Abstract
Metabolomics as one of the most rapidly growing technologies in the “-omics” field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [ {text{volume under ROC surface}};left( {text{VUS}} right) = 0. 8 9 1 { }left( { 9 5,% {text{ CI }}0. 7 9 4- 0. 9 6 8} right)]. We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and—despite all its current limitations—can deliver marker panels with high selectivity even in multi-class settings.
Highlights
Pancreatic cancer is the fourth leading cause of cancer death in the United States, and most patients diagnosed with pancreatic cancer develop clinical symptoms usually late in the course of the disease (Lowenfels and Maisonneuve 2006)
Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and—despite all its current limitations—can deliver marker panels with high selectivity even in multi-class settings
We recruited patients suffering from pancreatic cancer (n 1⁄4 40), healthy controls (n 1⁄4 40), and patients hospitalized due to acute pancreatitis (n 1⁄4 26) at the University Hospital of Leipzig in the context of previously published studies (Fiedler et al 2009; Leichtle et al 2012)
Summary
Pancreatic cancer is the fourth leading cause of cancer death in the United States, and most patients diagnosed with pancreatic cancer develop clinical symptoms usually late in the course of the disease (Lowenfels and Maisonneuve 2006). Whereas Brand et al (2011) focused on known tumor markers, tumor-associated peptides, etc., other studies have employed several of the emerging ‘‘-omics’’ subspecialties, such as proteomics (Fiedler et al 2009), transcriptomics (Zhang et al 2010), and—as the probably closest to the ‘‘bedside’’ (Van and Veenstra 2009)—metabolomics (Bathe et al 2011; Ceglarek et al 2009; Nishiumi et al 2010; OuYang et al 2011; Urayama et al 2010; Zhang et al 2011) The latter bears the chance to learn from the intricacies that have plagued ‘‘-omics’’ researchers over the last years, standardization (Van and Veenstra 2009), data processing (Blekherman et al 2011) and data interpretation (Kholodenko et al 2012), amongst others
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