A Spatio-Temporal Model of Macrophage-Mediated Drug Resistance in Glioma Immunotherapy

Zheng, Yu; Bao, Jiguang; Zhao, Qiyi; Sun, Xiaoqiang

doi:10.1158/1535-7163.mct-17-0634

Cited by 24 publications

(18 citation statements)

References 49 publications

(58 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, T‐cell mediated IL‐4 production following CSF1R inhibition induces macrophage‐mediated secretion of IGF‐1 that in turn activates intratumoral PI3K signaling (via glioma‐expressed IGF‐1 receptor) and the subsequent survival and growth of glioma cells . Furthermore, this mechanism of drug resistance was subsequently validated and quantitatively described through spatio‐temporal mathematical modeling using input from molecular networks involved in myeloid‐glioma crosstalk signaling . Our findings shed new light on the molecular nodes and signaling processes that underlie monocytes‐to‐M2 differentiation and myeloid‐tumor crosstalk, and provides a better understanding of myeloid‐mediated drug resistance mechanisms and facilitates the design of future immunotherapy strategies.…”

Section: Discussionmentioning

confidence: 71%

Dissecting intratumoral myeloid cell plasticity by single cell RNA‐seq

et al. 2019

View full text Add to dashboard Cite

Tumor‐infiltrating myeloid cells are the most abundant leukocyte population within tumors. Molecular cues from the tumor microenvironment promote the differentiation of immature myeloid cells toward an immunosuppressive phenotype. However, the in situ dynamics of the transcriptional reprogramming underlying this process are poorly understood. Therefore, we applied single cell RNA‐seq (scRNA‐seq) to computationally investigate the cellular composition and transcriptional dynamics of tumor and adjacent normal tissues from 4 early‐stage non‐small cell lung cancer (NSCLC) patients. Our scRNA‐seq analyses identified 11 485 cells that varied in identity and gene expression traits between normal and tumor tissues. Among these, myeloid cell populations exhibited the most diverse changes between tumor and normal tissues, consistent with tumor‐mediated reprogramming. Through trajectory analysis, we identified a differentiation path from CD14+ monocytes to M2 macrophages (monocyte‐to‐M2). This differentiation path was reproducible across patients, accompanied by increased expression of genes (eg, MRC1/CD206, MSR1/CD204, PPARG, TREM2) with significantly enriched functions (Oxidative phosphorylation and P53 pathway) and decreased expression of genes (eg, CXCL2, IL1B) with significantly enriched functions (TNF‐α signaling via NF‐κB and inflammatory response). Our analysis further identified a co‐regulatory network implicating upstream transcription factors (JUN, NFKBIA) in monocyte‐to‐M2 differentiation, and activated ligand‐receptor interactions (eg, SFTPA1‐TLR2, ICAM1‐ITGAM) suggesting intratumoral mechanisms whereby epithelial cells stimulate monocyte‐to‐M2 differentiation. Overall, our study identified the prevalent monocyte‐to‐M2 differentiation in NSCLC, accompanied by an intricate transcriptional reprogramming mediated by specific transcriptional activators and intercellular crosstalk involving ligand‐receptor interactions.

show abstract

Section: Discussionmentioning

confidence: 71%

Dissecting intratumoral myeloid cell plasticity by single cell RNA‐seq

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Several studies have reported that the tumor microenvironment plays important roles in glioma progression and therapeutic response [10–12, 28–31]. TAMs, a type of immune cell, account for the majority of nonneoplastic cells in the glioma microenvironment [26].…”

Section: Discussionmentioning

confidence: 99%

Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas

Sun

Xia

et al. 2019

J Transl Med

Self Cite

View full text Add to dashboard Cite

Background The tumor-associated microenvironment plays important roles in tumor progression and drug resistance. However, systematic investigations of macrophage–tumor cell interactions to identify novel macrophage-related gene signatures in gliomas for predicting patient prognoses and responses to targeted therapies are lacking. Methods We developed a multicellular gene network approach to investigating the prognostic role of macrophage–tumor cell interactions in tumor progression and drug resistance in gliomas. Multicellular gene networks connecting macrophages and tumor cells were constructed from re-grouped drug-sensitive and drug-resistant samples of RNA-seq data in mice gliomas treated with BLZ945 (a CSF1R inhibitor). Subsequently, a differential network-based COX regression model was built to identify the risk signature using a cohort of 310 glioma samples from the Chinese Glioma Genome Atlas database. A large independent validation set of 690 glioma samples from The Cancer Genome Atlas database was used to test the prognostic significance and accuracy of the gene signature in predicting prognosis and targeted therapeutic response of glioma patients. Results A macrophage-related gene signature was developed consisting of twelve genes (ANPEP, DPP4, PRRG1, GPNMB, TMEM26, PXDN, CDH6, SCN3A, SEMA6B, CCDC37, FANCA, NETO2), which was tested in the independent validation set to examine its prognostic significance and accuracy. The generation of 1000 random gene signatures by a bootstrapping scheme justified the non-random nature of the macrophage-related gene signature. Moreover, the discovered gene signature was verified to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics and outperformed other existing gene signatures. Additionally, the macrophage-related gene signature was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures. Conclusion The multicellular gene network approach developed herein indicates profound roles of the macrophage-mediated tumor microenvironment in the progression and drug resistance of gliomas. The identified macrophage-related gene signature has good prognostic value for predicting resistance to targeted therapeutics and survival of glioma patients, implying that combining current targeted therapies with new macrophage-targeted therapy may be beneficial for the long-term treatment outcomes of glioma patients. Electronic supplementary material The online version of this article (10.1186/s12967-019-1908-1) contains supplementary material, which is available to authorized users.

show abstract

“…However, acquisition of drug resistance during CSF1R inhibition was observed in preclinical mouse models. To understand how resistance emerges during this therapy, Zheng et al 42 . developed a spatio‐temporal model of macrophage‐mediated drug resistance to understand the interplay between glioma cells and macrophages targeted by CSF1R inhibitors through CSF1 and IGF1 pathways.…”

Section: Legacy Models Of Cancer Immunobiologymentioning

confidence: 99%

Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

Chelliah¹,

Lazarou²,

Bhatnagar

et al. 2020

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

show abstract

A Spatio-Temporal Model of Macrophage-Mediated Drug Resistance in Glioma Immunotherapy

Cited by 24 publications

References 49 publications

Dissecting intratumoral myeloid cell plasticity by single cell RNA‐seq

Dissecting intratumoral myeloid cell plasticity by single cell RNA‐seq

Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas

Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

Contact Info

Product

Resources

About