Jonathan Tammela

Detection of epithelial ovarian cancer using 1H-NMR-based metabonomics

Currently available serum biomarkers are insufficiently reliable to distinguish patients with epithelial ovarian cancer (EOC) from healthy individuals. Metabonomics, the study of metabolic processes in biologic systems, is based on the use of 1H‐NMR spectroscopy and multivariate statistics for biochemical data generation and interpretation and may provide a characteristic fingerprint in disease. In an effort to examine the utility of the metabonomic approach for discriminating sera from women with EOC from healthy controls, we performed 1H‐NMR spectroscopic analysis on preoperative serum specimens obtained from 38 patients with EOC, 12 patients with benign ovarian cysts and 53 healthy women. After data reduction, we applied both unsupervised Principal Component Analysis (PCA) and supervised Soft Independent Modeling of Class Analogy (SIMCA) for pattern recognition. The sensitivity and specificity tradeoffs were summarized for each variable using the area under the receiver‐operating characteristic (ROC) curve. In addition, we analyzed the regions of NMR spectra that most strongly influence separation of sera of EOC patients from healthy controls. PCA analysis allowed correct separation of all serum specimens from 38 patients with EOC (100%) from all of the 21 premenopausal normal samples (100%) and from all the sera from patients with benign ovarian disease (100%). In addition, it was possible to correctly separate 37 of 38 (97.4%) cancer specimens from 31 of 32 (97%) postmenopausal control sera. SIMCA analysis using the Coomans plot demonstrated that sera classes from patients with EOC, benign ovarian cysts and the postmenopausal healthy controls did not share multivariate space, providing validation for the class separation. ROC analysis indicated that the sera from patients with and without disease could be identified with 100% sensitivity and specificity at the 1H‐NMR regions 2.77 parts per million (ppm) and 2.04 ppm from the origin (AUC of ROC curve = 1.0). In addition, the regression coefficients most influential for the EOC samples compared to postmenopausal controls lie around δ3.7 ppm (due mainly to sugar hydrogens). Other loadings most influential for the EOC samples lie around δ2.25 ppm and δ1.18 ppm. These findings indicate that 1H‐NMR metabonomic analysis of serum achieves complete separation of EOC patients from healthy controls. The metabonomic approach deserves further evaluation as a potential novel strategy for the early detection of epithelial ovarian cancer.

New modalities in detection of recurrent ovarian cancer.

Purpose of review The vast majority of women diagnosed with ovarian cancer and subsequently treated with debulking surgery and adjuvant chemotherapy will ultimately relapse. As is the case with primary diagnosis, detection of recurrent ovarian cancer is limited due to lack of sensitivity and specificity. Specific guidelines for surveillance of this disease are controversial, partly because evidence to support such guidelines is scant and partly because the management of identified recurrences continues to be of minimal success. Subsequently, whether early detection actually can make a difference is not necessarily made clear in the literature. However, there are advances in radiological and molecular biology technology that may offer new possibilities in cancer surveillance. This review will outline the latest evidence to address their use in ovarian cancer. Recent findings Most of the recent literature involving detection of recurrent ovarian cancer addresses the use of positron emission tomography. There are also some data addressing the use of magnetic resonance imaging and computed tomography in this arena. Data pertaining to other modalities such as biological markers are limited. Ca-125 is the accepted assay used for ovarian cancer surveillance, but other options are introduced that may hold promise for the future. Summary A review of the recent literature concerning ovarian cancer surveillance techniques offers few new definitive avenues. While radiological technology and discoveries in detection assays are noteworthy, their potential impact on surveillance appears to be minimal at this time. Low sensitivity and specificity, along with expense, continue to be limiting factors.

Characterization of a Putative Ovarian Oncogene, Elongation Factor 1α, Isolated by Panning a Synthetic Phage Display Single-Chain Variable Fragment Library with Cultured Human Ovarian Cancer Cells

Purpose: In an effort to identify cell surface targets and single short-chain antibody (scFv) for ovarian cancer therapy, we used a phage display approach to isolate an antibody with high reactivity against ovarian cancer. Experimental Design: A phage scFv library was subjected to panning against human SK-OV-3 ovarian cancer cells. A clone with high reactivity was selected and tested in immunoperoxidase staining on a panel of normal tissues and ovarian carcinoma. Using immunoprecipitation, a differentially expressed band was analyzed by mass spectrometry. The antigen subclass was characterized with reverse transcription-PCR on cDNA library of normal tissues, and 91 ovarian cancer specimens, and correlated with clinicohistopathologic characteristics. Results: Ninety-six individual scFv clones were screened in ELISA following panning. scFv F7 revealed high reactivity with ovarian cancer cell lines and showed intense staining of 15 fresh ovarian cancer specimens and no staining of a panel of normal tissues. A 40-kDa protein was identified to be translation elongation factor 1α1 (EEF1A1; P < 0.05). The expression of EEF1A2, a highly homologous and functionally similar oncogene, was found to be restricted only to the normal tissues of the heart, brain, and skeletal muscle. Aberrant EEF1A2 mRNA expression was found in 21 of 91 (23%) of ovarian cancer specimens and significantly correlated with increased likelihood of recurrence (P = 0.021). Conclusions: scFv F7 may represent an ovarian cancer–specific antibody against translation EEF1A family of translational factors. We propose that EEF1A2 may be a useful target for therapy of human ovarian cancer.