A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition
Authored by Paolo Vicini, Chang Gong, Bing Wang, Aleksander S Popel, Oleg Milberg, Rajesh Narwal, Lorin Roskos
Date Published: 2017
DOI: 10.1098/rsif.2017.0320
Sponsors:
United States National Institutes of Health (NIH)
Platforms:
C++
Model Documentation:
Other Narrative
Model Code URLs:
Model code not found
Abstract
When the immune system responds to tumour development, patterns of
immune infiltrates emerge, highlighted by the expression of immune
check-point-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.
Tags
systems biology
Microenvironment
progression
growth
Mathematical-model
Immunotherapy
In-situ dcis
Cell lung-cancer
Immuno-oncology
Immune checkpoint
Biomarker
Carcinoma
Nivolumab
Blockade