Marking the Path Ahead: Integrating Oncology Biomarkers Into Clinical Development
In recent years, therapeutic capabilities in oncology have grown dramatically, with a vast array of advanced treatments progressing through clinical trials and onto the market. However, there has been a lag between the development of promising precision treatments and the identification, development and implementation of biomarkers that identify patients most likely to benefit from them. In fact, in a survey of oncology developers, more than half of respondents (53%) reported that difficulty identifying predictive biomarkers limited the utility of their therapeutic approach.
Further, the implementation of biomarkers in oncology clinical trials can increase the complexity and cost of clinical development. Here, we discuss how the landscape of biomarker development and implementation has evolved alongside precision medicines in oncology, and provide strategies for implementing biomarkers in clinical development so that oncology clinical developers save time and money, while positioning their therapy for clinical success.
Evolving biomarkers in oncology
Predictive biomarkers are invaluable tools for identifying patients who are likely to respond favorably to a therapy. Given the heterogeneous nature of cancer, the ability to discern a therapy’s probability of benefitting a particular patient can be critical to effective treatment. Companion diagnostics, which aid in determining whether a patient is a good candidate for a given therapy, have become a prerequisite for targeted therapies and a requirement that has become highly relevant as oncology increasingly progresses into precision medicine. For all these reasons, the development of biomarkers has necessarily evolved alongside the field of oncology.
Yet, biomarkers have not advanced at quite the same rate as oncology therapies. A large contributor to this lag is the complex relationship between a biomarker and treatment response. Only rarely is the presence of a single measurable characteristic sufficient to predict response to a treatment. Instead, a biomarker is more usually a component of a broader biological environment and only one of numerous factors interacting and influencing a patient’s response. Cases in which a patient tests positive for a single mutation (such as BRAF V600E) and is indicated for a specific drug treatment (such as vemurafenib) are the exception rather than the rule.
Finding appropriate biomarkers, therefore, may take advanced strategies, such as multiplex sequencing technology or the use of artificial intelligence (AI) algorithms. In particular, AI-driven biomarker identification and assessment holds a great deal of potential for closing the gap between oncology biomarkers and future treatments. However, despite 49% of survey respondents viewing it as an area with great potential to accelerate oncology drug development, only 11% reported using AI for biomarker identification and assessment — leaving it an area open to future exploration and innovation.
Clinical trial enrichment
The use of predictive biomarkers has the ability to improve the efficiency of clinical trials. A typical trial that does not employ biomarkers to select participants must enroll a large number of patients, to account for non-responders in the treatment group. Utilizing a predictive biomarker to target specific patient populations has the potential to demonstrate favorable responses to an investigational agent by reducing sample size requirements in clinical trials. With this reduction of participant numbers, clinical trials can also reduce the costs associated with recruiting and maintaining large trial cohorts. Additionally, it can be critical to enroll biomarker-positive patients in safety trials, so that developers can assess safety and efficacy early in the clinical trial process.
However, it can be worthwhile to include some biomarker-negative patients in clinical trials. This allows for the possibility of biomarker-negative responders, which may enable a broader application of the therapy. In addition to providing information on a therapy’s performance in marker-negative populations, it also validates the use of the predictive biomarker. To assuage the ethical and regulatory concerns of enrolling a group of cancer patients who are unlikely to benefit from a therapy, employing innovative trial designs, such as adaptive basket or umbrella studies, is an advantageous strategy. By doing so, it is possible to evaluate the marker-negative cohort’s performance as the trial is ongoing, and close that arm of the trial as soon as it becomes clear that it is not futile.
Planning for accessibility
A therapy’s accessibility is a major factor in its commercial success once it has entered the market. Elements, such as cost, biomarker sample requirements and specialized lab access, limit the number of patients who can reasonably use a therapy. As a result, it is beneficial to ensure that a biomarker test fits within the standards of patient management, as well as the resources that are commonly available to clinicians.
In oncology, regulatory and third-party payer environments have evolved so that companion diagnostics—which use predictive biomarkers to identify the target patient population for a specific therapy—are fully integrated into the label of the drug. Reimbursement systems increasingly appreciate the value of a companion biomarker to ensure the drug is being used selectively in the correct subpopulation of patients.
For companion diagnostics or other therapy-associated biomarkers, the ability to easily perform a biomarker test can impact scale-up and uptake, making it worthwhile to simplify protocols. Accordingly, one way to facilitate wider therapeutic adoption is to develop tests that can be shipped and stored at ambient temperatures, lowering costs and expanding the range of healthcare facilities that can readily access them.
The costs of genetic and molecular testing should also be a consideration when implementing biomarkers. Although using genetic and molecular biomarkers to identify patients for cancer therapies has been shown to benefit care and even lead to lower care costs, payers are not always willing to provide coverage for such tests. Furthermore, clinical trials with genetic or molecular biomarker entry criteria must often cover the cost of testing for participants, which can incur significant expenses. Considering such costs and questions of accessibility is central to understanding how the use of biomarkers will impact clinical development.
Moving oncology forward
Complex tumor microenvironments, heterogeneity of cancer cells and the mismatch between treatment capabilities and biomarker advancement contribute to the challenges of developing and utilizing biomarkers. Nevertheless, biomarkers have an important role to play in the future of oncology, which will likely continue to grow as more are identified, validated and start to be implemented in clinical practice. As a result, knowing how best to leverage biomarkers in clinical development can only help to move the field of oncology forward.
