Hydrogen/deuterium exchange mass spectrometry (HDX-MS) is an established method for the interrogation of protein conformation and dynamics. While the data analysis challenge of HDX-MS has been addressed by a number of software packages, new computational tools are needed to keep pace with the improved methods and throughput of this technique. To address these needs, we report an integrated desktop program titled HDX Workbench, which facilitates automation, management, visualization, and statistical cross-comparison of large HDX data sets. Using the software, validated data analysis can be achieved at the rate of generation. The application is available at the project home page http://hdx.florida.scripps.edu.

Recent observations connected DNA cytosine deaminase APOBEC3B to the genetic evolution of breast cancer. We addressed whether APOBEC3B is associated with breast cancer clinical outcomes. APOBEC3B messenger RNA (mRNA) levels were related in 1,491 primary breast cancers to disease-free (DFS), metastasis-free (MFS), and overall survival (OS). For independent validation, APOBEC3B mRNA expression was associated with patient outcome data in five additional cohorts (over 3,500 breast cancer cases). In univariate Cox regression analysis, increasing APOBEC3B expression as a continuous variable was associated with worse DFS, MFS, and OS (hazard ratio [HR] = 1.20, 1.21, and 1.24, respectively; all P < .001). Also, in untreated ER-positive (ER+), but not in ER−, lymph-node-negative patients, high APOBEC3B levels were associated with a poor DFS (continuous variable: HR = 1.29, P = .001; dichotomized at the median level, HR = 1.66, P = .0002). This implies that APOBEC3B is a marker of pure prognosis in ER + disease. These findings were confirmed in the analyses of five independent patient sets. In these analyses, APOBEC3B expression dichotomized at the median level was associated with adverse outcomes (METABRIC discovery and validation, 788 and 706 ER + cases, disease-specific survival (DSS), HR = 1.77 and HR = 1.77, respectively, both P < .001; Affymetrix dataset, 754 ER + cases, DFS, HR = 1.57, P = 2.46E-04; NKI295, 181 ER + cases, DFS, HR = 1.72, P = .054; and BIG 1-98, 1,219 ER + cases, breast-cancer-free interval (BCFI), HR = 1.42, P = 0.0079). APOBEC3B is a marker of pure prognosis and poor outcomes for ER + breast cancer, which strongly suggests that genetic aberrations induced by APOBEC3B contribute to breast cancer progression.Electronic supplementary materialThe online version of this article (doi: 10.1007/s12672-014-0196-8) contains supplementary material, which is available to authorized users.

Motivation: BioJava is an open-source project for processing of biological data in the Java programming language. We have recently released a new version (3.0.5), which is a major update to the code base that greatly extends its functionality.Results: BioJava now consists of several independent modules that provide state-of-the-art tools for protein structure comparison, pairwise and multiple sequence alignments, working with DNA and protein sequences, analysis of amino acid properties, detection of protein modifications and prediction of disordered regions in proteins as well as parsers for common file formats using a biologically meaningful data model.Availability: BioJava is an open-source project distributed under the Lesser GPL (LGPL). BioJava can be downloaded from the BioJava website (http://www.biojava.org). BioJava requires Java 1.6 or higher. All inquiries should be directed to the BioJava mailing lists. Details are available at http://biojava.org/wiki/BioJava:MailingListsContact: andreas.prlic@gmail.com

Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.

The extent of tumor-infiltrating lymphocytes (TILs), along with immunomodulatory ligands, tumor-mutational burden and other biomarkers, has been demonstrated to be a marker of response to immune-checkpoint therapy in several cancers. Pathologists have therefore started to devise standardized visual approaches to quantify TILs for therapy prediction. However, despite successful standardization efforts visual TIL estimation is slow, with limited precision and lacks the ability to evaluate more complex properties such as TIL distribution patterns. Therefore, computational image analysis approaches are needed to provide standardized and efficient TIL quantification. Here, we discuss different automated TIL scoring approaches ranging from classical image segmentation, where cell boundaries are identified and the resulting objects classified according to shape properties, to machine learning-based approaches that directly classify cells without segmentation but rely on large amounts of training data. In contrast to conventional machine learning (ML) approaches that are often criticized for their "black-box" characteristics, we also discuss explainable machine learning. Such approaches render ML results interpretable and explain the computational decision-making process through high-resolution heatmaps that highlight TILs and cancer cells and therefore allow for quantification and plausibility checks in biomedical research and diagnostics.

PurposeTo discover novel prognostic biomarkers in ovarian serous carcinomas.MethodsA meta-analysis of all single genes probes in the TCGA and HAS ovarian cohorts was performed to identify possible biomarkers using Cox regression as a continuous variable for overall survival. Genes were ranked by p-value using Stouffer’s method and selected for statistical significance with a false discovery rate (FDR) <.05 using the Benjamini-Hochberg method.ResultsTwelve genes with high mRNA expression were prognostic of poor outcome with an FDR <.05 (AXL, APC, RAB11FIP5, C19orf2, CYBRD1, PINK1, LRRN3, AQP1, DES, XRCC4, BCHE, and ASAP3). Twenty genes with low mRNA expression were prognostic of poor outcome with an FDR <.05 (LRIG1, SLC33A1, NUCB2, POLD3, ESR2, GOLPH3, XBP1, PAXIP1, CYB561, POLA2, CDH1, GMNN, SLC37A4, FAM174B, AGR2, SDR39U1, MAGT1, GJB1, SDF2L1, and C9orf82).ConclusionA meta-analysis of all single genes identified thirty-two candidate biomarkers for their possible role in ovarian serous carcinoma. These genes can provide insight into the drivers or regulators of ovarian cancer and should be evaluated in future studies. Genes with high expression indicating poor outcome are possible therapeutic targets with known antagonists or inhibitors. Additionally, the genes could be combined into a prognostic multi-gene signature and tested in future ovarian cohorts.

Hydrogen/deuterium exchange (HDX) mass spectrometry has been widely applied to the characterization of protein dynamics. More recently, differential HDX has been shown to be effective for the characterization of ligand binding. Previously we have described a fully automated HDX system for use as a ligand screening platform. Here we describe and validate the required data analysis workflow to facilitate the use of HDX as a robust approach for ligand screening. Following acquisition of HDX data at a single on-exchange time point (n ≥ 3), one way analysis of variance in conjunction with the Tukey multiple comparison procedure is used to establish the significance of any measured difference. Analysis results are graphed with respect to a single peptide, ligand or group of ligands, or displayed as an overview within a heat map. For the heat map display, only Δ%D values with a Tukey-adjusted P value less than 0.05 are colored. Hierarchical clustering is used to bin compounds with highly similar HDX signatures. The workflow is evaluated with a small data set showing the ligand binding domain (LDB) of the nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) screened against 10 functionally selective ligands. More significantly, data for the vitamin D receptor (VDR) in complex with 87 ligands are presented. To highlight the robustness and precision of our automated HDX platform we analyzed the data from 4191 replicate HDX measurements acquired over an eight month timeframe. Ninety six percent of these measurements were within 10 percent of the mean value. Work has begun to integrate these analysis and graphing components within our HDX software suite.

clinicaltrials.gov Identifier: NCT00045032.