictive diagnostics define the category of diagnostics we have been discussing, i.e. the efficient matching of a subset of the disease population to the appropriate clinical study and ultimately, to the most promising treatment path.
Diagnostics have historically moved from qualitative, to binary, to now quantitative measurements. Both the binary and quantitative have relied on measurement of protein, DNA, and more recently, RNA. While measuring a single analyte has proven to be our lowest hanging fruit in guiding patient treatment and even the basis for inclusion/exclusion in clinical trials, there is room for improvement. For example, only 37% of patients are found to have an actionable mutation for those undergoing DNA mutation testing using some of the most comprehensive and common testing panels in our industry. Technological advances have allowed us to not only look at many of these individual markers at once, but also are now allowing us to build multi-analyte or multi-dimensional biomarkers. These multi-dimensional biomarkers have been a more systems biology approach to diagnostics and the biomarkers underlying these diagnostics. One measurement or analyte alone may not be informative or predictive enough but when measuring many markers involved in a particular biological process, we observe that signal separates from the noise.
The body’s immune system has been a recent focus for drug development and predictive diagnostics. This focus has been a theme in infectious disease, autoimmune conditions, and in oncology. In oncology, one area of immunotherapy has focused on inhibiting cancer’s blocking mechanism that turns off T-cells aimed at eliminating cancer cells. As is the theme in precision medicine, when it works, this therapy works very well but the challenge is that the therapy works in less than 25% of the patient population. Therefore, we look to predictive diagnostics to fill this gap in precision medicine, to guide more qualified patients to the current therapies on the market but also for the numerous clinical trials being planned and underway. Predictive diagnostics in immunotherapy have followed the path like many other areas, where the drug target and biomarker have been one and the same (as is the case with drugs that bind either PD-1 or its ligand PD-L1, and there is measurement of PD-L1).
Regardless of the target and the biomarker being one and the same, we see that measuring a single analyte is not sufficient information in both sensitivity and specificity to accurately predict which patients will benefit from new classes of drugs, like immune checkpoint inhibitors. Therefore, more recent advances have looked beyond the single analyte approach and looked more comprehensively at the tumour microenvironment. Approaches in this area include tumour mutational burden (TMB), which is a measure of mutations present in the tumour. TMB is a proxy or predictor for immune activity since, the more foreign a cell becomes, the more likely there is to be an immune response to foreign cells. Some studies have reported that while this global approach improves upon the specificity lacking in the single analyte measurement of PD-L1, it does so at the expense of sensitivity, which results in false negatives. These prevent treatment from getting to patients that would benefit; therefore, it is often deemed more tolerable to have false positives than false negatives when it comes to treatment.
Along the same lines as a multi-dimensional biomarker approach to diagnostics predicting which patients will ultimately benefit from immunotherapy, has been a movement to more comprehensively and quantitatively characterise the immune component in the tumour microenvironment. This has included the measure of key genes involved in the tumour immune response, markers for key immune cells playing an active role, and most recently, the state or status of those immune cells (for example, whether the T-cells are activated or exhausted). This more comprehensive approach has been fruitful in more accurately predicting which patients will benefit from these new classes of therapies on the market and many more in the pipeline of clinical trials.
The evolution of therapies has given rise to new needs and roles for diagnostics. Predictive diagnostics in particular has been an emerging category tasked with matching a greater subset of patients to the trials and ultimately to the therapies that are most likely to deliver positive outcomes. It is interesting as we have never seen diagnostics take on such importance in disease and drug development. It begs the question: with the emergence of these diagnostics with greater predictive power, should we reconsider the term ‘companion diagnostic’? Rather than diagnostics being subordinate to therapy, perhaps it is now diagnostics in the lead position in matching patients to treatments. Perhaps it would be more accurate as the age of precision medicine unfolds to coin a new term, ‘companion therapies’. This would better reflect today’s landscape, where diagnostics are emerging as the key to ensuring the right patients get matched to the right therapy, at the right time.
Volume 23 – Issue 4, Fall 2022
About the author
Jarret Glasscock is a geneticist and computational biologist. He is the founder and CEO of Cofactor Genomics, a diagnostic company leveraging the power of RNA and machine learning to build disease models. Previously, he was faculty in the Department of Genetics at Washington University and part of The Genome Institute where he worked on the Human Genome Project.