The emergence of tumor metastases

Dingli, David; Michor, Franziska; Antal, Tibor; Pacheco, José Augusto

doi:10.4161/cbt.6.3.3720

Cited by 27 publications

(38 citation statements)

References 48 publications

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“…Our approach is then used to identify optimum therapeutic interventions, which can be tested in the clinic. This work is part of an ongoing effort to analyze cancer metastases using mathematical and computational techniques (Andasari et al, 2011; Chauviere et al, 2010; Dingli et al, 2007; Jadhav et al, 2007; Klein and Holzel, 2006; Michor et al, 2006; Quaranta et al, 2008; Sherratt, 2001) and provides new insight into the complexity of metastatic dissemination as well as suggests optimal treatment strategies for patients diagnosed with this devastating disease.…”

Section: Introductionmentioning

confidence: 99%

Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies

Haeno

Gönen

Davis

et al. 2012

Cell

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374

327

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SUMMARY Pancreatic cancer is a leading cause of cancer-related death, largely due to metastatic dissemination. We investigated pancreatic cancer progression by utilizing a mathematical framework of metastasis formation together with comprehensive data of 228 patients, 101 of whom had autopsies. We found that pancreatic cancer growth is initially exponential. After estimating the rates of pancreatic cancer growth and dissemination, we determined that patients likely harbor metastases at diagnosis and predicted the number and size distribution of metastases as well as patient survival. These findings were validated in an independent database. Finally, we analyzed the effects of different treatment modalities, finding that therapies which efficiently reduce the growth rate of cells earlier in the course of treatment appear to be superior to upfront tumor resection. These predictions can be validated in the clinic. Our interdisciplinary approach provides insights into the dynamics of pancreatic cancer metastasis and identifies optimum therapeutic interventions.

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Section: Introductionmentioning

confidence: 99%

Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies

Haeno

Gönen

Davis

et al. 2012

Cell

Self Cite

374

327

View full text Add to dashboard Cite

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“…For instance, variability has emerged to be a key factor in understanding the development of tumours [12][13][14]. Even when tumours may have smaller growth rates compared to normal cells [15], its growth will have significantly larger variation than normal cells.…”

Section: Introductionmentioning

confidence: 99%

“…This variation increases with the complexity of the tumour as it progresses towards metastasis with the involvement of an increasing number of different processes on a broad range of scales, accompanied by the propagation of the loss of cellular stability on multiscales (e.g. [12][13][14]). Ultimately, the danger of a tumour is not measured simply by its average growth rate or variance, but by the rare occurrence of extreme events of such aggressive tumour growth (metastasis).…”

Section: Introductionmentioning

confidence: 99%

Time-dependent probability density functions and information geometry in stochastic logistic and Gompertz models

2017

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A probabilistic description is essential for understanding growth processes far from equilibrium.In this paper, we compute time-dependent Probability Density Functions (PDFs) in order to investigate stochastic logistic and Gompertz models, which are two of the most popular growth models.We consider different types of short-correlated internal (multiplicative) and external (additive) stochastic noises and compare the time-dependent PDFs in the two models, elucidating the effects of the additive and multiplicative noises on the form of PDFs. We demonstrate an interesting transition from a unimodal to a bimodal PDF as the multiplicative noise increases for a fixed value of the additive noise. A much weaker (leaky) attractor in the Gompertz model leads to a significant (singular) growth of the population of a very small size. We point out the limitation of using stationary PDFs, mean value and variance in understanding statistical properties of the growth far from equilibrium, highlighting the importance of time-dependent PDFs. We further compare these two models from the perspective of information change that occurs during the growth process. Specifically, we define an infinitesimal distance at any time by comparing two PDFs at times infinitesimally apart and sum these distances in time. The total distance along the trajectory quantifies the total number of different states that the system undergoes in time, and is called the information length. We show that the time-evolution of the two models become more similar when measured in units of the information length and point out the merit of using the information length in unifying and understanding the dynamic evolution of different growth processes.

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“…Regarding cancer research, in the last years it has found valuable support in a wide range of modeling and simulation approaches, which cover a wide spectrum ranging from mathematical models -e.g., continuous models [2][3][4] and stochastic models [5][6][7][8] to computational models -e.g., Monte Carlo method and cellular automata [9][10][11], Boolean networks [12], Petri nets [13], artificial neural networks [14][15][16] and expert systems [17]. These approaches have allowed the in silico experimentation in cancer at the cellular, system and patient level.…”

Section: Bodymentioning

confidence: 99%

An In Silico Cell Signaling-Based Approach for Exploring the Activities Involved in Pre-Metastasis and Metastasis

González-Pérez

Cárdenas-García

2017

Genomics Comput Biol

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SUMMARYIn order to understand, identify and explore the activities involved in metastasis, as well as possible control points, in this work we model and simulate the sequential steps that a cell must follow from its transformation to metastasis, using the Cellulat bioinformatics tool, an in silico experimentation environment that complements and guides in vitro experimentation concerning intra and intercellular signaling networks. KEYWORDSMetastasis; Cancer cells; In silico experiments; Cellulat bioinformatics tool. BODYMetastasis is the clinical term for the process by which tumor cells leave a primary cancer tumor to transfer to different organs. The metastasis is considered to be the true killer in cancer patients, since usually when this process is triggered, it inevitably leads to death. To prevent the spreading of cancer, the most important is to avoid the metastasis of the primary tumor, as well as to find markers that allow early identification of the presence of a primary tumor [1]. Features that promote metastasis -i.e., sustained proliferation, replicative immortality, and evasion of growth suppression -allow cancer cells to grow uncontrollably in a tumor large enough to invade neighboring tissues. The ability of cells to resist cell death and prevent their destruction caused by the immune system response, allow them to survive on their way to metastasis.The intrinsic complexity of biological systems and phenomena -such as protein-protein interaction, protein-ligand docking and protein folding, just to mention a few examples -has required the development of a wide range of computational tools dedicated to the modeling and simulation of them, so that these computational approaches -also known as computer simulation or in silico experimentation -can complement, corroborate and enrich both the advances in theoretical and experimental research in the study of such systems. Regarding cancer research, in the last years it has found valuable support in a wide range of modeling and simulation approaches, which cover a wide spectrum ranging from mathematical models -e.g., continuous models [2][3][4] [17]. These approaches have allowed the in silico experimentation in cancer at the cellular, system and patient level.In order to understand, identify and explore the activities involved in metastasis, as well as possible control points, in this work we model and simulate the sequential steps that a cell must follow from its transformation to metastasis.We simulate and explore the complex interaction patterns of signaling pathways involved in pre-metastasis and metastasis using the Cellulat bioinformatics tool (http://bioinformatics.cua.uam.mx/node/10) [18,19], a computational simulation tool developed by us and inspired by Biochemical Tuple Spaces for Self-Organizing Coordination model (BTSSOC) [20]. The main idea behind the Cellulat bioinformatics tool is to provide an in silico experimentation environment that complements and guides in vitro experimentation concerning intra and intercellular signaling networks.C...

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The emergence of tumor metastases

Cited by 27 publications

References 48 publications

Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies

Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies

Time-dependent probability density functions and information geometry in stochastic logistic and Gompertz models

An In Silico Cell Signaling-Based Approach for Exploring the Activities Involved in Pre-Metastasis and Metastasis

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