Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and affects more than 37,000 women each year. Previous work has shown that the presence of immune cells in the tumor microenvironment of TNBC influences overall survival. This has prompted clinical trials testing immunomodulatory drugs for the treatment of this disease. Although immunotherapy has demonstrated success across a range of tumor types, only a subset of patients experience significant benefit. Identifying biomarkers to predict which patients will respond to these drugs has been extremely challenging. Sequencing based approaches require the tissue to be dissociated prior to analysis, and hence do not capture the spatial relationships between different cell types. Imaging methods do capture these spatial relationships, but can only visualize a small number of proteins at a time. This results in an incomplete picture of the complexity of the tumor microenvironment, since not all cell types can be identified at once. Our group has recently developed Multiplexed Ion Beam Imaging, which allows for a nearly 10-fold increase in the number of antibodies that can be visualized simultaneously. Our hypothesis is that by combining this novel imaging modality with DNA and RNA sequencing, we will be able to comprehensively profile the tumor microenvironment of TNBC patients, and thus significantly improve prediction of response.
In Aim 1, I will improve the computational tools our lab uses to identify the boundaries between adjacent cells in tissue, in order to accurately assign imaging signal to the correct cell.
In Aim 2, I will use our lab?s novel imaging platform to profile samples from patients enrolled in a clinical trial targeting PD-1, a key immune regulatory protein. I will then use this rich information to predict patient response to therapy.
In Aim 3 I will integrate sequencing data from the same samples with the imaging data we generated to determine how genetic alterations influence the composition of immune cells present in the tumor microenvironment. This work will increase our understanding of the immune interactions in TNBC, and will generate significantly improved models to predict response to immunotherapy.
This proposal has direct public health relevance due to the insights we will gain into the mechanisms of response and resistance to immunotherapy. The findings generated in this project will guide follow-up studies aimed at understanding the mechanisms of response to immunotherapy, as well as clinical trials testing the ability of the identified biomarkers to prospectively predict patient response.
