Dataset Information

A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade.

ABSTRACT: Over the past decade, several immunotherapies have been approved for the treatment of melanoma. The most prominent of these are the immune checkpoint inhibitors, which are antibodies that block the inhibitory effects on the immune system by checkpoint receptors, such as CTLA-4, PD-1 and PD-L1. Preclinically, blocking these receptors has led to increased activation and proliferation of effector cells following stimulation and antigen recognition, and subsequently, more effective elimination of cancer cells. Translation from preclinical to clinical outcomes in solid tumors has shown the existence of a wide diversity of individual patient responses, linked to several patient-specific parameters. We developed a quantitative systems pharmacology (QSP) model that looks at the mentioned checkpoint blockade therapies administered as mono-, combo- and sequential therapies, to show how different combinations of specific patient parameters defined within physiological ranges distinguish different types of virtual patient responders to these therapies for melanoma. Further validation by fitting and subsequent simulations of virtual clinical trials mimicking actual patient trials demonstrated that the model can capture a wide variety of tumor dynamics that are observed in the clinic and can predict median clinical responses. Our aim here is to present a QSP model for combination immunotherapy specific to melanoma.

SUBMITTER: Milberg O

PROVIDER: S-EPMC6677731 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade.

Milberg Oleg O Gong Chang C Jafarnejad Mohammad M Bartelink Imke H IH Wang Bing B Vicini Paolo P Narwal Rajesh R Roskos Lorin L Popel Aleksander S AS

Scientific reports 20190802 1

Over the past decade, several immunotherapies have been approved for the treatment of melanoma. The most prominent of these are the immune checkpoint inhibitors, which are antibodies that block the inhibitory effects on the immune system by checkpoint receptors, such as CTLA-4, PD-1 and PD-L1. Preclinically, blocking these receptors has led to increased activation and proliferation of effector cells following stimulation and antigen recognition, and subsequently, more effective elimination of ca ...[more]

PMID: 31375756

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Project description:T cells have been recognized as core effectors for cancer immunotherapy. How to restore the anti-tumor ability of suppressed T cells or improve the lethality of cytotoxic T cells has become the main focus in immunotherapy. Bispecific antibodies, especially bispecific T cell engagers (TCEs), have shown their unique ability to enhance the patient's immune response to tumors by stimulating T cell activation and cytokine production in an MHC-independent manner. Antibodies targeting the checkpoint inhibitory molecules such as programmed cell death protein 1 (PD-1), PD-ligand 1 (PD-L1) and cytotoxic lymphocyte activated antigen 4 are able to restore the cytotoxic effect of immune suppressed T cells and have also shown durable responses in patients with malignancies. However, both types have their own limitations in treating certain cancers. Preclinical and clinical results have emphasized the potential of combining these two antibodies to improve tumor response and patients' survival. However, the selection and evaluation of combination partners clinically is a costly endeavor. In addition, despite advances made in immunotherapy, there are subsets of patients who are non-responders, and reliable biomarkers for different immunotherapies are urgently needed to improve the ability to prospectively predict patients' response and improve clinical study design. Therefore, mathematical and computational models are essential to optimize patient benefit, and guide combination approaches with lower cost and in a faster manner. In this study, we continued to extend the quantitative systems pharmacology (QSP) model we developed for a bispecific TCE to explore efficacy of combination therapy with an anti-PD-L1 monoclonal antibody in patients with colorectal cancer. Patient-specific response to TCE monotherapy, anti-PD-L1 monotherapy and the combination therapy were predicted using this model according to each patient's individual characteristics. Individual biomarkers for TCE monotherapy, anti-PD-L1 monotherapy and their combination have been determined based on the QSP model. Best treatment options for specific patients could be suggested based on their own characteristics to improve clinical trial efficiency. The model can be further used to assess plausible combination strategies for different TCEs and immune checkpoint inhibitors in different types of cancer.

Project description:Blocking the interaction between the programmed cell death (PD)-1 protein and one of its ligands, PD-L1, has been reported to have impressive antitumor responses. Therapeutics targeting this pathway are currently in clinical trials. Pembrolizumab and nivolumab are the first of this anti-PD-1 pathway family of checkpoint inhibitors to gain accelerated approval from the US Food and Drug Administration (FDA) for the treatment of ipilimumab-refractory melanoma. Nivolumab has been associated with improved overall survival compared with dacarbazine in patients with previously untreated wild-type serine/threonine-protein kinase B-raf proto-oncogene BRAF melanoma. Although the most mature data are in the treatment of melanoma, the FDA has granted approval of nivolumab for squamous cell lung cancer and the breakthrough therapy designation to immune- checkpoint inhibitors for use in other cancers: nivolumab, an anti-PD-1 monoclonal antibody, for Hodgkin lymphoma, and MPDL-3280A, an anti-PD-L1 monoclonal antibody, for bladder cancer and non-small cell lung cancer. Here we review the literature on PD-1 and PD-L1 blockade and focus on the reported clinical studies that have included patients with melanoma.PubMed was searched to identify relevant clinical studies of PD-1/PD-L1-targeted therapies in melanoma. A review of data from the current trials on clinicaltrial.gov was incorporated, as well as data presented in abstracts at the 2014 annual meeting of the American Society of Clinical Oncology, given the limited number of published clinical trials on this topic.The anti-PD-1 and anti-PD-L1 agents have been reported to have impressive antitumor effects in several malignancies, including melanoma. The greatest clinical activity in unselected patients has been seen in melanoma. Tumor expression of PD-L1 is a suggestive, but inadequate, biomarker predictive of response to immune-checkpoint blockade. However, tumors expressing little or no PD-L1 are less likely to respond to PD-1 pathway blockade. Combination checkpoint blockade with PD-1 plus cytotoxic T-lymphocyte antigen (CTLA)-4 blockade appears to improve response rates in patients who are less likely to respond to single-checkpoint blockade. Toxicity with PD-1 blocking agents is less than the toxicity with previous immunotherapies (eg, interleukin 2, CTLA-4 blockade). Certain adverse events can be severe and potentially life threatening, but most can be prevented or reversed with close monitoring and appropriate management.This family of immune-checkpoint inhibitors benefits not only patients with metastatic melanoma but also those with historically less responsive tumor types. Although a subset of patients responds to single-agent blockade, the initial trial of checkpoint-inhibitor combinations has reported a potential to improve response rates. Combination therapies appear to be a means of increasing response rates, albeit with increased immune-related adverse events. As these treatments become available to patients, education regarding the recognition and management of immune-related effects of immune-checkpoint blockade will be essential for maximizing clinical benefit.

Project description:ImportancePD-L1 (programmed cell death ligand 1) immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), and multiplex immunohistochemistry/immunofluorescence (mIHC/IF) assays have been used to assess pretreatment tumor tissue to predict response to anti-PD-1/PD-L1 therapies. However, the relative diagnostic performance of these modalities has yet to be established.ObjectiveTo compare studies that assessed the diagnostic accuracy of PD-L1 IHC, TMB, GEP, and mIHC/IF in predicting response to anti-PD-1/PD-L1 therapy.Evidence reviewA search of PubMed (from inception to June 2018) and 2013 to 2018 annual meeting abstracts from the American Association for Cancer Research, American Society of Clinical Oncology, European Society for Medical Oncology, and Society for Immunotherapy of Cancer was conducted to identify studies that examined the use of PD-L1 IHC, TMB, GEP, and mIHC/IF assays to determine objective response to anti-PD-1/PD-L1 therapy. For PD-L1 IHC, only clinical trials that resulted in US Food and Drug Administration approval of indications for anti-PD-1/PD-L1 were included. Studies combining more than 1 modality were also included. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed. Two reviewers independently extracted the clinical outcomes and test results for each individual study.Main outcomes and measuresSummary receiver operating characteristic (sROC) curves; their associated area under the curve (AUC); and pooled sensitivity, specificity, positive and negative predictive values (PPV, NPV), and positive and negative likelihood ratios (LR+ and LR-) for each assay modality.ResultsTumor specimens representing over 10 different solid tumor types in 8135 patients were assayed, and the results were correlated with anti-PD-1/PD-L1 response. When each modality was evaluated with sROC curves, mIHC/IF had a significantly higher AUC (0.79) compared with PD-L1 IHC (AUC, 0.65, P < .001), GEP (AUC, 0.65, P = .003), and TMB (AUC, 0.69, P = .049). When multiple different modalities were combined such as PD-L1 IHC and/or GEP + TMB, the AUC drew nearer to that of mIHC/IF (0.74). All modalities demonstrated comparable NPV and LR-, whereas mIHC/IF demonstrated higher PPV (0.63) and LR+ (2.86) than the other approaches.Conclusions and relevanceIn this meta-analysis, tumor mutational burden, PD-L1 IHC, and GEP demonstrated comparable AUCs in predicting response to anti-PD-1/PD-L1 treatment. Multiplex immunohistochemistry/IF and multimodality biomarker strategies appear to be associated with improved performance over PD-L1 IHC, TMB, or GEP alone. Further studies with mIHC/IF and composite approaches with a larger number of patients will be required to confirm these findings. Additional study is also required to determine the most predictive analyte combinations and to determine whether biomarker modality performance varies by tumor type.

Dataset Information

A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade.

Publications

A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade.

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