Live Cell Lineage Tracing of Dormant Cancer Cells

Introduction:: Breast cancer is a leading cause of global cancer-related deaths, and metastasis is the overwhelming culprit of poor patient prognosis. The most nefarious aspect of metastasis is dormancy, a prolonged period between primary tumor resection and relapse. Current therapies are insufficient at killing dormant cells; thus, they can remain quiescent in the body for decades until eventually undergoing a phenotypic switch, resulting in metastases that are more adaptable and more drug resistant. Labs have been trying to establish the causal basis of dormancy and the molecular mechanisms driving the regrowth of disseminated tumor cells (DTCs); however, it has been a challenge to demonstrate that an individual DTC is both viable and dormant and most of the field’s knowledge of tumor and cellular dormancy is limited to static, endpoint measurements in vivo. Moreover, dormancy has few in vitro models, largely because lab-derived cell lines are highly proliferative. Existing models address tumor dormancy, not cellular dormancy, because tracking individual cells is technically challenging. To combat this problem, we adapted a live cell lineage approach to find and track individual dormant cells, distinguishing them from proliferative and dying cells over multiple days.


Materials and Methods:: Different types of human breast cancer cells (MCF7, MDA-MB-231, HCC1954, and HCC1143) were seeded on plastic, functionalized glass coverslips, 2D collagen gel, 2D Matrigel, or in 3D collagen gel or 3D Matrigel. 24 hours after cell seeding, cell culture media were changed to serum-containing or serum-free media. 6 hours after the media change, cells were imaged for 50 hours at 15- (for 2D) or 30-minute intervals (for 3D) under microscope, and individual cell proliferation was plotted onto cell lineage trees and the population ratios were visualized by stacked bar graphs (Fig. 1a-h). Two types of mouse breast cancer cells (dormant D2.0R, proliferative D2A1) were seeded in 3D Matrigel for the cell lineage tree analysis, and they were stained with dormancy markers of ERK and p38 to validate the cell lineage tree method was working to distinguish dormant cells (Fig 1i-j).


Results, Conclusions, and Discussions:: Results & Discussion: When comparing HCC1954 (ER-, HER2+) and MCF7 (ER+) cells, there was no significant difference in dormant cell population on 2D Matrigel (Fig. 1c-d), while there were more dormant cells found in MCF7 than in HCC1954 in 3D Matrigel. When comparing a single cell line (HCC1954) in different environments, highest number of dormant cell population was found in 3D collagen gels although there was no significant difference in dormant cell population between 2D collagen and Matrigel (Fig. 1b-c, e-f). This data tells us that both cell subtypes and culturing environment affect cellular dormancy. We also compared dormant cell population analyzed by the cell lineage tree approach to that analyzed by immunofluorescent staining of dormant markers, such as low Ki-67, low ERK and high p38, which is a traditional method to distinguish dormant cells (Fig. 1i-j). The immunofluorescent staining showed that there was the highest number or dormant cells found in D2.0R cells in serum-free condition, validating our results from cell lineage tree analysis. In addition, the cell lineage tree analysis method was applied for serum recovery experiments, to distinguish dormant cells from senescent cells (data not shown). We are also applying this cell lineage tree analysis method to study if AXL (putative dormancy regulator) is required to regulate dormancy in breast and lung cancers, by using AXL wild-type and AXL knock-out cell lines of human breast cancer (MDA-MB-231) and lung cancer (H1299).

Conclusions: We applied this approach across a range of different in vitro microenvironments. Our approach revealed that the proportion of cells that exhibited long-term quiescence was regulated by both cell intrinsic and extrinsic factors, with the most dormant cells found in 3D collagen gels. We envision that this approach will prove useful to biologists and bioengineers in the dormancy community to identify, quantify, and study dormant tumor cells.


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