Unified Tumor Growth Mechanisms from Multimodel Inference and Dataset Integration

Harris, Leonard A.; Kochen, Michael A.; Sage, Julien; Quaranta, Vito; López, Carlos F.

doi:10.1101/2022.07.22.500696

Cited by 3 publications

(3 citation statements)

References 100 publications

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“…These precise measurements helped capture variability in the output resulting from the above two parameters (β and Ke) variation. Extensive computational platforms are being developed for informing future experimental design to ensure that the best set of system variables are captured at the precise timepoints and model parameter estimates from the data have well-defined confidence bounds [41][42][43] .…”

Section: Discussionmentioning

confidence: 99%

Cell-state transitions and frequency-dependent interactions among subpopulations together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity in breast cancer

Jain,

Kizhuttil,

Nair

et al. 2023

Preprint

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Individual cells in a tumour can be distributed among Epithelial (E) and Mesenchymal (M) cell-states, as characterised by the levels of canonical E and M markers. Even after E and M (E-M) subpopulations are isolated and then cultured independently, E-M heterogeneity can re-equilibrate in each population over time, sometimes regaining the initial distribution of the parental cell population. However, it remains unclear which population-level processes give rise to the dynamical changes in E-M heterogeneity observed experimentally, including 1) differential growth, 2) cell-state switching, and 3) frequency-dependent growth or state-transition rates. Here, we analyse the necessity of these three processes in explaining the dynamics of E-M population distributions as observed in PMC42-LA and HCC38 breast cancer cells. We find that growth differences among E and M subpopulations, with and without any frequency-dependent interactions (cooperation or suppression) among E-M sub-populations, are insufficient to explain the observed population dynamics. This insufficiency is ameliorated by including cell-state transitions, albeit at slow rates, in explaining both PMC42-LA and HCC38 cells data. Further, our models predict that treatment of HCC38 cells with TGFβ signalling and JAK2/3 inhibitors could significantly enhance the transition rates from M state to E state, but does not prevent transitions from E to M. Finally, we devise a selection criterion to identify the next most informative time points for which future experimental data can optimally improve the identifiability of our estimated best fit model parameters. Overall, our study identifies the necessary population-level processes shaping the dynamics of E-M heterogeneity in breast cancer cells.

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Section: Discussionmentioning

confidence: 99%

Cell-state transitions and frequency-dependent interactions among subpopulations together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity in breast cancer

Jain,

Kizhuttil,

Nair

et al. 2023

Preprint

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“…Based on these analyses and limited applications to protein-protein interaction data, the authors concluded that it is generally important to choose a good set of models for building accurate MMI estimates. More recently, Beik et al utilized MMI with BMA to select candidate tumor growth mechanisms that are consistent with several experimental datasets [26]. However, MMI has not yet been investigated as a method to increase the certainty of intracellular signaling predictions in systems biology.…”

Section: Introductionmentioning

confidence: 99%

Increasing certainty in systems biology models using Bayesian multimodel inference

Linden-Santangeli,

Zhang,

Kramer

et al. 2024

Preprint

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Mathematical models are indispensable to the system biology toolkit for studying the structure and behavior of intracellular signaling networks. A common approach to modeling is to develop a system of equations that encode the known biology using approximations and simplifying assumptions. As a result, the same signaling pathway can be represented by multiple models, each with its set of underlying assumptions, which opens up challenges for model selection and decreases certainty in model predictions. Here, we use Bayesian multimodel inference to develop a framework to increase certainty in systems biology models. Using models of the extracellular regulated kinase (ERK) pathway, we first show that multimodel inference increases predictive certainty and yields predictors that are robust to changes in the set of available models. We then show that predictions made with multimodel inference are robust to data uncertainties introduced by decreasing the measurement duration and reducing the sample size. Finally, we use multimodel inference to identify a new model to explain experimentally measured sub-cellular location-specific ERK activity dynamics. In summary, our framework highlights multimodel inference as a disciplined approach to increasing the certainty of intracellular signaling activity predictions.

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“…However, the situation is beginning to change. With recent advances in parameter estimation (Eydgahi et al, 2013;Shockley et al, 2018), model analysis and model selection methodologies (Beik et al, 2023), the construction of large-scale, executable models of whole cells, whole tissues and whole patients may now be within reach. Indeed, efforts to develop such detailed computational models, termed "medical digital twins" (Laubenbacher et al, 2021;Masison et al, 2021), are underway.…”

Section: Introductionmentioning

confidence: 99%

Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia

Velleuer,

Domínguez-Hüttinger,

Rodríguez

et al. 2023

Front. Genet.

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Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the “hallmarks of cancer in FA”, which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.

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Unified Tumor Growth Mechanisms from Multimodel Inference and Dataset Integration

Cited by 3 publications

References 100 publications

Cell-state transitions and frequency-dependent interactions among subpopulations together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity in breast cancer

Cell-state transitions and frequency-dependent interactions among subpopulations together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity in breast cancer

Increasing certainty in systems biology models using Bayesian multimodel inference

Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia

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