Formalizing an Integrative, Multidisciplinary Cancer Therapy Discovery Workflow

McGuire, Mary F.; Enderling, Heiko; Wallace, Dorothy; Batra, Jaspreet S.; Jordan, Marie; Kumar, Sushil; Panetta, John C.; Pasquier, Eddy

doi:10.1158/0008-5472.can-13-0310

Cited by 21 publications

(15 citation statements)

References 47 publications

(41 reference statements)

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“…To date, only a few mathematical models of metronomic therapy have been published, none of which accounts for interactions among cancer cells, CSCs, immune cells, and tumor blood vessels (26)(27)(28)(29)(30)(31)(32). To this end, we have developed a mathematical model for tumor growth that accounts for three different phenotypes of cancer cells-nonstem cancer cells (CCs), CSCs (which are more resistant to drugs, hypoxia, and the immune system), and CCs that are induced by chemotherapy to acquire a more stem-like phenotype, which we refer to as treatment-induced cancer cells (ICCs) (33), as well as immune cells [natural killer (NK) cells, CD8 + T cells, and Tregs], tumor vasculature, and their interactions (Fig.…”

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confidence: 99%

Role of vascular normalization in benefit from metronomic chemotherapy

Mpekris

Baish

Stylianopoulos

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

145

119

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Metronomic dosing of chemotherapy-defined as frequent administration at lower doses-has been shown to be more efficacious than maximum tolerated dose treatment in preclinical studies, and is currently being tested in the clinic. Although multiple mechanisms of benefit from metronomic chemotherapy have been proposed, how these mechanisms are related to one another and which one is dominant for a given tumor-drug combination is not known. To this end, we have developed a mathematical model that incorporates various proposed mechanisms, and report here that improved function of tumor vessels is a key determinant of benefit from metronomic chemotherapy. In our analysis, we used multiple dosage schedules and incorporated interactions among cancer cells, stem-like cancer cells, immune cells, and the tumor vasculature. We found that metronomic chemotherapy induces functional normalization of tumor blood vessels, resulting in improved tumor perfusion. Improved perfusion alleviates hypoxia, which reprograms the immunosuppressive tumor microenvironment toward immunostimulation and improves drug delivery and therapeutic outcomes. Indeed, in our model, improved vessel function enhanced the delivery of oxygen and drugs, increased the number of effector immune cells, and decreased the number of regulatory T cells, which in turn killed a larger number of cancer cells, including cancer stem-like cells. Vessel function was further improved owing to decompression of intratumoral vessels as a result of increased killing of cancer cells, setting up a positive feedback loop. Our model enables evaluation of the relative importance of these mechanisms, and suggests guidelines for the optimal use of metronomic therapy.thrompospondin-1 | tumor perfusion | oxygenation | immune response | drug delivery

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mentioning

confidence: 99%

Role of vascular normalization in benefit from metronomic chemotherapy

Mpekris

Baish

Stylianopoulos

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

145

119

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“…Its utility as a predictor fits the proposed workflow model in McGuire et al [19]. In particular, it can help a researcher decide on a set of protocols for in vivo experiments.…”

Section: Discussionmentioning

confidence: 88%

A Model for Spheroid versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ₂

Wallace

Dunham

Chen

et al. 2016

Computational and Mathematical Methods in Medicine

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Researchers have observed that response of tumor cells to treatment varies depending on whether the cells are grown in monolayer, as in vitro spheroids or in vivo. This study uses data from the literature on monolayer treatment of SK-N-SH neuroblastoma cells with 15-deoxy-PGJ 2 and couples it with data on growth rates for untreated SK-N-SH neuroblastoma cells grown as multicellular spheroids. A linear model is constructed for untreated and treated monolayer data sets, which is tuned to growth, death, and cell cycle data for the monolayer case for both control and treatment with 15-deoxy-PGJ 2. The monolayer model is extended to a five-dimensional nonlinear model of in vitro tumor spheroid growth and treatment that includes compartments of the cell cycle (G 1, S, G 2/M) as well as quiescent (Q) and necrotic (N) cells. Monolayer treatment data for 15-deoxy-PGJ 2 is used to derive a prediction of spheroid response under similar treatments. For short periods of treatment, spheroid response is less pronounced than monolayer response. The simulations suggest that the difference in response to treatment of monolayer versus spheroid cultures observed in laboratory studies is a natural consequence of tumor spheroid physiology rather than any special resistance to treatment.

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“…This can be supported by mathematical modeling as an in silico prediction tool for preclinical, experimental treatments and analyses of patient data in clinical trials. A "discovery workflow" has been discussed describing a working relationship between experimental and computational methods [187]. Between genetic engineering and the resulting effects on the immune system, a myriad of combination therapies involving oncolytic virus therapy is inevitable (and ongoing in clinical trials [188]).…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Fighting Cancer with Mathematics and Viruses

Santiago¹,

Heidbuechel²,

Kandell³

et al. 2017

Preprint

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After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex anti-tumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these fields of research for more efficient development and effective treatments.

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Formalizing an Integrative, Multidisciplinary Cancer Therapy Discovery Workflow

Cited by 21 publications

References 47 publications

Role of vascular normalization in benefit from metronomic chemotherapy

Role of vascular normalization in benefit from metronomic chemotherapy

A Model for Spheroid versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ₂

Fighting Cancer with Mathematics and Viruses

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Formalizing an Integrative, Multidisciplinary Cancer Therapy Discovery Workflow

Cited by 21 publications

References 47 publications

Role of vascular normalization in benefit from metronomic chemotherapy

Role of vascular normalization in benefit from metronomic chemotherapy

A Model for Spheroid versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ2

Fighting Cancer with Mathematics and Viruses

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Product

Resources

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A Model for Spheroid versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ₂