Order-of-mutation effects on cancer progression: models for myeloproliferative neoplasm

TL;DR

This paper introduces a modeling framework using nonlinear ODEs and Markov chains to analyze how the order of mutations affects cancer progression, specifically in myeloproliferative neoplasms.

Contribution

It presents a novel mathematical modeling approach to understand the impact of mutation order on disease progression and prognosis in cancer.

Findings

Models explain non-additive effects of mutation order on gene expression.

Framework accounts for differences in cell mutation proportions and age at diagnosis.

Proposes experiments to validate the models.

Abstract

We develop a modeling framework for cancer progression that distinguishes the order of two possible mutations. Recent observations and information on myeloproliferative neoplasms are analyzed within our framework. In some patients with myeloproliferative neoplasms, two genetic mutations can be found, JAK2 V617F and TET2. Whether or not one mutation is present will influence how the other subsequent mutation affects the regulation of gene expression. When both mutations are present, the order of their occurrence has been shown to influence disease progression and prognosis. In this paper, we propose a nonlinear ordinary differential equation (ODE) and Markov chain models to explain the non-additive and non-commutative clinical observations with respect to different orders of mutations: gene expression patterns, proportions of cells with different mutations, and ages at diagnosis. We also propose potential experiments measurements that can be used to verify our models.