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arxiv: 2308.09941 · v1 · submitted 2023-08-19 · 🧬 q-bio.MN · q-bio.PE

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

Yue Wang , Blerta Shtylla , Tom Chou This is my paper

Pith reviewed 2026-05-24 07:20 UTC · model grok-4.3

classification 🧬 q-bio.MN q-bio.PE
keywords mutationsmodelsmyeloproliferativeprogressioncancerdifferentexpressionframework
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The pith

Nonlinear ODE and Markov chain models explain how the order of JAK2 V617F and TET2 mutations produces distinct clinical outcomes in myeloproliferative neoplasms.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper constructs a modeling framework that treats mutation sequence as a distinguishing variable rather than treating the two mutations as interchangeable. Nonlinear ordinary differential equations track regulatory interactions that change depending on which mutation arrives first, while Markov chains follow the resulting shifts in cell-type proportions over time. These constructions are shown to reproduce three classes of order-dependent data: altered gene-expression profiles, different fractions of cells carrying one versus both mutations, and shifts in patient age at diagnosis. A sympathetic reader would care because the presence of mutations alone would then be insufficient for prognosis; the temporal sequence would become an independent driver of disease trajectory.

Core claim

The authors state that a coupled nonlinear ODE–Markov framework, built on the regulatory effects each mutation exerts on the other, accounts for the non-additive and non-commutative observations: gene-expression patterns differ according to order, the steady-state proportions of singly and doubly mutant cells are order-dependent, and the inferred progression rates produce measurably different ages at diagnosis for the two possible sequences of JAK2 V617F and TET2.

What carries the argument

Nonlinear ordinary differential equation and Markov chain models that encode order-dependent regulatory interactions between the JAK2 V617F and TET2 mutations.

If this is right

  • Different mutation orders produce measurably different steady-state gene-expression levels.
  • The fractions of cells carrying only JAK2, only TET2, or both mutations reach different equilibria depending on sequence.
  • The time to reach symptomatic cell burdens, and therefore the typical age at diagnosis, shifts with order.
  • Specific experimental measurements of gene expression and cell proportions are proposed to test the models directly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the models are accurate, determining mutation order at diagnosis could refine risk stratification beyond mutation presence alone.
  • The same order-sensitive regulatory logic could be tested in other pairs of driver mutations where clinical order effects have been reported.
  • Adding stochastic microenvironmental terms to the Markov chains would constitute a direct extension that preserves the paper’s core distinction between sequences.
  • keywords:[
  • myeloproliferative neoplasms
  • JAK2 V617F
  • TET2
  • mutation order

Load-bearing premise

The non-commutative clinical observations are assumed to arise from the regulatory interactions encoded in the models rather than from unmodeled factors such as additional mutations or patient-specific variables.

What would settle it

A cohort study that measures gene-expression levels and mutant-cell fractions in patients with both mutations and finds no statistically significant difference between the two possible orders would falsify the claim that the models capture the observed non-commutativity.

Figures

Figures reproduced from arXiv: 2308.09941 by Blerta Shtylla, Tom Chou, Yue Wang.

Figure 1
Figure 1. Figure 1: as a function of λ and show the high and low expression level branches. For this nondimensionalized model, when λ < 1.6, there is one stable, low-value fixed point at x ∗ . 0.8. If λ > 2.4, there is one stable fixed point x ∗ & 3.2 continued from the stable high-value branch. At intermediate values 1.6 < λ < 2.4, both values of x ∗ (high and low) are locally stable and are connected by an unstable middle b… view at source ↗
Figure 2
Figure 2. Figure 2: (b) for a schematic of this scenario [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) Schematic of a model that explains x ∗ O < x∗ J but x ∗ T > x∗ TJ. In this model, the steady-state expression levels of Y are y ∗ O ≈ 0.8, y ∗ J ≈ 3.2, y ∗ T ≈ 0.6, y ∗ TJ ≈ 0.8. The basal value of x = 1, while the different stationary expression levels of X are x ∗ O ≈ 1.8, x ∗ J ≈ 3.2, x ∗ T ≈ 1.6, x ∗ TJ ≈ 0.8. (b) The gene regulatory network that explains x ∗ O < x∗ J but x ∗ T > x∗ TJ for a number… view at source ↗
Figure 4
Figure 4. Figure 4: (a) A schematic of the model λ = 2 + 1J − 1T in Eq. 2 which explains x ∗ TJ < x∗ JT. If the effects of JAK2 and TET2 mutations towards the input λ are together greater than 0.4 (i.e., with JAK2 but not TET2), the system is forced to be on the high-x ∗ branch; if the contribution to λ input JAK2 and TET2 is smaller than −0.4 (i.e., with TET2 but not JAK2), the system ends up on the low-value branch. (b) The… view at source ↗
Figure 5
Figure 5. Figure 5: A schematic of the steps in our Moran process. At some time, the system contains sixteen wild-type cells and four JAK2-mutated cells. In each timestep, one cell (wild-type) is chosen for removal (red-dashed circle), while another (J) is chosen for replication (green-dashed circle), during which one daughter may acquire a mutation. In this example, a J cell divides into a J cell and a double-mutant JT cell,… view at source ↗
Figure 6
Figure 6. Figure 6: The probability distribution over gene expressio [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The effective potential function U(x) of gene expression levels x for different scenarios within the Markov chain model. (a) The potential function in the absence of mutations. The system can switch between the shallow wells near x = 1000 and x = 2000. (b) U(x) when only the JAK2 mutation is present. The system is confined to the deep well near x = 1000. (c) U(x) when only the TET2 mutation is present. The… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript develops a modeling framework using nonlinear ODEs and Markov chains to distinguish the order of JAK2 V617F and TET2 mutations in myeloproliferative neoplasms. It claims these models explain observed non-additive and non-commutative effects on gene expression patterns, proportions of cells carrying different mutations, and ages at diagnosis, and proposes experiments to verify the models.

Significance. If the models reproduce the cited clinical patterns with the stated two parameters and without direct fitting to the same diagnosis-age or expression datasets, the framework would supply a mechanistic account of mutation-order effects that could guide targeted experiments on regulatory interactions.

major comments (2)
  1. [Model construction] Model construction section: the central explanatory claim requires that the encoded JAK2-TET2 regulatory interactions generate the non-commutative outcomes, yet the description indicates that mutation-order-dependent rate constants are chosen to match the clinical observations on cell proportions and diagnosis ages; without an independent determination of these rates or a demonstration that the two-parameter system reproduces the data quantitatively rather than by construction, the explanatory power remains unverified.
  2. [Results/Comparison] Comparison with data (likely § on results or figures): no explicit quantitative match is shown between model trajectories and the cited clinical patterns for gene expression or age distributions under the two mutation orders; this leaves open whether the nonlinear terms suffice or whether additional unmodeled factors (microenvironment, further mutations) dominate.
minor comments (2)
  1. Notation for the two mutation orders (JAK2-first vs. TET2-first) should be defined once at the outset and used consistently in both the ODE and Markov sections.
  2. [Proposed experiments] The proposed experiments section would benefit from specifying measurable quantities (e.g., time-resolved expression levels or clone sizes) that directly map to the model variables.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below, clarifying the model structure and committing to revisions where appropriate to strengthen the presentation of explanatory power.

read point-by-point responses

  1. Referee: [Model construction] Model construction section: the central explanatory claim requires that the encoded JAK2-TET2 regulatory interactions generate the non-commutative outcomes, yet the description indicates that mutation-order-dependent rate constants are chosen to match the clinical observations on cell proportions and diagnosis ages; without an independent determination of these rates or a demonstration that the two-parameter system reproduces the data quantitatively rather than by construction, the explanatory power remains unverified.

    Authors: The rate constants are identical across both mutation orders; non-commutativity arises solely from the nonlinear interaction terms that encode how the first mutation alters the regulatory state for the second. These terms are motivated by published JAK2-TET2 pathway crosstalk rather than fitted to the target clinical datasets. The two free parameters set overall interaction strength and mutation acquisition timescale, with values drawn from typical hematopoietic cell kinetics. We will revise the model-construction section to list the biological sources for each parameter and include a supplementary demonstration that the identical parameter set generates the observed order-dependent patterns without order-specific adjustments. revision: partial

  2. Referee: [Results/Comparison] Comparison with data (likely § on results or figures): no explicit quantitative match is shown between model trajectories and the cited clinical patterns for gene expression or age distributions under the two mutation orders; this leaves open whether the nonlinear terms suffice or whether additional unmodeled factors (microenvironment, further mutations) dominate.

    Authors: The current version emphasizes qualitative reproduction of the non-commutative signatures. We agree that explicit quantitative overlays and goodness-of-fit measures against the cited expression and age-at-diagnosis data would strengthen the claim. We will add these comparisons in a revised results section, including direct trajectory overlays and residual statistics, to evaluate whether the two-parameter nonlinear structure accounts for the patterns or whether extensions are required. revision: yes

Circularity Check

0 steps flagged

No circularity: models proposed as explanatory framework without reduction to fitted inputs shown

full rationale

The provided abstract and context describe a modeling framework (nonlinear ODE and Markov chain) constructed to account for observed non-additive and non-commutative effects of mutation order on gene expression, cell proportions, and diagnosis ages. No equations, parameter values, or fitting procedures are exhibited that would make any claimed prediction equivalent to its inputs by construction. No self-citations, uniqueness theorems, or ansatzes are referenced. The central claim remains a modeling proposal whose parameters and regulatory interactions are presented as independent of the target data in the given text, satisfying the requirement for self-contained derivation against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on several rate parameters that must be chosen or fitted to match the non-commutative clinical patterns; the ODE and Markov structures themselves are standard.

free parameters (2)
  • mutation-order-dependent rate constants
    Parameters that differ according to whether JAK2 precedes TET2 or vice versa; required to produce the non-commutative behavior described in the abstract.
  • expression-regulation coefficients
    Coefficients controlling how each mutation alters gene-expression levels; fitted or chosen to match observed patterns.
axioms (2)
  • domain assumption Cell populations obey mass-action or logistic growth laws modified by mutation state
    Standard assumption in ODE models of clonal expansion invoked when the authors state they use nonlinear ODEs.
  • domain assumption Mutation order can be represented as a finite-state Markov chain with order-dependent transition rates
    Core modeling choice stated in the abstract.

pith-pipeline@v0.9.0 · 5679 in / 1537 out tokens · 58630 ms · 2026-05-24T07:20:15.216741+00:00 · methodology

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