Elsevier

The Lancet Oncology

Volume 17, Issue 12, December 2016, Pages e542-e551
The Lancet Oncology

Review
Predictive biomarkers for checkpoint inhibitor-based immunotherapy

https://doi.org/10.1016/S1470-2045(16)30406-5Get rights and content

Summary

The clinical development of checkpoint inhibitor-based immunotherapy has ushered in an exciting era of anticancer therapy. Durable responses can be seen in patients with melanoma and other malignancies. Although monotherapy with PD-1 or PD-L1 agents are typically well tolerated, the risk of immune-related adverse events increases with combination regimens. The development of predictive biomarkers is needed to optimise patient benefit, minimise risk of toxicities, and guide combination approaches. The greatest focus has been on tumour-cell PD-L1 expression. Although PD-L1 positivity enriches for populations with clinical benefit, PD-L1 testing alone is insufficient for patient selection in most malignancies. In this Review, we discuss the status of PD-L1 testing and explore emerging data on new biomarker strategies with tumour-infiltrating lymphocytes, mutational burden, immune gene signatures, and multiplex immunohistochemistry. Future development of an effective predictive biomarker for checkpoint inhibitor-based immunotherapy will integrate multiple approaches for optimal characterisation of the immune tumour microenvironment.

Introduction

The immune system is important in cancer cell surveillance and elimination, and immune evasion of cancer cell populations by various mechanisms is considered one of the hallmarks of cancer.1 The cancer immunity cycle described by Chen and Mellman2 describes the foundation for strategies involved in augmenting antitumour immune responses. These strategies include steps such as: cancer antigen release and presentation by dendritic cells, priming and activation of peripheral immune cells, trafficking and infiltration of T cells to the tumour compartment, and tumour-cell recognition and immune-mediated cell death. The steps after priming and activation of peripheral immune cells result in what has been described as the T-cell inflamed phenotype, which includes the local production of chemokines, interferon signalling, and expansion of CD8+ cytotoxic T cells.3 Mechanisms of tolerance are common, such as upregulation of PD-L1 and IDO in response to interferon γ,4 which diminishes the ability for immune-mediated tumour eradication (figure). Immunotherapies are thought to be most effective in patients with this T-cell inflamed phenotype.

High-dose interleukin 2 and adoptive T-cell transfer have shown that durable clinical benefit can be achieved with immunotherapy in patients with advanced malignancies.5, 6 Focus has now shifted to targeted manipulation of immune checkpoints. The CTLA-4 antibody ipilimumab was the first approved checkpoint inhibitor after it improved overall survival in patients with advanced melanoma in two randomised phase 3 trials.7, 8 However, objective responses are low with ipilimumab monotherapy and 22% of patients with advanced melanoma survived at least 3 years after therapy, based on pooled data from past ipilimumab studies.9 Greater clinical activity has been shown in melanoma when either the PD-1 or PD-L1 checkpoint is targeted. The anti-PD-1 agents pembrolizumab and nivolumab are now approved by the US Food and Drug Administration (FDA) for patients with advanced unresectable melanoma and non-small-cell lung cancer (NSCLC). Objective responses are seen in 40–45% of patients with melanoma who were given pembrolizumab or nivolumab in the first-line setting and 20% of patients with NSCLC after failure of chemotherapy.10, 11, 12, 13, 14 Nivolumab is also FDA approved as second-line therapy for patients with metastatic renal-cell carcinoma, of whom 25% achieved an overall response.15 FDA approvals have been announced for nivolumab in patients with refractory Hodgkin's lymphoma and for the anti-PD-L1 agent atezolizumab in patients with advanced bladder cancer. Furthermore, promising clinical activity of these anti-PD-1 and anti-PD-L1 therapies, as well as the anti-PD-L1 agents durvalumab and avelumab, has now been shown in a wide range of solid tumour and haematological malignancies.16

The CheckMate 067 trial,13 which compared nivolumab plus ipilimumab with ipilimumab monotherapy and nivolumab monotherapy in patients with metastatic melanoma, confirmed higher antitumour activity with combination immune checkpoint blockade than monotherapy. In CheckMate 067, 181 (58%) of 314 patients given the combination regimen achieved objective responses, and progression-free survival was longer than that in the ipilimumab monotherapy and nivolumab monotherapy groups. Data emerging for combined therapy with nivolumab plus ipilimumab in other disease types, such as small-cell lung cancer and renal-cell carcinoma, have also shown enhanced clinical activity.17, 18 However, the risk of immune-related adverse events, such as dermatitis, colitis, and hepatitis, substantially increases with combination checkpoint blockade.13 In the CheckMate 067 trial, severe immune-related adverse events (grades 3 or 4) occurred in 172 (55%) of 313 patients given nivolumab plus ipilimumab, compared with 51 (16%) of 313 patients receiving nivolumab monotherapy, and 85 (27%) of 311 patients receiving ipilimumab monotherapy.13

The establishment of predictive biomarkers for checkpoint immunotherapy is therefore of utmost importance to maximise therapeutic benefit. One or more biomarker approaches that have high positive and negative predictive values are needed to assist oncologists in treatment recommendations for patients. Here, positive predictive value is referring to the number of correctly predicted responders or survivors divided by the total number of patients with a positive biomarker result, whereas negative predictive value is referring to the number of correctly predicted non-responders or non-survivors divided by the total number of patients with a negative biomarker result. Establishing predictive biomarkers is especially important for more aggressive treatment strategies, such as the nivolumab plus ipilimumab combination, in which the risk of severe (but manageable) toxicities is as high as the proportion of patients with an overall response. Biomarkers could be used to stratify patients between single-agent and combination immunotherapy or to prioritise when immunotherapy is given (first line vs salvage). Also, in patients predicted to not respond to current checkpoint immunotherapies, avoidance of unnecessary toxicities and use of alternative treatment strategies would have a major impact on patient care. So far, multiple biomarker strategies have emerged that focus on identifying aspects of the T-cell inflamed phenotype and so-called tumour foreignness (eg, mutational load, neoantigens) as approaches that are associated with clinical outcomes for anti-CTLA-4 and anti-PD-1 or anti-PD-L1 therapies. This Review investigates the progress of biomarkers as aids to checkpoint inhibitor immunotherapy in cancer (table).

Section snippets

PD-L1 expression

Direct assessment of PD-L1 expression on tumour cells is a logical biomarker for the prediction of treatment response to anti-PD-1 or anti-PD-L1 therapies. Initial data from the phase 1 study19 on the use of nivolumab in patients with melanoma, NSCLC, renal-cell carcinoma, prostate cancer, or colorectal cancer supported a potential role for measuring tumour-cell PD-L1 expression by immunohistochemistry on tumour biopsy specimens. Using a threshold of 5% PD-L1-positive tumour cells to define

Tumour-infiltrating lymphocytes

Lymphocyte infiltration in tumour biopsy samples has been associated with improved survival in retrospective studies of patients with a range of cancers such as colorectal cancer, melanoma, and NSCLC.40, 41, 42 Similarly, the presence of ectopic lymph node-like structures within solid tumour masses, such as colorectal cancer and melanoma metastases, might predict better patient survival.43 Data have also shown that patients with stage III NSCLC given chemoradiation have longer progression-free

T-cell receptor clonality

Tumeh and colleagues27 further investigated whether baseline tumour-infiltrating lymphocytes had a narrow T-cell receptor repertoire focused on a tumour-specific immune response and whether this narrow repertoire correlated with response to pembrolizumab. Next-generation sequencing was done on pretreatment melanoma tumours to capture all uniquely rearranged variable T-cell receptor β-chain regions. Of the 23 patients with available response and sequencing data receiving pembrolizumab treatment,

Mutational or neoantigen burden

Preclinical studies have identified neoantigens produced by somatic mutations in passenger genes of tumour cells as primary drivers of antitumour adaptive immune responses.45, 46 Rooney and colleagues47 showed that immune cytolytic activity, measured by intratumoural perforin 1 and granzyme B gene expression (presumably produced by effector lymphocytes), is associated with higher mutational count, and they predicted antigenic neoepitopes in a range of solid tumour malignancies. Their findings

Peripheral blood markers

Testing of peripheral blood markers is a non-invasive source of potential biomarkers in patients receiving immune checkpoint therapies. Although associations with clinical benefit and survival have been noted, none so far have been validated as predictive biomarkers in prospective studies. For ipilimumab studies, improved overall and progression-free survival was associated with baseline values including low absolute neutrophil count (<7500 cells/μL), low neutrophil to lymphocyte ratio (<3),

Immune gene signatures

A wider assessment of active innate and adaptive immune responses within the tumour microenvironment by gene expression profiling might effectively predict clinical benefit to checkpoint inhibitor strategies. A retrospective analysis38 of patients with advanced melanoma given ipilimumab in a phase 2 clinical trial (CA184004) provided evidence that gene expression profiling could indeed be a useful predictive biomarker. In this analysis,38 total RNA was extracted and analysed in 50 pretreatment

Multiplex immunohistochemistry

Direct assessment of both tumour and immune-cell phenotypes and their spatial relationships by multiplex immunohistochemistry techniques provides information on the immune state of the tumour microenvironment that might be superior or complementary to gene expression profiling. These techniques involve serial staining of tumour slides with individual primary antibodies for the proteins of interest and detection by either chromogenic or immunofluorescence methods.59 Current approaches allow for

Combined biomarker strategies

Strategies that combine two or more methods for capturing the immune status of the tumour microenvironment might be more effective as a composite predictive biomarker for immune checkpoint inhibitor therapy. High tumour PD-L1 expression can be present even when tumour-infiltrating lymphocyte counts are low, and tumours with high tumour-infiltrating lymphocyte density might not express PD-L1.61, 62 In both scenarios, clinical activity of anti-PD-1 or anti-PD-L1 monotherapy might be low but could

Conclusion

Thus far, use of PD-L1 immunohistochemistry alone has not been sufficient for ruling in or ruling out the use of anti-PD-1 or anti-PD-L1 expression-based therapies. Characterisation of the tumour microenvironment immune state needs to be improved, including the presence of recognised tumour antigens, effector T-cell function, and immune suppressive mechanisms. Because of the potential for redundancy, further investigation into the relationships between PD-1 and PD-L1 expression,

Search strategy and selection criteria

We identified references for this Review through searches of PubMed using the search terms “biomarker”, “predictive”, “mutation”, “tumor infiltrating lymphocytes”, “TCR repertoire”, “immunotherapy”, “PD-1”, “PD-L1”, and “CTLA-4”. Articles were also identified through searches of the authors' own files. Only papers and presentations or abstracts published in English between Jan 1, 2008, and June 30, 2016, were included for review. The final reference list was generated on the basis of

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