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      Predicting Response to Bevacizumab in Ovarian Cancer: A Panel of Potential Biomarkers Informing Treatment Selection

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          Abstract

          Purpose: The aim of this study was to identify and validate novel predictive and/or prognostic serum proteomic biomarkers in patients with epithelial ovarian cancer (EOC) treated as part of the phase III international ICON7 clinical trial.

          Experimental Design: ICON7 was a phase III international trial in EOC which showed a modest but statistically significant benefit in progression-free survival (PFS) with the addition of bevacizumab to standard chemotherapy. Serum samples from 10 patients who received bevacizumab (five responders and five nonresponders) were analyzed by mass spectrometry to identify candidate biomarkers. Initial validation and exploration by immunoassay was undertaken in an independent cohort of 92 patients, followed by a second independent cohort of 115 patients (taken from across both arms of the trial).

          Results: Three candidate biomarkers were identified: mesothelin, fms-like tyrosine kinase-4 (FLT4), and α1-acid glycoprotein (AGP). Each showed evidence of independent prognostic potential when adjusting for high-risk status in initial (P < 0.02) and combined (P < 0.01) validation cohorts. In cohort I, individual biomarkers were not predictive of bevacizumab benefit; however, when combined with CA-125, a signature was developed that was predictive of bevacizumab response and discriminated benefit attributable to bevacizumab better than clinical characteristics. The signature showed weaker evidence of predictive ability in validation cohort II, but was still strongly predictive considering all samples (P = 0.001), with an improvement in median PFS of 5.5 months in signature-positive patients in the experimental arm compared with standard arm.

          Conclusions: This study shows a discriminatory signature comprising mesothelin, FLT4, AGP, and CA-125 as potentially identifying those patients with EOC more likely to benefit from bevacizumab. These results require validation in further patient cohorts. Clin Cancer Res; 19(18); 5227–39. ©2013 AACR.

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          Most cited references49

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          MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

          Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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            Universal sample preparation method for proteome analysis.

            We describe a method, filter-aided sample preparation (FASP), which combines the advantages of in-gel and in-solution digestion for mass spectrometry-based proteomics. We completely solubilized the proteome in sodium dodecyl sulfate, which we then exchanged by urea on a standard filtration device. Peptides eluted after digestion on the filter were pure, allowing single-run analyses of organelles and an unprecedented depth of proteome coverage.
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              Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

              Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
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                Author and article information

                Journal
                Clinical Cancer Research
                American Association for Cancer Research (AACR)
                1078-0432
                1557-3265
                September 15 2013
                September 16 2013
                September 15 2013
                September 16 2013
                : 19
                : 18
                : 5227-5239
                Article
                10.1158/1078-0432.CCR-13-0489
                3780518
                23935036
                2b334fa2-04ad-4f75-8276-4f76b3c7c762
                © 2013
                History

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