Classical JAK2V617F+ Myeloproliferative Neoplasms emergence and development based on real life incidence and mathematical modeling

TL;DR

This work develops a two-phase, age-dependent framework to explain the emergence and progression of JAK2V617F+ MPN (ET and PV) using registry data from the Côte d'Or region and the FIMBANK cohort. The active mutation rate follows Gompertz aging, with a mean time to mutation emergence of $T_1 \approx 63$ years and a fixed expansion time $T_2 \approx 8.8$ years, yielding a mean detection age around $76$; PV shows about $1.5$ years longer $T_1$ than ET, while $T_2$ is similar. Model selection and validation favor the Gompertz-age-dependent mutation rate with a fixed expansion time, rejecting age-independent and Weibull forms. The findings link mutation emergence to aging processes and suggest screening strategies for older adults with JAK2V617F clonal hematopoiesis, highlighting an 8–9 year window from mutation to clinical MPN diagnosis.

Abstract

Mathematical modeling allows us to better understand myeloproliferative neoplasms (MPN), a group of blood cancers, emergence and development. We test different mathematical models on an initial cohort to determine the emergence and evolution times before diagnosis of JAK2V617F+ classical MPN (Polycythemia Vera (PV) and Essential Thrombocythemia (ET)). We consider the time before diagnosis as the sum of two independent periods: the time (from embryonic development) for the JAK2V617F mutation to occur, not disappear and enter proliferation, and a second time corresponding to the expansion of the clonal population until diagnosis. We prove that the rate of active mutation occurrence increases exponentially with age following the Gompertz model rather than being constant. We find that the first tumorous cell takes an average time of $63.1 \pm 13$ years to appear and start proliferation. On the other hand, the expansion time is constant: $8.8$ years once the mutation has emerged. These results are validated in an external cohort. Using this model, we analyze JAK2V617F ET versus PV, and obtain that the time of active mutation occurrence for PV takes approximately $1.5$ years more than for ET to develop, while the expansion time was similar. In conclusion, our age-dependent approach for the emergence and development of MPN demonstrates that the emergence of a JAKV617F mutation should be linked to an aging mechanism, and indicates a $8-9$ years period of time to develop a full MPN.

Figures (5)

• Figure 1: A: Incidence by age of JAK2V617F Classical MPN (ET and PV, excluding myelofibrosis) in the Cote d’Or Regional Registry Database. B: Incidence by age of JAK2V617F classical MPN (ET and PV, excluding myelofibrosis) in the Cote d’Or Regional Registry Database after adjustment excluding other causes of death before the MPN diagnosis. C: Incidence by age of JAK2V617F Classical MPN (ET and PV, excluding myelofibrosis) in the National BCB-FIMBANK Cohort. D: Incidence by age of JAK2V617F classical MPN (ET and PV, excluding myelofibrosis) in the National BCB-FIMBANK Cohort after adjustment excluding other causes of death before the MPN diagnosis.
• Figure 2: A: Frequency of cases for the data (in black) and probability density function of the estimation (in red) for the model with aging \ref{['eq:mod1']}. B: Accumulated frequencies of cases for the data (black) and cumulative distribution of the estimation (in red) for the model with aging \ref{['eq:mod1']}. C: Frequency of cases for the data (in black) and probability density function of the estimation (in red) for the model with aging and lognormal MPN growing time \ref{['eq:mod2']}D: Accumulated frequencies of cases for the data (black) and cumulative distribution of the estimation (in red) for the model with aging and lognormal MPN growing time \ref{['eq:mod2']}.
• Figure 3: A: Incidence by age of JAK2V617F Classical MPN (ET and PV, excluding myelofibrosis) in the National BCB-FIMBANK Cohort. B: Incidence by age of JAK2V617F classical MPN (ET and PV, excluding myelofibrosis) in the National BCB-FIMBANK Cohort after adjustment excluding other causes of death before the MPN diagnosis.
• Figure 4: A: Frequency of cases for the data (in black) and probability density function of the estimation (in red) for the age-independent model (Model B.1 \ref{['eq:M31']}). B: Accumulated frequencies of cases for the data from the (black) and cumulative distribution of the estimation (in red) for the age-independent model (Model B.1 \ref{['eq:M31']}). C: Frequency of cases for the data (in black) and probability density function of the estimation (in red) for the age-independent model with lognormal MPN growing time (Model B.2 \ref{['eq:M32']}). D: Accumulated frequencies of cases for the data (in black) and cumulative density of the estimation (in red) for the age-independent model with lognormal MPN growing time (Model B.2 \ref{['eq:M32']}). E: Accumulated frequencies of cases for the data (in black) and $90\%$ confidence interval of the estimation (in red) for the age-independent model with individual variability (Model B.3 \ref{['eq:M33']})
• Figure 5: Frequencies of JAK2V617F ET (red line) and JAK2V617F PV (blue line) by age at diagnosis in the National BCB-FIMBANK Cohort