(111e) Ovarian Cancer Early Detection Using Proteomic Data - A Systems Engineering Approach for Biomarker Discovery

Ovarian cancer has the characteristics of few early symptoms and poor survival rate, therefore, early detection of ovarian cancer is very critical and new technologies for ovarian cancer early detection are urgently needed, especially for women who have high risk of ovarian cancer. In order to achieve early detection of ovarian cancer, specific and sensitive molecular markers are essential. Because pathological changes within an organ might be reflected in serum proteome, proteomics may offer the best chance of discovering the early stage changes (Petricoin et al., 2002). Recent advances in mass spectrometry (MS) offer exciting opportunities for novel biomarker discovery and several methods have been published to identify biomarkers using serum proteomic data (Petricoin et al., 2002; Sorace & Zhan, 2003; Zhu et al., 2003; Wu et al., 2003). However, considerable controversy has been generated by some initial results (Wagner, 2004; Diamandis, 2004; Check, 2004; Garber, 2004; Baggerly et al., 2005; Liotta et al., 2005), and there remain some critical issues such as reproducibility and robustness of these methods (Jocobs & Menon, 2004; Ransohoff, 2005).

So far the identified biomarkers for ovarian cancer detection are mainly single or combination of multiple m/z peaks of MS data that show different means in cancer patients compared to healthy controls. Because of large variations among individuals, these biomarkers vary significantly within normal and cancer groups, which yield unsatisfactory sensitivity and specificity. The disagreement of biomarker levels between training and testing datasets also indicates the possible issue of reliability and robustness of some existing methods. Because ovarian cancer can be viewed as a ?fault? in human body, in this research, we aim to study the ovarian cancer early detection problem from a systems engineering perspective, i.e., to apply principles and adapt proven techniques for fault detection and diagnosis in systems engineering to ovarian cancer detection and diagnosis in clinical research. It has been realized that despite the extremely different physical implementations, a human body can be viewed as a complicated biochemical plant. More importantly, at the system level human body shares important common features with a chemical plant. As a result of these common features, there are some remarkable similarities between disease detection in clinical research and fault detection in systems engineering. Because of these similarities, a systems engineering perspective can deepen our understanding of the disease mechanism, and provide an alternative set of tools for disease detection and diagnosis.

In this research, two different approaches, sequential approach and parallel approach are developed to identify significant correlation changes, instead of certain protein level changes, to tackle ovarian cancer detection problem using proteomic data. A publicly available ovarian cancer proteomic dataset(NCI 8-7-02) from National Cancer Institute are analyzed using both methods. The performances of the proposed approaches were compared to existing methods and their advantages and limitations are discussed.

Key words: Proteomic data, ovarian cancer detection, systems engineering, biomarkers, correlation changes.

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