Evidence map›Paper›PMID 36699399›Full record

ArticleBioinformatics advances2022

MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach.

Giulia Cesaro, Mikele Milia, Giacomo Baruzzo, Giovanni Finco, Francesco Morandini, Alessio Lazzarini, Piergiorgio Alotto, Noel Filipe da Cunha Carvalho de Miranda, Zlatko Trajanoski, Francesca Finotello and 1 more

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Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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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

11 authors.

Giulia CesaroDepartment of Information Engineering, University of Padova, 35131 Padova, Italy.ORCID https://orcid.org/0000-0001-7971-963X
Mikele MiliaDepartment of Information Engineering, University of Padova, 35131 Padova, Italy.ORCID https://orcid.org/0000-0002-2386-5475
Giacomo BaruzzoDepartment of Information Engineering, University of Padova, 35131 Padova, Italy.ORCID https://orcid.org/0000-0001-6129-5007
Giovanni FincoDepartment of Information Engineering, University of Padova, 35131 Padova, Italy.ORCID https://orcid.org/0000-0002-1218-1924
Francesco MorandiniDepartment of Information Engineering, University of Padova, 35131 Padova, Italy.ORCID https://orcid.org/0000-0003-1040-8539
Alessio LazzariniDepartment of Information Engineering, University of Padova, 35131 Padova, Italy.
Piergiorgio AlottoDepartment of Industrial Engineering, University of Padova, 35131 Padova, Italy.
Noel Filipe da Cunha Carvalho de MirandaDepartment of Pathology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands.
Zlatko TrajanoskiBiocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Francesca FinotelloBiocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Barbara Di CamilloDepartment of Information Engineering, University of Padova, 35131 Padova, Italy.ORCID https://orcid.org/0000-0001-8415-4688

Funding

Austrian Science Fund FWF T 974
6 · The paper itself

Abstract

Motivation: Recently, several computational modeling approaches, such as agent-based models, have been applied to study the interaction dynamics between immune and tumor cells in human cancer. However, each tumor is characterized by a specific and unique tumor microenvironment, emphasizing the need for specialized and personalized studies of each cancer scenario. Results: We present MAST, a hybrid Multi-Agent Spatio-Temporal model which can be informed using a data-driven approach to simulate unique tumor subtypes and tumor-immune dynamics starting from high-throughput sequencing data. It captures essential components of the tumor microenvironment by coupling a discrete agent-based model with a continuous partial differential equations-based model.The application to real data of human colorectal cancer tissue investigating the spatio-temporal evolution and emergent properties of four simulated human colorectal cancer subtypes, along with their agreement with current biological knowledge of tumors and clinical outcome endpoints in a patient cohort, endorse the validity of our approach. Availability and implementation: MAST, implemented in Python language, is freely available with an open-source license through GitLab (https://gitlab.com/sysbiobig/mast), and a Docker image is provided to ease its deployment. The submitted software version and test data are available in Zenodo at https://dx.doi.org/10.5281/zenodo.7267745. Supplementary information: Supplementary data are available at

Identifiers

PMID36699399
PMCPMC9744439

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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.