A method for finding putative causes of gene expression variation

The majority of microarray studies evaluate gene ex- pression differences between various specimens or con- ditions. However, the causes of this variability often re- main unknown. Our aim is to identify underlying causes of these patterns, a process that would eventually enable a mechanistic understanding of the deregulation of gene expression in cancer. The procedure consists of three phases: pre-processing, data integration and statistical analysis. We have applied the strategy to identify genes that are overexpressed due to amplification in breast cancer. The data were obtained from 14 breast cancer cell lines, which were subjected to cDNA microarray based copy number and expression experiments. The re- sult of the analysis was a list that consisted of 92 genes. This set includes several genes that are known to be both overexpressed and amplified in breast cancer. The com- plete study was published in Journal of the Franklin In- stitute 2004, and in this paper we focus on the main issues of the study

Clinical relevance of integrin alpha 4 in gastrointestinal stromal tumours

The molecular mechanisms for the dissemination and metastasis of gastrointestinal stromal tumours (GIST) are incompletely understood. The purpose of the study was to investigate the clinical relevance of integrin alpha 4 (ITGA4) expression in GIST. GIST transcriptomes were first compared with transcriptomes of other types of cancer and histologically normal gastrointestinal tract tissue in the MediSapiens in silico database. ITGA4 was identified as an unusually highly expressed gene in GIST. Therefore, the effects of ITGA4 knock-down and selective integrin alpha 4 beta 1 (VLA-4) inhibitors on tumour cell proliferation and invasion were investigated in three GIST cell lines. In addition, the prognostic role of ITGA4 expression in cancer cells was investigated in a series of 147 GIST patients with immunohistochemistry. Inhibition of ITGA4-related signalling decreased GIST cell invasion in all investigated GIST cell lines. ITGA4 protein was expressed in 62 (42.2%) of the 147 GISTs examined, and expression was significantly associated with distant metastases during the course of the disease and several adverse prognostic features. Patients whose GIST expressed strongly ITGA4 had unfavourable GIST-specific survival and overall survival compared to patients with low or no ITGA4 expression. Taken together, ITGA4 is an important integrin in the molecular pathogenesis of GIST and may influence their clinical behaviour.Peer reviewe

High-throughput cell-based compound screen identifies pinosylvin methyl ether and tanshinone IIA as inhibitors of castration-resistant prostate cancer

Current treatment options for castration-resistant prostate cancer (CRPC) are limited. In this study, a high-throughput screen of 4910 drugs and drug-like molecules was performed to identify antiproliferative compounds in androgen ablated prostate cancer cells. The effect of compounds on cell viability was compared in androgen ablated LNCaP prostate cancer cells and in LNCaP cells grown in presence of androgens as well as in two non-malignant prostate epithelial cells (RWPE-1 and EP156T). Validation experiments of cancer specific anti-proliferative compounds indicated pinosylvin methyl ether (PSME) and tanshinone IIA as potent inhibitors of androgen ablated LNCaP cell proliferation. PSME is a stilbene compound with no previously described antineoplastic activity whereas tanshinone IIA is currently used in cardiovascular disorders and proposed as a cancer drug. To gain insights into growth inhibitory mechanisms in CRPC, genome-wide gene expression analysis was performed in PSME- and tanshinone IIA-exposed cells. Both compounds altered the expression of genes involved in cell cycle and steroid and cholesterol biosynthesis in androgen ablated LNCaP cells. Decrease in androgen signalling was confirmed by reduced expression of androgen receptor and prostate specific antigen in PSME- or tanshinone IIA-exposed cells. Taken together, this systematic screen identified a novel anti-proliferative agent, PSME, for CRPC. Moreover, our screen confirmed tanshinone IIA as well as several other compounds as potential prostate cancer growth inhibitors also in androgen ablated prostate cancer cells. These results provide valuable starting points for preclinical and clinical studies for CRPC treatment

Functional Profiling of Precursor MicroRNAs Identifies MicroRNAs Essential for Glioma Proliferation

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High-Throughput Transcriptomic and RNAi Analysis Identifies AIM1, ERGIC1, TMED3 and TPX2 as Potential Drug Targets in Prostate Cancer

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A Comprehensive Panel of Three-Dimensional Models for Studies of Prostate Cancer Growth, Invasion and Drug Responses

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Comprehensive Drug Testing of Patient-derived Conditionally Reprogrammed Cells from Castration-resistant Prostate Cancer

Background Technology development to enable the culture of human prostate cancer (PCa) progenitor cells is required for the identification of new, potentially curative therapies for PCa. Objective We established and characterized patient-derived conditionally reprogrammed cells (CRCs) to assess their biological properties and to apply these to test the efficacies of drugs. Design, setting, and participants CRCs were established from seven patient samples with disease ranging from primary PCa to advanced castration-resistant PCa (CRPC). The CRCs were characterized by genomic, transcriptomic, protein expression, and drug profiling. Outcome measurements and statistical analysis The phenotypic quantification of the CRCs was done based on immunostaining followed by image analysis with Advanced Cell Classifier using Random Forest supervised machine learning. Copy number aberrations (CNAs) were called from whole-exome sequencing and transcriptomics using in-house pipelines. Dose-response measurements were used to generate multiparameter drug sensitivity scores using R-statistical language. Results and limitations We generated six benign CRC cultures which all had an androgen receptor-negative, basal/transit-amplifying phenotype with few CNAs. In three-dimensional cell culture, these cells could re-express the androgen receptor. The CRCs from a CRPC patient (HUB.5) displayed multiple CNAs, many of which were shared with the parental tumor. We carried out high-throughput drug-response studies with 306 emerging and clinical cancer drugs. Using the benign CRCs as controls, we identified the Bcl-2 family inhibitor navitoclax as the most potent cancer-specific drug for the CRCs from a CRPC patient. Other drug efficacies included taxanes, mepacrine, and retinoids. Conclusions Comprehensive cancer pharmacopeia-wide drug testing of CRCs from a CRPC patient highlighted both known and novel drug sensitivities in PCa, including navitoclax, which is currently being tested in clinical trials of CRPC. Patient summary We describe an approach to generate patient-derived cancer cells from advanced prostate cancer and apply such cells to discover drugs that could be applied in clinical trials for castration-resistant prostate cancer.Peer reviewe

HOX gene expression predicts response to BCL-2 inhibition in acute myeloid leukemia

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GTI: A Novel Algorithm for Identifying Outlier Gene Expression Profiles from Integrated Microarray Datasets

Background Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type (‘outlier genes’), a hallmark of potential oncogenes. Methodology A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. Conclusions/Significance Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).Peer reviewe

Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2

We present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics