The overall goal of this application is to discover and validate multiple novel plasma protein biomarkers of human ovarian cancer. We will initially discover a substantial number of candidate biomarkers through parallel analysis of two complementary models of ovarian cancers and will subsequently validate the most promising candidate biomarkers in a human patient cohort.
In Aim 1, we will use a xenograft model where human ovarian cancer cell lines are injected into both ovaries of NOD/SCID/3c-/- (NOG) mice and human primary tumors are allowed to grow prior to collection of the plasma. Several existing early passage serous cancer cell lines will be used and additional early passage cell lines will be derived from human ovarian tumors for this study. Low abundance plasma proteins will be identified using a 4-D protein profiling method that is capable of detecting many proteins in the low ng/ml to pg/ml range in plasma. Proteins secreted or shed by the human tumors will be unambiguously identified using high mass accuracy mass spectrometry coupled with rigorous data analysis to distinguish human and mouse proteins based on species-specific sequence differences. Low abundance human proteins that can be identified with >99% confidence and where repeat targeted LC-MS/MS analysis of the fraction can confirm the protein identification will be considered as candidate biomarkers.
In Aim 2, we will analyze the secretome (shed and secreted proteins) from short term organ cultures using fresh ovarian tumors. The effects of normoxic and hypoxic conditions on protein shedding will be compared. In addition, the secretomes of short term organ culture, early passage established cultures from the same tumor and xenograft tumors from the same cell line will be compared to determine how different environmental and physiological context affects ovarian tumor cell protein shedding.
In Aim 3, we will select the most promising candidate biomarkers from Aims 1 and 2 for validation analyses. Candidate biomarkers from the discovery studies in Aims 1 and 2 will be prioritized based upon: abundance in normal human plasma;specificity for ovarian tumors: consistency of shedding by different ovarian tumors, and biological function. The highest priority candidate biomarkers will be subsequently validated using multiplexed multiple reaction monitoring (MRM) mass spectrometry assays to quantify biomarker levels in plasma of ovarian patients and controls. Individual biomarkers as well as groups of biomarkers will be evaluated for their capacity to predict early stage ovarian cancers
Survival rates of ovarian cancer patients are about 90% if the disease is confined to the ovaries at diagnosis. Unfortunately, early stage ovarian tumors are usually asymptomatic and about 75% of cases are diagnosed after the cancer has spread. Recently developed powerful mass spectrometry-based plasma proteome analysis methods coupled with mouse ovarian cancer models provide unique opportunities to systematically discover many new ovarian cancer biomarker candidates that will lead to improved early diagnosis and clinical management of this disease.
|Beer, Lynn A; Wang, Huan; Tang, Hsin-Yao et al. (2013) Identification of multiple novel protein biomarkers shed by human serous ovarian tumors into the blood of immunocompromised mice and verified in patient sera. PLoS One 8:e60129|
|Tang, Hsin-Yao; Beer, Lynn A; Tanyi, Janos L et al. (2013) Protein isoform-specific validation defines multiple chloride intracellular channel and tropomyosin isoforms as serological biomarkers of ovarian cancer. J Proteomics 89:165-78|
|Tang, Hsin-Yao; Beer, Lynn A; Chang-Wong, Tony et al. (2012) A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer. J Proteome Res 11:678-91|
|Cao, Zhijun; Tang, Hsin-Yao; Wang, Huan et al. (2012) Systematic comparison of fractionation methods for in-depth analysis of plasma proteomes. J Proteome Res 11:3090-100|
|Tang, Hsin-Yao; Beer, Lynn A; Barnhart, Kurt T et al. (2011) Rapid verification of candidate serological biomarkers using gel-based, label-free multiple reaction monitoring. J Proteome Res 10:4005-17|
|Beer, Lynn A; Tang, Hsin-Yao; Barnhart, Kurt T et al. (2011) Plasma biomarker discovery using 3D protein profiling coupled with label-free quantitation. Methods Mol Biol 728:3-27|
|Wang, Huan; Tang, Hsin-Yao; Tan, Glenn C et al. (2011) Data analysis strategy for maximizing high-confidence protein identifications in complex proteomes such as human tumor secretomes and human serum. J Proteome Res 10:4993-5005|
|Tang, Hsin-Yao; Beer, Lynn A; Speicher, David W (2011) In-depth analysis of a plasma or serum proteome using a 4D protein profiling method. Methods Mol Biol 728:47-67|