Evidence map›Paper›PMID 29353950›Full record

ArticleMathematical models & methods in applied sciences : M3AS2018

A HYBRID THREE-SCALE MODEL OF TUMOR GROWTH.

H L Rocha, R C Almeida, E A B F Lima, A C M Resende, J T Oden, T E Yankeelov

Open access · greenAbstract read
In one paragraph

Article in Mathematical models & methods in applied sciences : M3AS, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
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

18 citing papers in PubMed, 44 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. A multi-layered hybrid model for cancer cell invasion.Medical & biological engineering & computing · 2022
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
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

6 authors at 2 institutions in 2 countries.

H L RochaNational Laboratory for Scientific Computing (LNCC), Av. Getúlio Vargas, 333, Quitandinha, Petrópolis, Rio de Janeiro, 25651-075, Brazil.
R C AlmeidaNational Laboratory for Scientific Computing (LNCC), Av. Getúlio Vargas, 333, Quitandinha, Petrópolis, Rio de Janeiro, 25651-075, Brazil.
E A B F LimaCenter of Computational Oncology, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, TX, 78712-1229, USA.
A C M ResendeNational Laboratory for Scientific Computing (LNCC), Av. Getúlio Vargas, 333, Quitandinha, Petrópolis, Rio de Janeiro, 25651-075, Brazil.
J T OdenCenter of Computational Oncology, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, TX, 78712-1229, USA.
T E YankeelovCenter of Computational Oncology, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, TX, 78712-1229, USA.
Laboratório Nacional de Computação Científica · BRThe University of Texas at Austin · US

Funding

Quantitative Multiscale Imaging to Optimize Cancer Treatment StrategiesR01CA186193 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, RERICHA, ERIN C · 2014 to 2019
$3.1M
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast CancerU01CA174706 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, YANKEELOV, THOMAS E · 2013 to 2018
$2.5M
NCI NIH HHS R01 CA186193NCI NIH HHS U01 CA174706
6 · The paper itself

Abstract

Cancer results from a complex interplay of different biological, chemical, and physical phenomena that span a wide range of time and length scales. Computational modeling may help to unfold the role of multiple evolving factors that exist and interact in the tumor microenvironment. Understanding these complex multiscale interactions is a crucial step towards predicting cancer growth and in developing effective therapies. We integrate different modeling approaches in a multiscale, avascular, hybrid tumor growth model encompassing tissue, cell, and sub-cell scales. At the tissue level, we consider the dispersion of nutrients and growth factors in the tumor microenvironment, which are modeled through reaction-diffusion equations. At the cell level, we use an agent based model (ABM) to describe normal and tumor cell dynamics, with normal cells kept in homeostasis and cancer cells differentiated apoptotic, hypoxic, and necrotic states. Cell movement is driven by the balance of a variety of forces according to Newton's second law, including those related to growth-induced stresses. Phenotypic transitions are defined by specific rule of behaviors that depend on microenvironment stimuli. We integrate in each cell/agent a branch of the epidermal growth factor receptor (EGFR) pathway. This pathway is modeled by a system of coupled nonlinear differential equations involving the mass laws of 20 molecules. The rates of change in the concentration of some key molecules trigger proliferation or migration advantage response. The bridge between cell and tissue scales is built through the reaction and source terms of the partial differential equations. Our hybrid model is built in a modular way, enabling the investigation of the role of different mechanisms at multiple scales on tumor progression. This strategy allows representating both the collective behavior due to cell assembly as well as microscopic intracellular phenomena described by signal transduction pathways. Here, we investigate the impact of some mechanisms associated with sustained proliferation on cancer progression. Specifically, we focus on the intracellular proliferation/migration-advantage-response driven by the EGFR pathway and on proliferation inhibition due to accumulation of growth-induced stresses. Simulations demonstrate that the model can adequately describe some complex mechanisms of tumor dynamics, including growth arrest in avascular tumors. Both the sub-cell model and growth-induced stresses give rise to heterogeneity in the tumor expansion and a rich variety of tumor behaviors.

Indexed as

Cell-agent based modelHybrid multiscale modelSignaling pathway

Identifiers

PMID29353950
PMCPMC5773147
OpenAlexW2765898593

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.