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

A computational model of MGUS progression to Multiple Myeloma identifies optimum screening strategies and their effects on mortality

View ORCID ProfilePhilipp M. Altrock, Jeremy Ferlic, View ORCID ProfileTobias Galla, View ORCID ProfileMichael H. Tomasson, Franziska Michor
doi: https://doi.org/10.1101/208645
Philipp M. Altrock
1Department of Integrated Mathematical Oncology, Department of Malignant Hematology, and Department of Blood and Marrow Transplantation and Cellular Immunotherapy, Moffitt Cancer Center and Research Institute, Tampa, FL33612, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Philipp M. Altrock
  • For correspondence: philipp.altrock@moffitt.org
Jeremy Ferlic
2Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tobias Galla
3Theoretical Physics, The University of Manchester, Manchester M13 9PL, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tobias Galla
Michael H. Tomasson
4Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, The University of Iowa, Iowa City, IA 52242, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Michael H. Tomasson
Franziska Michor
2Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
5Department of Stem Cell and Regenerative Biology, Harvard University, and The Broad Institute of Harvard and MIT, Cambridge, MA 02138, USA
6Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

Recent advances uncovered therapeutic interventions that might reduce the risk of progression of premalignant diagnoses, such as from Monoclonal Gammopathy of Undetermined Significance (MGUS) to multiple myeloma (MM). It remains unclear how to best screen populations at risk and how to evaluate the ability of these interventions to reduce disease prevalence and mortality at the population level. To address these questions, we developed a computational modeling framework. We used individual-based computational modeling of MGUS incidence and progression across a population of diverse individuals, to determine best screening strategies in terms of screening start, intervals, and risk-group specificity. Inputs were life tables, MGUS incidence and baseline MM survival. We measured MM-specific mortality and MM prevalence following MGUS detection from simulations and mathematical precition modeling. We showed that our framework is applicable to a wide spectrum of screening and intervention scenarios, including variation of the baseline MGUS to MM progression rate and evolving MGUS, in which progression increases over time. Given the currently available progression risk-point estimate of 61% risk, starting screening at age 55 and follow-up screening every 6yrs reduced total MM prevalence by 19%. The same reduction could be achieved with starting age 65 and follow-up every 2yrs. A 40% progression risk reduction per MGUS patient per year would reduce MM-specific mortality by 40%. Generally, age of screening onset and frequency impact disease prevalence, progression risk reduction impacts both prevalence and disease-specific mortality, and screeenign would generally be favorable in high-risk individuals. Screening efforts should focus on specifically identified groups of high lifetime risk of MGUS, for which screening benefits can be significant. Screening low-risk MGUS individuals would require improved preventions.

Footnotes

  • Financial Support: This work was supported by Deutsche Akademie der Naturforscher Leopoldina, grant number LPDS 2012-12 (to P.M.A. for work at Harvard University), the Dana-Farber Cancer Institute Physical Sciences-Oncology Center, NCI U54CA193461 (to F.M.), and by support by the Engineering and Physical Sciences Research Council (EPSRC), grant reference EP/K037145/1 (to T.G). P.M.A. also acknowledges generous support from the Moffitt Cancer Center and Research Institute.

  • The authors declare no competing financial interests.

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-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted October 25, 2017.
Download PDF

Supplementary Material

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 computational model of MGUS progression to Multiple Myeloma identifies optimum screening strategies and their effects on mortality
(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 computational model of MGUS progression to Multiple Myeloma identifies optimum screening strategies and their effects on mortality
Philipp M. Altrock, Jeremy Ferlic, Tobias Galla, Michael H. Tomasson, Franziska Michor
bioRxiv 208645; doi: https://doi.org/10.1101/208645
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A computational model of MGUS progression to Multiple Myeloma identifies optimum screening strategies and their effects on mortality
Philipp M. Altrock, Jeremy Ferlic, Tobias Galla, Michael H. Tomasson, Franziska Michor
bioRxiv 208645; doi: https://doi.org/10.1101/208645

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

  • Epidemiology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4113)
  • Biochemistry (8816)
  • Bioengineering (6519)
  • Bioinformatics (23463)
  • Biophysics (11791)
  • Cancer Biology (9209)
  • Cell Biology (13324)
  • Clinical Trials (138)
  • Developmental Biology (7439)
  • Ecology (11410)
  • Epidemiology (2066)
  • Evolutionary Biology (15151)
  • Genetics (10438)
  • Genomics (14044)
  • Immunology (9171)
  • Microbiology (22155)
  • Molecular Biology (8812)
  • Neuroscience (47570)
  • Paleontology (350)
  • Pathology (1428)
  • Pharmacology and Toxicology (2491)
  • Physiology (3730)
  • Plant Biology (8081)
  • Scientific Communication and Education (1437)
  • Synthetic Biology (2221)
  • Systems Biology (6038)
  • Zoology (1253)