Chalmers Conferences, 9th European Conference on Mathematical and Theoretical Biology

An evolutionary model of micrometastatic progression: are driver mutations in the primary tumor important?
Christopher McFarland

Last modified: 2014-06-09


Tumor cells are unlikely to metastasize even after intravasation, a phenomenon termed metastatic inefficiency. Approximately 80% of circulating tumor cells extravasate into foreign stroma, while only a small fraction of these extravasated cells (~2.5%) ever divide, forming micrometastases. Lastly, and most critical to our study, very few micrometastases (~1%) ever grow to clinical size, while most regress and disappear. Previously, we developed a model of tumor progression that seldomly grew to macroscopic size and most often regressed, like micrometastases. This was a result of a tug-of-war between advantageous (epi)genetic driver mutations that increased cancer cell’s proliferation rate, and frequent passenger mutations that were moderately harmful to cancer cells. By extended this theory to the evolution of micrometastases, we were able to describe many clinical phenomena, including metastatic inefficiency at this final step. The model predicted that tumors with more mutations would be less likely to metastasize, which proved true in a mouse model and suggests that a tumor’s mutational load may serve as a biomarker for response to mutagenic chemotherapy and metastatic disease.  

Dynamics in our model depend critically on a stroma factor, which defines the similarity of the microenvironment of new stroma to the microenvironment of the primary tumor. When large, colonizing cells are more likely to disperse early in the primary tumor’s evolution (long before clinical detection), are more likely to form metastases that are genetically very distinct from the primary tumor, and are less likely to respond to therapies targeting drivers in the primary tumor. Genomic evidence from the The Cancer Genome Atlas suggests that the stromal factor is indeed large. Hence, to successfully treat metastatic disease, clinicians must go beyond targeting the phenotypes of primary tumors, and instead characterize and exploit the phenotypes of descended metastases.