Tumor necrosis is an important hallmark of aggressive endometrial cancer and associates with hypoxia, angiogenesis and inflammation responses.

AIMS: Tumor necrosis is associated with aggressive features of endometrial cancer and poor prognosis. Here, we investigated gene expression patterns and potential treatment targets related to presence of tumor necrosis in primary endometrial cancer lesions. METHODS AND RESULTS: By DNA microarray analysis, expression of genes related to tumor necrosis reflected multiple tumor-microenvironment interactions like tissue hypoxia, angiogenesis and inflammation pathways. A tumor necrosis signature of 38 genes and a related patient cluster (Cluster I, 67% of the cases) were associated with features of aggressive tumors such as type II cancers, estrogen receptor negative tumors and vascular invasion. Further, the tumor necrosis signature was increased in tumor cells grown in hypoxic conditions in vitro. Multiple genes with increased expression are known to be activated by HIF1A and NF-kB. CONCLUSIONS: Our findings indicate that the presence of tumor necrosis within primary tumors is associated with hypoxia, angiogenesis and inflammation responses. HIF1A, NF-kB and PI3K/mTOR might be potential treatment targets in aggressive endometrial cancers with presence of tumor necrosis.

Genes of cell-cell interactions, chemotherapy detoxification and apoptosis are induced during chemotherapy of acute myeloid leukemia.

BACKGROUND: The molecular changes in vivo in acute myeloid leukemia cells early after start of conventional genotoxic chemotherapy are incompletely understood, and it is not known if early molecular modulations reflect clinical response. METHODS: The gene expression was examined by whole genome 44 k oligo microarrays and 12 k cDNA microarrays in peripheral blood leukocytes collected from seven leukemia patients before treatment, 2-4 h and 18-24 h after start of chemotherapy and validated by real-time quantitative PCR. Statistically significantly upregulated genes were classified using gene ontology (GO) terms. Parallel samples were examined by flow cytometry for apoptosis by annexin V-binding and the expression of selected proteins were confirmed by immunoblotting. RESULTS: Significant differential modulation of 151 genes were found at 4 h after start of induction therapy with cytarabine and anthracycline, including significant overexpression of 31 genes associated with p53 regulation. Within 4 h of chemotherapy the BCL2/BAX and BCL2/PUMA ratio were attenuated in proapoptotic direction. FLT3 mutations indicated that non-responders (5/7 patients, 8 versus 49 months survival) are characterized by a unique gene response profile before and at 4 h. At 18-24 h after chemotherapy, the gene expression of p53 target genes was attenuated, while genes involved in chemoresistance, cytarabine detoxification, chemokine networks and T cell receptor were prominent. No signs of apoptosis were observed in the collected cells, suggesting the treated patients as a physiological source of pre-apoptotic cells. CONCLUSION: Pre-apoptotic gene expression can be monitored within hours after start of chemotherapy in patients with acute myeloid leukemia, and may be useful in future determination of therapy responders. The low number of patients and the heterogeneity of acute myeloid leukemia limited the identification of gene expression predictive of therapy response. Therapy-induced gene expression reflects the complex biological processes involved in clinical cancer cell eradication and should be explored for future enhancement of therapy.

Comparison of nucleic acid targets prepared from total RNA or poly(A) RNA for DNA oligonucleotide microarray hybridization.

The aim of this work was to compare DNA microarray results using either total RNA or affinity-purified poly(A) RNA from the same biological sample for target preparation. The high-density oligonucleotide microarrays of both Agilent Technologies (based on two-color detection) and Applied Biosystems (based on single-color detection) were evaluated. Real-time quantitative PCR was used to quantify messenger RNA (mRNA) and ribosomal RNA (rRNA) at different stages of target preparations. Poly(A) RNA versus total RNA target hybridizations exhibited slightly lower correlation coefficients than did self versus self hybridizations (i.e., poly(A) RNA targets vs. poly(A) RNA targets or total RNA targets vs. total RNA targets). Only a small fraction of all transcripts appeared to be significantly over- or underrepresented when total RNA targets or poly(A) RNA targets from the same biological sample were compared. Therefore, the conclusion is that poly(A) affinity purification from total RNA can be omitted during target preparation for routine mRNA expression analysis using high-density oligonucleotide microarrays. Among consistently overrepresented transcripts in total RNA targets were histone mRNAs known to lack poly(A) tails. Therefore, structurally exceptional RNA species can be identified by comparing targets derived from either poly(A) RNA or total RNA using microarray hybridization.

Increased expression of SIM2-s protein is a novel marker of aggressive prostate cancer.

PURPOSE: The human SIM2 gene is located within the Down's syndrome critical region of chromosome 21 and encodes transcription factors involved in brain development and neuronal differentiation. SIM2 has been assigned a possible role in the pathogenesis of solid tumors, and the SIM2-short isoform (SIM2-s) was recently proposed as a molecular target for cancer therapy. We previously reported SIM2 among the highly up-regulated genes in 29 prostate cancers, and the purpose of our present study was to examine the expression status of SIM2 at the transcriptional and protein level as related to outcome in prostate cancer. EXPERIMENTAL DESIGN: By quantitative PCR, mRNA in situ hybridization, and immunohistochemistry, we evaluated the expression and significance of SIM2 isoforms in 39 patients with clinically localized prostate cancer and validated the expression of SIM2-s protein in an independent cohort of 103 radical prostatectomies from patients with long and complete follow-up. RESULTS: The SIM2 isoforms (SIM2-s and SIM2-l) were significantly coexpressed and increased in prostate cancer. Tumor cell expression of SIM2-s protein was associated with adverse clinicopathologic factors like increased preoperative serum prostate-specific antigen, high histologic grade, invasive tumor growth with extra-prostatic extension, and increased tumor cell proliferation by Ki-67 expression. SIM2-s protein expression was significantly associated with reduced cancer-specific survival in multivariate analyses. CONCLUSIONS: These novel findings indicate for the first time that SIM2 expression might be important for clinical progress of human cancer and support the recent proposal of SIM2-s as a candidate for targeted therapy in prostate cancer.

Global gene expression in classification, pathogenetic understanding and identification of therapeutic targets in acute myeloid leukemia.

Global gene expression analysis by way of DNA microarrays and real time quantitative PCR provides an important supplement to established diagnosis and classification of malignant disease. A comprehensive molecular understanding of the regulatory modules involved in carcinogenesis should also be important for improved identification of therapeutic targets and thus for future individualized therapy, e.g., by allowing therapeutic synergy when designing combination therapy against vulnerable points in the malignant cells. The therapeutic potential of knowledge obtained from global gene expression analysis of malignant cells is crucially dependent upon a similarly fine-grained knowledge of gene regulation in normal cells. The deviant gene expression patterns should therefore be assessed on a background of gene expression associated with housekeeping functions, particular differentiation stages and epiphenomena due to genomic instability. Since malignant cells originate from transformed precursor cells, such reference information can be obtained from investigations of embryonic and somatic stem cells. Much has recently been learned about the regulatory modules of normal hematopoietic stem cells and their malignant counterparts, and new biologically and clinically relevant patient subgroups as well as novel prognostic and therapeutic molecular markers have been identified. The present review weighs up the results and their potentialities with reference to gene expression analysis in acute myeloid leukemia (AML).

ERG upregulation and related ETS transcription factors in prostate cancer.

The aim of this study was to identify and validate differentially expressed genes in matched pairs of benign and malignant prostate tissue. Samples included 29 histologically verified primary tumors and 23 benign controls. Microarray analysis was initially performed using a sequence verified set of 40,000 human cDNA clones. Among the genes most consistently and highly upregulated in prostate cancer was the ETS family transcription factor ERG (ETS related gene). This finding was validated in an expanded patient series (37 tumors and 38 benign samples) using DNA oligonucleotide microarray and real-time quantitative PCR assays. ERG was 20- to more than 100-fold overexpressed in prostate cancer compared with benign prostate tissue in more than 50% of patients according to quantitative PCR. Surprisingly, ERG mRNA levels were found to be significantly higher in the endothelial cell line, HUVEC, than in the prostate cell lines PC3, DU145 and LNCaP. In situ hybridization of prostate cancer tissue revealed that ERG was abundantly expressed in both prostate cancer cells and associated endothelial cells. The consistency and magnitude of ERG overexpression in prostate cancer appeared unique, but several related ETS transcription factors were also overexpressed in matched pairs of tumor and benign samples, whereas ETS2 was significantly underexpressed. Our findings support the hypothesis that ERG overexpression and related ETS transcription factors are important for early prostate carcinogenesis.

cDNA microarray analysis of non-selected cases of acute myeloid leukemia demonstrates distinct clustering independent of cytogenetic aberrations and consistent with morphological signs of differentiation.

Acute myeloid leukemia (AML) is a heterogeneous disease with respect to biology and clinical course. Until now the basis for prognostic evaluation and therapeutic decision has been the karyotype, genetic FLT3 abnormalities and the initial chemotherapy response. A question that has emerged is if extensive gene expression analysis may supplement or partly replace current diagnostics. In an attempt to address this question, we performed cDNA microarray analysis on peripheral blood samples of 25 patients with newly diagnosed AML with high blast counts. The patients were randomly selected from a large group of consecutive patients. Leave-one-out crossvalidation (LOOCV) showed with high accuracy that gene expression classifiers could predict if leukaemia samples belonged to the FAB AML-M1 or to the FAB AML-M2 groups. An unsupervised two-dimensional hierarchical cluster analysis generated 3 patient subgroups. Except for an accumulation of samples classified as FAB M1 and M2 in cluster 3, there was no evident relationship between the clusters and the FAB classification. Each subgroup displayed clearly distinguished gene expression patterns validated using real-time quantitative PCR analysis. The identification of specific gene expressions that together constitute regulatory modules must complement cluster analyses in order to achieve an accurate basis for prognosis and prediction.

CD34 expression in native human acute myelogenous leukemia blasts: differences in CD34 membrane molecule expression are associated with different gene expression profiles.

BACKGROUND: The stem cell marker CD34 is expressed by leukemia blasts only for a subset of patients with acute myelogenous leukemia (AML). It is still controversial as to whether CD34 expression (defined as at least 10-20% positive cells) has any prognostic effect in patients with AML who receive intensive chemotherapy. The present study investigated whether gene expression profiling could be used to further subclassify CD34(+) AML cell populations. METHODS: AML blasts derived from 25 patients were examined; these patients were randomly selected from a larger consecutive group of patients. CD34 protein expression was determined by flow cytometry and expressed as the percentage of positive cells. Gene expression profiles were determined by complementary DNA microarrays. RESULTS: By unsupervised hierarchical clustering our patients could be grouped into two or three major subsets depending on the methodologic approach before clustering analysis (filtering or flooring of data, respectively). However, both approaches identified a cluster characterized by high gene expression and membrane molecule level of CD34. When using the floored expression profiles, the patient cluster characterized by increased CD34 gene expression was also characterized by a high percentage of CD34(+) cells (median 82%, range 56-100%) compared with the two other major clusters (median 19%, range <1-55%), but three of four outpatients also showed a high percentage of CD34(+) cells. CONCLUSION: A major proportion of patients with AML and high CD34 expression (usually >80% CD34(+) cells; nearly all patients had >50% positive cells) showed similarities in gene expression profile. In contrast, patients with lower CD34 expression often had a profile similar to those of patients regarded as CD34(-) according to conventional criteria. Our results suggest that the possible prognostic effect of CD34 expression should be reevaluated in clinical studies using additional or alternative cutoff values to describe CD34 expression.

Gene expression profiles in prostate cancer: association with patient subgroups and tumour differentiation.

Prostate carcinoma is the most common cancer of western men and is a markedly heterogeneous disease. The aim of this study was to identify signatures of differentially expressed genes in prostate cancer using DNA microarray technology, evaluating expression profiles in matched pairs of benign and malignant tissue. Samples were collected from 33 radical prostatectomies, and 52 specimens were included, representing 29 histologically verified primary tumours, 19 paired samples of malignant and benign tissue, and 4 non-paired benign tissue samples. Microarray analysis was performed using an expanded sequence verified set of 40,000 human cDNA clones, revealing several genes with significant differences between malignant and benign tissue, including recently reported genes like alpha-methylacyl-CoA racemase (AMACR) and hepsin, as well as genes relevant for tumour development and progression. Leave out cross validation (LOCV) test correctly predicted tumour or benign tissue in 47 (90.3%) out of 52 cases, significantly better than cross validation tests using randomly permuted tissue labels. Unsupervised clustering analysis revealed 3 distinct patient clusters significantly associated with Gleason score, and high grade tumours (Gleason score >/=7) accumulated in cluster 1 (C1). Gene expression profiles correctly predicted 100% of tumour samples segregating to C1, as also validated by LOCV. Gene expression profiles were analysed in filtered and floored datasets with similar results, and a pair-wise design was also tested. Gene expression profiles provided tumour clusters linked to differentiation, and revealed novel markers relevant for molecular classification, grading and therapy of prostate cancer.

LSimpute: accurate estimation of missing values in microarray data with least squares methods.

Microarray experiments generate data sets with information on the expression levels of thousands of genes in a set of biological samples. Unfortunately, such experiments often produce multiple missing expression values, normally due to various experimental problems. As many algorithms for gene expression analysis require a complete data matrix as input, the missing values have to be estimated in order to analyze the available data. Alternatively, genes and arrays can be removed until no missing values remain. However, for genes or arrays with only a small number of missing values, it is desirable to impute those values. For the subsequent analysis to be as informative as possible, it is essential that the estimates for the missing gene expression values are accurate. A small amount of badly estimated missing values in the data might be enough for clustering methods, such as hierachical clustering or K-means clustering, to produce misleading results. Thus, accurate methods for missing value estimation are needed. We present novel methods for estimation of missing values in microarray data sets that are based on the least squares principle, and that utilize correlations between both genes and arrays. For this set of methods, we use the common reference name LSimpute. We compare the estimation accuracy of our methods with the widely used KNNimpute on three complete data matrices from public data sets by randomly knocking out data (labeling as missing). From these tests, we conclude that our LSimpute methods produce estimates that consistently are more accurate than those obtained using KNNimpute. Additionally, we examine a more classic approach to missing value estimation based on expectation maximization (EM). We refer to our EM implementations as EMimpute, and the estimate errors using the EMimpute methods are compared with those our novel methods produce. The results indicate that on average, the estimates from our best performing LSimpute method are at least as accurate as those from the best EMimpute algorithm.

Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data.

BACKGROUND: Using DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fisher's linear discriminant). RESULTS: The proposed models were tested on four published datasets: (1) Leukemia (2) Colon cancer (3) Brain tumors and (4) NCI cancer cell lines. The models gave class prediction with markedly reduced error rates compared to other class prediction approaches, and the importance of feature selection on microarray data analysis was also emphasized. CONCLUSIONS: Our models identify marker genes with predictive potential, often better than other available methods in the literature. The models are potentially useful for medical diagnostics and may reveal some insights into cancer classification. Additionally, we illustrated two limitations in tumor classification from microarray data related to the biology underlying the data, in terms of (1) the class size of data, and (2) the internal structure of classes. These limitations are not specific for the classification models used.