Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that comprises approximately 20% of new breast cancer diagnoses each year. Because pathological evaluation of DCIS typically relies on histologic criteria that offer little value in predicting which lesions will progress to invasive breast cancer (IBC), many patients receive unnecessary surgical and adjuvant interventions that often lead to therapy-related complications. To date, most clinical studies examining protein expression of DCIS in breast cancer biopsies have employed immune-peroxidase staining using a single primary antibody, such that each protein is visualized in separate serial biopsy sections. Consequently, despite numerous studies implicating a critical role in the pathogenesis of IBC, multiplexed, quantitative single cell profiles relating the phenotype of breast tumor cells with the surrounding microenvironment have not been described. With this in mind, the work proposed here focuses on using a novel imaging platform that I recently developed, multiplexed ion beam imaging (MIBI), to define new disease models for mapping progressive perturbations in protein expression and tissue histology that correlate with developing DCIS and IBC. Multiplexed ion beam imaging (MIBI) is capable of analyzing up to 100 targets simultaneously and is compatible with standard formalin-fixed, paraffin-embedded (FFPE) tissue specimens, and the most common sample type in clinical repositories worldwide. I will first expand upon recent work to create an antibody panel for characterizing fibroblasts, macrophages, and epithelium in human breast tissue. This panel will be used to interrogate previously constructed tissue microarrays comprised of clinically annotated biopsies of normal breast, DCIS, and IBC. These experiments will, for the first time, simultaneously characterize phenotypic and histologic features of epithelal and stromal components of normal and neoplastic human breast tissue at the subcellular level. These features will then be analyzed in aggregate to construct predictive clinical classifiers that can be used to select optimal therapeutic regimens that prevent progression to IBC while also minimizing treatment-related morbidity. More generally, this work will also establish a novel platform for gaining an unprecedented view into disease pathogenesis that could be easily adapted in future work to risk stratify other pre-invasive lesions.
Ductal carcinoma in situ (DCIS) is a pre-invasive lesion who's potential for progressing to invasive breast cancer (IBC) remains difficult to predict. The goal of the project proposed here is to use a novel imaging platform I recently developed, multiplexed ion beam imaging, to define new disease models for mapping distinctive features that correlate with developing DCIS and IBC. Predictive signatures derived from these results will have direct implications for selecting optimal therapeutic regimens in DCIS that prevent progression to IBC while also minimizing treatment-related morbidity.