“This Might Sound Cheesy, But We Need to Know More About Ovarian Cancer Development”
Professor Laura Sanchez, University of Illinois at Chicago, Medicinal Chemistry and Pharmacognosy
Natural environments such as soil and food contain many molecules that convolute analyses, and identification of microbial metabolites amongst environmental metabolites is an informatic problem we begin to address here. Our microbes are derived from naturally aged cheese and grown on solid or liquid cheese curd media for the purpose of identifying metabolites involved in bacterial-fungal interactions. This medium, which is necessary for proper microbial growth, contains high amounts of salts, lipids, and casein breakdown products which make statistical analysis using mass spectrometry data difficult due to the high background from the media. We have developed a simple algorithm to carry out background subtraction from microbes grown on solid or liquid cheese curd media to aid in our ability to conduct statistical analysis so that we may prioritize metabolites for further structure elucidation.
Ovarian cancer is a severe gynecological disease and is currently the fifth leading cause of cancer-related deaths among women. This disease is typically diagnosed in its later stages, once tumor metastasis has occurred, due to the fact that there are no routine screens that are clinically available. Due to a stagnation of development in ovarian cancer diagnostics, the medical field is in significant need of tools to better detect ovarian cancer. Recent evidence suggests that DNA sourced from ovarian cancer tumor cells has been found in Pap smear samples, which are sourced from the cervix. This would imply that these cells migrate from their tumors of origin in the fallopian tube and/or ovaries and that it is feasible to detect these cells using a sensitive analytical technique, such as mass spectrometry (MS). We hypothesize that cells sourced from the local microenvironment of female reproductive tracts can be used to diagnose ovarian cancer in its early stages through the development of MS methods. Samples containing heterogeneous cell populations were obtained weekly from mice via vaginal lavage and spectral fingerprints were collected using MALDI-TOF MS. This technique has promise in its ability to differentiate between healthy and disease states based on cell populations taken from a local environment.
“Proteomic Identification of Galectin-1 Overexpression in Murine Primary and Metastatic Claudin-low Breast Tumors”
Kassie Balestrieri, Laboratory of Professor Kathryn Verbanac, East Carolina University, Brody School of Medicine, Department of Surgery
Triple negative breast cancer (TNBC) lacks expression of estrogen and progesterone receptors and has reduced expression of tyrosine kinase receptor HER2. Molecular subtyping has further refined the TNBC subtype into six classifications, one of which is claudin-low. Metastases and mortality among patients with claudin-low tumors remain high; these tumors are more clinically aggressive, and lack targeted therapies compared to other breast cancer subtypes. Our objective is to identify proteins associated with claudin-low primary and metastatic tumors in order to gain insight into pathways and mechanisms that could help identify biomarkers or targets for intervention. The present study uses a syngeneic murine T11 tumor model, which displays gene expression profiles mirroring human claudin-low breast tumors, and discovery-based proteomics. Nano-LC/MS analyses was conducted on trypsin-digested cultured murine T11 tumor cells, primary orthotopic tumors and lung metastases (n≥2), as well as mammary fat pad and lung tissue (n≥2) from naïve mice (healthy contemporary controls). Of the proteins identified with >95% confidence, nineteen were present in claudin-low T11 cultures, primary tumor, and metastatic tissue samples, but not in control tissues. Among the nineteen proteins was the N-acetyllactosamine-specific binding protein Galectin-1 (Gal-1). Four consistent Gal-1 peptides were identified in lysates of all tumor-containing samples, but absent from control tissues. Protein coverage was 72% in primary tumor and 47% in lungs with metastases. To spatially map peptides, MALDI/MSI was conducted on 10 µm sections of flash-frozen, trypsin-digested primary tumors, metastatic lung and normal lung. In all samples, Gal-1 peptides were detected with Mascot scores >106 after MS-Bridge. Gal-1 expression (intensity) levels were higher in primary tumors compared to tissue from healthy naïve mice and higher in metastatic areas of lung tissue, compared to adjacent areas. Eleven Gal-1 peptides identified in MALDI/MSI were present in >67% of tumor samples (n=3) and >75% of metastatic lung samples (n=4) with four consistent peptides in 100% of samples. Western blot and immunohistochemistry analysis demonstrated higher Gal-1 expression in T11 primary tumors compared to control mammary fat pad, and higher Gal-1 expression in T11 lung metastases compared to normal lung. Immunohistochemistry also confirmed Gal-1 spatial distribution in primary tumors and lungs, with increased expression within metastatic tumor foci of lung tissue. These findings are further supported by our analysis of both murine and human genomic data sets, which demonstrate Gal-1 overexpression in the claudin-low subtype compared to other breast cancer subtypes and normal breast tissue. Several reports in other TNBC subtypes have suggested a role for Gal-1 in tumor cell migration, metastasis and/or immune evasion. To our knowledge, this is the first report to identify and confirm Gal-1 overexpression in claudin-low TNBC metastases. Our findings support the need for further investigations that focus on determining the role and significance of Gal-1 overexpression in claudin-low tumor progression.