Cancer metabolism and the dynamics of metastasis

Dattoli, G.; Guiot, Caterina; Delsanto, Pier Paolo; Ottaviani, P.L.; Pagnutti, S.; Deisboeck, Ts

doi:10.1016/j.jtbi.2008.10.008

Cited by 14 publications

(17 citation statements)

References 23 publications

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“…Our prospective data further support the notion that tumors can start developing metastasis very early, i.e., before they may even become clinically detactable (29) and that aggressive neoplastic behavior could be manipulated by systemic metabolic factors. These concepts may aid in refinement of prostate cancer risk prediction (especially for lethal outcomes) by including BMI (30) and related biomarkers such as C-peptide and adiponectin in the clinical nomogram to improve performance.…”

Section: Discussionsupporting

confidence: 80%

A 25-Year Prospective Study of Plasma Adiponectin and Leptin Concentrations and Prostate Cancer Risk and Survival

et al. 2010

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Background: Adipocytokines may mediate the association between adiposity and lethal prostate cancer outcomes. Methods: In the Physicians’ Health Study, we prospectively examined the association of prediagnostic plasma concentrations of adiponectin and leptin with risk of developing incident prostate cancer (654 cases diagnosed 1982–2000 and 644 age-matched controls) and, among cases, risk of dying from prostate cancer by 2007. Results: Adiponectin concentrations were not associated with risk of overall prostate cancer. However, men with higher adiponectin concentrations had lower risk of developing high-grade or lethal cancer (metastatic or fatal disease). The relative risk (95% CI) comparing the highest quintile to the lowest (Q5 vs Q1) was 0.25 (95% CI 0.07–0.87; Ptrend = 0.02) for lethal cancer. Among all the cases, higher adiponectin concentrations predicted lower prostate cancer–specific mortality [hazard ratio (HR)Q5 vs Q1= 0.39; 95% CI 0.17–0.85; Ptrend = 0.02], independent of body mass index (BMI), plasma C-peptide (a marker of insulin secretion), leptin, clinical stage, and tumor grade. This inverse association was apparent mainly among men with a BMI ≥25 kg/m2 (HRQ5 vs Q1= 0.10; 95% CI 0.01–0.78; Ptrend = 0.02), but not among men of normal weight (Ptrend = 0.51). Although the correlation of leptin concentrations with BMI (r = 0.58, P < 0.001) was stronger than that of adiponectin (r = −0.17, P < 0.001), leptin was unrelated to prostate cancer risk or mortality. Conclusions: Higher prediagnostic adiponectin (but not leptin) concentrations predispose men to a lower risk of developing high-grade prostate cancer and a lower risk of subsequently dying from the cancer, suggesting a mechanistic link between obesity and poor prostate cancer outcome.

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Section: Discussionsupporting

confidence: 80%

A 25-Year Prospective Study of Plasma Adiponectin and Leptin Concentrations and Prostate Cancer Risk and Survival

et al. 2010

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“…Macro-scale models can provide information to a micro-scale model such as agent-based models, or they can treat the tumor as a continuum and simply ignore the interaction effects of individual cells. This is often used to model general patterns of growth, including the total cell number, and to determine the onset of metastasis [19]. Here, continuum modeling is acceptable because as scales become larger cellular responses can be treated as gradients in response to substrate gradients.…”

Section: Overview Of Multiscale Abm Methodsmentioning

confidence: 99%

Simulating cancer growth with multiscale agent-based modeling

Wang

Butner

Kerketta

et al. 2015

Seminars in Cancer Biology

200

147

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There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models.

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“…Metastasis is taken to occur when N L reaches a critical size N crit (≪ N Lmax ) because PC is likely cured [13] if treated early (i. e. N L < N crit ). Indeed, Dattoli et al [14] studied the interaction of a PC tumor with its host prostate and estimated a critical cell count for metastasis to be roughly 10 9 – 10 10 cells. When N L exceeds N crit , the systemic cells grow explosively from N HS = N HR = 0 as new tumor sites develop.…”

Section: Methodsmentioning

confidence: 99%

Use of tumor dynamics to clarify the observed variability among biochemical recurrence nomograms for prostate cancer

et al. 2011

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BACKGROUND Nomograms for biochemical recurrence (BCR) of prostate cancer (PC) after radical prostatectomy can yield very different prognoses for individual patients. Since the nomograms are optimized on different cohorts, the variations may be due to differences in patient risk-factor distributions. In addition, the nomograms assign different relative scores to the same PC risk factors and rarely stratify for tumor growth rate. METHODS We compared BCR-free probabilities from the GPSM model with a cell kinetics (CK) model that uses the individual’s tumor state and growth rate. We first created a cohort of 143 patients that reproduced the GPSM patient distribution in Gleason score, Prostate specific antigen (PSA), Seminal vesicle involvement and Margin status since they form the GPSM score. We then performed 143 CK calculations to determine BCR-free probabilities for comparison with the GPSM results for all scores and with four other prominent nomograms for a high-risk patient. RESULTS The BCR-free probabilities from the CK model agree within 10% with those from the GPSM study for all scores once the CK model parameters are stratified in terms of the GPSM risk factors and the PSA doubling time (PSADT). However, the probabilities from widely used nomograms vary significantly. CONCLUSIONS The CK model reproduces the observed GPSM BCR-free probabilities with a broad stratification of model parameters for PC risk factors and can thus be used to describe PC progression for individual patients. The analysis suggests that nomograms should stratify for PSADT to be predictive.

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Cancer metabolism and the dynamics of metastasis

Cited by 14 publications

References 23 publications

A 25-Year Prospective Study of Plasma Adiponectin and Leptin Concentrations and Prostate Cancer Risk and Survival

A 25-Year Prospective Study of Plasma Adiponectin and Leptin Concentrations and Prostate Cancer Risk and Survival

Simulating cancer growth with multiscale agent-based modeling

Use of tumor dynamics to clarify the observed variability among biochemical recurrence nomograms for prostate cancer

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