Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment

Gaddy, Thomas D.; Finley, Stacey D.

doi:10.1101/136531

Cited by 12 publications

(19 citation statements)

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“…Building upon the basic growth models (i.e. adapted Gompertz model on tumor phases) and vascular endothelial growth factor (VEGF), the work in [11] trained and validated a model using published in vivo measurements of xenograft tumor volume. The model employed Nonlinear Least Squares (NLS) optimization together with a global sensitivity analysis to determine which of the four tumor growth kinetic parameters impacts the predicted tumor volume most significantly.…”

Section: Predictive Models Of Tumor Growthmentioning

confidence: 99%

“…breast versus lung cancer, [5] and each type of treatment (i.e. administered drug) modifies the growth curve [11]. is heterogeneous and sometimes expensive to obtain (e.g.volume assessed from bio-markers, fMRI [1], fluorescence imaging [20], flow cytometry, or calipers [6]).…”

Section: Peculiarities Of Tumor Growth Datamentioning

confidence: 99%

“…is heterogeneous and sometimes expensive to obtain (e.g.volume assessed from bio-markers, fMRI [1], fluorescence imaging [20], flow cytometry, or calipers [6]). poses challenges in selecting the best model [1,11,19].…”

Section: Peculiarities Of Tumor Growth Datamentioning

confidence: 99%

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Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics

Axenie¹,

Kurz

2020

Preprint

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Neoplastic processes are described by complex and heterogeneous dynamics. The interaction of neoplastic cells with their environment describes tumor growth and is critical for the initiation of cancer invasion. Despite the large spectrum of tumor growth models, there is no clear guidance on how to choose the most appropriate model for a particular cancer and how this will impact its subsequent use in therapy planning. Such models need parametrization that is dependent on tumor biology and hardly generalize to other tumor types and their variability. Moreover, the datasets are small in size due to the limited or expensive measurement methods. Alleviating the limitations that incomplete biological descriptions, the diversity of tumor types, and the small size of the data bring to mechanistic models, we introduce Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics (GLUECK) a novel, data-driven model based on a neural network capable of unsupervised learning of cancer growth curves. Employing mechanisms of competition, cooperation, and correlation in neural networks, GLUECK learns the temporal evolution of the input data along with the underlying distribution of the input space. We demonstrate the superior accuracy of GLUECK, against four typically used tumor growth models, in extracting growth curves from a four clinical tumor datasets. Our experiments show that, without any modification, GLUECK can learn the underlying growth curves being versatile between and within tumor types.

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Section: Predictive Models Of Tumor Growthmentioning

confidence: 99%

Section: Peculiarities Of Tumor Growth Datamentioning

confidence: 99%

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Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics

Axenie¹,

Kurz

2020

Preprint

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“…Usually, tumor growth data is small, only a few data points with, typically, days level granularity [10] and irregular spacing among measurements [9]. Moreover, the data has high variability due to: within tumor types specifics, therapy effect on tumor growth [11], and heterogeneous measurement types (e.g. bio-markers, fMRI, fluorescence imaging [12], flow cytometry, or calipers [13]).…”

Section: Model Equationmentioning

confidence: 99%

Prediction of Individual Breast Cancer Evolution to Surgical Size

Axenie

Kurz

2020

Preprint

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Modelling surgical size is not inherently meant to replicate the tumor's exact form and proportions, but instead to elucidate the degree of the tissue volume that may be surgically removed in terms of improving patient survival and minimize the risk that subsequent operations will be needed to eliminate all malignant cells entirely. Given the broad range of models of tumor growth, there is no specific rule of thumb about how to select the most suitable model for a particular breast cancer type and whether that would influence its subsequent application in surgery planning. Typically, these models require tumor biologydependent parametrization, which hardly generalizes to cope with tumor heterogeneity. In addition, the datasets are limited in size, owing to the restricted or expensive measurement methods. We address the shortcomings that incomplete biological specifications, the variety of tumor types, and the limited size of the data bring to existing mechanistic tumor growth models and introduce a Machine Learning model for the PRediction of INdividual breast Cancer Evolution to Surgical Size (PRINCESS). This is a data-driven model based on neural networks capable of unsupervised learning of cancer growth curves. PRINCESS learns the temporal evolution of the tumor along with the underlying distribution of the measurement space. We demonstrate the superior accuracy of PRINCESS, against four typically used tumor growth models, in learning tumor growth curves from a set of four clinical breast cancer datasets. Our experiments show that, without any modification, PRINCESS can accurately predict tumor sizes while being versatile between breast cancer types.

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“…3. Phophatase inhibition ("Ptase"): Studies have shown [31,32] that inhibiting MAPK phosphatase (MKP) activity can promote apoptosis signaling. We simulated this effect by decreasing the association rate (K on_dephos ) of the phosphatase with phosphorylated p38MAPK (pp38) and the dephosphorylation rate (K dephos ) by 10-fold.…”

Section: Definition Of Apoptotic Cells Cleaved Poly(adp-ribose) Polymentioning

confidence: 99%

Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling

Finley

2017

Preprint

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This study explores strategies to enhance thrombospondin-1 (TSP1) induced apoptosis in endothelial cells, an important function that contributes to TSP1's anti-angiogenic effect. We established a mathematical model of TSP1-mediated intracellular signaling via the CD36 receptor. This model was used to investigate the effects of several approaches to perturb the TSP1-CD36 signaling network. Model simulations predict the population-based response to strategies to enhance TSP1-mediated apoptosis, such as downregulating the apoptosis inhibitor XIAP and inhibiting phosphatase activity. The model also postulates a new mechanism of low dosage doxorubicin treatment in combination with TSP1 stimulation. Using computational analysis, we predict which cells will undergo apoptosis, based on the initial intracellular concentrations of particular signaling species. Overall, the modeling framework predicts molecular strategies that increase TSP1-mediated apoptosis, which is useful in many disease settings.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/188003 doi: bioRxiv preprint first posted online Sep. 12, 2017; 3 Introduction Angiogenesis, the formation of new capillaries from pre-existing blood vessels, plays a critical role in tumor progression. Angiogenesis enables the tumor to generate its own blood supply and obtain oxygen and nutrition from the microenvironment. This process is regulated by a dynamic interplay between the angiogenic promoters, such as vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF), as well as angiogenic inhibitors, such as thrombospondin-1 (TSP1) [1][2][3][4][5]. Due to its importance in tumor development, invasion, and metastasis, angiogenesis has become a prominent target for cancer therapies. In addition to strategies targeting proangiogenic species, such as inhibiting VEGF signaling using antibodies and tyrosine kinase inhibitors, anti-angiogenic species hold promise in reducing tumor angiogenesis. TSP1 is a wellknown, potent endogenous angiogenesis inhibitor. TSP1 expression is lost in multiple cancer types;; however, its re-expression can delay cancer progression, promote tumor cell apoptosis, and decrease microvascular density. For these reasons, it has been of interest to mimic TSP1's functions in regulating angiogenesis [3,[7][8][9][10]. TSP1 is a multifunctional matricellular protein that acts to inhibit angiogenesis in multiple ways [2,11,12], which include altering the availability of pro-angiogenic factors and promoting antiangiogenic signaling through its receptors CD36 and CD47. Several studies have shown that TSP1 mediates its anti-proliferative and pro-apoptotic effects in a highly specific manner on endothelial cells. TSP1 primarily promotes these effects by binding to the CD36 receptor [3,13,14], which is associated with capillary blood vessel regression [11,13,15,16]. TSP1 interaction with CD36 leads to ...

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Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment

Cited by 12 publications

References 49 publications

Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics

Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics

Prediction of Individual Breast Cancer Evolution to Surgical Size

Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling

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