BackgroundReceiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.ResultsWith data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC.ConclusionspROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

Multiplex coherent anti-Stokes Raman scattering (CARS) microscopic imaging is demonstrated for the first time and used to visualize the thermodynamic state, either liquid crystalline or gel phase, of lipid membranes. Whereas in spontaneous Raman scattering the low scattering cross section is generally prohibitive for highresolution microscopic imaging, the signal enhancement in multiplex CARS, by more than 4 orders of magnitude, enables microscopic imaging with realistic frame rates (∼min -1 ). Because of the broad spectral window over which the CARS signal is detected simultaneously, the identification of the thermodynamic state of the lipid membranes can be accomplished directly, without any additional tuning of the lasers. We demonstrate that the experimental CARS spectral data are highly consistent with those obtained with spontaneous Raman scattering, with a signal-to-noise ratio determined only by Poisson noise. In addition, the inherent intensity normalization renders multiplex CARS a robust imaging technique.

Label-free quantification of high mass resolution LC-MS data has emerged as a promising technology for proteome analysis. Computational methods are required for the accurate extraction of peptide signals from LC-MS data and the tracking of these features across the measurements of different samples. We present here an open source software tool, SuperHirn, that comprises a set of modules to process LC-MS data acquired on a high resolution mass spectrometer. The program includes newly developed functionalities to analyze LC-MS data such as feature extraction and quantification, LC-MS similarity analysis, LC-MS alignment of multiple datasets, and intensity normalization. These program routines extract profiles of measured features and comprise tools for clustering and classification analysis of the profiles. SuperHirn was applied in an MS1-based profiling approach to a benchmark LC-MS dataset of complex protein mixtures with defined concentration changes. We show that the program automatically detects profiling trends in an unsupervised manner and is able to associate proteins to their correct theoretical dilution profile.

Comprehensive knowledge of the human leukocyte antigen (HLA) class-I and class-II peptides presented to T-cells is crucial for designing innovative therapeutics against cancer and other diseases. However methodologies for their purification for mass-spectrometry analysis have been a major limitation. We designed a novel high-throughput, reproducible and sensitive method for sequential immuno-affinity purification of HLA-I and -II peptides from up to 96 samples in a plate format, suitable for both cell lines and tissues. Our methodology drastically reduces sample-handling and can be completed within five hours. We challenged our methodology by extracting HLA peptides from multiple replicates of tissues (n = 7) and cell lines (n = 21, 108 cells per replicate), which resulted in unprecedented depth, sensitivity and high reproducibility (Pearson correlations up to 0.98 and 0.97 for HLA-I and HLA-II). Because of the method's achieved sensitivity, even single measurements of peptides purified from 107 B-cells resulted in the identification of more than 1700 HLA-I and 2200 HLA-II peptides. We demonstrate the feasibility of performing drug-screening by using ovarian cancer cells treated with interferon gamma (IFNγ). Our analysis revealed an augmented presentation of chymotryptic-like and longer ligands associated with IFNγ induced changes of the antigen processing and presentation machinery. This straightforward method is applicable for basic and clinical applications.

Malignant melanoma is a prime example of cancers that respond poorly to various treatment modalities including chemotherapy. A number of chemotherapeutic agents have been shown recently to act by inducing apoptosis, a type of cell death antagonized by the bcl-2 gene. Human melanoma expresses Bcl-2 in up to 90% of all cases. In the present study we demonstrate that bcl-2 antisense oligonucleotide treatment improves the chemosensitivity of human melanoma grown in severe combined immunodeficient (SCID) mice. Our findings suggest that reduction of Bcl-2 in melanoma, and possibly also in a variety of other tumors, may be a novel and rational approach to improve chemosensitivity and treatment outcome.

Efforts to precisely identify tumor human leukocyte antigen (HLA) bound peptides capable of mediating T cell-based tumor rejection still face important challenges. Recent studies suggest that non-canonical tumor-specific HLA peptides derived from annotated non-coding regions could elicit anti-tumor immune responses. However, sensitive and accurate mass spectrometry (MS)-based proteogenomics approaches are required to robustly identify these noncanonical peptides. We present an MS-based analytical approach that characterizes the noncanonical tumor HLA peptide repertoire, by incorporating whole exome sequencing, bulk and single-cell transcriptomics, ribosome profiling, and two MS/MS search tools in combination. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides, including an immunogenic peptide derived from an open reading frame downstream of the melanoma stem cell marker gene ABCB5. These findings hold great promise for the discovery of previously unknown tumor antigens for cancer immunotherapy.

Using the surface-specific vibrational technique of vibrational sum-frequency generation, we reveal that the double-peaked structure in the vibrational spectrum of hydrogen-bonded interfacial water molecules originates from vibrational coupling between the stretch and bending overtone, rather than from structural effects. This is demonstrated by isotopic dilution experiments, which reveal a smooth transition from two peaks to one peak, as D2O is converted into HDO. Our results show that the water interface is structurally more homogeneous than previously thought.

No abstract