By Nina Baluja
Senior Medical Director, Medical Services, Premier Research

Nina Baluja

 

Emboldened by 2017’s back-to-back FDA approvals of the first drugs that use genetically engineered patient immune cells – Kymriah (tisagenlecleucel) to treat leukemia and Yescarta (axicabtagene ciloleucel) to combat large-B-cell lymphomas – immunotherapy researchers are continuing to revolutionizing cancer treatment. But uneven patient response rates and the side effects often associated with immunotherapies are putting high priority on accurately identifying which patients would benefit most from particular treatment options.

Biomarkers have great potential to help identify the best individual treatments at every stage of cancer therapy, and these indicators offer crucial insights into the underlying mechanisms of patient response and resistance to immunotherapy – in addition to providing predictive and prognostic information.1 But due to tumors’ diverse characteristics, the plasticity and diversity of cancer cells, and numerous other factors, developing biomarkers is difficult, time-consuming, and expensive.

With that in mind, here are four important trends in cancer biomarker development.

Novel biomarker development technologies

Of the tens of thousands of identified biomarkers, just a fraction have been developed into validated genomic biomarkers for FDA-approved drugs, and none have become in vitro companion diagnostics.1 To be applied in a clinical setting, predictive biomarkers must have not just clinical utility, but analytic and clinical validity as well. Multiple organizations have published guidelines on validating diagnostic tests, providing recommendations on analytic sensitivity, specificity, reproducibility, and assay robustness.2,3

Translation of biological data into predictive biomarkers is complicated by the many host- and cancer-related factors that shape the complex interactions between tumors and the immune system. New genomic and proteomic technologies, alongside advanced bioinformatic tools, enable the simultaneous analysis of thousands of biological molecules – techniques that make possible discovery of new tumor signatures needed to advance truly personalized cancer therapy.

Mass cytometry, whole-exome sequencing, gene expression profiling, and sequencing technology for assessing T-cell receptor clonality are among the novel technologies and high-throughput approaches that simultaneously present opportunities and challenges for immune biomarker development. With these techniques, a single sample can be used to address many questions, with the resulting data quantity and complexity leading to unique analytical considerations.

Programmed death ligand 1

PD-L1 is the main programmed death ligand among the immune checkpoint inhibitory receptors known as PD-1s. A transmembrane protein expressed on a variety of cell types, including dendritic cells, PD-L1 plays a critical role in innate and adaptive immunity. Binding of PD-L1 inhibits the function of activated T-cells, allowing tumor cells to co-opt the PD-1/PD-L1 regulatory mechanism via expression of PD-L1. The subsequent PD-1 binding and inhibition of T-cell activation allow cancer cells to bypass the immune system.

Blockage of PD-1 or PD-L1 via therapeutic antibodies restores host immunity against tumors by removing the suppressive effects of PD-L1 on cytotoxic T-cells – underscoring the importance of defining biomarkers that predict therapeutic response to PD-1/PD-L1 blockade in determining which patients to treat.

Use of immunohistochemistry to detect PD-L1 protein expression by tumor cells has been evaluated in clinical studies for correlation with response to PD-1 and PD-L1 immune checkpoint inhibitors. PD-L1 IHC 22C3 pharmDx, manufactured by Agilent Technologies – used to select patients for treatment with pembrolizumab, a PD-1 inhibitor marketed as Keytruda – is the only FDA-approved companion diagnostic.

However, studies have shown that PD-L1 negativity is unreliable, with results prone to differ depending on antibody, assay, or tissue sample. Additionally, tumor heterogeneity, low expression, and inducible genes can lead to sampling errors or false negatives. Moreover, the impact of previous cancer treatments on the tumor microenvironment remains undefined.4 A Blueprint Working Group established in cooperation with the pharmaceutical industry recently compared immunohistochemistry tests and cell scoring methods for PD-L1 expression. Based on comparison of assays and cutoffs, the group concluded that more information is required before an alternative assay can be used to read specific therapy-related PD-L1 cutoffs.5

For now, therefore, PD-L1 IHC positivity is an imperfect biomarker of response and unsuitable 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

Tumor cell mutations

Tumor mutation burden measures the mutations carried by tumor cells. It’s the subject of numerous studies that are evaluating TMB’s association with response to immuno-oncology therapy. DNA sequencing typically is used to determine the number of acquired mutations in a tumor, and TMB is reported as the number of mutations in a specific area of genetic material – for example, mutations in a single cell, mutations in an entire tumor, or mutations per megabase.

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

At the International Association for the Study of Lung Cancer’s 2017 World Conference on Lung Cancer, researchers presented data from CheckMate-032, an ongoing Phase I/II open-label trial comparing the safety and efficacy of nivolumab monotherapy versus nivolumab plus ipilimumab in patients with advanced small-cell lung cancer. The response rate and one-year overall survival nearly doubled in patients with a high TMB who received combination therapy versus monotherapy.7

Additionally, a high TMB predicted better outcomes, regardless of the treatment arm, compared with a medium or low TMB.7 These findings strongly support the clinical utility of TMB as a biomarker for nivolumab therapy, alone and in combination with ipilimumab.

The tumor microenvironment

It’s well established that the tumor microenvironment, metabolic considerations, the microbiome, and signaling pathway modulation affect the immune system. Genetic-level 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 are biomarkers that might not correlate strongly with response to treatment – but may help direct development of cancer therapies.

In a study that used Ras mutations as immune target biomarkers, patients with advanced solid tumors bearing Ras mutations received vaccines containing autologous peptides and interleukin-2, granulocyte-macrophage colony-stimulating factor, or both. While most patients developed antigen-specific immune responses, only one out of 57 generated productive immunity that went on to eliminate the tumor cells.8 From this disparity, we learned that there is significant expansion of regulatory T-cells (Treg) in patients with colon cancer with mutated Ras compared to healthy individuals and patients with colon cancer with wild-type Ras. Mutant Ras activates the MEK-ERK-AP1 pathway to induce 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, indicating that the efficacy of a cancer vaccine in patients with Ras mutations may improve with addition of an agent that targets Treg.

Conclusion

Still in the early stages of biomarker development for cancer immune therapies, we’re finding that the opportunities presented by the use of biomarkers outweigh the challenges associated with their development. Biomarkers have great potential to allow patient-specific treatments that maximize the likelihood of efficacy, avoid use of ineffective therapies, and minimize exposure patients to unnecessary toxicities.

 

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.

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.