|VTT Technical Research Centre of Finland||University of Helsinki||University of Turku|
Olli Kallioniemi group
Olli Kallioniemi’s group is located at the newly-established Turku-based research center of the VTT Technical Research Centre in Finland, on Medical Biotechnology. The group is also affiliated with the University of Turku (Biotechnology Center and Dept. Pharmacology). Research in the Medical Biotechnology Center is carried out in three teams, each headed by a senior scientist. These include the Canceromics (headed by Matthias Nees, PhD), High-throughput Screening (headed by Merja Perälä, PhD), and Biochips teams (headed by Petri Saviranta, PhD).
Even though recently launched in Turku, the group has had a significant long-term program on genome-scale cancer research, both to explore germline predisposition as well to investigate mechanisms of disease progression. The group has an over 10-year track record in developing and early-stage application of high-throughput technologies, including comparative genomic hybridization (CGH, 1992), tissue microarrays (1998), CGH microarrays (2001), NMD-microarrays (2002) and cell-based (RNAi) microarrays (2003). These tools have facilitated the identification of critical genes in cancer and their role in tumor progression. These include the AR, IGFBPs, S100P, ERG and HDACs in prostate cancer, as well as AIB1, S6K, and PLK1 in breast cancer.
These previously mentioned tools and others now under development are being applied to prostate and breast cancer research. A new direction involves focussing on genes that would be potential drug targets, as well as to understand druggable mechanisms and genetic and chemical vulnerabilities. The group is developing and applying high-throughput genomics and transcriptomics analyses of cancer, bioinformatic modelling and the functional, high-throughput analysis with RNAi and drugs using cell and lysate microarrays. The strategy is to integrate data from the aforementioned platforms in order to identify key drug targets and explore mechanisms of drug action and resistance.
Specific competences and infrastructure
HTS: We have established a state-of-the-art cell-based High Throughput Screening (HTS) system. The primary focus is at siRNA screening for target identification and genome-scale cancer or cell biology research, but we can also screen for compounds against specific druggable targets or cellular processes (chemical biology). The HTS system includes robotic instrumentation (Hamilton Microlab STAR, Hamilton SWAP automation, Multidrop plate filler and Tecan plate washer), a Cytomat 6000 automated CO2 incubator, as well as PE-Envision plate reader, Acumen image cytometer and a FACS Array. Typical HTS screens are carried out with cells plated in 384 well plates that are robotically processed for either compound or RNA interference screening. Over 30 000 cell biological experiments can be performed at a time.
uHTS with Cell Arrays: We have developed a unique ultra-high density cell microarray screening system enables 100-1000x screening throughput compared to 384-well-based assays with a corresponding reduction in reagent consumption. siRNAs, mixed with transfection agents, are printed as a microarray. Cultured cells are then allowed to adhere on top of the array, where they become transfected with the siRNAs. Typically each spot has 200-300 transfected cells. After our early work (Mousses et al., Genome Research, 2003), we have made significant technical progress. Currently, we print arrays of 7000 individual siRNAs/slide, and can measure many endpoints such as cell proliferation, apoptosis and mitotic index, as well as individual protein markers. We use a Genetix Qarry2 printer for producing the arrays, and a Tecan LS400 four-laser confocal scanner for low resolution (down to 4 µm) imaging of the arrays. For higher resolution analysis (e.g. subcellular localization) we use a Zeiss Axiovert 200M microscope equipped with a motorized stage.
RNAi, cDNA and chemical libraries: We have acquired both specific sets of siRNA libraries, including all human kinases and phosphatases or human cancer genes, as well as a whole-genome library of 44,000 siRNAs. In addition, we have sequence-verified Origene full-length cDNAs for all human kinases in expression vectors. Finally, we have significant sets of chemical libraries. These include ~3000 drug-like molecules, including most of the currently used drugs in the clinic, ~80 kinase and phosphatase inhibitors, ~1500 natural products, and ~70,000 diverse small molecule compounds. We have state-of-the-art chemoinformatic possibilities for drug discovery using both ligand- and receptor structure based methods and a virtual compound library of up to 3.5 Million compounds with SDF structure files available.
Gene expression arrays: We are using the industry-standard Affymetrix U133 series platform (1.3 M oligos) and the new Affymetrix exon microarrays. These latter ones contain >5.5 Million oligos, essentially at least one probe set for each exon of each human gene. This provides with an opportunity to identify genes that are alternatively spliced or that are rearranged by genetic alterations. We have also set up array profiling of human miRNAs.
High-resolution array-CGH analyses: Our laboratory has been involved in the development and application of the comparative genomic hybridization (CGH) technology since its original description in 1992. We are now using 244,000 oligo-array elements, allowing the identification of even sub-gene (exon-level) copy number changes. This has revealed a number of individual gene targets involved in genetic rearrangements.
Bioinformatic analysis and data integration for systems biology: Microarray and HTS data are routinely normalized, QC controlled and analyzed by hierarchical clustering, principal component analysis, self-organizing-maps (SOM), significance analysis of microarrays (SAM) and gene-set enrichment analysis (GSEA). We have also created an extensive set of bioinformatic methods for high-throughput data integration. Briefly, in-house relational databases are used to store data and to enable connecting together the various types of identifiers (siRNA IDs, probe IDs, gene- and transcript IDs etc.) used in the different studies. Matlab, the R programming language and Bioconductor packages are used to build data integration, analysis and visualization capabilities. In addition, web-based data analysis and display software are used to enable biologists to analyze and interpret the data. Appropriate statistical methods are employed to deal with the multiple testing nature of high-throughput experiments.
Meta-analysis of transcriptional profiles in clinical cancers: We are setting up a database publicly available gene expression data from databases, journal web sites and published reports. The data have been normalized, QC-checked and annotated to form a single, fully integrated and searchable database. The database currently covers about 9800 samples, including roughly normal tissues, major cancer types and other disease states.
Recent key papers:
1) Iljin K, Wolf M, Edgren H, Gupta S, Kilpinen S, Skotheim RI, Peltola M, Smit F, Verhaegh G, Schalken J, Nees M, Kallioniemi O. TMPRSS2 fusions with oncogenic ETS factors in prostate cancer involve unbalanced genomic rearrangements and are associated with HDAC1 and epigenetic reprogramming. Cancer Res. 2006 Nov 1;66(21):10242-6.
2) Edgren H, Kallioniemi O. Integrated breast cancer genomics. Cancer Cell. 2006 Dec;10(6):453-4.
3) Huusko P, Ponciano-Jackson D, Wolf M, Kiefer JA, Azorsa DO, Tuzmen S, Weaver D, Robbins C, Moses T, Allinen M, Hautaniemi S, Chen Y, Elkahloun A, Basik M, Bova GS, Bubendorf L, Lugli A, Sauter G, Schleutker J, Ozcelik H, Elowe S, Pawson T, Trent JM, Carpten JD, Kallioniemi OP, Mousses S. Nonsense-mediated decay microarray analysis identifies mutations of EPHB2 in human prostate cancer. Nat Genet. 2004 Sep;36(9):979-83.
|© Translational Genome-Scale Biology, 2006|