Ovarian cancer is the fifth-leading cause of cancer-related death in women in the United States, and is the most lethal of all gynecological malignancies.1
In 2010, an estimated 21,880 women were diagnosed with ovarian cancer, and 13,850 deaths occurred in the United States alone.1
The most common and deadly form of ovarian cancer is epithelial ovarian cancer, which further can be divided into four major histopathological groups: serous, endometrioid, mucinous, and clear cell tumors.2,3
The high mortality rate of ovarian cancer is due largely to the lack of effective screening strategies for early detection. When ovarian cancer is diagnosed at an early stage (stages I or II), treatment is highly effective, with a five-year survival rate of up to 90%, whereas the five-year survival rate for patients with advanced disease (stages III and IV) is reduced to 30% or less.4, 5
Unfortunately, most ovarian cancers are not diagnosed until after the cancer has spread, primarily because earlier-stage diseases are asymptomatic and the ovaries are buried deep within the body.
Current screening methods for ovarian cancer typically use a combination of pelvic examination, transvaginal ultrasonography, and serum CA125, but these methods are not effective in detecting early-stage ovarian cancer.6–8
In addition, CA125 is recognized as a poor protein biomarker for early detection due to its high false positive rate and poor sensitivity and specificity.9, 10
Other promising biomarkers have been reported,11, 12
but a recently completed study comparing many of these protein biomarkers showed that none of them performed better than CA125 as a biomarker for ovarian cancer.13
A few groups also have used panels of biomarkers and obtained better sensitivity and specificity than CA125 alone when used in diagnostic samples.14–17
However, a recent study found that available biomarker panels did not outperform CA125 when used in prediagnostic samples.18
Therefore, better biomarkers that could diagnose early-stage ovarian cancer with high sensitivity and specificity are needed. Furthermore, it is unlikely that any single protein will have adequate specificity and sensitivity for early diagnosis of most solid-tumor cancers. Instead, multiple novel biomarkers must be identified and analyzed in combination to identify biomarker panels that can outperform the use of CA125 alone.
Proteomics technology offers a conceptually attractive platform for cancer biomarker discovery.19
Human blood, in the form of plasma or serum, is one of the most valuable specimens for protein biomarker discovery because it is routinely collected, collection is minimally invasive, and it contains thousands of proteins, including those secreted or shed into the blood by tumors.20
However, systematic discovery of serological biomarkers directly from human serum using proteomics has proven extremely challenging due to the extremely wide concentration range of blood proteins that span more than 10 orders of magnitude. In addition, the most tumor-specific proteins are very likely to primarily be shed by the tumor and will be very low abundant in blood, as exemplified by well-known cancer biomarkers such as PSA and CEA, which are present in serum in the low ng/mL to pg/mL range.20, 21
Most cancers and other diseases also elicit a wide range of host response mechanisms, producing many acute-phase or inflammation-related proteins. It is unlikely that most such relatively general host responses will have sufficient specificity and sensitivity for cancer detection in at-risk populations, although selected inflammation-related biomarkers could contribute to panels of biomarkers that include proteins specifically shed by the tumor. Regardless, it is clear that these common, acute-phase-related changes in serum proteins hamper discovery of tumor-specific proteins when directly profiling sera in human populations. Finally, individual protein levels in blood are highly variable in the human population due to extensive genetic, physiological, and environmental variations, requiring analysis of many patient and control samples before statistically significant, disease-related differences can be identified.
The dynamic range and complexity of the blood proteome can partially be addressed by major protein depletion and multidimensional sample prefractionation. We and others have shown that multidimensional sample prefractionation prior to mass spectrometry analysis greatly enhances proteome coverage and allows detection of low-abundance proteins, at least down to the low ng/mL range.22–27
To overcome the genetic, physiological, and environmental variability associated with analyzing human samples, many less complex experimental models, including cancer cell lines in culture,28, 29
cancer tissue specimens,30, 31
ascites fluid,32, 33
and mouse models,36–38
have been used in ovarian cancer biomarker discovery. Each model has its benefits, but most strategies, except for the use of mouse models, are not able to determine if the discovered biomarkers are actually shed into blood. In ovarian cancer, the use of both genetically engineered and xenograft mouse models to facilitate serum biomarker discovery has been described.36–38
Even though subject-to-subject heterogeneity is considerably reduced with the use of genetically engineered mouse models, these models still produce many host-response protein changes that can be difficult to distinguish from more tumor-specific protein changes.37
The use of xenograft mouse models has several advantages over other models. First, proteins shed by human tumors into mouse blood can be unambiguously distinguished from less-specific host responses by exploiting species differences in peptide sequences identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Second, the blood volume of a mouse is approximately 5,000 times less than an adult human. Therefore, proteins shed by similar-sized small tumors in a mouse and an adult human are likely to be at least 1,000 times more concentrated in a xenograft mouse as compared to the same size tumor in a human. Third, the minimal biological heterogeneity of the xenograft mouse model means that only a small number of samples need to be profiled in order to make inferences about putative biomarkers.
While the use of xenograft mouse models potentially can improve detection of novel cancer biomarkers, mouse serum is still a very complex proteome and requires multidimensional sample prefractionation for sufficient depth of analysis. For example, in a prior xenograft mouse study using two-dimensional gel electrophoresis and without any sample prefractionation, only acute-phase proteins were identified successfully.39
In the case of ovarian cancer, two different studies using a xenograft model with human SKOV-3 serous ovarian cancer cells have been described. In one study, mouse sera were trypsin-digested and analyzed directly by LC-MS/MS, resulting in identification of 13 human proteins.38
The other study focused on the low-molecular weight serum proteome/peptidome of the xenograft model and reported the identification of five human proteins.36
While both prior xenograft ovarian cancer studies successfully identified a few candidate biomarkers (14-3-3 zeta and S100A6), we reasoned that the combination of a xenograft mouse model with more extensive fractionation of serum proteins could identify much larger numbers of novel human candidate biomarkers. The most difficult-to-detect proteins are expected to be lower abundance and, therefore, may be more tumor specific.
In this study, we established a xenograft mouse model using the ovarian endometrioid TOV-112D cell line and analyzed the serum proteome using a 4-D protein profiling strategy. We demonstrated that it is possible to detect many tumor-derived human proteins, including low-abundance human proteins that are present at < 100 ng/mL in normal human serum. In a proof-of-concept validation analysis, we quantified the levels of three high-priority candidate biomarkers in serum from ovarian patients, as well as normal controls and patients with benign disease, using label-free MRM-MS.