Evidence map›Paper›PMID 28931635›Full record

ArticleJournal of the Royal Society, Interface2017

A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition.

Chang Gong, Oleg Milberg, Bing Wang, Paolo Vicini, Rajesh Narwal, Lorin Roskos, Aleksander S Popel

Abstract read
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In one paragraph

Article in Journal of the Royal Society, Interface, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

94 citing papers in PubMed.

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34 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Chang GongDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA cgong5@jhu.edu.ORCID 0000-0002-1315-8638
Oleg MilbergDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Bing WangMedImmune, Mountain View, CA, USA.
Paolo ViciniMedImmune, Cambridge, UK.
Rajesh NarwalMedImmune, Gaithersburg, MD, USA.
Lorin RoskosMedImmune, Gaithersburg, MD, USA.
Aleksander S PopelDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Funding

Predictive experiment-based multiscale models of the tumor immune microenvironment and immunotherapy in breast cancerR01CA138264 · NCI · JOHNS HOPKINS UNIVERSITY · PI POPEL, ALEKSANDER S. · 2009 to 2023
$8.1M
NCI NIH HHS R01 CA138264
6 · The paper itself

Abstract

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

Indexed as

Models, ImmunologicalB7-H1 AntigenHumansNeoplasm ProteinsNeoplasmsProgrammed Cell Death 1 ReceptorT-LymphocytesB7-H1 AntigenCD274 protein, humanNeoplasm ProteinsPDCD1 protein, humanProgrammed Cell Death 1 Receptorbiomarkerimmune checkpointimmuno-oncologyimmunotherapysystems biology

Identifiers

PMID28931635
PMCPMC5636269

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.