Eric T. Fung

Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer

Early detection remains the most promising approach to improve long-term survival of patients with ovarian cancer. In a five-center case-control study, serum proteomic expressions were analyzed on 153 patients with invasive epithelial ovarian cancer, 42 with other ovarian cancers, 166 with benign pelvic masses, and 142 healthy women. Data from patients with early stage ovarian cancer and healthy women at two centers were analyzed independently and the results cross-validated to discover potential biomarkers. The results were validated using the samples from two of the remaining centers. After protein identification, biomarkers for which an immunoassay was available were tested on samples from the fifth center, which included 41 healthy women, 41 patients with ovarian cancer, and 20 each with breast, colon, and prostate cancers. Three biomarkers were identified as follows: (a) apolipoprotein A1 (down-regulated in cancer); (b) a truncated form of transthyretin (down-regulated); and (c) a cleavage fragment of inter-α-trypsin inhibitor heavy chain H4 (up-regulated). In independent validation to detect early stage invasive epithelial ovarian cancer from healthy controls, the sensitivity of a multivariate model combining the three biomarkers and CA125 [74% (95% CI, 52–90%)] was higher than that of CA125 alone [65% (95% CI, 43–84%)] at a matched specificity of 97% (95% CI, 89–100%). When compared at a fixed sensitivity of 83% (95% CI, 61–95%), the specificity of the model [94% (95% CI, 85–98%)] was significantly better than that of CA125 alone [52% (95% CI, 39–65%)]. These biomarkers demonstrated the potential to improve the detection of early stage ovarian cancer.

Proteomic Approaches to Tumor Marker Discovery

CONTEXT Current tumor markers for ovarian cancer still lack adequate sensitivity and specificity to be applicable in large populations. High-throughput proteomic profiling and bioinformatics tools allow for the rapid screening of a large number of potential biomarkers in serum, plasma, or other body fluids. OBJECTIVE To determine whether protein profiles of plasma can be used to identify potential biomarkers that improve the detection of ovarian cancer. DESIGN We analyzed plasma samples that had been collected between 1998 and 2001 from patients with sporadic ovarian serous neoplasms before tumor resection at various International Federation of Gynecology and Obstetrics stages (stage I [n = 11], stage II [n = 3], and stage III [n = 29]) and from women without known neoplastic disease (n = 38) using proteomic profiling and bioinformatics. We compared results between the patients with and without cancer and evaluated their discriminatory performance against that of the cancer antigen 125 (CA125) tumor marker. RESULTS We selected 7 biomarkers based on their collective contribution to the separation of the 2 patient groups. Among them, we further purified and subsequently identified 3 biomarkers. Individually, the biomarkers did not perform better than CA125. However, a combination of 4 of the biomarkers significantly improved performance (P < or =.001). The new biomarkers were complementary to CA125. At a fixed specificity of 94%, an index combining 2 of the biomarkers and CA125 achieves a sensitivity of 94% (95% confidence interval, 85%-100.0%) in contrast to a sensitivity of 81% (95% confidence interval, 68%-95%) for CA125 alone. CONCLUSIONS The combined use of bioinformatics tools and proteomic profiling provides an effective approach to screen for potential tumor markers. Comparison of plasma profiles from patients with and without known ovarian cancer uncovered a panel of potential biomarkers for detection of ovarian cancer with discriminatory power complementary to that of CA125. Additional studies are required to further validate these biomarkers.

Protein biochips for differential profiling

Progress has been made in utilizing ProteinChip technology to profile and compare protein expression in normal and diseased states, particularly in the areas of cancer, infectious disease and toxicology. The past year has also seen the development of several novel chip types designed to analyze proteins in a fashion analogous to the array-based format of DNA microarrays. Some of these platforms may be used for differential profiling.

Classification of cancer types by measuring variants of host response proteins using SELDI serum assays

Protein expression profiling has been increasingly used to discover and characterize biomarkers that can be used for diagnostic, prognostic or therapeutic purposes. Most proteomic studies published to date have identified relatively abundant host response proteins as candidate biomarkers, which are often dismissed because of an apparent lack of specificity. We demonstrate that 2 host response proteins previously identified as candidate markers for early stage ovarian cancer, transthyretin and inter‐alpha trypsin inhibitor heavy chain 4 (ITIH4), are posttranslationally modified. These modifications include proteolytic truncation, cysteinylation and glutathionylation. Assays using Surface Enhanced Laser Desorption/Ionization Time of Flight Mass Spectrometry (SELDI‐TOF‐MS) may provide a means to confer specificity to these proteins because of their ability to detect and quantitate multiple posttranslationally modified forms of these proteins in a single assay. Quantitative measurements of these modifications using chromatographic and antibody‐based ProteinChip® array assays reveal that these posttranslational modifications occur to different extents in different cancers and that multivariate analysis permits the derivation of algorithms to improve the classification of these cancers. We have termed this process host response protein amplification cascade (HRPAC), since the process of synthesis, posttranslational modification and metabolism of host response proteins amplifies the signal of potentially low‐abundant biologically active disease markers such as enzymes.

β2-Microglobulin as a Biomarker in Peripheral Arterial Disease Proteomic Profiling and Clinical Studies

Background— Peripheral arterial disease (PAD) is common but commonly unrecognized. Improved recognition of PAD is needed. We used high-throughput proteomic profiling to find PAD-associated biomarkers. Methods and Results— Plasma was collected from PAD patients (ankle brachial index of <0.90; n=45) and subjects with risk factors but without PAD (n=43). Plasma was analyzed with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to quantify 1619 protein peaks. The peak intensity of a 12-kDa protein was higher in PAD patients. Western blot analyses and immunoaffinity studies confirmed that this protein was &bgr;2-microglobulin (B2M). In a validation study, B2M was measured by ELISA in plasma in age- and gender-matched PAD (n=20) and non-PAD (n=20) subjects. Finally, we studied a larger cohort of subjects (n=237) referred for coronary angiography but without known PAD. Plasma B2M levels were higher in PAD patients than in non-PAD patients with coronary artery disease. Plasma B2M correlated with ankle brachial index and functional capacity. Independent predictors of PAD were diabetes mellitus, age, and the combination of B2M and C-reactive protein level. Conclusions— In PAD patients, circulating B2M is elevated and correlates with the severity of disease independent of other risk factors. These findings might provide a needed biomarker for PAD and new insight into its pathophysiology. Further studies in other populations are needed to confirm the utility of measuring B2M in cardiovascular disease risk assessment.

A Framework for Evaluating Biomarkers for Early Detection: Validation of Biomarker Panels for Ovarian Cancer

A panel of biomarkers may improve predictive performance over individual markers. Although many biomarker panels have been described for ovarian cancer, few studies used prediagnostic samples to assess the potential of the panels for early detection. We conducted a multisite systematic evaluation of biomarker panels using prediagnostic serum samples from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial. Using a nested case–control design, levels of 28 biomarkers were measured laboratory-blinded in 118 serum samples obtained before cancer diagnosis and 951 serum samples from matched controls. Five predictive models, each containing 6 to 8 biomarkers, were evaluated according to a predetermined analysis plan. Three sequential analyses were conducted: blinded validation of previously established models (step 1); simultaneous split-sample discovery and validation of models (step 2); and exploratory discovery of new models (step 3). Sensitivity, specificity, sensitivity at 98% specificity, and AUC were computed for the models and CA125 alone among 67 cases diagnosed within one year of blood draw and 476 matched controls. In step 1, one model showed comparable performance to CA125, with sensitivity, specificity, and AUC at 69.2%, 96.6%, and 0.892, respectively. Remaining models had poorer performance than CA125 alone. In step 2, we observed a similar pattern. In step 3, a model derived from all 28 markers failed to show improvement over CA125. Thus, biomarker panels discovered in diagnostic samples may not validate in prediagnostic samples; utilizing prediagnostic samples for discovery may be helpful in developing validated early detection panels. Cancer Prev Res; 4(3); 375–83. ©2011 AACR.

A Recipe for Proteomics Diagnostic Test Development: The OVA1 Test, from Biomarker Discovery to FDA Clearance

Although diagnostic test development remains challenging, novel technologies, including proteomics, genomics, and microRNA analysis, provide opportunities to identify biomarkers that in principle could accelerate the development of new diagnostic tests. Unfortunately, the literature is littered with initial biomarker discoveries that have failed to reach the clinic. We sought to identify a recipe that combines the basic ingredients of clinical research with novel analytical tools to create a new diagnostic test. In the test kitchen, we understood that the most important ingredient is the unmet clinical need. Having made the decision to develop a test for ovarian cancer, we discussed with clinicians what they felt were the most pressing needs in this field. Our initial instinct was to pursue ovarian cancer screening, but in our conversations with key opinion leaders, including Ian Jacobs (University College London), Bob Bast (MD Anderson), and Daniel Chan (Johns Hopkins University), we came to understand that because of the low prevalence of ovarian cancer, development of a screening test would require large studies that would exceed our budget and time constraints. Additionally, because a positive initial result in a screening test would likely lead to pelvic surgery, the test would demand a level of clinical specificity that we were unlikely to achieve. Our colleagues, however, identified a critical unmet need in the area of ovarian tumor triage. Although ovarian tumors are relatively common, only a fraction of them are malignant. Being able to identify the malignant ones preoperatively would permit better preoperative management of women with ovarian tumors. In particular, women with a high likelihood of malignancy could benefit from referral to specialist surgeons (e.g., gynecologic oncologists), who would be able to perform debulking and staging surgeries that form the basis of optimal care for ovarian cancer (1). Having identified the clinical question, we found …

Current achievements using ProteinChip ® Array technology

Because of its inherent flexibility, the ProteinChip Array platform has demonstrated utility into basic research as well as clinical research. In the domain of basic research, it has been used to examine protein modifications, characterize protein-protein interactions and study signal transduction and enzymatic pathways. In clinical research, it has been used to elucidate and identify biomarkers of disease, and as a platform for predictive medicine.

Evaluation of Apolipoprotein A1 and Posttranslationally Modified Forms of Transthyretin as Biomarkers for Ovarian Cancer Detection in an Independent Study Population

Background: Although overall 5-year survival rates for ovarian cancer are poor (10-30%), stage I/IIa patients have a 95% 5-year survival. New biomarkers that improve the diagnostic performance of existing tumor markers are critically needed. A previous study by Zhang et al. reported identification and validation of three biomarkers using proteomic profiling that together improved early-stage ovarian cancer detection. Methods: To evaluate these markers in an independent study population, postdiagnostic/pretreatment serum samples were collected from women hospitalized at the Mayo Clinic from 1980 to 1989 as part of the National Cancer Institute Immunodiagnostic Serum Bank. Sera from 42 women with ovarian cancer, 65 with benign tumors, and 76 with digestive diseases were included in this study. Levels of various posttranslationally forms of transthyretin and apolipoprotein A1 were measured in addition to CA125. Results: Mean levels of five of the six forms of transthyretin were significantly lower in cases than in controls. The specificity of a model including transthyretin and apolipoprotein A1 alone was high [96.5%; 95% confidence interval (95% CI), 91.9-98.8%] but sensitivity was low (52.4%; 95% CI, 36.4-68.0%). A class prediction algorithm using all seven markers, CA125, and age maintained high specificity (94.3%; 95% CI, 89.1-97.5%) but had higher sensitivity (78.6%; 95% CI, 63.2-89.7%). Conclusions: We were able to replicate the findings reported by Zhang et al. in an independently conducted blinded study. These results provide some evidence that including age of patient and these markers in a model may improve specificity, especially when CA125 levels are ≥35 units/mL. Influences of sample handling, subject characteristics, and other covariates on biomarker levels require further consideration in discovery and replication or validation studies. (Cancer Epidemiol Biomarkers Prev 2006;15(9):1641–6)

SELDI-TOF-MS proteomics of breast cancer.

Abstract The detection, diagnosis, and management of breast cancer rely on an integrated approach using clinical history, physical examination, imaging, and histopathology. The discovery and validation of novel biomarkers will aid the physician in more effectively achieving this integration. This review discusses efforts in surface-enhanced laser desorption/ionization (SELDI)-based proteomics to address various clinical questions surrounding breast cancer, including diagnosis, monitoring, and stratification for treatment. Emphasis is placed on examining how study design and execution influence the discovery and validation process, which is critical to the proper development of potential clinical tests.