Greg P. Bertenshaw

Development and Preliminary Evaluation of a Multivariate Index Assay for Ovarian Cancer

Background Most women with a clinical presentation consistent with ovarian cancer have benign conditions. Therefore methods to distinguish women with ovarian cancer from those with benign conditions would be beneficial. We describe the development and preliminary evaluation of a serum-based multivariate assay for ovarian cancer. This hypothesis-driven study examined whether an informative pattern could be detected in stage I disease that persists through later stages. Methodology/Principal Findings Sera, collected under uniform protocols from multiple institutions, representing 176 cases and 187 controls from women presenting for surgery were examined using high-throughput, multiplexed immunoassays. All stages and common subtypes of epithelial ovarian cancer, and the most common benign ovarian conditions were represented. A panel of 104 antigens, 44 autoimmune and 56 infectious disease markers were assayed and informative combinations identified. Using a training set of 91 stage I data sets, representing 61 individual samples, and an equivalent number of controls, an 11-analyte profile, composed of CA-125, CA 19-9, EGF-R, C-reactive protein, myoglobin, apolipoprotein A1, apolipoprotein CIII, MIP-1α, IL-6, IL-18 and tenascin C was identified and appears informative for all stages and common subtypes of ovarian cancer. Using a testing set of 245 samples, approximately twice the size of the model building set, the classifier had 91.3% sensitivity and 88.5% specificity. While these preliminary results are promising, further refinement and extensive validation of the classifier in a clinical trial is necessary to determine if the test has clinical value. Conclusions/Significance We describe a blood-based assay using 11 analytes that can distinguish women with ovarian cancer from those with benign conditions. Preliminary evaluation of the classifier suggests it has the potential to offer approximately 90% sensitivity and 90% specificity. While promising, the performance needs to be assessed in a blinded clinical validation study.

Comprehensive serum profiling for the discovery of epithelial ovarian cancer biomarkers.

FDA-cleared ovarian cancer biomarkers are limited to CA-125 and HE4 for monitoring and recurrence and OVA1, a multivariate panel consisting of CA-125 and four additional biomarkers, for referring patients to a specialist. Due to relatively poor performance of these tests, more accurate and broadly applicable biomarkers are needed. We evaluated the dysregulation of 259 candidate cancer markers in serum samples from 499 patients. Sera were collected prospectively at 11 monitored sites under a single well-defined protocol. All stages of ovarian cancer and common benign gynecological conditions were represented. To ensure consistency and comparability of biomarker comparisons, all measurements were performed on a single platform, at a single site, using a panel of rigorously calibrated, qualified, high-throughput, multiplexed immunoassays and all analyses were conducted using the same software. Each marker was evaluated independently for its ability to differentiate ovarian cancer from benign conditions. A total of 175 markers were dysregulated in the cancer samples. HE4 (AUC = 0.933) and CA-125 (AUC = 0.907) were the most informative biomarkers, followed by IL-2 receptor α, α1-antitrypsin, C-reactive protein, YKL-40, cellular fibronectin, CA-72-4 and prostasin (AUC>0.800). To improve the discrimination between cancer and benign conditions, a simple multivariate combination of markers was explored using logistic regression. When combined into a single panel, the nine most informative individual biomarkers yielded an AUC value of 0.950, significantly higher than obtained when combining the markers in the OVA1 panel (AUC 0.912). Additionally, at a threshold sensitivity of 90%, the combination of the top 9 markers gave 88.9% specificity compared to 63.4% specificity for the OVA1 markers. Although a blinded validation study has not yet been performed, these results indicate that alternative biomarker combinations might lead to significant improvements in the detection of ovarian cancer.

Functional Profiling of Live Melanoma Samples Using a Novel Automated Platform

Aims This proof-of-concept study was designed to determine if functional, pharmacodynamic profiles relevant to targeted therapy could be derived from live human melanoma samples using a novel automated platform. Methods A series of 13 melanoma cell lines was briefly exposed to a BRAF inhibitor (PLX-4720) on a platform employing automated fluidics for sample processing. Levels of the phosphoprotein p-ERK in the mitogen-activated protein kinase (MAPK) pathway from treated and untreated sample aliquots were determined using a bead-based immunoassay. Comparison of these levels provided a determination of the pharmacodynamic effect of the drug on the MAPK pathway. A similar ex vivo analysis was performed on fine needle aspiration (FNA) biopsy samples from four murine xenograft models of metastatic melanoma, as well as 12 FNA samples from patients with metastatic melanoma. Results Melanoma cell lines with known sensitivity to BRAF inhibitors displayed marked suppression of the MAPK pathway in this system, while most BRAF inhibitor-resistant cell lines showed intact MAPK pathway activity despite exposure to a BRAF inhibitor (PLX-4720). FNA samples from melanoma xenografts showed comparable ex vivo MAPK activity as their respective cell lines in this system. FNA samples from patients with metastatic melanoma successfully yielded three categories of functional profiles including: MAPK pathway suppression; MAPK pathway reactivation; MAPK pathway stimulation. These profiles correlated with the anticipated MAPK activity, based on the known BRAF mutation status, as well as observed clinical responses to BRAF inhibitor therapy. Conclusion Pharmacodynamic information regarding the ex vivo effect of BRAF inhibitors on the MAPK pathway in live human melanoma samples can be reproducibly determined using a novel automated platform. Such information may be useful in preclinical and clinical drug development, as well as predicting response to targeted therapy in individual patients.

A method for assessing and maintaining the reproducibility of mass spectrometric analyses of complex samples.

Direct injection mass spectrometric analysis of biological samples is potentially an attractive approach to the discovery of diagnostic patterns for specific pathophysiological conditions because of its speed and simplicity. Despite the possible benefits offered by such a method, its extensive application has been limited so far by several factors, including the inadequate reproducibility of the analytical results. We describe a method for monitoring and optimizing the performance of mass spectrometers used for biomarker discovery studies, based on the analysis of patterns of standardized spectral features. The method was successfully applied to maintaining spectral reproducibility during a multi-day analysis of hundreds of serum samples despite an ion source failure, which necessitated minor maintenance. The monitoring method allowed the early detection of that failure and the restoration of the spectral profiles after the system was restarted.

Abstract 3498: Functional profiling of Champions TumorGraft™ models from metastatic melanoma patients.

Introduction: Molecularly targeted agents, such as the BRAF inhibitor vemurafenib, may produce short-term responses in some patients; however, most patients are intrinsically resistant, or develop resistance through restructuring of signal transduction pathways. SnapPath™ is a live-cell-processing platform that utilizes ex vivo signal transduction modulation of live tumor samples to produce Functional Signaling Profiles (FSPs). Application of this technology to Champions TumorGraft models may provide novel insights to guide oncology drug development as these models preserve the biological properties of the original human tumor. Methods: Fresh melanoma tumor specimens were collected from patients and implanted into immunodeficient mice. Fine needle aspiration biopsies were performed on each melanoma TumorGraft model and processed on the SnapPath™ platform (BioMarker Strategies) to modulate tumor cell signal transduction networks through brief ex vivo exposure to the vemurafenib tool compound PLX-4720. Cell lysates were then analyzed using a multiplexed immunoassay to assess the inhibition of the downstream MAPK markers pMEK1 and pERK-1/2. FSPs were then created for each TumorGraft model based on baseline and modulated levels of each phosphoprotein. In parallel, the in vivo sensitivity to vemurafenib and BRAF mutation status was evaluated in each Champions TumorGraft model. FSPs were then compared with in vivo efficacy, gene expression and genotype data. Results: Functional profiling stratified the TumorGraft models into two distinct groups upon ex vivo exposure to a BRAF inhibitor: 1) MAPK markers suppressed and 2) MAPK markers not suppressed. As anticipated, TumorGraft models that showed resistance to ex vivo BRAF inhibition demonstrated vemurafenib resistance in vivo and were BRAF wild type. There were other models that displayed MAPK suppression with ex vivo BRAF inhibition and vemurafenib sensitivity in vivo or MAPK suppression with ex vivo BRAF inhibition but demonstrated vemurafenib resistance in vivo. One of these TumorGrafts contained a BRAF V600E mutation, suggesting the activation of an alternate pathway that conferred resistance. The other TumorGraft contained a novel BRAF insertion. The functional profiling suggests that this insertion may activate BRAF and is susceptible to vemurafenib inhibition, but the tumor may contain an alternate pathway that confers resistance. Analysis of gene expression data demonstrated hierarchical clustering of BRAF mutated TumorGraft models. Conclusions: These results demonstrate the capability of the SnapPath™ platform to generate FSPs from FNAs of Champions melanoma TumorGraft models. Overall, the combination of Champions TumorGraft models with functional profiling represents a powerful tool for pharmacodynamic assessment of targeted therapeutics in clinically relevant models and has the potential to guide oncology therapy. Citation Format: Elizabeth M. Bruckheimer, Adam Schayowitz, Kala Barnes, Greg Bertenshaw, Tin Khor, James Cotton, Jay Friedman, Dhanrajan Tiruchinapalli, Douglas P. Clark. Functional profiling of Champions TumorGraft™ models from metastatic melanoma patients. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3498. doi:10.1158/1538-7445.AM2013-3498