Thomas Breslin

Early B-cell factor, E2A, and Pax-5 cooperate to activate the early B cell-specific mb-1 promoter.

ABSTRACT Previous studies have suggested that the early-B-cell-specific mb-1(Igα) promoter is regulated by EBF and Pax-5. Here, we used in vivo footprinting assays to detect occupation of binding sites in endogenous mb-1 promoters at various stages of B-cell differentiation. In addition to EBF and Pax-5 binding sites, we detected occupancy of a consensus binding site for E2A proteins (E box) in pre-B cells. EBF and E box sites are crucial for promoter function in transfected pre-B cells, and EBF and E2A proteins synergistically activated the promoter in transfected HeLa cells. Other data suggest that EBF and E box sites are less important for promoter function at later stages of differentiation, whereas binding sites for Pax-5 (and its Ets ternary complex partners) are required for promoter function in all mb-1-expressing cells. Using DNA microarrays, we found that expression of endogenous mb-1 transcripts correlates most closely with EBF expression and negatively with Id1, an inhibitor of E2A protein function, further linking regulation of the mb-1 gene with EBF and E2A. Together, our studies demonstrate the complexity of factors regulating tissue-specific transcription and support the concept that EBF, E2A, and Pax-5 cooperate to activate target genes in early B-cell development.

Comparing functional annotation analyses with Catmap

BackgroundRanked gene lists from microarray experiments are usually analysed by assigning significance to predefined gene categories, e.g., based on functional annotations. Tools performing such analyses are often restricted to a category score based on a cutoff in the ranked list and a significance calculation based on random gene permutations as null hypothesis.ResultsWe analysed three publicly available data sets, in each of which samples were divided in two classes and genes ranked according to their correlation to class labels. We developed a program, Catmap (available for download at http://bioinfo.thep.lu.se/Catmap), to compare different scores and null hypotheses in gene category analysis, using Gene Ontology annotations for category definition. When a cutoff-based score was used, results depended strongly on the choice of cutoff, introducing an arbitrariness in the analysis. Comparing results using random gene permutations and random sample permutations, respectively, we found that the assigned significance of a category depended strongly on the choice of null hypothesis. Compared to sample label permutations, gene permutations gave much smaller p-values for large categories with many coexpressed genes.ConclusionsIn gene category analyses of ranked gene lists, a cutoff independent score is preferable. The choice of null hypothesis is very important; random gene permutations does not work well as an approximation to sample label permutations.

Signal transduction pathway profiling of individual tumor samples

BackgroundSignal transduction pathways convey information from the outside of the cell to transcription factors, which in turn regulate gene expression. Our objective is to analyze tumor gene expression data from microarrays in the context of such pathways.ResultsWe use pathways compiled from the TRANSPATH/TRANSFAC databases and the literature, and three publicly available cancer microarray data sets. Variation in pathway activity, across the samples, is gauged by the degree of correlation between downstream targets of a pathway. Two correlation scores are applied; one considers all pairs of downstream targets, and the other considers only pairs without common transcription factors. Several pathways are found to be differentially active in the data sets using these scores. Moreover, we devise a score for pathway activity in individual samples, based on the average expression value of the downstream targets. Statistical significance is assigned to the scores using permutation of genes as null model. Hence, for individual samples, the status of a pathway is given as a sign, + or -, and a p-value. This approach defines a projection of high-dimensional gene expression data onto low-dimensional pathway activity scores. For each dataset and many pathways we find a much larger number of significant samples than expected by chance. Finally, we find that several sample-wise pathway activities are significantly associated with clinical classifications of the samples.ConclusionThis study shows that it is feasible to infer signal transduction pathway activity, in individual samples, from gene expression data. Furthermore, these pathway activities are biologically relevant in the three cancer data sets.

Intratumor versus intertumor heterogeneity in gene expression profiles of soft-tissue sarcomas.

Soft‐tissue sarcomas (STSs) constitute more than 30 histologic entities. In addition, within each entity, tumors are often heterogeneous in macroscopic features, genetic alterations, microscopic appearance, and clinical course. Therefore, there has been concern about whether a single tumor sample can provide a gene expression profile representative of the entire tumor. We used 27‐k cDNA microarray slides to assess the importance of intratumor versus intertumor heterogeneity of the gene expression profiles of 2 morphologically heterogeneous STSs. Multiple pieces of tumor (8 and 10 pieces) were obtained from a myxoid variant of malignant fibrous histiocytoma (MFH) and a leiomyosarcoma (LMS), respectively, and the expression patterns were compared with single tumor samples from 20 MFHs and 16 LMSs. Hierarchical clustering analysis of the expression profiles showed that samples from the same tumor clustered together. The average intratumor distance was considerably shorter than the average intertumor distance in both LMS and MFH. In addition, tumor subclusters that distinguished different macroscopic parts of the tumor could be discerned. We concluded that intratumor variability exists but that accurate gene expression profiling also could be obtained using single samples from a large STS.

A New Look at Drugs Targeting Malignant Melanoma – An Application for Mass Spectrometry Imaging

Malignant melanoma (MM) patients are being treated with an increasing number of personalized medicine (PM) drugs, several of which are small molecule drugs developed to treat patients with specific disease genotypes and phenotypes. In particular, the clinical application of protein kinase inhibitors has been highly effective for certain subsets of MM patients. Vemurafenib, a protein kinase inhibitor targeting BRAF‐mutated protein, has shown significant efficacy in slowing disease progression. In this paper, we provide an overview of this new generation of targeted drugs, and demonstrate the first data on localization of PM drugs within tumor compartments. In this study, we have introduced MALDI‐MS imaging to provide new information on one of the drugs currently used in the PM treatment of MM, vemurafenib. In a proof‐of‐concept in vitro study, MALDI‐MS imaging was used to identify vemurafenib applied to metastatic lymph nodes tumors of subjects attending the regional hospital network of Southern Sweden. The paper provides evidence of BRAF overexpression in tumors isolated from MM patients and localization of the specific drug targeting BRAF, vemurafenib, using MS fragment ion signatures. Our ability to determine drug uptake at the target sites of directed therapy provides important opportunity for increasing our understanding about the mode of action of drug activity within the disease environment.

A Combined Shotgun and Targeted Mass Spectrometry Strategy for Breast Cancer Biomarker Discovery

It is of highest importance to find proteins responsible for breast cancer dissemination, for use as biomarkers or treatment targets. We established and performed a combined nontargeted LC-MS/MS and a targeted LC-SRM workflow for discovery and validation of protein biomarkers. Eighty breast tumors, stratified for estrogen receptor status and development of distant recurrence (DR ± ), were collected. After enrichment of N-glycosylated peptides, label-free LC-MS/MS was performed on each individual tumor in triplicate. In total, 1515 glycopeptides from 778 proteins were identified and used to create a map of the breast cancer N-glycosylated proteome. Based on this specific proteome map, we constructed a 92-plex targeted label-free LC-SRM panel. These proteins were quantified across samples by LC-SRM, resulting in 10 proteins consistently differentially regulated between DR+/DR- tumors. Five proteins were further validated in a separate cohort as prognostic biomarkers at the gene expression level. We also compared the LC-SRM results to clinically reported HER2 status, demonstrating its clinical accuracy. In conclusion, we demonstrate a combined mass spectrometry strategy, at large scale on clinical samples, leading to the identification and validation of five proteins as potential biomarkers for breast cancer recurrence. All MS data are available via ProteomeXchange and PASSEL with identifiers PXD001685 and PASS00643.

Establishing a Southern Swedish Malignant Melanoma OMICS and biobank clinical capability.

BackgroundThe objectives and goals of the Southern Swedish Malignant Melanoma (SSMM)are to develop, build and utilize cutting edge biobanks and OMICS platformsto better understand disease pathology and drug mechanisms. The SSMMresearch team is a truly cross-functional group with members from oncology,surgery, bioinformatics, proteomics, and genomics initiatives. Within theresearch team there are members who daily diagnose patients with suspectmelanomas, do follow-ups on malignant melanoma patients and remove primaryor metastatic lesions by surgery. This inter-disciplinary clinical patientcare ensures a competence build as well as a best practice procedure wherethe patient benefits.MethodsClinical materials from patients before, during and after treatments withclinical end points are being collected. Tissue samples as well as bio-fluidsamples such as blood fractions, plasma, serum and whole blood will bearchived in 384-high density sample tube formats. Standardized approachesfor patient selections, patient sampling, sample-processing and analysisplatforms with dedicated protein assays and genomics platforms that willhold value for the research community are used. The patient biobank archivesare fully automated with novel ultralow temperature biobank storage unitsand used as clinical resources.ResultsAn IT-infrastructure using a laboratory information management system (LIMS)has been established, that is the key interface for the research teams inorder to share and explore data generated within the project. The cross-sitedata repository in Lund forms the basis for sample processing, together withbiological samples in southern Sweden, including blood fractions and tumortissues. Clinical registries are associated with the biobank materials,including pathology reports on disease diagnosis on the malignant melanoma(MM) patients.ConclusionsWe provide data on the developments of protein profiling and targeted proteinassays on isolated melanoma tumors, as well as reference blood standardsthat is used by the team members in the respective laboratories. These pilotdata show biobank access and feasibility of performing quantitativeproteomics in MM biobank repositories collected in southern Sweden. Thescientific outcomes further strengthen the build of healthcare benefit inthe complex challenges of malignant melanoma pathophysiology that isaddressed by the novel personalized medicines entering the market.

RNA analysis of B cell lines arrested at defined stages of differentiation allows for an approximation of gene expression patterns during B cell development

The development of a mature B lymphocyte from a bone marrow stem cell is a highly ordered process involving stages with defined features and gene expression patterns. To obtain a deeper understanding of the molecular genetics of this process, we have performed RNA expression analysis of a set of mouse B lineage cell lines representing defined stages of B cell development using Affymetrix™ microarrays. The cells were grouped based on their previously defined phenotypic features, and a gene expression pattern for each group of cell lines was established. The data indicated that the cell lines representing a defined stage generally presented a high similarity in overall expression profiles. Numerous genes could be identified as expressed with a restricted pattern using dCHIP‐based, quantitative comparisons or presence/absence‐based, probabilistic state analysis. These experiments provide a model for gene expression during B cell development, and the correctly identified expression patterns of a number of control genes suggest that a series of cell lines can be useful tools in the elucidation of the molecular genetics of a complex differentiation process.

Analysis of Alpha-Synuclein in Malignant Melanoma – Development of a SRM Quantification Assay

Globally, malignant melanoma shows a steady increase in the incidence among cancer diseases. Malignant melanoma represents a cancer type where currently no biomarker or diagnostics is available to identify disease stage, progression of disease or personalized medicine treatment. The aim of this study was to assess the tissue expression of alpha-synuclein, a protein implicated in several disease processes, in metastatic tissues from malignant melanoma patients. A targeted Selected Reaction Monitoring (SRM) assay was developed and utilized together with stable isotope labeling for the relative quantification of two target peptides of alpha-synuclein. Analysis of alpha-synuclein protein was then performed in ten metastatic tissue samples from the Lund Melanoma Biobank. The calibration curve using peak area ratio (heavy/light) versus concentration ratios showed linear regression over three orders of magnitude, for both of the selected target peptide sequences. In support of the measurements of specific protein expression levels, we also observed significant correlation between the protein and mRNA levels of alpha-synuclein in these tissues. Investigating levels of tissue alpha-synuclein may add novel aspect to biomarker development in melanoma, help to understand disease mechanisms and ultimately contribute to discriminate melanoma patients with different prognosis.

Probabilistic estimation of microarray data reliability and underlying gene expression

BackgroundThe availability of high throughput methods for measurement of mRNA concentrations makes the reliability of conclusions drawn from the data and global quality control of samples and hybridization important issues. We address these issues by an information theoretic approach, applied to discretized expression values in replicated gene expression data.ResultsOur approach yields a quantitative measure of two important parameter classes: First, the probability P(σ|S) that a gene is in the biological state σ in a certain variety, given its observed expression S in the samples of that variety. Second, sample specific error probabilities which serve as consistency indicators of the measured samples of each variety. The method and its limitations are tested on gene expression data for developing murine B-cells and a t-test is used as reference. On a set of known genes it performs better than the t-test despite the crude discretization into only two expression levels. The consistency indicators, i.e. the error probabilities, correlate well with variations in the biological material and thus prove efficient.ConclusionsThe proposed method is effective in determining differential gene expression and sample reliability in replicated microarray data. Already at two discrete expression levels in each sample, it gives a good explanation of the data and is comparable to standard techniques.