Mass spectrometry-based serum metabolic profiling is a promising tool to analyse complex cancer associated metabolic alterations, which may broaden our pathophysiological understanding of the disease and may function as a source of new cancer-associated biomarkers. Highly standardized serum samples of patients suffering from colon cancer (n = 59) and controls (n = 58) were collected at the University Hospital Leipzig. We based our investigations on amino acid screening profiles using electrospray tandem-mass spectrometry. Metabolic profiles were evaluated using the Analyst 1.4.2 software. General, comparative and equivalence statistics were performed by R 2.12.2. 11 out of 26 serum amino acid concentrations were significantly different between colorectal cancer patients and healthy controls. We found a model including CEA, glycine, and tyrosine as best discriminating and superior to CEA alone with an AUROC of 0.878 (95% CI 0.815–0.941). Our serum metabolic profiling in colon cancer revealed multiple significant disease-associated alterations in the amino acid profile with promising diagnostic power. Further large-scale studies are necessary to elucidate the potential of our model also to discriminate between cancer and potential differential diagnoses. In conclusion, serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination between colorectal cancer patients and controls.

In malignancies, enhanced nuclear factor-kB (NF-kB) activity is largely viewed as an oncogenic property that also confers resistance to chemotherapy. Recently, NF-kB has been postulated to participate in a senescence-associated and possibly senescence-reinforcing cytokine response, thereby suggesting a tumor-restraining role for NF-kB. Using a mouse lymphoma model and analyzing transcriptome and clinical data from lymphoma patients, we show here that therapy-induced senescence presents with and depends on active NF-kB signaling, whereas NF-kB simultaneously promotes resistance to apoptosis. Further characterization and genetic engineering of primary mouse lymphomas according to distinct NF-kB-related oncogenic networks reminiscent of diffuse large B-cell lymphoma (DLBCL) subtypes guided us to identify Bcl2-overexpressing germinal center B-cell-like (GCB) DLBCL as a clinically relevant subgroup with significantly superior outcome when NF-kB is hyperactive. Our data illustrate the power of cross-species investigations to functionally test genetic mechanisms in transgenic mouse tumors that recapitulate distinct features of the corresponding human entity, and to ultimately use the mouse model-derived genetic information to redefine novel, clinically relevant patient subcohorts.

Purpose: Mass spectrometry-based serum peptidome profiling is a promising tool to identify novel disease-associated biomarkers, but is limited by preanalytic factors and the intricacies of complex data processing. Therefore, we investigated whether standardized sample protocols and new bioinformatic tools combined with external data validation improve the validity of peptidome profiling for the discovery of pancreatic cancerassociated serum markers. Experimental Design: For the discovery study, two sets of sera from patients with pancreatic cancer (n = 40) and healthy controls (n = 40) were obtained from two different clinical centers. For external data validation, we collected an independent set of samples from patients (n = 20) and healthy controls (n = 20). Magnetic beads with different surface functionalities were used for peptidome fractionation followed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). Data evaluation was carried out by comparing two different bioinformatic strategies. Following proteome database search, the matching candidate peptide was verified by MALDI-TOF MS after specific antibody-based immunoaffinity chromatography and independently confirmed by an ELISA assay. Results: Two significant peaks (m/z 3884; 5959) achieved a sensitivity of 86.3% and a specificity of 97.6% for the discrimination of patients and healthy controls in the external validation set. Adding peak m/z 3884 to conventional clinical tumor markers (CA 19-9 and CEA) improved sensitivity and specificity, as shown by receiver operator characteristics curve analysis (AUROC combined = 1.00). Mass spectrometry-based m/z 3884 peak identification and following immunologic quantitation revealed platelet factor 4 as the corresponding peptide. Conclusions: MALDI-TOF MS-based serum peptidome profiling allowed the discovery and validation of platelet factor 4 as a new discriminating marker in pancreatic cancer.

Background: Peptidome profiling of human urine is a promising tool to identify novel disease-associated biomarkers; however, a wide range of preanalytical variables influence the results of peptidome analysis. Our aim was to develop a standardized protocol for reproducible urine peptidome profiling by means of magnetic bead (MB) separation followed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). Methods: MBs with defined surface functionalities (hydrophobic interaction, cation exchange, and metal ion affinity) were used for peptide fractionation of urine. Mass accuracy and imprecision were calculated for 9 characteristic mass signals (M r , 1000 -10 000). Exogenous variables (instrument performance, urine sampling/storage conditions, freezing conditions, and freeze-thaw cycles) and endogenous variables (pH, urine salt and protein concentrations, and blood and bacteria interferences) were investigated with urine samples from 10 male and 10 female volunteers. Results: We detected 427 different mass signals in the urine of healthy donors. Within-and between-day imprecision in relative signal intensities ranged from 1% to 14% and from 4% to 16%, respectively. Weak cationexchange and metal ion affinity MB preparations required adjustment of the urinary pH to 7. Storage time,

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 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.