The Challenge for Development of Valuable Immuno-oncology Biomarkers

Abstract

The development of immunotherapy is an important breakthrough for the treatment of cancer, with antitumor efficacy observed in a wide variety of tumors. To optimize immunotherapy use, approaches must be developed to identify which patients are likely to achieve benefit. To minimize therapeutic toxicities and costs, understanding the ideal choice and sequencing of the numerous immuno-oncology agents available for individual patients is thus critical, but fraught with challenges. The immune tumor microenvironment (TME) is a unique aspect of the response to immuno-oncology agents and measurement of single biomarkers does not adequately capture these complex interactions. Therefore, multiple potential biomarkers are likely needed. Current candidates in this area include PD-L1 expression, CD8⁺ tumor-infiltrating lymphocytes, tumor mutation load and neoantigen burden, immune-related gene signatures, and multiplex IHC assays that examine the pharmacodynamic and spatial interactions of the TME. The most fruitful investigations are likely to use several techniques to predict response and interrogate mechanisms of resistance. Immuno-oncology biomarker research must employ validated assays to ask focused research questions utilizing clinically annotated tissue collections and biomarker-focused clinical trial designs to investigate specific endpoints. Real-time input from patients and their advocates into biomarker discovery is necessary to ensure that the investigations pursued will improve both clinical outcomes and quality of life. We herein provide a framework of recommendations to guide the search for immuno-oncology biomarkers of value.

Figure 1. Putative Immuno-Oncology Biomarkers in the TME

1. PD-L1 expression PD-L1 expression in the tumor microenvironment may indicate increased responsiveness to blockade of the PD-1/PD-L1 checkpoint. 2. TILs The presence of tumor infiltrating lymphocytes may be indicative of a pre-existing anti-tumor immune response which can be re-invigorated by immunotherapy. 3. Mutational Load and neo-antigens Increased tumor mutational load and putative neo-antigens may be a marker for increased immunogenicity of a tumor. 4. Immunosuppressive cell types Immunosuppressive cells, such as immature dendritic cells, myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages, and T-regulatory cells are recruited to or generated in the tumor microenvironment. The presence of these cell types may indicate resistance or sensitivity to specific types of therapy. 5. Macrophage and DC polarization Macrophages can be pro-inflammatory (M1) or anti-inflammatory (M2). M2 macrophages and MDSCs inhibit T-cell responses through a variety of mechanisms and their presence may indicate resistance to certain types of immunotherapy. Dendritic Cells (DCs) can also be polarized from immature into primed immunosuppressive/tolerogenic regulatory DCs, which limit activity of effector T cells and support tumor growth and progression. 6. Immunosuppressive molecules The presence of other modes of immune suppression such as PD-1, PD-L1, CTLA-4, IDO, Tim-3 and others may indicate non-overlapping mechanisms of immune resistance which may predict sensitivity or resistance to immunotherapy. 7. Cytokine signatures The presence of immunostimulatory or immune inhibitory cytokines in the tumor microenvironment may predict sensitivity or resistance. Multiple methods exist to interrogate the tumor microenvironment including: IHC/IF, WES, transciptome analysis, proteomics, flow cytometry and others.

Figure 2. Subtypes of Tumor Immunity in MicroEnvironment (TIME) Classification

This figure, modelled after Zhang and Chen’s (28), illustrates the four tumor subtypes of TIME; T1-T4.