Abstract:Pancreatic ductal adenocarcinoma (PDAC) exhibits a variety of phenotypes with regard to disease progression and treatment response. This variability complicates clinical decision-making despite the improvement of survival due to the recent introduction of FOLFIRINOX (FFX) and nab-paclitaxel. Questions remain as to the timing and sequence of therapies and the role of radiotherapy for unresectable PDAC. Here we developed a computational analysis platform to investigate the dynamics of growth, metastasis and trea… Show more
“…Additionally, healthy tissues such as white matter, gray matter, muscle, adipose, or fibro glandular tissue also may be identified from these images. Such data have been used to parameterize models of tumor growth and metastasis ( Hormuth et al., 2019b ; Neal et al., 2013 ; Yamamoto et al., 2019 ). Figure 4 Representative Images from a Murine Model of Glioma (A) Contrast-enhanced magnetic resonance image with the brain indicated by the dashed box.…”
Section: Mathematical Modeling Of Cancer At the Tissue And Organ Scalmentioning
confidence: 99%
“…Moreover, mathematical models parameterized by biological data need not be comprehensive to make to gain insight into underlying tumor dynamics and make valid predictions. For example, studies investigating scaling laws ( Pérez-García et al., 2020 ) and metastasis ( Yamamoto et al., 2019 ) made use of only volumetric measurements from imaging data to model longitudinal tumor growth and response. Additionally, serum prostate-specific antigen (PSA) is a standard clinical measure for patients with prostate cancer undergoing treatment and has been used to parameterize patient-specific models of prostate cancer response.…”
Section: Emerging Applications For Practical Mathematical Modelingmentioning
confidence: 99%
“…Additionally, healthy tissues such as white matter, gray matter, muscle, adipose, or fibro glandular tissue also may be identified from these images. Such data have been used to parameterize models of tumor growth and metastasis (Hormuth et al, 2019b;Neal et al, 2013;Yamamoto et al, 2019).…”
Section: Tissue and Organ Scale Data Appropriate For Incorporating Into Mathematical Modelsmentioning
confidence: 99%
“…Moreover, mathematical models parameterized by biological data need not be comprehensive to make to gain insight into underlying tumor dynamics and make valid predictions. For example, studies investigating scaling laws (Pé rez-García et al, 2020) and metastasis (Yamamoto et al, 2019) (Lorenzo et al, 2019b). These parameters were then used to define biomarkers to successfully identify tumor recurrence.…”
Section: Emerging Applications For Practical Mathematical Modelingmentioning
Summary
We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and
in vivo
imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.
“…Additionally, healthy tissues such as white matter, gray matter, muscle, adipose, or fibro glandular tissue also may be identified from these images. Such data have been used to parameterize models of tumor growth and metastasis ( Hormuth et al., 2019b ; Neal et al., 2013 ; Yamamoto et al., 2019 ). Figure 4 Representative Images from a Murine Model of Glioma (A) Contrast-enhanced magnetic resonance image with the brain indicated by the dashed box.…”
Section: Mathematical Modeling Of Cancer At the Tissue And Organ Scalmentioning
confidence: 99%
“…Moreover, mathematical models parameterized by biological data need not be comprehensive to make to gain insight into underlying tumor dynamics and make valid predictions. For example, studies investigating scaling laws ( Pérez-García et al., 2020 ) and metastasis ( Yamamoto et al., 2019 ) made use of only volumetric measurements from imaging data to model longitudinal tumor growth and response. Additionally, serum prostate-specific antigen (PSA) is a standard clinical measure for patients with prostate cancer undergoing treatment and has been used to parameterize patient-specific models of prostate cancer response.…”
Section: Emerging Applications For Practical Mathematical Modelingmentioning
confidence: 99%
“…Additionally, healthy tissues such as white matter, gray matter, muscle, adipose, or fibro glandular tissue also may be identified from these images. Such data have been used to parameterize models of tumor growth and metastasis (Hormuth et al, 2019b;Neal et al, 2013;Yamamoto et al, 2019).…”
Section: Tissue and Organ Scale Data Appropriate For Incorporating Into Mathematical Modelsmentioning
confidence: 99%
“…Moreover, mathematical models parameterized by biological data need not be comprehensive to make to gain insight into underlying tumor dynamics and make valid predictions. For example, studies investigating scaling laws (Pé rez-García et al, 2020) and metastasis (Yamamoto et al, 2019) (Lorenzo et al, 2019b). These parameters were then used to define biomarkers to successfully identify tumor recurrence.…”
Section: Emerging Applications For Practical Mathematical Modelingmentioning
Summary
We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and
in vivo
imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.
“…Stochastic models introduce additional challenges due to their intrinsic stochasticity and, therefore, there is a lack of tools for easily finding parameter estimates for such models. Although a few frameworks, such as StochSS, exist to help users to automatically complete this task, many studies found in literature treat the system as deterministic instead of stochastic when estimating the parameters of the model to be able to use common procedures, such as maximum likelihood estimation [ 25 ]. In those cases, the estimates of the parameter obtained through the deterministic formulation may be plugged back into the stochastic model for the simulation of the stochastic process [ 26 ].…”
Section: Differences Between Deterministic and Stochastic Modeling Apmentioning
The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug–disease models more versatile and realistic.
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