Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A reduced Gompertz model for predicting tumor age using a population approach

View ORCID ProfileC. Vaghi, A. Rodallec, R. Fanciullino, J. Ciccolini, View ORCID ProfileJ. Mochel, M. Mastri, View ORCID ProfileC. Poignard, J. ML Ebos, View ORCID ProfileS. Benzekry
doi: https://doi.org/10.1101/670869
C. Vaghi
1MONC team, Inria Bordeaux Sud-Ouest, France
2Institut de Mathématiques de Bordeaux, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for C. Vaghi
A. Rodallec
3SMARTc, Center for Research on Cancer of Marseille, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
R. Fanciullino
3SMARTc, Center for Research on Cancer of Marseille, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
J. Ciccolini
3SMARTc, Center for Research on Cancer of Marseille, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
J. Mochel
4Iowa State University, Department of Biomedical Sciences, Ames, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for J. Mochel
M. Mastri
5Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
C. Poignard
1MONC team, Inria Bordeaux Sud-Ouest, France
2Institut de Mathématiques de Bordeaux, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for C. Poignard
J. ML Ebos
5Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
S. Benzekry
1MONC team, Inria Bordeaux Sud-Ouest, France
2Institut de Mathématiques de Bordeaux, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for S. Benzekry
  • For correspondence: sebastien.benzekry@inria.fr
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Tumor growth curves are classically modeled by ordinary differential equations. In analyzing the Gompertz model several studies have reported a striking correlation between the two parameters of the model.

We analyzed tumor growth kinetics within the statistical framework of nonlinear mixed-effects (population approach). This allowed for the simultaneous modeling of tumor dynamics and interanimal variability. Experimental data comprised three animal models of breast and lung cancers, with 843 measurements in 94 animals. Candidate models of tumor growth included the Exponential, Logistic and Gompertz. The Exponential and – more notably – Logistic models failed to describe the experimental data whereas the Gompertz model generated very good fits. The population-level correlation between the Gompertz parameters was further confirmed in our analysis (R2 > 0.96 in all groups). Combining this structural correlation with rigorous population parameter estimation, we propose a novel reduced Gompertz function consisting of a single individual parameter. Leveraging the population approach using bayesian inference, we estimated the time of tumor initiation using three late measurement timepoints. The reduced Gompertz model was found to exhibit the best results, with drastic improvements when using bayesian inference as compared to likelihood maximization alone, for both accuracy and precision. Specifically, mean accuracy was 12.1% versus 74.1% and mean precision was 15.2 days versus 186 days, for the breast cancer cell line.

These results offer promising clinical perspectives for the personalized prediction of tumor age from limited data at diagnosis. In turn, such predictions could be helpful for assessing the extent of invisible metastasis at the time of diagnosis.

Author summary Mathematical models for tumor growth kinetics have been widely used since several decades but mostly fitted to individual or average growth curves. Here we compared three classical models (Exponential, Logistic and Gompertz) using a population approach, which accounts for inter-animal variability. The Exponential and the Logistic models failed to fit the experimental data while the Gompertz model showed excellent descriptive power. Moreover, the strong correlation between the two parameters of the Gompertz equation motivated a simplification of the model, the reduced Gompertz model, with a single individual parameter and equal descriptive power. Combining the mixed-effects approach with Bayesian inference, we predicted the age of individual tumors with only few late measurements. Thanks to its simplicity, the reduced Gompertz model showed superior predictive power. Although our method remains to be extended to clinical data, these results are promising for the personalized estimation of the age of a tumor from limited measurements at diagnosis. Such predictions could contribute to the development of computational models for metastasis.

Footnotes

  • Figure 3 revised. Due to mistake in figure compilation, the model used for the VPC in previous version was not the reduced Gompertz.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted June 25, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A reduced Gompertz model for predicting tumor age using a population approach
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A reduced Gompertz model for predicting tumor age using a population approach
C. Vaghi, A. Rodallec, R. Fanciullino, J. Ciccolini, J. Mochel, M. Mastri, C. Poignard, J. ML Ebos, S. Benzekry
bioRxiv 670869; doi: https://doi.org/10.1101/670869
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
A reduced Gompertz model for predicting tumor age using a population approach
C. Vaghi, A. Rodallec, R. Fanciullino, J. Ciccolini, J. Mochel, M. Mastri, C. Poignard, J. ML Ebos, S. Benzekry
bioRxiv 670869; doi: https://doi.org/10.1101/670869

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Cancer Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4378)
  • Biochemistry (9569)
  • Bioengineering (7081)
  • Bioinformatics (24818)
  • Biophysics (12595)
  • Cancer Biology (9943)
  • Cell Biology (14332)
  • Clinical Trials (138)
  • Developmental Biology (7941)
  • Ecology (12091)
  • Epidemiology (2067)
  • Evolutionary Biology (15975)
  • Genetics (10913)
  • Genomics (14723)
  • Immunology (9856)
  • Microbiology (23615)
  • Molecular Biology (9471)
  • Neuroscience (50809)
  • Paleontology (369)
  • Pathology (1538)
  • Pharmacology and Toxicology (2677)
  • Physiology (4005)
  • Plant Biology (8651)
  • Scientific Communication and Education (1508)
  • Synthetic Biology (2388)
  • Systems Biology (6420)
  • Zoology (1345)