Cancer Immunotherapy Biomarkers: Four Trends to Watch

On the heels of the 2017 FDA approvals of Kymriah® (tisagenlecleucel) to treat leukemia and Yescarta® (axicabtagene ciloleucel) to combat large-B-cell lymphomas, researchers are continuing to make bold strides in revolutionizing cancer therapy. While some patients experience potent and durable clinical responses to these breakthrough immunotherapies, response rates are highly variable, and these treatments are often associated with side effects that differ from those of cytotoxic chemotherapy. As a result, researchers and clinicians are in search of tools that help them identify which patients are most likely to benefit from specific immunotherapies.

There is a critical need for standardized and validated biomarkers that yield actionable insights into immunotherapy efficacy at every stage of development. In addition to helping identify patients who could benefit from available therapies, biomarkers may be useful for monitoring treatment response. These indicators also have the potential to shed light on a treatment’s mechanism of action, which would provide important insight for optimizing treatment approaches and defining rational combination therapies. However, the intrinsic characteristics of malignant tumors-such as their heterogeneity, plasticity, and diversity-pose challenges to biomarker development.

Here are four trends to watch as researchers continue to make headway in the development of biomarkers for cancer immunotherapy.

Using state of the art technologies for biomarker development

Only a tiny fraction of the tens of thousands of identified biomarkers have been developed into validated genomic biomarkers for FDA-approved drugs, and none have become in vitro companion diagnostics.¹ To be used effectively in a clinical setting, predictive biomarkers must have both clinical utility and analytic and clinical validity. Many organizations have published guidelines on validating diagnostic tests, which provide recommendations on analytic sensitivity, specificity, reproducibility, and assay robustness.2,3

The translation of biological data into predictive or prognostic biomarkers is complicated by the many host-and cancer-related factors that influence the complex interactions between tumors and the immune system. New genomic and proteomic technologies, combined with advanced bioinformatic tools, enable the simultaneous analysis of thousands of biological molecules. These cutting-edge techniques help enable the discovery of new tumor signatures, which are critical for making the leap to precision medicine and personalized cancer therapy.

Mass cytometry, whole-exome sequencing, gene expression profiling, and T-cell receptor clonality sequencing technology are just a few of the novel technologies and high-throughput approaches being used for biomarker development. With these techniques, a single sample can be used to address many questions, but the resulting quantity and complexity of data leads to unique analytical challenges and may require multi-disciplinary expertise for interpretation.

Validating PD-L1 as a biomarker of response

Programmed death ligand-1 (PD-L1) a transmembrane protein expressed on a variety of cell types, including dendritic cells. The main programmed death ligand among the immune checkpoint inhibitory receptors known as PD-1s, PD-L1 plays a critical role in innate and adaptive immunity. PD-L1 binding inhibits the normal function of activated T-cells. By expressing PD-L1, tumor cells can co-opt the PD-1/PD-L1 regulatory mechanism and inhibit T-cell activation, allowing cancer cells to bypass the immune system.

Therapeutic antibodies can be used to block PD-1 or PD-L1 and restore host anti-tumor immunity by removing the suppressive effects of PD-L1 on cytotoxic T-cells. As such, defining biomarkers that predict therapeutic response to PD-1/PD-L1 blockade is critical for identifying patients who are likely to respond to PD-1 and PD-L1 immune checkpoint inhibitors.  

In clinical studies, immunohistochemistry has been used to detect PD-L1 protein expression by tumor cells and evaluate it for correlation with response to PD-1 and PD-L1 immune checkpoint inhibitors. Currently, the only FDA-approved companion diagnostic is PD-L1 IHC 22C3 pharmDx, which is used to select patients for treatment with pembrolizumab, a PD-1 inhibitor marketed as Keytruda.

Unfortunately, PD-L1 negativity is unreliable and results may vary depending on antibody, assay, or tissue sample. Tumor heterogeneity, low expression, and inducible genes can also lead to sampling errors or false negatives. In addition, at this time, it is not known how previous cancer treatments affect the tumor microenvironment.4 According to a recent Blueprint Working Group analysis comparing immunohistochemistry tests and cell scoring methods for PD-L1 expression, more data is needed before an alternative assay can be used to read specific therapy-related PD-L1 cutoffs.5

For now, PD-L1 IHC positivity remains an imperfect biomarker of response and cannot be used as a definitive biomarker to select patients for PD-1/PD-L1 inhibitor therapy. A more complex, multi-component predictive biomarker system likely will be required to refine patient selection.6

Linking tumor mutation burden to response rate

Tumor mutation burden (TMB), a measure of the mutations carried by tumor cells, has been studied to evaluate its association with response to immuno-oncology therapy. DNA sequencing is used to determine the number of acquired mutations in a tumor, and TMB is generally reported as the number of mutations in a specific area of genetic material.

The theory behind TMB as a biomarker is that tumor cells with high TMB may have more neoantigens, cell-surface molecules produced by DNA mutations that are unique to cancer cells. These neoantigens can be recognized by T-cells to stimulate an anti-tumor immune response in the tumor microenvironment. Consequently, a high TMB may correlate positively with the likelihood of responding to immunotherapy.

At the 2017 World Conference on Lung Cancer, researchers presented data from CheckMate-032, an ongoing Phase I/II open-label trial comparing nivolumab monotherapy with nivolumab plus ipilimumab combination therapy in patients with advanced small-cell lung cancer. The data showed that a high TMB predicted better outcomes, regardless of the treatment arm, compared with medium of low TMB. In addition, patients with a high TMB who received combination therapy had significantly higher response rates and one-year overall survival than those who received monotherapy. Taken together, these findings provide strong support for the clinical utility of TMB as a biomarker for nivolumab therapy, both alone and in combination with ipilimumab.7

Leveraging the tumor microenvironment to direct therapeutic development

The tumor microenvironment comprises the cellular environment in which a tumor exists, including surrounding vasculature, immune cells, fibroblasts, inflammatory cells, signaling molecules, and the extracellular matrix. Recent investigations into the tumor microenvironment seek to determine whether genetic changes can guide the design of cancer immunotherapeutics. Unlike predictive or prognostic biomarkers, immune targets may not correlate with treatment response, but may help direct the development of new cancer therapies.

In one study using Ras mutations as immune target biomarkers, 57 patients with advanced solid tumors bearing Ras mutations received a cancer vaccine containing autologous peptides and interleukin-2, granulocyte-macrophage colony-stimulating factor, or both. While most patients developed antigen-specific immune responses, only one generated productive, tumor-eliminating immunity.⁸ This finding led to the discovery that there is a significant expansion of regulatory T-cells (Tregs) in patients with colon tumors bearing Ras mutations.

Mutant Ras induces secretion of high levels of IL-10 and transforming growth factor-β1, which generate local induction of Treg in the tumor microenvironment.⁹ Induction of Treg supports tumor immune escape by creating a suppressive tumor microenvironment that inhibits the anti-tumor response, suggesting that the efficacy of cancer vaccines in patients with Ras mutations may improve with addition of an agent that targets Treg.

Conclusion

Immunotherapies represent a paradigm shift in cancer treatment and are expanding the therapeutic landscape for cancer patients. However, they are not a one-size-fits-all solution and there is growing need to identify predictive and prognostic markers that enhance our understanding of the complex interactions between cancer and the immune system, as well as our ability to identify the right therapy for each patient at every stage of their disease. We are still at the dawn of cancer immunotherapy biomarker development, with exciting opportunities ahead for using biomarkers to aid in treatment selection and rational design of the combination therapies that will be the next revolution in cancer treatment.

References

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

2 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.

3 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.

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

5 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.

6 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 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.

8 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.

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

Nina Baluja, M.D., Senior Medical Director, Premier Research