Identification of multiple novel protein biomarkers shed by human serous ovarian tumors into the blood of immunocompromised mice and verified in patient sera.

The most cancer-specific biomarkers in blood are likely to be proteins shed directly by the tumor rather than less specific inflammatory or other host responses. The use of xenograft mouse models together with in-depth proteome analysis for identification of human proteins in the mouse blood is an under-utilized strategy that can clearly identify proteins shed by the tumor. In the current study, 268 human proteins shed into mouse blood from human OVCAR-3 serous tumors were identified based upon human vs. mouse species differences using a four-dimensional plasma proteome fractionation strategy. A multi-step prioritization and verification strategy was subsequently developed to efficiently select some of the most promising biomarkers from this large number of candidates. A key step was parallel analysis of human proteins detected in the tumor supernatant, because substantially greater sequence coverage for many of the human proteins initially detected in the xenograft mouse plasma confirmed assignments as tumor-derived human proteins. Verification of candidate biomarkers in patient sera was facilitated by in-depth, label-free quantitative comparisons of serum pools from patients with ovarian cancer and benign ovarian tumors. The only proteins that advanced to multiple reaction monitoring (MRM) assay development were those that exhibited increases in ovarian cancer patients compared with benign tumor controls. MRM assays were facilely developed for all 11 novel biomarker candidates selected by this process and analysis of larger pools of patient sera suggested that all 11 proteins are promising candidate biomarkers that should be further evaluated on individual patient blood samples.

A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer.

Proteomics discovery of novel cancer serum biomarkers is hindered by the great complexity of serum, patient-to-patient variability, and triggering by the tumor of an acute-phase inflammatory reaction. This host response alters many serum protein levels in cancer patients, but these changes have low specificity as they can be triggered by diverse causes. We addressed these hurdles by utilizing a xenograft mouse model coupled with an in-depth 4-D protein profiling method to identify human proteins in the mouse serum. This strategy ensures that identified putative biomarkers are shed by the tumor, and detection of low-abundance proteins shed by the tumor is enhanced because the mouse blood volume is more than a thousand times smaller than that of a human. Using TOV-112D ovarian tumors, more than 200 human proteins were identified in the mouse serum, including novel candidate biomarkers and proteins previously reported to be elevated in either ovarian tumors or the blood of ovarian cancer patients. Subsequent quantitation of selected putative biomarkers in human sera using label-free multiple reaction monitoring (MRM) mass spectrometry (MS) showed that chloride intracellular channel 1, the mature form of cathepsin D, and peroxiredoxin 6 were elevated significantly in sera from ovarian carcinoma patients.

Comparison of extensive protein fractionation and repetitive LC-MS/MS analyses on depth of analysis for complex proteomes.

In-depth, reproducible coverage of complex proteomes is challenging because the complexity of tryptic digests subjected to LC-MS/MS analysis frequently exceeds mass spectrometer analytical capacity, which results in undersampling of data. In this study, we used cancer cell lysates to systematically compare the commonly used GeLC-MS/MS (1-D protein + 1-D peptide separation) method using four repetitive injections (2-D/repetitive) with a 3-D method that included solution isoelectric focusing and involved an equal number of LC-MS/MS runs. The 3-D method detected substantially more unique peptides and proteins, including higher numbers of unique peptides from low-abundance proteins, demonstrating that additional fractionation at the protein level is more effective than repetitive analyses at overcoming LC-MS/MS undersampling. Importantly, more than 90% of the 2-D/repetitive protein identifications were found in the 3-D method data in a direct protein level comparison, and the reproducibility between data sets increased to greater than 96% when factors such as database redundancy and use of rigid scoring thresholds were considered. Hence, high reproducibility of complex proteomes, such as human cancer cell lysates, readily can be achieved when using multidimensional separation methods with good depth of analysis.