The evolution of tumor metastases during clonal expansion

Haeno, Hiroshi; Michor, Franziska

doi:10.1016/j.jtbi.2009.11.005

Cited by 43 publications

(44 citation statements)

References 29 publications

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“…Haeno and Michor [12] developed a descriptive model of the growth of tumors, which begins with consideration of tumor cells in three groups, type-0 cells which reside in the primary tumor and are not yet able to metastasize, type-1 cells which can metastasize and type-2 cells which have metastasized and form new lesions [12]. They used this model to derive several analytical approximations, including: (i) the probability of having at least one metastatic site at diagnosis and at autopsy; (ii) the expected number of metastatic sites at diagnosis and at autopsy; and (iii) the expected total number of cells in all metastatic sites at diagnosis and at autopsy [12].…”

Section: Other Models Of Carcinoma Of the Pancreasmentioning

confidence: 99%

“…They used this model to derive several analytical approximations, including: (i) the probability of having at least one metastatic site at diagnosis and at autopsy; (ii) the expected number of metastatic sites at diagnosis and at autopsy; and (iii) the expected total number of cells in all metastatic sites at diagnosis and at autopsy [12]. By considering the cells of a tumor in these three groups and having analytic approximations to relate the size of each group, they can study the effects of various changes on the overall cell population.…”

Section: Other Models Of Carcinoma Of the Pancreasmentioning

confidence: 99%

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Simulating the growth of hepatic metastases from carcinoma of the pancreas using LabVIEW

Lefor¹

2017

Dig Sys

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The mathematical modeling of tumors has potential to contribute to individualized therapy for a wide range of malignancies. There have been many approaches to this complex subject over the last 50 years or more. The current study is an attempt to adapt a model, originally developed for neuro-oncology, to the understanding of hepatic metastases from pancreatic carcinoma. The Diffusion-Proliferation (DP) model uses the sum of a diffusion term and a proliferation term to calculate changes in tumor cell concentration over time. The model was adapted directly from previous investigators who have used it to study brain tumors. The model is based on two parameters, a diffusion parameter (D i ) and a proliferation index (p). LabVIEW is a widely used software package, which enables simulation of physical systems, especially in engineering applications, and has been applied to the diagnosis and treatment of malignancies. This is the first study to use LabVIEW for the simulation of tumor growth. The simulation allows direct entry of the two parameters, D and p, with the resulting tumor growth curve appearing as a graph. The user has a rapid graphical understanding of the impact of the two parameters, D and p, on tumor growth. The next step in the development of this model will be to use patient-specific data to understand tumor growth and calculate survival based on the parameters D and p.

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Section: Other Models Of Carcinoma Of the Pancreasmentioning

confidence: 99%

Section: Other Models Of Carcinoma Of the Pancreasmentioning

confidence: 99%

Simulating the growth of hepatic metastases from carcinoma of the pancreas using LabVIEW

Lefor¹

2017

Dig Sys

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“…Therefore, to understand cancer as anomalous, we need to rethink cancer at a population level and not at a single cell level. This means that instead of merely tracing the journey from a single, normal cell (physiology) to when it becomes a 'diseased cell' by contraposition (pathophysiology), we can consider, at a macro-level, with a broader temporal approach, that anomalous cell developments are part of the wider evolutionary process by which life develops (Haeno and Michor, 2010). Understanding the sweep of time in terms of what has already happened provides only limited insight.…”

Section: Anomalies and The Temporal Dimension Of Cancermentioning

confidence: 99%

Rethinking therapeutic strategies in cancer: Wars, fields, anomalies and monsters

Stewart

Rauch

2016

Soc Theory Health

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Rethinking therapeutic strategies in cancer: wars, fields, anomalies and monsters (accepted for publication in Social Theory and Health)In this article, we argue that the excessive focus on cancer as an insidious living defect that needs to be destroyed has obscured the fact that cancer develops inside human beings.Therefore, in order to contribute to debates about new cancer therapies, we argue that it is important to gain a broader understanding of what cancer is and how it might be otherwise.First, in order to reframe the debate, we utilize Pierre Bourdieu's (2004) field analysis in order to gain a stronger understanding of the structure of the (sub)field of cancer research. In doing so, we are able to see that those in a dominant position in the field, with high levels of scientific capital at their disposal, are in the strongest position to determine the type of research that is carried out and, more significantly, how cancer is perceived. Field analysis enables us to gain a greater understanding of the complex interplay between the field of science (and, more specifically, the subfield of cancer research) and broader sources of power. Second, we draw attention to new possible ways of understanding cancer in its evolutionary context. One of the problems facing cancer research is the narrow time frame within which cancer is perceived: the lives of cancer cells are considered from the moment the cells initially change. In contrast, the approach put forward here requires a different way of thinking: we take a longer view and consider cancer as a living entity, with cancer perceived as anomalous rather than abnormal. Third, we theorize the possibility of therapeutic strategies that might involve the redirection (rather than the eradication) of cancer cells. This approach also necessitates new ways of perceiving cancer. 2 Fields, subfields and external forcesBourdieu's notion of field is useful in helping us to understand the complex interplay of forces guiding cancer research.

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“…Although models on tumor cellular evolution have been developed, such as a stochastic mathematical model of the evolution of tumor metastases in an expanding cancer cell population (Haeno and Michor 2010) or a model that describes the accumulation of driver (and passenger) mutations during tumor development (Bozic et al 2010), there is no approach in cancer modeling available that can model very large pathway systems with consideration of patient specific environment. But since many of the cancer drugs available are only effective in an often rather small fraction of the patient population, with the majority of patients showing little or no benefits or even suffering often quite severe side effects shows that the progress in the treatment of tumors in individual patients will depend critically on being able to predict the effect of such treatments in the context of the genome/ transcriptome involved.…”

Section: Introductionmentioning

confidence: 99%

The “Virtual Patient” system: modeling cancer using deep sequencing technologies for personalized cancer treatment

Kühn

Lehrach

2012

J. Verbr. Lebensm.

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Cancer is a complex disease involving many different genomic and epigenomic changes, mutations, copy number changes, loss of heterozygosity etc. These differences cause tumors with the same pathological classification to respond very differently to the drugs, making therapy decisions difficult. As a result of intensive research in the field of oncology much information is available on molecular interactions and pathways involved in cancer onset and progression. In addition recent increases in the capacity of next-generation sequencing systems will provide huge amounts of genome, epigenome and transcriptome data, making it feasible to apply deep sequencing in the clinic to characterize tumor/patient samples. Both, the complexity of disturbances in interaction networks of biological processes in cancer and the new molecular information generated by this sequence analysis urgently require the development of systems that are able to derive clinically relevant predictions from all available data. The ''Virtual Patient'' modeling system combines general information available about cancer relevant pathways with the individual (genome, transcriptome) information available on the individual tumor/patient to generate models able to predict the effects and side effects of individual drugs or drug combinations. This opens the way to experiment with the response of the model of the individual patient to different therapy options in the computer, offering new routes to improve oncological practice, reduce health costs but also to accelerate the development and the approval process for new drugs in this area.

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The evolution of tumor metastases during clonal expansion

Cited by 43 publications

References 29 publications

Simulating the growth of hepatic metastases from carcinoma of the pancreas using LabVIEW

Simulating the growth of hepatic metastases from carcinoma of the pancreas using LabVIEW

Rethinking therapeutic strategies in cancer: Wars, fields, anomalies and monsters

The “Virtual Patient” system: modeling cancer using deep sequencing technologies for personalized cancer treatment

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