Guest Column | February 21, 2018

As Cancer Immune Therapies Advance, 4 Biomarker Trends To Watch

By Nina Baluja, M.D.

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With the advent of immune checkpoint inhibitors and the recent approvals of the first two chimeric antigen receptor (CAR) T-cell therapeutics, the field of cancer immunotherapy has revolutionized cancer treatment. Despite this progress, the current patient response rates and side effects associated with immunotherapies have created a sense of urgency to more accurately identify which patients would most benefit from a particular treatment option.

Every patient’s cancer is unique, and biomarkers have the potential to help researchers and clinicians select the right treatment for each individual patient at every stage of cancer therapy. The identification of biomarkers will help to fill knowledge gaps by providing not only predictive and prognostic information, but also additional insight on the underlying mechanisms of patient response or resistance to immunotherapy.[1] However, due to tumor heterogeneity, the plasticity and diversity of cancer cells, and a multitude of other factors, biomarker development is a challenging task.

In this article, we explore four cancer biomarker development trends to watch.


Programmed death 1 (PD-1) is a key immune checkpoint inhibitory receptor expressed on activated tumor-specific CD4+ helped and CD8+ killer T lymphocytes. Programmed death ligand 1 (PD-L1), the main PD-1 ligand, is a transmembrane protein expressed on a variety of cell types, including dendritic cells, which play a critical role in both innate and adaptive immunity. Binding of PD-L1 inhibits the function of activated T-cells. Tumor cells can co-opt the PD-1/PD-L1 regulatory mechanism via expression of PD-L1, with subsequent PD-1 binding and inhibition of T-cell activation, allowing cancer cells to evade the immune system.

Therapeutic antibody-mediated blockage of PD-1 or PD-L1 removes the suppressive effects of PD-L1 on cytotoxic T-cells, restoring host immunity against the tumor. Two PD-1 inhibitors [nivolumab (marketed as Opdivo) and pembrolizumab (marketed as Keytruda)] and three PD-L1 inhibitors, atezolizumab (marketed as Tecentriq), avelumab (marketed as Bavencio) and durvalumab (marketed as Imfinzi)] have been approved for cancer immunotherapy. Consequently, defining biomarkers that predict therapeutic response to PD-1/PD-L1 blockade is important for determining which patients to treat.

Detection of PD-L1 protein expression by tumor cells using immunohistochemistry (IHC) has been evaluated in clinical studies for correlation with response to PD-1 and PD-L1 immune checkpoint inhibitors. Currently, PD-L1 IHC 22C3 pharmDx (manufactured by Dako A/S, now Agilent Technologies), which is used to select patients for treatment with pembrolizumab, is the only FDA-approved companion diagnostic. The other three FDA-approved PD-L1 IHC assays serve as complementary diagnostics that may provide physicians with more data and inform patient dialogue around treatment decisions.

Studies have shown that PD-L1 negativity is unreliable, as results may differ depending on the antibody, assay, or tissue sample. Low expression, tumor heterogeneity, and inducible genes can also lead to sampling errors or false negatives. Moreover, the impact of previous cancer treatments on the tumor microenvironment is still undefined.[2] Recently, a Blueprint Working Group established in cooperation with the pharmaceutical industry compared the different IHC tests and cell scoring methods for PD-L1 expression. By comparing assays and cut-offs, the Group concluded that more data are required before an alternative assay can be used to read different specific therapy-related PD-L1 cut-offs.[3] Consequently, for now, PD-L1 IHC positivity is an imperfect biomarker of response and is currently not suitable as a definitive biomarker for selection for therapy with PD-1/PD-L1 inhibitors. It is likely that a more complex, multi-component predictive biomarker system will be required to refine patient selection.[4]

Use of Novel Technologies for Biomarker Development

The GVK Biosciences Online Clinical Biomarker Database, developed in collaboration with the U.S. Food and Drug Administration, has identified tens of thousands of biomarkers. However, only a tiny fraction of these have been developed into validated genomic biomarkers for FDA-approved drugs and none have become in vitro companion diagnostics.1 For a predictive biomarker to be applied in the clinic, it must have analytic and clinical validity, in addition to clinical utility. Multiple organizations have published guidelines for the validation of diagnostic tests, with guidelines and recommendations regarding analytic sensitivity, specificity, reproducibility, and assay robustness.[5],[6]

The process of translating biological data into a predictive biomarker is complicated by the multitude of host- and cancer-related factors that shape the complex interactions between the tumor and the immune system. The emergence of powerful genomic and proteomic technologies, along with advanced bioinformatic tools, has made it possible to simultaneously analyze thousands of biological molecules. These techniques enable the discovery of new tumor signatures which are both sensitive and specific enough for early cancer detection, monitoring of disease progression and appropriate treatment selection, paving the way to truly personalized cancer therapy.

The availability of novel technologies and high throughput approaches, such as mass cytometry, whole exome sequencing, gene expression profiling, and sequencing technology for T-cell receptor clonality assessment, offers both opportunities and challenges for immune biomarker development. With these techniques, a single sample can be used to address myriad questions, but the resulting quantity and complexity of data lead to unique analytical considerations. The Society for Immunotherapy of Cancer (SITC) convened a working group which published a white paper that evaluated new technologies and emerging biomarkers relevant to cancer immunotherapy and provided recommendations on best practices.[7]

Tumor Mutation Burden

Tumor mutation burden (TMB) is a measurement of the mutations carried by tumor cells, and numerous studies are evaluating its association with response to immuno-oncology therapy. Typically, DNA sequencing is used to determine the number of acquired mutations in the tumor and TMB is reported as the number of mutations in a specific area of genetic material, such as mutations in a single cell, mutations in an entire tumor, or mutations per megabase.

Tumor cells with high TMB may have more neoantigens, cell surface molecules produced by DNA mutations that are present only in cancer cells and not in normal cells. These neoantigens can be recognized by T-cells, inciting an anti-tumor immune response both in the tumor microenvironment and beyond. As such, it is postulated that a high TMB may correlate with a higher likelihood of responding to immunotherapy.

At the 2017 International Association for the Study of Lung Cancer (IASLC) 18th World Conference on Lung Cancer in Yokohama, Japan, researchers presented data from CheckMate-032, an ongoing phase I/II open-label trial evaluating the safety and efficacy of nivolumab monotherapy and nivolumab plus ipilimumab (marketed as Yervoy) combination therapy in patients with advanced small cell lung cancer patients. The data demonstrated that response rate and one-year overall survival nearly doubled in patients with a high TMB who were treated with combination therapy versus monotherapy. In addition, a high TMB predicted better outcomes, regardless of the treatment arm, as compared with a medium or low TMB.[8] These new findings provide compelling evidence supporting the clinical utility of TMB as a biomarker for nivolumab therapy, both alone and in combination with ipilimumab.

The Tumor Microenvironment

We now know that metabolic considerations, the tumor microenvironment, the microbiome, and signaling pathway modulation all affect the immune system. Investigations into the tumor microenvironment at a genetic level seek to determine whether genetic changes within the tumor microenvironment can guide the design of cancer immunotherapeutics. Unlike predictive or prognostic biomarkers, immune targets are biomarkers that might not correlate strongly with response to treatment, but may help direct the development of cancer therapies.

In one study, Ras mutations were used as immune target biomarkers. Patients with advanced solid tumors bearing Ras mutations were given a cancer vaccine comprised of autologous peptides along with interleukin (IL)-2, granulocyte-macrophage colony-stimulating factor (GM-CSF), or both. Although the majority of patients developed antigen-specific immune responses, only one patient out of 57 generated productive immunity that went on to eliminate the tumor cells.[9]

This disparity led to the discovery that there is significant expansion of regulatory T-cells (Treg) in patients with colon cancer with mutated Ras, compared to both healthy individuals and patients with colon cancer with wild-type Ras. It was found that mutant Ras activates the MEK-ERK-AP1 pathway to induce secretion of high levels of IL-10 and transforming growth factor (TGF)-β1, which generate local induction of Treg in the tumor microenvironment.[10] Induction of Treg serves to support tumor immune escape by creating a suppressive tumor microenvironment that inhibits the anti-tumor response. Thus, the efficacy of a cancer vaccine in patients with Ras mutations may be increased by adding an agent that targets Treg.


The development of biomarkers for cancer immune therapies is still in its nascent stages. The challenges associated with biomarker development are outweighed by the opportunities presented by the use of biomarkers. Biomarkers have the potential to allow development of treatment plans specific to each patient, which could help avoid selection of ineffective therapies, unnecessary toxicities, and the subsequent need to treat those toxicities. In addition, the rational design of combination therapies will only be possible when mechanisms of action and resistance are fully elucidated.1


Bio: Nina Baluja, M.D., is senior medical director for medical services at Premier Research.

[1] Gulley JL, et al. Immunotherapy biomarkers 2016: overcoming the barriers. J Immunother Cancer 2017;5:29.

[2] Topalian SL, et al. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer 2016;5:275-287.

[3] Hirsch FR, et al. PD-LD Immunohistochemistry assays for lung cancer: Results from Phase 1 of the Blueprint PD-L1 IHC Assay Comparison Project. J Thorac Oncol 2017;12(2):208-222.

[4] Boussiotis VA. Molecular and biochemical aspects of the PD-1 checkpoint pathway. N Engl J Med 2016;375:1767-1778.

[5] Chau CH, Rixe O, McLeod H, Figg WD. Validation of analytic methods for biomarkers used in drug development. Clin Cancer Res 2008;14(19);5967-5976.

[6] Lee JW, et al. Method validation and measurement of biomarkers in nonclinical and clinical samples in drug development: a conference report. Pharm Res 2005;22(4):499-511.

[7] Yuan J, et al. Novel technologies and emerging biomarkers for personalized cancer immunotherapy. J Immunother Cancer 2016;4:3.

[8] Rizvi N, et al. Impact of tumor mutation burden on the efficacy of nivolumab or nivolumab plus ipilimumab in small cell lung cancer: An exploratory analysis of CheckMate 032. 2017 World Conference on Lung Cancer. Abstract OA 07.03a. Presented October 16, 2017.

[9] Rahma OE, et al. The immunological and clinical effects of mutated ras peptide vaccine in combination with IL-1, GM-CSF, or both in patients with solid tumors. J Trans Med 2014;12:55.

[10] Zdanov S, et al. Mutant KRAS conversion of conventional T cells into regulatory T cells. Cancer Immunol Res 2016;4(4):354-365.