The call to pursue oncology is different for everyone. For me, it was the field's immediacy: It stood out among medical subspecialties as a place where the bench and the bedside intersected frequently.
To make the biggest possible difference for patients, oncologists need to constantly bring the latest research, technology, and knowledge into the clinic. While we have made massive strides in our understanding of cancer biology, applying this knowledge to each of our patients on a daily basis is incredibly difficult.
New tools are needed to help oncologists select the best treatment in each case. The emergence of massively parallel DNA sequencing platforms, often called comprehensive genomic profiling (CGP), drastically increased the amount of tumor genomic data available to oncologists in a clinically relevant timeframe. More recently, a promising predictive biomarker called tumor mutational burden (TMB) is expanding the applicability of CGP to the evolving field of immuno-oncology by aiding in the identification of patients for whom checkpoint inhibitors may be considered.
Increasingly, clinical data support CGP as a more efficient and effective approach than traditional “hotspot” testing for detecting molecular alterations. Rather than probe areas of a single gene or small set of genes, CGP evaluates the coding exons of hundreds of cancer-related genes simultaneously. In many cases, CGP may even reveal novel targetable mutations not detected by traditional hotspot tests (Clinical Cancer Research 2015;21(16):3631-3639, The Oncologist 2016;21(6):684-691).
There are numerous examples of this including the identification of novel and rare HER2 mutations (J Thorac Oncol 2017;12(3):446-457), actionable fusions (The Oncologist 2016;21:762-770, J Natl Cancer Inst 2016;108, Am J Surg Pathol 2016;40:1298-1301), MET exon 14 skipping alterations (Cancer Discov 2015;5:850-859), kinase domain duplications (JAMA Oncol 2016;2:272-274, Cancer Discov 2016;6:601-611), and targetable resistance mutations (J Thorac Oncol 2014;9:549-553), among others (Cancer Discov 2016;6:594-600, Cancer Discov 2015;5:1262-1270). The incorporation of CGP is now included in the NCCN guidelines for non-small cell lung cancer (NSCLC), and I anticipate the role will continue to expand as new data emerge.
Keeping Up With Immunotherapy
The ability of CGP to reliably identify molecular targets is established, and recent attention has focused on the intersection of genomics and immuno-oncology. Several landmark studies have demonstrated a role for immune checkpoint inhibitors across multiple anatomic tumor types (N Engl J Med 2015;372:2521-2532, N Engl J Med 2015;372:2018-2028, Lancet 2016;87:1909-1920, N Engl J Med 2015;373:1803-1813). While checkpoint inhibitors are changing the cancer paradigm, a pattern of response with durable benefit in 15-40 percent of patients has spurred the search for robust response biomarkers (Ann Transl Med 2015;3(18):267, N Engl J Med 2013;369(2):122-133).
The current diagnostic standard for checkpoint inhibitors measures PD-L1 protein expression using immunohistochemistry (IHC). However, PD-L1 testing is highly qualitative and varies depending on the specific assay, each one potentially relying on a different threshold for a positive result (JAMA Oncology 2016;2(1):15-16). The differing thresholds and methodologic approaches limit broad applicability and may introduce confusion among practicing oncologists. TMB, as determined using existing CGP assays, offers a quantitative and robust predictive marker for immunotherapy across tumor types. Expressed as the total number of mutations per coding area of the tumor genome, it can be derived from the same CGP assay used to simultaneously probe hundreds of cancer-related genes for actionable alterations. Tumors with high TMB are thought to express higher levels of neoantigens (cancer-specific antigens from altered proteins), which may increase the chances of immune recognition (Genome Research 2014;24:743-750, Science 2015;348(6230):69-74). Essentially, TMB may be a proxy for identifying neoantigen load and subsequently help predict a patient's response to immunotherapy.
Several recent studies have shown the utility of TMB as a predictive marker. Balar, et al, and Rosenberg, et al, independently found that in advanced bladder cancer patients, high TMB was associated with better responses to anti-PD-L1 immunotherapy and was a better predictor than PD-L1 IHC testing (Lancet 2016; 87:1909-1920, Lancet 2017;389:67-76). Data presented at the ESMO 2016 Congress found an association between higher TMB and improved outcomes with PD-L1 blockade in second line NSCLC (Annals of Oncology 2016;27(6):15-42). Similar findings have been reported in melanoma with anti-PD-1/PD-L1 and anti-CTLA-4 treated patients (Cancer Immunol Res 2016;4(11):1-9, N Engl J Med 2015;373:1984).
A Solution for Everyone
I have incorporated the additional data offered by TMB into my own practice and observed benefits first hand. For example, we recently treated an 84-year-old patient who presented with locally advanced esophageal cancer and several medical comorbidities, limiting clinical trial, standard chemotherapy, radiotherapy, and surgical options. In an attempt to expand therapeutic options, we used a comprehensive genomic profiling assay that found him to be TMB-high with 36 mutations per DNA megabase. He received expanded access checkpoint inhibitor with a complete clinical response with resolution of dysphagia.
Experiences like this highlight how CGP can straddle the intersection of genomics and immunotherapies and have motivated me to collaborate in improving clinical trial design to expand patient options. For example, rather than restricting to PD-L1 positive or negative, perhaps we should consider a trial design that investigates checkpoint inhibitors in all TMB-high tumors, independent of anatomic origin. Recently, the phase III CheckMate-026 trial for first-line NSCLC failed to demonstrate improved PFS for nivolumab versus chemotherapy in patients with 1 percent or greater PD-L1 expression in tumor cells (Annals of Oncology 2016(27(suppl_6):LBA7_PR). Ultimately prospective trials are needed to validate any therapeutic approach, though in the absence of approved or appropriate options, smaller data sets including proof-of-principle case reports are important. With reliable, quantitative markers, such as TMB, to guide clinical trial inclusion or stratify participants, we'll be able to draw more concrete conclusions about how a treatment works (or doesn't) and why.
More work is necessary to characterize TMB's ability to predict responses across cancer types, but the initial data are exciting. In combination with CGP, this new approach is distinct from standard diagnostics. Clinical trial opportunities are not available in all practice settings and the adjunct information from TMB may provide helpful information in considering immunotherapy options. When TMB is paired with the ability of CGP to simultaneously interrogate for actionable alterations, there is potential to maximize patient options. A similar approach is being followed by the addition of the SWOG DART trial to the NCI-MATCH efforts, which will help to elucidate the genomic signatures of rare cancers and how they respond to combination immunotherapy.
As we learn more about cancer, we also gain a greater appreciation for its complexity and associated challenges. All signs point toward a future where we need to look beyond single-gene dissection of a tumor. CGP and biomarkers like TMB are complementary data addressing targeted and immuno-oncology approaches. Better tools facilitate improved outcomes, and I see CGP and increasingly TMB as tools to navigate cancer's complexity to ultimately improve patient survival.
SAMUEL J. KLEMPNER, MD, is Director of the Precision Medicine Program at The Angeles Clinic & Research Institute, an affiliate of Cedars-Sinai, Los Angeles.