Are Metastases from Metastases Clinical Relevant? Computer Modelling of Cancer Spread in a Case of Hepatocellular Carcinoma

Abstract

BackgroundMetastasis formation remains an enigmatic process and one of the main questions recently asked is whether metastases are able to generate further metastases. Different models have been proposed to answer this question; however, their clinical significance remains unclear. Therefore a computer model was developed that permits comparison of the different models quantitatively with clinical data and that additionally predicts the outcome of treatment interventions.MethodsThe computer model is based on discrete events simulation approach. On the basis of a case from an untreated patient with hepatocellular carcinoma and its multiple metastases in the liver, it was evaluated whether metastases are able to metastasise and in particular if late disseminated tumour cells are still capable to form metastases. Additionally, the resection of the primary tumour was simulated. The simulation results were compared with clinical data.ResultsThe simulation results reveal that the number of metastases varies significantly between scenarios where metastases metastasise and scenarios where they do not. In contrast, the total tumour mass is nearly unaffected by the two different modes of metastasis formation. Furthermore, the results provide evidence that metastasis formation is an early event and that late disseminated tumour cells are still capable of forming metastases. Simulations also allow estimating how the resection of the primary tumour delays the patient's death.ConclusionThe simulation results indicate that for this particular case of a hepatocellular carcinoma late metastases, i.e., metastases from metastases, are irrelevant in terms of total tumour mass. Hence metastases seeded from metastases are clinically irrelevant in our model system. Only the first metastases seeded from the primary tumour contribute significantly to the tumour burden and thus cause the patient's death.

Spreaders and Sponges Define Metastasis in Lung Cancer: A Markov Chain Monte Carlo Mathematical Model

“…Alternative methods have been proposed, but by and large, tracking survival of a cohort of patients remains the industry-standard way of tracking progression. There is very little in the literature that tracks the timescale of progression from metastatic site to metastatic site (24-27, 29). …”

“…These authors further comment that ‘it is tempting to speculate that self-seeding might occur not only at the primary tumor site, but also at distinct metastatic sites, … each site being a nesting ground’. The possibility of metastasis from metastases has also been discussed (23,29). While the underlying ‘agent’ responsible for the spread of cancer is the CTC, the disease progression pathways in different patients can be both predictable (from a statistical viewpoint), but often unpredictable and surprisingly distinct in patients with nominally the same disease (26,27), prompting the question ‘how can metastatic pathways be predictable and unpredictable at the same time’ (10)?…”

A mathematical model of systemic inhibition of angiogenesis in metastatic development

“…There is no clear consensus in the biological literature about metastases being able to metastasize or not [9,34,35]. However, we argue here that cancer cells that acquired the ability to metastasize should conserve it when establishing in a new site.…”

“…We present now simulations of the nondimensionalized model (1,(5)(6)(7)(8)(9). For simulations based on comparison to experimental data we refer to [8] and focus here on the theoretical dynamical behavior of the model.…”

“…However, we argue here that cancer cells that acquired the ability to metastasize should conserve it when establishing in a new site. Moreover, since metastasis is a long process before being detectable [9,34,35] (in particular because tumors could remain dormant during large time periods), the absence of clear proof in favor of metastases from metastases could be due to the short duration of the experiments compared to the time required for a secondary generation of tumors to reach a visible size. Here we are interested in long time behaviors and, although metastases from metastases could be neglected in first approximation, we think this second order term is relevant in our setting and chose to include it in our modeling, following clinical evidences of second-generation metastases [2].…”