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Anti-PD-1/Anti-PD-L1 Drugs and Radiation Therapy: Combinations and Optimization Strategies

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Although immune checkpoint blockade has yielded unprecedented and durable responses in cancer patients, the efficacy of this treatment remains limited. Radiation therapy can induce immunogenic cell death that contributes to the local efficacy of irradiation. However, radiation-induced systemic responses are scarce. Studies combining radiation with checkpoint inhibitors suggest a synergistic potential of this strategy. In this review, we focused on parameters that can be optimized to enhance the anti-tumor immune response that results from this association, in order to achieve data on dose, fractionation, target volume, lymph nodes sparing, radiation particles, and other immunomodulatory agents. These factors should be considered in future trials for better clinical outcomes. To this end, we discussed the main preclinical and clinical data available to optimize the efficacy of the treatment combination.

Abstract

Immune checkpoint inhibitors have been associated with long-term complete responses leading to improved overall survival in several cancer types. However, these novel immunotherapies are only effective in a small proportion of patients, and therapeutic resistance represents a major limitation in clinical practice. As with chemotherapy, there is substantial evidence that radiation therapy promotes anti-tumor immune responses that can enhance systemic responses to immune checkpoint inhibitors. In this review, we discuss the main preclinical and clinical evidence on strategies that can lead to an enhanced response to PD-1/PD-L1 blockade in combination with radiation therapy. We focused on central issues in optimizing radiation therapy, such as the optimal dose and fractionation for improving the therapeutic ratio, as well as the impact on immune and clinical responses of dose rate, target volume, lymph nodes irradiation, and type of radiation particle. We explored the addition of a third immunomodulatory agent to the combination such as other checkpoint inhibitors, chemotherapy, and treatment targeting the tumor microenvironment components. The strategies described in this review provide a lead for future clinical trials.

SUBMITTER: Boustani J

PROVIDER: S-EPMC8508444 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:OBJECTIVE:To evaluate rates of serious organ specific immune-related adverse events, general adverse events related to immune activation, and adverse events consistent with musculoskeletal problems for anti-programmed cell death 1 (PD-1) drugs overall and compared with control treatments. DESIGN:Systematic review and meta-analysis. DATA SOURCES:Medline, Embase, Cochrane Library, Web of Science, and Scopus searched to 16 March 2017 and combined with data from ClinicalTrials.gov. STUDY SELECTION:Eligible studies included primary clinical trial data on patients with cancer with recurrent or metastatic disease. DATA EXTRACTION:Three independent investigators extracted data on adverse events from ClinicalTrials.gov and the published studies. Risk of bias was assessed using the Cochrane tool by three independent investigators. RESULTS:13 relevant studies were included; adverse event data were available on ClinicalTrials.gov for eight. Studies compared nivolumab (n=6), pembrolizumab (5), or atezolizumab (2) with chemotherapy (11), targeted drugs (1), or both (1). Serious organ specific immune-related adverse events were rare, but compared with standard treatment, rates of hypothyroidism (odds ratio 7.56, 95% confidence interval 4.53 to 12.61), pneumonitis (5.37, 2.73 to 10.56), colitis (2.88, 1.30 to 6.37), and hypophysitis (3.38, 1.02 to 11.08) were increased with anti-PD-1 drugs. Of the general adverse events related to immune activation, only the rate of rash (2.34, 2.73 to 10.56) increased. Incidence of fatigue (32%) and diarrhea (19%) were high but similar to control. Reporting of adverse events consistent with musculoskeletal problems was inconsistent; rates varied but were over 20% in some studies for arthraligia and back pain. CONCLUSIONS:Organ specific immune-related adverse events are uncommon with anti-PD-1 drugs but the risk is increased compared with control treatments. General adverse events related to immune activation are largely similar. Adverse events consistent with musculoskeletal problems are inconsistently reported but adverse events may be common.

Project description:BackgroundPD-L1 is an immune inhibitory receptor ligand that leads to T cell dysfunction and apoptosis by binding to its receptor PD-1, which works in braking inflammatory response and conspiring tumor immune evasion. However, in gliomas, the cause of PD-L1 expression in the tumor microenvironment is not yet clear. Besides, auxiliary biomarkers are urgently needed for screening possible responsive glioma patients for anti-PD-1/PD-L1 therapies.MethodsThe distribution of tumor-infiltrating T cells and PD-L1 expression was analyzed via immunofluorescence in orthotopic murine glioma model. The expression of PD-L1 in immune cell populations was detected by flow cytometry. Data excavated from TCGA LGG/GBM datasets and the Ivy Glioblastoma Atlas Project was used for in silico analysis of the correlation among genes and survival.ResultsThe distribution of tumor-infiltrating T cells and PD-L1 expression, which parallels in murine orthotopic glioma model and human glioma microdissections, was interrelated. The IFN-γ level was positively correlated with PD-L1 expression in murine glioma. Further, IFN-γ induces PD-L1 expression on primary cultured microglia, bone marrow-derived macrophages, and GL261 glioma cells in vitro. Seven IFN-γ-induced genes, namely GBP5, ICAM1, CAMK2D, IRF1, SOCS3, CD44, and CCL2, were selected to calculate as substitute indicator for IFN-γ level. By combining the relative expression of the listed IFN-γ-induced genes, IFN-γ score was positively correlated with PD-L1 expression in different anatomic structures of human glioma and in glioma of different malignancies.ConclusionOur study identified the distribution of tumor-infiltrating T cells and PD-L1 expression in murine glioma model and human glioma samples. And we found that IFN-γ is an important cause of PD-L1 expression in the glioma microenvironment. Further, we proposed IFN-γ score aggregated from the expressions of the listed IFN-γ-induced genes as a complementary prognostic indicator for anti-PD-1/PD-L1 therapy.

Project description:BACKGROUND:Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies. METHODS:A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx. RESULTS:The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1. CONCLUSIONS:This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.

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Anti-PD-1/Anti-PD-L1 Drugs and Radiation Therapy: Combinations and Optimization Strategies

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