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Parameter estimation for multistage clonal expansion models from cancer incidence data: A practical identifiability

Andrew F Brouwer1, Rafael Meza1, Marisa C Eisenberg1

  • 1Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America.

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Multistage clonal expansion models help study cancer development. This research shows some cancer model parameters are practically unidentifiable with real data, but reparameterization can improve estimation.

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Area of Science:

  • Cancer epidemiology
  • Mathematical modeling of carcinogenesis
  • Computational biology

Background:

  • Multistage clonal expansion (MSCE) models describe cancer as a multi-hit process.
  • These models are crucial for understanding cancer risk, carcinogen impact, and prevention strategies.
  • Previous studies explored the structural identifiability of MSCE models, but practical identifiability with real data remains a challenge.

Purpose of the Study:

  • To investigate the practical identifiability of two-, three-, and four-stage MSCE models using pancreatic cancer incidence data.
  • To assess limitations of real-world data in identifying key model parameters.
  • To propose solutions for parameter estimation challenges in MSCE models.

Main Methods:

  • Utilized age-specific pancreatic cancer incidence data.
  • Employed a numerical profile-likelihood approach to assess practical identifiability.
  • Examined two-, three-, and four-stage clonal expansion models.

Main Results:

  • Demonstrated that several theoretically identifiable parameters in three- and four-stage models are practically unidentifiable with real data.
  • Identified that intermediate cell mutation rates are often not individually identifiable and lead to unstable parameter estimation.
  • Showed that products of unidentifiable parameters are identifiable, leading to new model reparameterizations.

Conclusions:

  • Practical identifiability is crucial for interpreting MSCE model parameter estimates, going beyond theoretical structural identifiability.
  • New reparameterizations of model hazards can resolve practical parameter estimation problems.
  • This work enhances the reliability of MSCE models in cancer research.