Patients with liver metastases from primary colorectal cancers (CRC) are characterized by a poor prognosis. The 5-year survival rate for primary nonmetastatic CRC (stages I - III) is estimated as 75 - 88%3,4, while patients with liver metastases (stage IV) have a 5-year survival rate of only 8 - 12%5,6. However, metastatic patients represent a heterogeneous group, presenting with different numbers of metastases and different recurrence times. Clinical observations indicate that the number of metastases (which may be proportional to colonizing ability or frequency of colonization) and the size of any single metastasis (proportional to the local growth rate) are independent prognostic factors1,7. In other words, the success of metastatic clones colonizing the liver depends on two major properties: their ability to grow and their ability to disseminate and survive in the liver microenvironment.
The design of successful clinical models with the capability of capturing and quantifying the properties of metastatic clones can drastically improve our understanding of liver metastasis biology and provide an effective tool for the design of potential therapeutic approaches. Models of experimental liver metastasis have been previously reported8,9, but neither of them provided the ability to quantitatively capture and describe properties of individual metastatic clones both in vivo and ex vivo.
Here, we present a new, advanced model of liver metastasis that includes the generation of tumor clones with different liver colonization efficiencies and growth properties. We employed a combination of dual-labeling of cancer cells with luciferase and tdTomato fluorescent protein with the generation of monoclonal cell lines that have intrinsic differences in metastatic capacity. In this experimental model, the data indicate that the development of liver metastases can be described in terms of colonization frequency and doubling time (Td), which is consistent with clinical observations. The quantitative nature of this model makes it easily adoptable for drug discovery and diagnostic purposes.