Breast cancer is prevalent both in the United States and worldwide. While both screening tests and targeted therapies are available, there are still challenges in the diagnosis, prognosis, and treatment of breast cancer. We were motivated to develop a new approach for both studying the etiology of heterogeneity in breast cancer phenotypes and predicting the course of disease in tumor biopsies using an emerging chemical imaging technology.
It has been described extensively that the tumor microenvironment, or stroma, can promote or suppress cancer phenotypes in many tissues. We hypothesized that interaction with fibroblasts, the predominant cell type found in the breast tumor stroma, is a critical regulatory step in the progression of breast cancer from confined to invasive disease. Hence, we developed a novel three-dimensional co-culture model to investigate this interaction. The development and validation of this model is described in chapter two. We used it first to determine how cancerous molecular signatures can propagate from cancerous to normal epithelium through the activation of fibroblasts. Changes in the architectural morphology of normal mammary acini were used as a metric to determine cancer progression, in addition to gene expression analysis and cell-based assays such as proliferation and migration assays. This system is a robust and easily applied tool for investigating fibroblast-epithelial communication in a physiologically-relevant context.
The 3D co-culture system was used to investigate how fibroblasts impact the growth of estrogen receptor-positive (ER+) breast cancer cells and this is described in chapter three. ER+ is the most common subtype of breast cancer (>75%) and while these patients are eligible to receive targeted endocrine therapies, up to 30% of patients will experience a recurrence. Others will fail to respond to front-line endocrine therapies, while more patients will become resistant to endocrine therapies over time. We aimed to understand how fibroblasts play a role in the progression from hormone-dependent to hormone-independent growth. The cell culture data was translated to patient samples using bioinformatics approaches and label-free chemical imaging. Further, we define one aspect of the interaction between breast cancer cells and fibroblasts through identifying secreted proteins that are involved in the stromal-epithelial communication. The 43-protein signature can be used to classify breast cancer patients based on their corresponding gene expression profile, and we found that the signature is significantly upregulated in patients with more invasive disease.
In order to continue translating our results from cell culture to patient samples, we describe the application of label-free Fourier transform infrared spectroscopic imaging to monitoring breast cancer cell phenotypes. Chapter four details how biological changes can be spatially resolved in heterogeneous samples while in chapter five an approach to determine estrogen receptor presence and function in cell culture samples and patient biopsies is discussed. We show how FT-IR imaging can be used to define label-free spectroscopic signatures that are consistent between cell culture and patients, and explore how this approach may be used in the future to add additional information to current pathology practice.
We have developed a method to both understand the molecular mechanisms involved in how the microenvironment regulates early breast cancer phenotypes and to detect altered cellular phenotypes using label-free FT-IR imaging. We aim to apply this systems pathology approach to the development of novel diagnostic and prognostic signatures for determining the trajectory of cancer progression at very early stages
