Comparison of classical tumour growth models for patient derived and cell-line derived xenografts using the nonlinear mixed-effects framework

Voulgarelis, Dimitrios; Bulusu, Krishna C.; Yates, James

doi:10.1080/17513758.2022.2061615

Cited by 9 publications

(8 citation statements)

References 25 publications

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“…One of the primary aims of the study was to formulate a computational framework that captures the dynamics of tumour growth and treatment effect, including resistance, offering a continuous description of drug response and finally integrates the model output with 'omics' data to extract markers of resistance. This framework builds upon a recently published study by the authors that performs a comprehensive assessment of tumour growth models applied to untreated PDX and CDX (Cell line-derived Xenograft) models [43]. While here it is applied to olaparib in triple-negative breast cancer (TNBC) PDX models, the framework itself can be generalised and is sufficiently robust to be applied for different treatments as well as various tumour types.…”

Section: Capturing and Quantifying Tumour Growth Variation In Vivomentioning

confidence: 99%

Understanding tumour growth variability in patient-derived breast cancer xenograft models identifies early responders and biomarkers of resistance to PARP inhibition

O'Connor,

Voulgarelis,

Forment

et al. 2024

Preprint

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Understanding mechanisms of resistance to PARP inhibitors (PARPi) represents a clinically relevant goal that is addressed in this study using a novel methodology. A framework has been developed formulating a mathematical model accounting for intrinsic resistance to the PARPi olaparib, identified by fitting the model to tumour growth metrics from breast cancer patient-derived xenograft (PDX) data. Pre-treatment transcriptomic profiles were used together with the calculated resistance in order to extract baseline biomarkers of resistance to olaparib, as well as potential combination targets. Predicted biomarkers were then assessed for validity and novelty through differential survival analysis, modelling of combination data and pathway enrichment analysis. The model provided both a classification of responses, as well as a continuous description of resistance, allowing for more robust biomarker associations and capturing the variability observed. 36 resistance gene markers were identified, including multiple Homologous Recombination Repair (HRR) pathway genes that are a key part of olaparib’s mechanism-of-action. High levels of WEE1 expression were also linked to resistance, highlighting an opportunity for combining a PARPi with the WEE1 inhibitor. This framework facilitates a fully automated way of capturing response to treatment, including intrinsic resistance, and accounts for the biological and pharmacological response variability captured within PDX studies and hence provides a precision medicine approach.

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Section: Capturing and Quantifying Tumour Growth Variation In Vivomentioning

confidence: 99%

Understanding tumour growth variability in patient-derived breast cancer xenograft models identifies early responders and biomarkers of resistance to PARP inhibition

O'Connor,

Voulgarelis,

Forment

et al. 2024

Preprint

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“…Nonlinear mixed-effects models can handle complex, nonlinear dependent variables. Tumor growth within mice ( 20 ) and pharmacokinetics of animals ( 21 ) have been modeled using nonlinear mixed-effects models.…”

Section: Alternative Statistical Approach: Mixed-effects Modelsmentioning

confidence: 99%

Guidelines for repeated measures statistical analysis approaches with basic science research considerations

Muhammad¹

2023

Journal of Clinical Investigation

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“…The nature of tumor growth is not well known, and the exact laws which govern the growth of tumor cells will likely be context-dependent (cancer type, location in the body, etc.). However, even when many of these dependencies are held constant, it is difficult to discern between various models of cancer cell growth [43][44][45][46]. We fit generalized logistic growth models [24] to the time series cell growth data for eight gastric cancer cell lines (Appendix A Table A1):…”

Section: Mathematical Models Of In-vitro Cell Growthmentioning

confidence: 99%

Mathematical Modeling of Clonal Interference by Density-Dependent Selection in Heterogeneous Cancer Cell Lines

Veith

Schultz

Alahmari

et al. 2023

Cells

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Many cancer cell lines are aneuploid and heterogeneous, with multiple karyotypes co-existing within the same cell line. Karyotype heterogeneity has been shown to manifest phenotypically, thus affecting how cells respond to drugs or to minor differences in culture media. Knowing how to interpret karyotype heterogeneity phenotypically would give insights into cellular phenotypes before they unfold temporally. Here, we re-analyzed single cell RNA (scRNA) and scDNA sequencing data from eight stomach cancer cell lines by placing gene expression programs into a phenotypic context. Using live cell imaging, we quantified differences in the growth rate and contact inhibition between the eight cell lines and used these differences to prioritize the transcriptomic biomarkers of the growth rate and carrying capacity. Using these biomarkers, we found significant differences in the predicted growth rate or carrying capacity between multiple karyotypes detected within the same cell line. We used these predictions to simulate how the clonal composition of a cell line would change depending on density conditions during in-vitro experiments. Once validated, these models can aid in the design of experiments that steer evolution with density-dependent selection.

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Comparison of classical tumour growth models for patient derived and cell-line derived xenografts using the nonlinear mixed-effects framework

Cited by 9 publications

References 25 publications

Understanding tumour growth variability in patient-derived breast cancer xenograft models identifies early responders and biomarkers of resistance to PARP inhibition

Understanding tumour growth variability in patient-derived breast cancer xenograft models identifies early responders and biomarkers of resistance to PARP inhibition

Guidelines for repeated measures statistical analysis approaches with basic science research considerations

Mathematical Modeling of Clonal Interference by Density-Dependent Selection in Heterogeneous Cancer Cell Lines

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