Project:
Nanostructure-Initiator Mass Spectrometry Based Tissue Imaging to Identify Metabolic Biomarkers of Basal and Luminal Breast Cancer Subtypes

Principal Investigator - Trent Northen, PhD

ABSTRACT

Functional ("Omics") imaging of tissues provides a tremendous opportunity to gain insights into the pathological processes of breast cancer including the effects of tissue microenvironment. Current approaches are focused on genomic and proteomic imaging. However, the importance of cellular metabolisms in cancer anthology coupled with the utility of small molecule biomarkers make it critical to develop the complementary metabolite imaging approaches. Unfortunately, technical limitations of existing mass spectrometry approaches have largely limited this possibility. Specifically, extensive fragmentation of Secondary Ion Mass Spectrometry (SIMS) makes metabolite identification extremely difficult, matrix interference limits application of imaging Matrix Assisted Laser Desorption Ionization (MALDI) for metabolite analysis, and the low resolution of Desorption Electrospray Ionization (DESI, 0.5 mm) limit its application. We have developed a new surface-based mass spectrometry approach that is well suited for metabolite profiling and imaging from frozen tissue sections (Nanostructure-Initiator Mass Spectrometry, NIMS) with a unique combination of high lateral resolution (10-75 μ), sensitivity (highest reported including single cancer cells), and lack of matrix. Preliminary results reveal dramatic differences between abnormal and normal adjacent breast tissue. However, breast cancer is a very heterogeneous disease making it difficult to select the appropriate treatment. For example, basal and luminal are two molecular subtypes of breast cancer. The basal subtype has been associated with strongly reduced survival durations in patients treated with surgery and radiation. Recent work suggests that these two subtypes should be metabolically distinct. We hypothesize that our NIMS imaging approach will also be able to identify metabolic biomarkers to distinguish between two. The aims of this project are to: 1) use NIMS-based tissue imaging to identify metabolic biomarkers which can discriminate between basal and luminal subtypes, and 2) initial validation of this approach by correctly discriminating subtype in blinded experiments.