Comprehensive Immunogenomics Profiling for the Discovery of the Next Generation of Oncology Biomarkers
Although hypothesized back in the early 1900s that the immune system may provide a protective mechanism against tumorigenesis, only recently have the fields of immunology and oncology been better linked. It is well accepted today that the immune system protects the host from cancer by eliminating malignant cells. However, tumor cells may gain an ability to evade the immune system by blocking key interactions needed for immune response. Overcoming this barrier has been the general goal of recent immunotherapies and combination agents that can restore immune function. The use of predictive biomarkers has helped researchers identify promising targets for immunotherapy, as well as enrich for patients who are more likely to respond to such therapy.
The term “biomarker” is broad, as it includes any biological molecule that can be detected and measured in human tissues or body fluids. A consensus definition given by a Biomarkers Working Group is ‘an objective, measurable indicator of normal physiology, or pathophysiological process, or response to pharmacological intervention’ (1). When discussing immunotherapies, it is this latter part of the definition that becomes most relevant. Emphasis was placed on the need to separate “clinical endpoints” from “surrogate endpoints” in therapeutic interventions (1). Patient survival and quality of life are considered examples of clinical endpoints. On the other hand, biomarkers serve as surrogate endpoints to evaluate patient response to treatment.
PD-L1, MSI, and TMB in clinical practice
Biomarkers aid in predicting beneficial drug response. A classic example of this strategy is immunohistochemistry (IHC) staining of tumor biopsies to determine PD-L1 expression status. Patients expressing PD-L1 on tumor cells are candidates for anti-PD1 or anti-PD-L1 therapy, immune checkpoint blockade agents that function to restore T cells’ cytotoxic ability in the tumor microenvironment. The approved cut-offs for PD-L1 positivity range from 1 to 50%, depending on the kit used and the cancer indication (2). This wide range and variability of testing make it difficult to draw any reliable conclusion across clinical trials. Another example has been the tissue-agnostic microsatellite instability (MSI) status measured by PCR or IHC staining of key DNA mismatch repair genes. Merck’s anti-PD1 therapy, Keytruda, gained accelerated approval for adult and pediatric cancer patients with advanced solid tumors that are MSI high. This was the first approval based on the genomic alteration of tumors rather than the origin of the primary tumor (3). A more recent biomarker example is tumor mutational burden (TMB) assessment by next generation sequencing (NGS). Overall TMB is believed to be an important indicator of prognosis and response to immunotherapy. A current challenge in the field is establishing reliable cut-offs for TMB to guide immunotherapy treatment, including the need to determine different cut-off values across tumor types. Friends of Cancer Research has initiated multiple studies in an effort to standardize the TMB measurement across diagnostic panels benchmarked against whole exome as the “ground truth” for accurate somatic mutation measurement, and has outlined key guidelines to ensure consistency across tests (4). These examples, currently in clinical practice, have ushered in a new era of immune checkpoint inhibitors, expanding their efficacy in various cancer types. However, despite their application as clinical biomarkers, PD-L1 status and MSI do not reliably predict individual response, and TMB is merely a surrogate measurement of tumor immunogenicity and neoantigens.