Spreaders and sponges define metastasis in lung cancer: A Markov chain mathematical model
A partir de bases de données recueillies au cours d'autopsies, cette étude de modélisation mathématique identifie deux types de processus métastatique d'un cancer du poumon
The classic view of metastatic cancer progression is that it is a unidirectional process initiated at the primary tumor site, progressing to variably distant metastatic sites in a fairly predictable, though not perfectly understood, fashion. A Markov chain Monte Carlo mathematical approach can determine a pathway diagram that classifies metastatic tumors as 'spreaders' or 'sponges' and orders the timescales of progression from site to site. In light of recent experimental evidence highlighting the potential significance of self-seeding of primary tumors, we use a Markov chain Monte Carlo (MCMC) approach, based on large autopsy data sets, to quantify the stochastic, systemic, and often multi-directional aspects of cancer progression. We quantify three types of multi-directional mechanisms of progression: (i) self-seeding of the primary tumor; (ii) re-seeding of the primary tumor from a metastatic site (primary re-seeding); and (iii) re-seeding of metastatic tumors (metastasis re-seeding). The model shows that the combined characteristics of the primary and the first metastatic site to which it spreads largely determine the future pathways and timescales of systemic disease. For lung cancer, the main `spreaders' of systemic disease are the adrenal gland and kidney, whereas the main `sponges' are regional lymph nodes, liver, and bone. Lung is a significant self-seeder, although it is a `sponge' site with respect to progression characteristics.