Androgen Receptor Functional Analyses by High Throughput Imaging: Determination of Ligand, Cell Cycle, and Mutation-Specific Effects

Understanding how androgen receptor (AR) function is modulated by exposure to steroids, growth factors or small molecules can have important mechanistic implications for AR-related disease therapies (e.g., prostate cancer, androgen insensitivity syndrome, AIS), and in the analysis of environmental endocrine disruptors.We report the development of a high throughput (HT) image-based assay that quantifies AR subcellular and subnuclear distribution, and transcriptional reporter gene activity on a cell-by-cell basis. Furthermore, simultaneous analysis of DNA content allowed determination of cell cycle position and permitted the analysis of cell cycle dependent changes in AR function in unsynchronized cell populations. Assay quality for EC50 coefficients of variation were 5–24%, with Z' values reaching 0.91. This was achieved by the selective analysis of cells expressing physiological levels of AR, important because minor over-expression resulted in elevated nuclear speckling and decreased transcriptional reporter gene activity. A small screen of AR-binding ligands, including known agonists, antagonists, and endocrine disruptors, demonstrated that nuclear translocation and nuclear “speckling” were linked with transcriptional output, and specific ligands were noted to differentially affect measurements for wild type versus mutant AR, suggesting differing mechanisms of action. HT imaging of patient-derived AIS mutations demonstrated a proof-of-principle personalized medicine approach to rapidly identify ligands capable of restoring multiple AR functions.HT imaging-based multiplex screening will provide a rapid, systems-level analysis of compounds/RNAi that may differentially affect wild type AR or clinically relevant AR mutations

Androgen Receptor Mutations Associated with Androgen Insensitivity Syndrome: A High Content Analysis Approach Leading to Personalized Medicine

Androgen insensitivity syndrome (AIS) is a rare disease associated with inactivating mutations of AR that disrupt male sexual differentiation, and cause a spectrum of phenotypic abnormalities having as a common denominator loss of reproductive viability. No established treatment exists for these conditions, however there are sporadic reports of patients (or recapitulated mutations in cell lines) that respond to administration of supraphysiologic doses (or pulses) of testosterone or synthetic ligands. Here, we utilize a novel high content analysis (HCA) approach to study AR function at the single cell level in genital skin fibroblasts (GSF). We discuss in detail findings in GSF from three historical patients with AIS, which include identification of novel mechanisms of AR malfunction, and the potential ability to utilize HCA for personalized treatment of patients affected by this condition

A cell spot microarray method for high-throughput biology

High-throughput screening of cellular effects of RNA interference (RNAi) libraries is now being increasingly applied to explore the role of genes in specific cell biological processes and disease states. However, the technology is still limited to specialty laboratories, due to the requirements for robotic infrastructure, access to expensive reagent libraries, expertise in high-throughput screening assay development, standardization, data analysis and applications. In the future, alternative screening platforms will be required to expand functional large-scale experiments to include more RNAi constructs, allow combinatorial loss-of-function analyses (e.g. genegene or gene-drug interaction), gain-of-function screens, multi-parametric phenotypic readouts or comparative analysis of many different cell types. Such comprehensive perturbation of gene networks in cells will require a major increase in the flexibility of the screening platforms, throughput and reduction of costs. As an alternative for the conventional multi-well based high-throughput screening -platforms, here the development of a novel cell spot microarray method for production of high density siRNA reverse transfection arrays is described. The cell spot microarray platform is distinguished from the majority of other transfection cell microarray techniques by the spatially confined array layout that allow highly parallel screening of large-scale RNAi reagent libraries with assays otherwise difficult or not applicable to high-throughput screening. This study depicts the development of the cell spot microarray method along with biological application examples of high-content immunofluorescence and phenotype based cancer cell biological analyses focusing on the regulation of prostate cancer cell growth, maintenance of genomic integrity in breast cancer cells, and functional analysis of integrin protein-protein interactions in situ.RNA-interferenssin (RNAi) käyttö geenituotteiden toimintojen tutkimuksessa on vuosikymmenessä kehittynyt solubiologisen tutkimuksen merkittävimpien teknologioiden joukkoon. RNAi-menetelmät ovat mahdollistaneet myös eri solubiologisten signaalivälitysreittien kartoittamisen koko perimänlaajuisten geenitoimintojen suurtehoseulonnan avulla. Teknologian nopeasta kehityksestä ja laajamittaisesta hyödyntämisestä huolimatta, RNAi-menetelmien käyttö suurtehoseulontaan rajoittuu edelleen erikoislaboratorioihin huomattavien laite-, reagenssi- sekä tietotaitovaatimusten johdosta. Lisäksi yleisimmin käytetyt kuoppalevypohjaiset seulonta-menetelmät rajoittavat seulontatutkimusten laajuutta, eri analyysimenetelmien käyttöä sekä useiden mallisolulinjojen rinnakkaista seulontaa suuren reagenssikulutuksen takia. RNAi-suurtehoseulonnan kustannuksien ja laiteriippuvuuden alentamiseksi sekä monipuolisuuden lisäämiseksi tulevaisuudessa tarvitaan uusia vaihtoehtoisia seulontamenetelmiä. Tämän väitöskirjatyön lähtökohtana oli uuden kuoppalevyillä tehtäviä seulontamenetelmiä edullisemman ja monipuolisemman RNAi-suurtehoseulontamenetelmän kehittäminen. Tutkimuksessa kehitettiin solujen siRNA käänteistransfektioon perustuva solumikrosirumenetelmä, joka tarkasti rajattujen siRNA näytepisteiden avulla mahdollistaa jopa koko ihmisen perimänlaajuisten siRNA näytekirjastojen seulonnan yksittäisellä mittasirulla. Menetelmän suuri näytetiheys sekä sirujen pieni pinta-ala mahdollistavat myös erikoismittamenetelmien käytön suurtehoseulonnassa, jotka teknisistä tai kustannussyistä eivät sovellu käytettäväksi suurtehoseulontaan kuoppalevyillä. Väitöskirjan ensimmäinen osatyö kuvaa menetelmän kehittämistyön. Väitöskirjan toisessa, kolmannessa ja neljännessä osatyössä menetelmää on hyödynnetty syöpäbiologisissa seulontatutkimuksissa selvitettäessä geenejä jotka vaikuttavat eturauhassyöpäsolujen kasvuun, rintasyöpäsolujen solunjakautumiseen sekä integriini tarttumisreseptorien aktiivisuuden säätelyyn.Siirretty Doriast

The X-linked tumor suppressor TSPX downregulates cancer-drivers/oncogenes in prostate cancer in a C-terminal acidic domain dependent manner.

TSPX is a tumor suppressor gene located at Xp11.22, a prostate cancer susceptibility locus. It is ubiquitously expressed in most tissues but frequently downregulated in various cancers, including lung, brain, liver and prostate cancers. The C-terminal acidic domain (CAD) of TSPX is crucial for the tumor suppressor functions, such as inhibition of cyclin B/CDK1 phosphorylation and androgen receptor transactivation. Currently, the exact role of the TSPX CAD in transcriptional regulation of downstream genes is still uncertain. Using different variants of TSPX, we showed that overexpression of either TSPX, that harbors a CAD, or a CAD-truncated variant (TSPX[∆C]) drastically retarded cell proliferation in a prostate cancer cell line LNCaP, but cell death was induced only by overexpression of TSPX. Transcriptome analyses showed that TSPX or TSPX[∆C] overexpression downregulated multiple cancer-drivers/oncogenes, including MYC and MYB, in a CAD-dependent manner and upregulated various tumor suppressors in a CAD-independent manner. Datamining of transcriptomes of prostate cancer specimens in the Cancer Genome Atlas (TCGA) dataset confirmed the negative correlation between the expression level of TSPX and those of MYC and MYB in clinical prostate cancer, thereby supporting the hypothesis that the CAD of TSPX plays an important role in suppression of cancer-drivers/oncogenes in prostatic oncogenesis

Identification of new candidate biomarkers for prostate cancer by affinity proteomics

Prostate cancer (PCA) is a complex malignancy that needs to be more thoroughly studied and understood at a molecular level to fill the current knowledge gap, and optimize diagnosis and to treatment. Prostate specific antigen (PSA) showed to be not specific for PCA, therefore a demand for novel specific biomarkers exists. The aim of our work was to identify new specific candidate biomarkers for PCA in tissue and plasma samples by means of affinity proteomics approaches such as reverse phase protein array and antigen arrays. Tissue samples are an invaluable source of biomarkers for cancer, but very limited in amount and requiring invasive procedures for collection. Still, they allow to directly profiling the molecular status of tumor itself. Beside tissue, a screening procedure on biological fluid such as plasma would be highly desirable, thanks to the less invasiveness and low-costs of samples collection. Among the biomarkers detectable in plasma are the autoantibodies. The first part of this thesis summarizes the current status of PCA epidemiology, treatment, and biomarkers research. Beside this, an overview of the affinity proteomics platforms available for biomarkers research, and the critical variables to consider in the biomarkers validation process are presented. The second part of the thesis reports the main results of two original studies where the author of the thesis is the main contributor. Paper I is based on the profiling of PCA tissue samples using RPPA. Our results indicate the feasibility of combining laser capture microdissection (LCM) and RPPA for evaluating the molecular architecture and cross-talking of epithelial and stromal compartments. Paper II is based on profiling the autoimmune response to PCA patients, comparing early and late stage of the disease. The authors identified and characterized the IgG reactivity toward a novel epitope for the candidate biomarker prostein. The data presented in this thesis provide two robust frameworks based on affinity proteomics platforms applied for protein profiling in tissue, and autoantibodies profiling in plasma in the context of PCA biomarkers discovery.Co-supervisors: Peter Nilsson (Biotechnology Department, KTH-Royal Institute of Technology, Stockholm, Sweden), Mariaelena Pierobon (Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA)openDottorato di ricerca in Medicina cellulare e molecolareopenPin, Elis

CoPub Mapper: mining MEDLINE based on search term co-publication

BACKGROUND: High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to identify biological similarities between genes, publications from MEDLINE were identified in which pairs of gene names and combinations of gene name with specific keywords were co-mentioned. RESULTS: MEDLINE search strings for 15,621 known genes and 3,731 keywords were generated and validated. PubMed IDs were retrieved from MEDLINE and relative probability of co-occurrences of all gene-gene and gene-keyword pairs determined. To assess gene clustering according to literature co-publication, 150 genes consisting of 8 sets with known connections (same pathway, same protein complex, or same cellular localization, etc.) were run through the program. Receiver operator characteristics (ROC) analyses showed that most gene sets were clustered much better than expected by random chance. To test grouping of genes from real microarray data, 221 differentially expressed genes from a microarray experiment were analyzed with CoPub Mapper, which resulted in several relevant clusters of genes with biological process and disease keywords. In addition, all genes versus keywords were hierarchical clustered to reveal a complete grouping of published genes based on co-occurrence. CONCLUSION: The CoPub Mapper program allows for quick and versatile querying of co-published genes and keywords and can be successfully used to cluster predefined groups of genes and microarray data

Differential proteomic alterations between localised and metastatic prostate cancer

Molecular alterations in the prostate cancer proteome mediate the functional and phenotypic transformation from clinically localised to metastatic cancer, a transition that drives patient's mortality and challenges therapeutic intervention. A first approximation of differential proteomic alterations stratified by disease stage has yielded repertoires of potential diagnostic and prognostic markers, multiplex signatures of predictive value, and yield fundamental insight into molecular commonalities in cancer progression. Deciphering these causative proteomic alterations from the molecular noise will continue to mature our understanding of tumour biology and drive new computational and integrative approaches to model a system's view that accommodates the heterogeneity of prostate cancer progression

BIOINFORMATICS ANALYSIS OF OMICS DATA TOWARDS CANCER DIAGNOSIS AND PROGNOSIS

I would first like to thank my mentor, Dr. Arul M. Chinnaiyan, for his expert guidance, support, encouragement, and inspiration. I would also like to thank Dr. Debashis Ghosh for his continuous statistical support and great advice, Dr. David G. Beer, Dr. Jill A. Macoska, and Dr. Kerby A. Shedden for serving on my Doctoral committee and giving me valuable suggestions on this thesis work. I would like to thank Jindan Yu, Xiaoju Wang, Guoan Chen, Saravana Dhanasekaran, Daniel Rhodes, Scott A. Tomlins, and Sooryanarayana Varambally, who have contributed to most of the work described here. I would like to express my gratitude to all the members in the Chinnaiyan lab for their support. Without them, none of the work described here could have been completed. I would also like to thank William P. Worzel and Arpit A. Almal for their support on genetic programming project. I would like to express my deepest gratitude to my wife and my love, Yipin, without whom I would be nowhere. Thanks for putting up with my late nights and giving me unconditional love and encouragement through my Doctoral study and the writing of this work. I would also like to thank my parents, my sister, and my grandparents for giving constant support and love. And last but not least, I would like to thank my friends and all whose support helped me completing this thesis in time. ii TABLE OF CONTENT

Optimization of Invasion-Specific Effects of Betulin Derivatives on Prostate Cancer Cells through Lead Development

The anti-invasive and anti-proliferative effects of betulins and abietane derivatives was systematically tested using an organotypic model system of advanced, castration-resistant prostate cancers. A preliminary screen of the initial set of 93 compounds was performed in two-dimensional (2D) growth conditions using non-transformed prostate epithelial cells (EP156T), an androgen-sensitive prostate cancer cell line (LNCaP), and the castration-resistant, highly invasive cell line PC-3. The 25 most promising compounds were all betulin derivatives. These were selected for a focused secondary screen in three-dimensional (3D) growth conditions, with the goal to identify the most effective and specific anti-invasive compounds. Additional sensitivity and cytotoxicity tests were then performed using an extended cell line panel. The effects of these compounds on cell cycle progression, mitosis, proliferation and unspecific cytotoxicity, versus their ability to specifically interfere with cell motility and tumor cell invasion was addressed. To identify potential mechanisms of action and likely compound targets, multiplex profiling of compound effects on a panel of 43 human protein kinases was performed. These target de-convolution studies, combined with the phenotypic analyses of multicellular organoids in 3D models, revealed specific inhibition of AKT signaling linked to effects on the organization of the actin cytoskeleton as the most likely driver of altered cell morphology and motility.Peer reviewe