Cancer Biology

Obesity has been linked to breast cancer progression but the underlying mechanisms remain obscure. Being overweight or obese for a woman at the time she is diagnosed with breast cancer is linked to a high risk of recurrence regardless of treatment factors. In rodents, high body weight is also associated with increased incidence of spontaneous and chemically induced tumors. To study the complex interaction between the mammary epithelia and the microenvironment, with a focus on the mechanism underlying the role obesity plays in the regulation of the cancer stem cell traits and the development of mammary cancer in vivo, we have established a diet-induced obesity (DIO) rat model of breast cancer (Chang et al., 2015).

In 1999, Hahnfeldt et al. proposed a mathematical model for tumor growth as dictated by reciprocal communications between tumor and its associated vasculature, introducing the idea that a tumor is supported by a dynamic, rather than a static, carrying capacity. In this original paper, the carrying capacity was equated with the variable tumor vascular support resulting from the net effect of tumor-derived angiogenesis stimulators and inhibitors. This dynamic carrying capacity model was further abstracted and developed in our recent publication to depict the more general situation where there is an interaction between the tumor and its supportive host tissue; in that case, as a function of host aging (Benzekry et al., 2014). This allowed us to predict a range of host changes that may be occurring with age that impact tumor dynamics. More generally, the basic formalism described here can be (and has been), extended to the therapeutic context using additional optimization criteria (Hahnfeldt et al., 1999). The model depends on three parameters: One for the tumor cell proliferation kinetics, one for the stimulation of the stromal support, and one for its inhibition, as well as two initial conditions. We describe here the numerical method to estimate these parameters from longitudinal tumor volume measurements.

Tumors are comprised of heterogeneous subpopulations that may exhibit differing capacity for differentiation, self-renewal, and tumorigenicity. In vivo lineage-tracing studies are a powerful tool for defining the role of tumor subpopulations in tumor growth and as targets for therapeutic agents. This protocol describes using a neuroblastoma cancer cell line transduced with two different fluorescent proteins (GFP and tdTomato) to track the specific contributions of cells expressing the GCSF receptor (CD114⁺) or not (CD114^-) on tumor growth in vivo.