TY - JOUR
T1 - A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition
AU - Gong, Chang
AU - Milberg, Oleg
AU - Wang, Bing
AU - Vicini, Paolo
AU - Narwal, Rajesh
AU - Roskos, Lorin
AU - Popel, Aleksander S.
N1 - Funding Information:
Windows 7 and Windows 10) with parameter file template can be accessed at: popellab.johnshopkins.edu/software/CancerImmu-neABM/. Authors’ contributions. C.G. formulated the model, performed the simulations, interpreted the data, made the figures and drafted the manuscript. B.W., P.V. and A.S.P. helped formulate the model, interpreted the data and edited the manuscript. R.N. and L.R. participated in the discussions on the model and results, and edited the manuscript. Competing interests. We declare we have no competing interests. Funding. This study is supported by a grant from MedImmune to JHU (A.S.P.) and by NIH grant no. R01CA138264 (A.S.P.). Acknowledgements. The authors thank Drs Janis Taube and Robert Anders, Department of Pathology at the Johns Hopkins School of Medicine for their advice on the methods for scoring histopathology slides. This work was partially presented at the American Association for Cancer Research (AACR) Annual Meeting, 1–5 April 2017 in Washington, DC, and was published in the conference proceedings as Abstract no. 975. The authors thank Drs Janis Taube, Robert Anders and Jonathan Powell for useful discussions.
Publisher Copyright:
© 2017 The Authors. Published by the Royal Society.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - When the immune system responds to tumour development, patterns of immune infiltrates emerge, highlighted by the expression of immune checkpoint- related molecules such as PDL1 on the surface of cancer cells. Such spatial heterogeneity carries information on intrinsic characteristics of the tumour lesion for individual patients, and thus is a potential source for biomarkers for anti-tumour therapeutics. We developed a systems biology multiscale agent-based model to capture the interactions between immune cells and cancer cells, and analysed the emergent global behaviour during tumour development and immunotherapy. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during tumour progression, as well as three-dimensional spatial distributions of these cells. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and antigen strength, a spectrum of pretreatment spatial patterns of PDL1 expression is generated in our simulations, resembling immuno-architectures obtained via immunohistochemistry from patient biopsies. By correlating these spatial characteristics with in silico treatment results using immune checkpoint inhibitors, the model provides a framework for use to predict treatment/biomarker combinations in different cancer types based on cancer-specific experimental data.
AB - When the immune system responds to tumour development, patterns of immune infiltrates emerge, highlighted by the expression of immune checkpoint- related molecules such as PDL1 on the surface of cancer cells. Such spatial heterogeneity carries information on intrinsic characteristics of the tumour lesion for individual patients, and thus is a potential source for biomarkers for anti-tumour therapeutics. We developed a systems biology multiscale agent-based model to capture the interactions between immune cells and cancer cells, and analysed the emergent global behaviour during tumour development and immunotherapy. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during tumour progression, as well as three-dimensional spatial distributions of these cells. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and antigen strength, a spectrum of pretreatment spatial patterns of PDL1 expression is generated in our simulations, resembling immuno-architectures obtained via immunohistochemistry from patient biopsies. By correlating these spatial characteristics with in silico treatment results using immune checkpoint inhibitors, the model provides a framework for use to predict treatment/biomarker combinations in different cancer types based on cancer-specific experimental data.
KW - Biomarker
KW - Immune checkpoint
KW - Immuno-oncology
KW - Immunotherapy
KW - Systems biology
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U2 - 10.1098/rsif.2017.0320
DO - 10.1098/rsif.2017.0320
M3 - Article
C2 - 28931635
AN - SCOPUS:85031100698
VL - 14
JO - Journal of the Royal Society Interface
JF - Journal of the Royal Society Interface
SN - 1742-5689
IS - 134
M1 - 20170320
ER -