In order to grow, replicate, and communicate with one another, healthy cells produce signaling molecules (typically proteins) in strictly regulated amounts and at strictly regulated stages during their life cycle. A hallmark of cancerous cells is the loss of this strict regulation—they produce proteins at inappropriate times and greater than normal quantities, enabling the unlimited replication, rapid growth, and metastasis responsible for cancer’s damaging effects on the human body.
While this out-of-control production of molecules is one of cancer’s greatest weapons, it also presents scientists and clinicians a means by which to detect the presence of potentially cancerous cells long before they have a chance to damage the body. By comparing the protein profile of an ovarian cancer patient to that of a healthy woman, imbalances in protein production can be rapidly identified and monitored over time. These diagnostic molecules are known as biomarkers. Some ovarian cancer biomarkers are already utilized in clinical settings, such as cancer antigen 125 (CA-125) and HE4, which are approved by the Federal Drug Administration for use in monitoring the recurrence of ovarian cancer.
The goal of the OCEDP is to discover highly sensitive, highly specific ovarian cancer biomarkers that can be used in conjunction with existing biomarkers, like CA-125 and HE4, to produce a screening system that can detect ovarian cancer signals at the earliest stages of the disease.
For technical information regarding the OCEDP's experiments in biomarker discovery, see the information below.
Early detection of ovarian cancer is needed to increase patient survival. However, a screening tool that is adequately sensitive and specific for use in the general population has yet to be developed.
No biomarker by itself, including CA125 and HE4, has been sufficient to detect early stages of ovarian cancer; rather, we postulate that multiple biomarkers need to be combined into a detection test. We are in the process of screening blood samples from hundreds of women with early stages of ovarian cancer or from healthy women on a multiplex platform. We are using the latest technology to determine the levels of 92 cancer-related proteins in patients' samples. By performing a series of complex statistical analyses, we will identify the candidate biomarkers that are the most specific and sensitive for ovarian cancer detection. We will then combine the candidate biomarkers into a mathematical formula that will enable us to discriminate between samples from healthy women and women with early stages of ovarian cancer. Our goal is to develop a test to detect ovarian cancer in its earliest, most readily treatable stage.
At a cost of $100, the typical rate that a hospital clinical laboratory charges for quantification of one serum biomarker, the levels of 92 different protein biomarkers can be quantified in an individual’s serum using this new platform. Once we determine which of the 92 biomarkers are most relevant for detecting early stages of ovarian cancer, this technology can be readily translated into the clinical laboratory. The findings from this study will eventually lead to a new profile/signature of biomarkers that can be used to screen the blood of women on an annual basis for the detection of ovarian cancer. Using a multiplex approach, rather than just one biomarker at a time, it will be possible to: (a) diagnose ovarian cancer in a woman with an abdominal mass prior to surgery, (b) screen high-risk women for ovarian cancer, and (c) ultimately screen the general population of women for ovarian cancer.
A multiplex platform for the identification of ovarian cancer biomarkers.
Boylan KLM, Geschwind K, Koopmeiners JS, Geller MA, Starr TK, Skubitz APN
Background: Currently, there are no FDA approved screening tools for detecting early-stage ovarian cancer in the general population. Development of a biomarker-based assay for early detection would significantly improve the survival of ovarian cancer patients.
Methods: We used a multiplex approach to identify protein biomarkers for detecting early stage ovarian cancer. This new technology (Proseek® Multiplex Oncology Plates) can simultaneously measure the expression of 92 proteins in serum based on a proximity extension assay. We analyzed serum samples from 81 women representing healthy, benign pathology, early, and advanced stage serous ovarian cancer patients.
Results: Principal component analysis and unsupervised hierarchical clustering separated patients into cancer versus non-cancer subgroups. Data from the Proseek® plate for CA125 levels exhibited a strong correlation with current clinical assays for CA125 (correlation coefficient of 0.89, 95% CI 0.83, 0.93). CA125 and HE4 were present at very low levels in healthy controls and benign cases, while higher levels were found in early-stage cases, with highest levels found in the advanced stage cases. Overall, significant trends were observed for 38 of the 92 proteins (p < 0.001), many of which are novel candidate serum biomarkers for ovarian cancer. The area under the ROC curve (AUC) for CA125 was 0.98 and the AUC for HE4 was 0.85 when comparing early-stage ovarian cancer versus healthy controls. In total, 23 proteins had an estimated AUC of 0.7 or greater. Using a naïve Bayes classifier that combined 12 proteins, we improved the sensitivity corresponding to 95% specificity from 93 to 95% when compared to CA125 alone. Although small, a 2% increase would have a significant effect on the number of women correctly identified when screening a large population.
Conclusions: These data demonstrate that the Proseek® technology can replicate the results established by conventional clinical assays for known biomarkers, identify new candidate biomarkers, and improve the sensitivity and specificity of CA125 alone. Additional studies using a larger cohort of patients will allow for validation of these biomarkers and lead to the development of a screening tool for detecting early stage ovarian cancer in the general population.
View the full paper on PubMed.
Simultaneous Measurement of 92 Serum Protein Biomarkers for the Development of a Multuprotein Classifier for Ovarian Cancer Detection.
Skubitz APN, Boylan KLM, Geschwind K, Cao Q, Starr TK, Geller MA, Celestino J, Bast Jr RC, Lu KH, Koopmeiners JS.
Abstract: The best known ovarian cancer biomarker, CA125, is neither adequately sensitive nor specific for screening the general population. By using a combination of proteins for screening, it may be possible to increase the sensitivity and specificity over CA125 alone. In this study, we used Proseek Multiplex Oncology II plates to simultaneously measure the expression of 92 cancer-related proteins in serum using proximity extension assays. This technology combines the sensitivity of the PCR with the specificity of antibody-based detection methods, allowing multiplex biomarker detection and high-throughput quantification. We analyzed 1 μL of sera from each of 61 women with ovarian cancer and compared the values obtained with those from 88 age-matched healthy women. Principle component analysis and unsupervised hierarchical clustering separated the ovarian cancer patients from the healthy, with minimal misclassification. Data from the Proseek plates for CA125 levels exhibited a strong correlation with clinical values for CA125. We identified 52 proteins that differed significantly (P < 0.006) between ovarian cancer and healthy samples, several of which are novel serum biomarkers for ovarian cancer. In total, 40 proteins had an estimated area under the ROC curve of 0.70 or greater, suggesting their potential to serve as biomarkers for ovarian cancer. CA125 alone achieved a sensitivity of 93.4% at a specificity of 98%. By adding the Oncology II values for five proteins to CA125 in a multiprotein classifier, we increased the assay sensitivity to 98.4% at a specificity of 98%, thereby improving the sensitivity and specificity of CA125 alone.
View the full paper here.
Differential gene expression in ovarian carcinoma: identification of potential biomarkers.
Hibbs K, Skubitz KM, Pambuccian SE, Casey RC, Burleson KM, Oegema TR Jr, Thiele JJ, Grindle SM, Bliss RL, Skubitz AP.
Abstract: Ovarian cancer remains the fifth leading cause of cancer death for women in the United States. In this study, the gene expression of 20 ovarian carcinomas, 17 ovarian carcinomas metastatic to the momentum, and 50 normal ovaries were determined by Gene Logic Inc. using Affymetrix GeneChip HU_95 arrays containing approximately 12,000 known genes. Differences in gene expression were quantified as fold changes in gene expression in ovarian carcinomas compared to normal ovaries and ovarian carcinoma metastases. Genes up-regulated in ovarian carcinoma tissue samples compared to more than 300 other normal and diseased tissue samples were identified. Seven genes were selected for further screening by immunohistochemistry to determine the presence and localization of the proteins. These seven genes were: the beta8 integrin subunit, bone morphogenetic protein-7, claudin-4, collagen type IX alpha2, cellular retinoic acid binding protein-1, forkhead box J1, and S100 calcium-binding protein A1. Statistical analyses showed that the beta8 integrin subunit, claudin-4, and S100A1 provided the best distinction between ovarian carcinoma and normal ovary tissues, and may serve as the best candidate tumor markers among the seven genes studied. These results suggest that further exploration into other up-regulated genes may identify novel diagnostic, therapeutic, and/or prognostic biomarkers in ovarian carcinoma.
View the full paper here.
Early detection of ovarian cancer using group biomarkers.
Tchagang AB, Tewfik AH, DeRycke MS, Skubitz KM, Skubitz AP.
Abstract: One reason that ovarian cancer is such a deadly disease is that it is not usually diagnosed until it has reached an advanced stage. In this study, we developed a novel algorithm for group biomarkers identification using gene expression data. Group biomarkers consist of coregulated genes across normal and different stage diseased tissues. Unlike prior sets of biomarkers identified by statistical methods, genes in group biomarkers are potentially involved in pathways related to different types of cancer development. They may serve as an alternative to the traditional single biomarkers or combination of biomarkers used for the diagnosis of early-stage and/or recurrent ovarian cancer. We extracted group biomarkers by applying biclustering algorithms that we recently developed on the gene expression data of over 400 normal, cancerous, and diseased tissues. We identified several groups of coregulated genes that encode for secreted proteins and exhibit expression levels in ovarian cancer that are at least 2-fold (in log2 scale) higher than in normal ovary and nonovarian tissues. In particular, three candidate group biomarkers exhibited a conserved biological pattern that may be used for early detection or recurrence of ovarian cancer with specificity greater than 99% and sensitivity equal to 100%. We validated these group biomarkers using publicly available gene expression datasets downloaded from an NIH Web site (www.ncbi.nlm.nih.gov/geo). Statistical analysis showed that our methodology identified an optimum combination of genes that have the highest effect on the diagnosis of the disease compared with several computational techniques that we tested. Our study also suggests that single or group biomarkers correlate with the stage of the disease.
View the full paper here.
Mathematical prognostic biomarker models for treatment response and survival in epithelial ovarian cancer.
Nikas JB, Boylan KL, Skubitz AP, Low WC.
Abstract: Following initial standard chemotherapy (platinum/taxol), more than 75% of those patients with advanced stage epithelial ovarian cancer (EOC) experience a recurrence. There are currently no accurate prognostic tests that, at the time of the diagnosis/surgery, can identify those patients with advanced stage EOC who will respond to chemotherapy. Using a novel mathematical theory, we have developed three prognostic biomarker models(complex mathematical functions) that-based on a global gene expression analysis of tumor tissue collected during surgery and prior to the commencement of chemotherapy-can identify with a high accuracy those patients with advanced stage EOC who will respond to the standard chemotherapy [long-term survivors (>7 yrs)] and those who will not do so [short-term survivors (<3 yrs)]. Our three prognostic biomarker models were developed with 34 subjects and validated with 20 unknown (new and different) subjects. Both the overall biomarker model sensitivity and specificity ranged from 95.83% to 100.00%. The 12 most significant genes identified, which are also the input variables to the three mathematical functions, constitute three distinct gene networks with the following functions: 1) production of cytoskeletal components, 2) cell proliferation, and 3) cell energy production. The first gene network is directly associated with the mechanism of action of anti-tubulin chemotherapeutic agents, such as taxanes and epothilones. This could have a significant impact in the discovery of new, more effective pharmacological treatments that may significantly extend the survival of patients with advanced stage EOC.
View the full paper here.
Differential gene expression identifies subgroups of ovarian carcinoma.
Skubitz AP, Pambuccian SE, Argenta PA, Skubitz KM.
View the full paper here.
Mass Spectrometry Proteomics
Quantitative proteomic analysis by iTRAQ(R) for the identification of candidate biomarkers in ovarian cancer serum.
Boylan KL, Andersen JD, Anderson LB, Higgins L, Skubitz AP.
Ovarian cancer is the most lethal gynecologic malignancy, with the majority of cases diagnosed at an advanced stage when treatments are less successful. Novel serum protein markers are needed to detect ovarian cancer in its earliest stage; when detected early, survival rates are over 90%. The identification of new serum biomarkers is hindered by the presence of a small number of highly abundant proteins that comprise approximately 95% of serum total protein. In this study, we used pooled serum depleted of the most highly abundant proteins to reduce the dynamic range of proteins, and thereby enhance the identification of serum biomarkers using the quantitative proteomic method iTRAQ(R).
Medium and low abundance proteins from 6 serum pools of 10 patients each from women with serous ovarian carcinoma, and 6 non-cancer control pools were labeled with isobaric tags using iTRAQ(R) to determine the relative abundance of serum proteins identified by MS. A total of 220 unique proteins were identified and fourteen proteins were elevated in ovarian cancer compared to control serum pools, including several novel candidate ovarian cancer biomarkers: extracellular matrix protein-1, leucine-rich alpha-2 glycoprotein-1, lipopolysaccharide binding protein-1, and proteoglycan-4. Western immunoblotting validated the relative increases in serum protein levels for several of the proteins identified.
This study provides the first analysis of immunodepleted serum in combination with iTRAQ(R) to measure relative protein expression in ovarian cancer patients for the pursuit of serum biomarkers. Several candidate biomarkers were identified which warrant further development.
View the full paper here.
Identification of candidate biomarkers in ovarian cancer serum by depletion of highly abundant proteins and differential in-gel electrophoresis.
Andersen JD, Boylan KL, Xue FS, Anderson LB, Witthuhn BA, Markowski TW, Higgins L, Skubitz AP.
Abstract: Ovarian cancer is the fifth leading cause of cancer death for women in the US, yet survival rates are over 90% when it is diagnosed at an early stage, highlighting the need for biomarkers for early detection. To enhance the discovery of tumor-specific proteins that could represent novel serum biomarkers for ovarian cancer, we depleted serum of highly abundant proteins which can mask the detection of proteins present in serum at low concentrations. Three commercial immunoaffinity columns were used in parallel to deplete the highly abundant proteins in serum from 60 patients with serous ovarian carcinoma and 60 non-cancer controls. Medium and low abundance serum proteins from each serum pool were then evaluated by the quantitative proteomic technique of differential in-gel electrophoresis. The number of protein spots that were elevated in ovarian cancer sera by at least twofold ranged from 36 to 248, depending upon the depletion and separation methods. From the 33 spots picked for MS analysis, nine different proteins were identified, including the novel candidate ovarian cancer biomarkers leucine-rich alpha2 glycoprotein-1 and ficolin 3. Western blotting validated the relative increases in serum protein levels for three of the proteins identified, demonstrating the utility of this approach for the identification of novel serum biomarkers for ovarian cancer.
View the full paper here.