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Addressing the Gap in Precision Medicine
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Addressing the Gap in Precision Medicine

David A. Fabrizio, BS
We need the development of new, complex biomarkers to address the increasing complexity in treatment modalities that, in and of themselves, have characteristics of a continuous variable; they require innovation and outcomes data, which perhaps will be partly addressed by some of the emerging real-world evidence databases amassed by pairing sequence information and clinical outcomes. Tumor mutational burden is a great example of this innovation in practice.
The age of precision medicine has officially entered its adolescent years. A decade ago, we were beginning to unlock the potential of next-generation sequencing (NGS) to inform treatment options for cancer patients that weren’t restricted by the origin of the tumor in the body, but rather informed by the unique genetic mutations responsible for driving tumor growth. This tailored treatment approach made sense: It not only provided the rationale for targeted treatment options, but it also led to new discoveries for drug sensitivity and resistance. Copy number amplifications in MET, for example, were complemented by splicing mutations in exon 141; deletions in EGFR exon 19 added to the plethora of ways to be sensitive to EGFR targeting therapies2; and the number of ways to delete function in BRCA1 or BRCA2 seemed to grow by the day. Further, in a landmark approval from the FDA, all patients with solid tumors that were microsatellite instability–high (MSI-H) could receive the immunotherapy (IO) pembrolizumab regardless of their respective cancer type.3 In another important milestone, the union of molecularly guided targeted therapy with a broadly available commercial assay was finally achieved, with the FDA approval of FoundationOne CDx in 2018.4 Despite these enormous successes in advancing treatment options for patients, there was still much to be learned.

Targeted therapies—that is, treatments developed based on a specific molecular driver of cancer—are no longer the only mechanism to fight cancer in a precise and durable way. Immunotherapies have revolutionized the treatment paradigm and, using perhaps the most complex biological system in our bodies, found a way to provide meaningful benefit in cancers for which no other treatment options previously existed. Additionally, a new mechanism of treating cancer that relied on synthetic lethality involved targeting PARP to allow tumors to effectively destroy themselves by taking advantage of holes in the DNA damage repair pathways.

These additional advancements represented rapid growth in the evolution of cancer treatments, and as such, required new and complicated methods to determine which patients would benefi t most from their application. Yet, despite our initial efforts, we struggled to neatly “fit” these biomarkers into the precision medicine paradigm that worked so cleanly for traditional targeted therapies. A new way of thinking would be required if we were to successfully realize the full potential of personalized health care and, specifi cally, immunotherapy.

Immunotherapy-Guided Biomarkers and Genomic Signatures

Despite its revolutionary eff ect on cancer treatments, immunotherapies weren’t born with companion diagnostics by their side. It took years of trials and failures before we began to focus on PD-L1 immunohistochemistry as a method to identify likely responders. Discrete cutoff s were chosen to segment the cancer population into 2 cohorts: positive (high) and negative (low). However, the application in clinical trials was largely inconsistent, leading to success in some areas and failures in others.5,6 MSI was the fi rst IO biomarker based on the premise that tumors harboring more somatic mutations might be more likely to elicit an immune response through expression of neoantigens, and subsequently quiesce that response by upregulating checkpoint proteins. It also didn’t necessarily require NGS for its application, and in colorectal cancer, the results were staggering.7 Establishing proof of concept for the neoantigenicity hypothesis was crucial; however, MSI still left a gaping sensitivity hole since it was only 1 of many ways for tumors to become hypermutant, and many hypermutant cancers had virtually 0 cases of MSI-H but could still benefit from IO.8 Tumor mutational burden (TMB) eff ectively filled this gap by assessing the overall somatic mutation rate as a proxy for the neoantigenicity of tumors, and preliminary studies using retrospective clinical trial data demonstrated that its clinical utility could be remarkable.9 Disease-specific TMB cutoffs were chosen to evaluate the marker’s performance, and control arms compared with standard-of-care chemotherapy largely demonstrated that TMB was predictive of IO response and not generally prognostic when applied in non-IO treated patients.10

The Evolution of TMB

CheckMate 227 was the first prospective application of TMB in a phase 3, fully randomized clinical trial. One of the coprimary end points in this first-line non–small cell lung cancer (NSCLC) study evaluating the combination of ipilimumab and nivolumab versus platinum-based chemotherapy was based on progression-free survival (PFS), using TMB to defi ne the population. Results presented by Hellmann and colleagues in 2018 at the American Association of Cancer Research meeting, and simultaneously published in the New England Journal of Medicine, showed that the landmark 1-year PFS rate more than tripled for the IO combination compared with chemotherapy, a remarkable and dramatic effect driven by TMB that was not observed in the overall, unselected population.11 Furthermore, results presented that year at the European Society for Medical Oncology (ESMO) by Kim and colleagues from the B-FIRST study evaluating atezolizumab in first-line NSCLC patients identified a rate of enrichment more than 6 times higher in the TMB high vs low subgroups, measured using a using a blood-based TMB calculated from the circulating tumor DNA in plasma.12 Most recently, Mirabelle et al. presented results at ESMO 2019 from the KEYNOTE-158 study evaluating pembrolizumab monotherapy in select solid tumors using TMB calculated from tissue biopsies, finding a 29% response rate in TMB-high versus 6% in nonhigh groups, representing a nearly 5-times enrichment in response.13

A Need for TMB Harmonization

These results are potentially practice-changing; however, the need for harmonization of this complex genomic signature was apparent well before these studies ensued. As a result, a TMB Harmonization Consortium was created and led by Friends of Cancer Research (Friends) to focus on 3 main aspects of TMB: common defi nitions across diagnostic platforms; consistent methods for analytic validation, including the use of reference standards; and proposals for the clinical application of TMB in tumor agnostic studies. The last component followed the guidance of FDA’s Oncology Center for Excellence, which stated that if a biomarker would be used to define a disease category, such as TMB, then it should include participation across both industry and academia.14 These eff orts were recently published by Merino and colleagues in the Journal for Immunotherapy of Cancer15 and guidelines on the tumor-agnostic application of TMB in clinical trials were published on the Friends website,16 with a similar approach being adopted and published by our European counterparts.17 This enormously complex, precompetitive harmonization on TMB required a thoughtful approach that included multiple key stakeholders, and despite the meaningful outcome that should serve to create consistency of TMB across different diagnostic platforms, there is still the unmet need associated with the continuous variable nature of this biomarker.  

Measuring TMB as a Continuous Variable

Alignment on measuring TMB is even more complex because of its nature as a continuous variable genomic signature. An inherent property of continuous variables is their ability to provide more information beyond restricting populations into 2 discrete segments based on a singular cutoff . TMB produces a somatic mutations-per-megabase score that falls along a continuum that can be associated with a probability of achieving clinical outcome. It’s important to understand that continuous variables don’t begin and end with TMB. For instance, another common continuous variable is age, one that can be generally associated with overall risk of death.

Contrasting this against a discrete biomarker, like a pregnancy test result or EGFR L858R, and it becomes more obvious how, in these latter scenarios, the practical application lends itself to defining the population according to 2 groups: positive or negative. However, forcing continuous variables into an oncology infrastructure that was designed for discrete biomarkers leaves behind valuable information that can’t be gleaned from restricting populations into 2 broad segments. For example, a singular TMB cutoff to define high versus low doesn’t account for the patient who falls just below or just above the cutoff and would likely have statistically equivalent chances of response. In this way, we are ignoring the probabilistic relationship associated with the continuous variables that can provide significantly more information at the point of care for clinicians, who themselves are facing ever-increasing challenges in determining a treatment plan amid the multitude of options available for their patients.

Closing the Gaps in Precision Medicine

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