A Computational Model of Neoadjuvant PD-1 Inhibition in Non-Small Cell Lung Cancer

Jafarnejad, Mohammad; Gong, Chang; Gabrielson, Edward; Bartelink, Imke H.; Vicini, Paolo; Wang, Bing; Narwal, Rajesh; Roskos, Lorin; Popel, Aleksander S.

doi:10.1208/s12248-019-0350-x

Cited by 58 publications

(104 citation statements)

References 53 publications

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“…We also did not include lymphatic vessels, which play an important role in the immune response (1). Finally, calculating a partial rank correlation coefficient for the sensitivity of the solution to various model parameters can potentially improve the fidelity of the model when individual simulations can be run much faster than currently feasible (∼24 h per simulation; SI Appendix) (44,73). Nevertheless, the model is relatively comprehensive and reproduces complex, rich behaviors.…”

Section: Discussionmentioning

confidence: 99%

“…We hypothesize that a mathematical model that incorporates the known mechanisms of immune response as well as vascular and stroma normalization can explain the experimental observations reported in the literature and provide guidelines for its optimal use and for future experiments. Systems-level mathematical models for the prediction of anti-CTLA-4 and anti-PD-1/anti-PD-L1 response have been developed previously (43)(44)(45); however, they do not account for spatial variation in the TME. Here, based on our previous study (41) and the work of others (46,47), we developed a continuum mathematical modeling framework to account for interactions among different types of cancer cells, immune cells, tumor blood vessels, oxygen supply, and drug delivery.…”

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confidence: 99%

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Combining microenvironment normalization strategies to improve cancer immunotherapy

Mpekris

Voutouri

Baish

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

206

143

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Advances in immunotherapy have revolutionized the treatment of multiple cancers. Unfortunately, tumors usually have impaired blood perfusion, which limits the delivery of therapeutics and cytotoxic immune cells to tumors and also results in hypoxia-a hallmark of the abnormal tumor microenvironment (TME)-that causes immunosuppression. We proposed that normalization of TME using antiangiogenic drugs and/or mechanotherapeutics can overcome these challenges. Recently, immunotherapy with checkpoint blockers was shown to effectively induce vascular normalization in some types of cancer. Although these therapeutic approaches have been used in combination in preclinical and clinical studies, their combined effects on TME are not fully understood. To identify strategies for improved immunotherapy, we have developed a mathematical framework that incorporates complex interactions among various types of cancer cells, immune cells, stroma, angiogenic molecules, and the vasculature. Model predictions were compared with the data from five previously reported experimental studies. We found that low doses of antiangiogenic treatment improve immunotherapy when the two treatments are administered sequentially, but that high doses are less efficacious because of excessive vessel pruning and hypoxia. Stroma normalization can further increase the efficacy of immunotherapy, and the benefit is additive when combined with vascular normalization. We conclude that vessel functionality dictates the efficacy of immunotherapy, and thus increased tumor perfusion should be investigated as a predictive biomarker of response to immunotherapy.immunotherapy | vascular function | normalization | anti-angiogenic therapy | mechanotherapeutics

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Combining microenvironment normalization strategies to improve cancer immunotherapy

Mpekris

Voutouri

Baish

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

206

143

View full text Add to dashboard Cite

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“…To make clear the relationship between ICGs and other biomarkers for immune checkpoint treatment, we analyzed the relationship between ICGs and tumor mutational burden (TMB), MMRs, and neoantigens, respectively. TMB is a reliable indicator for predicting clinical efficacy of PD-1 inhibitor [24]. The more mutant genes in the tumor tissue, the more likely abnormal proteins will be largely produced.…”

Section: Discussionmentioning

confidence: 99%

Identification of immune prognostic signature of lung carcinoma based on Genome-wide immune expression profile

Ling

Zhao

et al. 2020

Preprint

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Background : Lung cancer is one of the most common types of cancer with low early diagnosis rate and poor prognosis. The integration of immune checkpoint gene expression data and patient prognosis information can help identify the immune subtypes of lung cancer and provide reference for individualized gene immunotherapy in patients with lung cancer. Methods : The data of immune gene expression for lung cancer patients were obtained from TCGA and GEO databases. The relationship between the expressions of 45 immune checkpoint genes (ICGs) and prognosis were analysed. In the other hand, the correlation between the expressions of 45 biomarkers , tumor mutation load (TMB), MMRs, neoantigens and other immunotherapy biomarkers were been identified. Ultimately, prognosis-related ICGs were combined with IDO1, CD274, and CTLA4 to divide lung cancer immune subgroups and the prognostic differences between lung cancer immune subgroups were identified. Results: Based on TCGA database and GEO database, 9 and 11 ICGs were obtained respectively, which were closely related to prognosis. There was a certain synergistic relationship between them. The expression of CD200R1 had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68 and GZMB genes, indicating that it may cause the expression disorder of adaptive immune resistance pathway genes. Based on CD200R1 and combination with IDO1, CD274 and CTLA4, the group with high expression of CD200R1 and low expression of IDO1, CD274 and CTLA4 had the best prognosis among the immune subtypes. Conclusion : Our research provides a method of integrating immune checkpoint gene expression profile and clinical prognosis information to identify immune subtypes of lung cancer, which can provide a unique reference for gene immunotherapy of lung cancer patients.

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“…The proposed QSP model has a general structure similar to the model introduced in our previous studies (Jafarnejad et al, 2019;Wang et al, 2019). It comprises four compartments: central, peripheral, tumor, and tumor-draining lymph node (TDLN).…”

Section: Model Overviewmentioning

confidence: 99%

“…Since the characteristics of patients who are likely to benefit from epigenetic modulation are still unknown, we propose an expanded quantitative systems pharmacology (QSP) model based on our previous steps (Jafarnejad et al, 2019;Milberg et al, 2019;Wang et al, 2019). It is built with a detailed MDSC module and pharmacokinetics and pharmacodynamics of entinostat, to investigate the effect of entinostat and its combination with nivolumab and ipilimumab by conducting an in silico virtual clinical trial.…”

Section: Introductionmentioning

confidence: 99%

Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model With an Epigenetic Modulator and Immune Checkpoint Inhibitors

Wang

Sové

Jafarnejad

et al. 2020

Front. Bioeng. Biotechnol.

Self Cite

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The survival rate of patients with breast cancer has been improved by immune checkpoint blockade therapies, and the efficacy of their combinations with epigenetic modulators has shown promising results in preclinical studies. In this prospective study, we propose an ordinary differential equation (ODE)-based quantitative systems pharmacology (QSP) model to conduct an in silico virtual clinical trial and analyze potential predictive biomarkers to improve the anti-tumor response in HER2-negative breast cancer. The model is comprised of four compartments: central, peripheral, tumor, and tumor-draining lymph node, and describes immune activation, suppression, T cell trafficking, and pharmacokinetics and pharmacodynamics (PK/PD) of the therapeutic agents. We implement theoretical mechanisms of action for checkpoint inhibitors and the epigenetic modulator based on preclinical studies to investigate their effects on antitumor response. According to model-based simulations, we confirm the synergistic effect of the epigenetic modulator and that pre-treatment tumor mutational burden, tumor-infiltrating effector T cell (Teff) density, and Teff to regulatory T cell (Treg) ratio are significantly higher in responders, which can be potential biomarkers to be considered in clinical trials. Overall, we present a readily reproducible modular model to conduct in silico virtual clinical trials on patient cohorts of interest, which is a step toward personalized medicine in cancer immunotherapy.

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A Computational Model of Neoadjuvant PD-1 Inhibition in Non-Small Cell Lung Cancer

Cited by 58 publications

References 53 publications

Combining microenvironment normalization strategies to improve cancer immunotherapy

Combining microenvironment normalization strategies to improve cancer immunotherapy

Identification of immune prognostic signature of lung carcinoma based on Genome-wide immune expression profile

Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model With an Epigenetic Modulator and Immune Checkpoint Inhibitors

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