Samir M. Hanash

Discordant Protein and mRNA Expression in Lung Adenocarcinomas

The relationship between gene expression measured at the mRNA level and the corresponding protein level is not well characterized in human cancer. In this study, we compared mRNA and protein expression for a cohort of genes in the same lung adenocarcinomas. The abundance of 165 protein spots representing 98 individual genes was analyzed in 76 lung adenocarcinomas and nine non-neoplastic lung tissues using two-dimensional polyacrylamide gel electrophoresis. Specific polypeptides were identified using matrix-assisted laser desorption/ionization mass spectrometry. For the same 85 samples, mRNA levels were determined using oligonucleotide microarrays, allowing a comparative analysis of mRNA and protein expression among the 165 protein spots. Twenty-eight of the 165 protein spots (17%) or 21 of 98 genes (21.4%) had a statistically significant correlation between protein and mRNA expression (r > 0.2445; p < 0.05); however, among all 165 proteins the correlation coefficient values (r) ranged from −0.467 to 0.442. Correlation coefficient values were not related to protein abundance. Further, no significant correlation between mRNA and protein expression was found (r = −0.025) if the average levels of mRNA or protein among all samples were applied across the 165 protein spots (98 genes). The mRNA/protein correlation coefficient also varied among proteins with multiple isoforms, indicating potentially separate isoform-specific mechanisms for the regulation of protein abundance. Among the 21 genes with a significant correlation between mRNA and protein, five genes differed significantly between stage I and stage III lung adenocarcinomas. Using a quantitative analysis of mRNA and protein expression within the same lung adenocarcinomas, we showed that only a subset of the proteins exhibited a significant correlation with mRNA abundance.

Mining the plasma proteome for cancer biomarkers

Systematic searches for plasma proteins that are biological indicators, or biomarkers, for cancer are underway. The difficulties caused by the complexity of biological-fluid proteomes and tissue proteomes (which contribute proteins to plasma) and by the extensive heterogeneity among diseases, subjects and levels of sample procurement are gradually being overcome. This is being achieved through rigorous experimental design and in-depth quantitative studies. The expected outcome is the development of panels of biomarkers that will allow early detection of cancer and prediction of the probable response to therapy. Achieving these objectives requires high-quality specimens with well-matched controls, reagent resources, and an efficient process to confirm discoveries through independent validation studies.

Global Profiling of the Cell Surface Proteome of Cancer Cells Uncovers an Abundance of Proteins with Chaperone Function

There is currently limited data available pertaining to the global characterization of the cell surface proteome. We have implemented a strategy for the comprehensive profiling and identification of surface membrane proteins. This strategy has been applied to cancer cells, including the SH-SY5Y neuroblastoma, the A549 lung adenocarcinoma, the LoVo colon adenocarcinoma, and the Sup-B15 acute lymphoblastic leukemia (B cell) cell lines and ovarian tumor cells. Surface membrane proteins of viable, intact cells were subjected to biotinylation then affinity-captured and purified on monomeric avidin columns. The biotinylated proteins were eluted from the monomeric avidin columns as intact proteins and were subsequently separated by two-dimensional PAGE, transferred to polyvinylidene difluoride membranes, and visualized by hybridization with streptavidin-horseradish peroxidase. Highly reproducible, but distinct, two-dimensional patterns consisting of several hundred biotinylated proteins were obtained for the different cell populations analyzed. Identification of a subset of biotinylated proteins among the different cell populations analyzed using matrix-assisted laser desorption ionization and tandem mass spectrometry uncovered proteins with a restricted expression pattern in some cell line(s), such as CD87 and the activin receptor type IIB. We also identified more widely expressed proteins, such as CD98, and a sushi repeat-containing protein, a member of the selectin family. Remarkably, a set of proteins identified as chaperone proteins were found to be highly abundant on the cell surface, including GRP78, GRP75, HSP70, HSP60, HSP54, HSP27, and protein disulfide isomerase. Comprehensive profiling of the cell surface proteome provides an effective approach for the identification of commonly occurring proteins as well as proteins with restricted expression patterns in this compartment.

Mining gene expression databases for association rules.

MOTIVATION Global gene expression profiling, both at the transcript level and at the protein level, can be a valuable tool in the understanding of genes, biological networks, and cellular states. As larger and larger gene expression data sets become available, data mining techniques can be applied to identify patterns of interest in the data. Association rules, used widely in the area of market basket analysis, can be applied to the analysis of expression data as well. Association rules can reveal biologically relevant associations between different genes or between environmental effects and gene expression. An association rule has the form LHS --> RHS, where LHS and RHS are disjoint sets of items, the RHS set being likely to occur whenever the LHS set occurs. Items in gene expression data can include genes that are highly expressed or repressed, as well as relevant facts describing the cellular environment of the genes (e.g. the diagnosis of a tumor sample from which a profile was obtained). RESULTS We demonstrate an algorithm for efficiently mining association rules from gene expression data, using the data set from Hughes et al. (2000, Cell, 102, 109-126) of 300 expression profiles for yeast. Using the algorithm, we find numerous rules in the data. A cursory analysis of some of these rules reveals numerous associations between certain genes, many of which make sense biologically, others suggesting new hypotheses that may warrant further investigation. In a data set derived from the yeast data set, but with the expression values for each transcript randomly shifted with respect to the experiments, no rules were found, indicating that most all of the rules mined from the actual data set are not likely to have occurred by chance. AVAILABILITY An implementation of the algorithm using Microsoft SQL Server with Access 2000 is available at http://dot.ped.med.umich.edu:2000/pub/assoc_rules/assoc_rules.zip. Our results from mining the yeast data set are available at http://dot.ped.med.umich.edu:2000/pub/assoc_rules/yeast_results.zip.

Mutations in NALP7 cause recurrent hydatidiform moles and reproductive wastage in humans

Hydatidiform mole (HM) is an abnormal human pregnancy with no embryo and cystic degeneration of placental villi. We report five mutations in the maternal gene NALP7 in individuals with familial and recurrent HMs. NALP7 is a member of the CATERPILLER protein family involved in inflammation and apoptosis. NALP7 is the first maternal effect gene identified in humans and is also responsible for recurrent spontaneous abortions, stillbirths and intrauterine growth retardation.

Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study

The Human Proteome Organization (HUPO) recently completed the first large-scale collaborative study to characterize the human serum and plasma proteomes. The study was carried out in different locations and used diverse methods and instruments to compare and integrate tandem mass spectrometry (MS/MS) data on aliquots of pooled serum and plasma from healthy subjects. Liquid chromatography (LC)-MS/MS data sets from 18 laboratories were matched to the International Protein Index database, and an initial integration exercise resulted in 9,504 proteins identified with one or more peptides, and 3,020 proteins identified with two or more peptides. This article uses a rigorous statistical approach to take into account the length of coding regions in genes, and multiple hypothesis-testing techniques. On this basis, we now present a reduced set of 889 proteins identified with a confidence level of at least 95%. We also discuss the importance of such an integrated analysis in providing an accurate representation of a proteome as well as the value such data sets contain for the high-confidence identification of protein matches to novel exons, some of which may be localized in alternatively spliced forms of known plasma proteins and some in previously nonannotated gene sequences.

Profiling Changes in Gene Expression during Differentiation and Maturation of Monocyte-derived Dendritic Cells Using Both Oligonucleotide Microarrays and Proteomics*

Dendritic cells (DCs) are antigen-presenting cells that play a major role in initiating primary immune responses. We have utilized two independent approaches, DNA microarrays and proteomics, to analyze the expression profile of human CD14+ blood monocytes and their derived DCs. Analysis of gene expression changes at the RNA level using oligonucleotide microarrays complementary to 6300 human genes showed that ∼40% of the genes were expressed in DCs. A total of 255 genes (4%) were found to be regulated during DC differentiation or maturation. Most of these genes were not previously associated with DCs and included genes encoding secreted proteins as well as genes involved in cell adhesion, signaling, and lipid metabolism. Protein analysis of the same cell populations was done using two-dimensional gel electrophoresis. A total of 900 distinct protein spots were included, and 4% of them exhibited quantitative changes during DC differentiation and maturation. Differentially expressed proteins were identified by mass spectrometry and found to represent proteins with Ca2+ binding, fatty acid binding, or chaperone activities as well as proteins involved in cell motility. In addition, proteomic analysis provided an assessment of post-translational modifications. The chaperone protein, calreticulin, was found to undergo cleavage, yielding a novel form. The combined oligonucleotide microarray and proteomic approaches have uncovered novel genes associated with DC differentiation and maturation and has allowed analysis of post-translational modifications of specific proteins as part of these processes.

An immune response manifested by the common occurrence of annexins I and II autoantibodies and high circulating levels of IL-6 in lung cancer

The identification of circulating tumor antigens or their related autoantibodies provides a means for early cancer diagnosis as well as leads for therapy. The purpose of this study was to identify proteins that commonly induce a humoral response in lung cancer by using a proteomic approach and to investigate biological processes that may be associated with the development of autoantibodies. Aliquots of solubilized proteins from a lung adenocarcinoma cell line (A549) and from lung tumors were subjected to two-dimensional PAGE, followed by Western blot analysis in which individual sera were tested for primary antibodies. Sera from 54 newly diagnosed patients with lung cancer and 60 patients with other cancers and from 61 noncancer controls were analyzed. Sera from 60% of patients with lung adenocarcinoma and 33% of patients with squamous cell lung carcinoma but none of the noncancer controls exhibited IgG-based reactivity against proteins identified as glycosylated annexins I and/or II. Immunohistochemical analysis showed that annexin I was expressed diffusely in neoplastic cells in lung tumor tissues, whereas annexin II was predominant at the cell surface. Interestingly, IL-6 levels were significantly higher in sera of antibody-positive lung cancer patients compared with antibody-negative patients and controls. We conclude that an immune response manifested by annexins I and II autoantibodies occurs commonly in lung cancer and is associated with high circulating levels of an inflammatory cytokine. The proteomic approach we have implemented has utility for the development of serum-based assays for cancer diagnosis as we report in this paper on the discovery of antiannexins I and/or II in sera from patients with lung cancer.

Molecular classification of papillary thyroid carcinoma: distinct BRAF , RAS , and RET/PTC mutation-specific gene expression profiles discovered by DNA microarray analysis

Thyroid cancer poses a significant clinical challenge, and our understanding of its pathogenesis is incomplete. To gain insight into the pathogenesis of papillary thyroid carcinoma, transcriptional profiles of four normal thyroids and 51 papillary carcinomas (PCs) were generated using DNA microarrays. The tumors were genotyped for their common activating mutations: BRAF V600E point mutation, RET/PTC1 and 3 rearrangement and point mutations of KRAS, HRAS and NRAS. Principal component analysis based on the entire expression data set separated the PCs into three groups that were found to reflect tumor morphology and mutational status. By combining expression profiles with mutational status, we defined distinct expression profiles for the BRAF, RET/PTC and RAS mutation groups. Using small numbers of genes, a simple classifier was able to classify correctly the mutational status of all 40 tumors with known mutations. One tumor without a detectable mutation was predicted by the classifier to have a RET/PTC rearrangement and was shown to contain one by fluorescence in situ hybridization analysis. Among the mutation-specific expression signatures were genes whose differential expression was a direct consequence of the mutation, as well as genes involved in a variety of biological processes including immune response and signal transduction. Expression of one mutation-specific differentially expressed gene, TPO, was validated at the protein level using immunohistochemistry and tissue arrays containing an independent set of tumors. The results demonstrate that mutational status is the primary determinant of gene expression variation within these tumors, a finding that may have clinical and diagnostic significance and predicts success for therapies designed to prevent the consequences of these mutations.

A biomarker panel for acute graft-versus-host disease.

No validated biomarkers exist for acute graft-versus-host disease (GVHD). We screened plasma with antibody microarrays for 120 proteins in a discovery set of 42 patients who underwent transplantation that revealed 8 potential biomarkers for diagnostic of GVHD. We then measured by enzyme-linked immunosorbent assay (ELISA) the levels of these biomarkers in samples from 424 patients who underwent transplantation randomly divided into training (n = 282) and validation (n = 142) sets. Logistic regression analysis of these 8 proteins determined a composite biomarker panel of 4 proteins (interleukin-2-receptor-alpha, tumor-necrosis-factor-receptor-1, interleukin-8, and hepatocyte growth factor) that optimally discriminated patients with and without GVHD. The area under the receiver operating characteristic curve distinguishing these 2 groups in the training set was 0.91 (95% confidence interval, 0.87-0.94) and 0.86 (95% confidence interval, 0.79-0.92) in the validation set. In patients with GVHD, Cox regression analysis revealed that the biomarker panel predicted survival independently of GVHD severity. A panel of 4 biomarkers can confirm the diagnosis of GVHD in patients at onset of clinical symptoms of GVHD and provide prognostic information independent of GVHD severity.