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  • Oral Presentation
  • Open Access

Dissection of molecular pathways of cancer by high-throughput biochip technologies and RNA interference

  • 1
Breast Cancer Research20057 (Suppl 2) :S.43

  • Published:


  • Comparative Genomic Hybridization
  • Tissue Microarray Analysis
  • Affymetrix Gene Expression
  • Candidate Drug Target
  • Gene Expression Platform


Our aim is to identify new molecular targets and mechanisms for therapeutic intervention in cancer. To achieve this aim, we develop and apply multiple high-throughput technologies including 'in silico' screening as well as technologies for molecular, cellular and clinical discovery research. Finally, data integration from these technology platforms is applied to facilitate interpretation and prioritization of the findings.

In silico screening

In order to make use of the exponential increase of published data on gene expression arrays, we have launched a project to acquire and make use of these data as a discovery resource. We currently have data on 5700 samples analyzed on the Affymetrix gene expression platform stored in our relational database. These samples include, for example, 64 normal tissues/cell types, 43 tumor types, many other diseases as well as functional experiments; altogether 84 million data points. We have developed methods to mine these data to identify tissue-specific and disease-specific expression patterns of transcripts, to identify gene coexpression profiles, to explore networks of gene regulation as well as methods to interpret new microarray experiments. In silico transcriptomic screening makes it possible to generate dozens of testable hypotheses for laboratory analysis based on datasets that are much larger and more extensive than any single academic laboratory can afford to generate. Analysis of gene expression profiles across hundreds of tissue and tumor types, diseases and experimental manipulations generates novel, often unexpected, insights of gene function as well as of the underlying biology and medicine.

Molecular screening

Large cohorts of clinical samples are now being investigated not only at the RNA level by gene expression profiling, but also at the DNA-level using comparative genomic hybridization (CGH) arrays for analysis of somatic genetic alterations or SNP arrays for studies of allelic gains and losses. There is also an emerging interest for large-scale proteomic and metabolic profiling. It will be increasingly important to integrate multiple levels of molecular profiling data to gain new insights and comprehensive views on mechanisms of cancer development. We are applying single-gene resolution oligo-CGH arrays and integrating these data with gene expression information on the same samples. The increased CGH resolution has highlighted several microdeletions as well as small amplifications, whose impact on gene expression can be substantial and highly specific. This has led to an opportunity for rapid identification of genes that may be targets of genetic alterations in cancer. As demonstrated by several recently approved drugs for cancer, such mutated genes represent attractive targets for the development of effective cancer-specific therapeutics.

Functional screening using RNA interference

The molecular profiling of DNA expression patterns, RNA expression patterns or protein expression patterns in patient samples is not sufficient for implicating these molecules or molecular mechanisms as therapeutic targets. It is also necessary to generate functional information on such genes and pathways. Towards this aim, we have developed a high-throughput screening system that is composed of a robotic, automated platform for the analysis of up to 20,000 functional experiments with living cells at a time using the 384-well microplate format. Cells are dispensed into culture wells, exposed to siRNAs or small molecule compounds, incubated for 1–3 days, washed, and stained with phenotype-specific markers for cell growth, cell cycle distribution or induction of apoptosis. The results are read by plate readers or cell cytometers. Functional studies with RNAi libraries (e.g. 1000–10,000 siRNAs) have implicated genes whose targeting by RNAi is lethal to specific cancer types, such as breast cancer. Integration of such functional RNAi data with gene expression and aCGH data has enabled us to identify genes that are targets of genetic alterations and whose expression is required for the maintenance of the malignant phenotype. Such genes represent attractive candidate drug targets.

Clinical screening

Data on molecular targets arising from functional in vitro studies need to be corroborated in studies of large-scale clinical sample cohorts in order to verify that such molecular targets are relevant in clinical patient samples. A number of technologies are being developed towards this aim. First, the in silico screening transcriptomics database with 5700 samples has made it possible to develop an approach for 'in silico clinical validation'. It is possible to determine the expression levels of any gene across a very large number of tumor types and normal sample types. Second, more established technologies, such as tissue microarrays, facilitate the analysis of individual DNA, RNA and protein targets in thousands of arrayed patient samples, typically from formalin-fixed tumors. Tissue microarray analysis with antibody binders produces definitive clinical data on the expression of therapeutic protein targets, and enables quantitation of drug target distributions at the population level (target epidemiology). In order to further increase the throughput of molecular analyses, we are developing methods to print tissue lysates from frozen samples in an array format. Tissue protein lysate arrays will help to automate and expand antibody-based validation of molecular targets in large cohorts of tissue specimens.


In summary, high-throughput technologies can be applied in the 'in silico', molecular, cellular and clinical screening studies, thereby substantially increasing the information and insights that can be obtained on a single platform. Integration of data from the various high-throughput platforms will facilitate a deeper, mechanistic interpretation and understanding of cancer biology, which is an essential requirement for the development of next-generation targeted therapies.

Authors’ Affiliations

Medical Biotechnology, VTT Technical Research Centre of Finland, University of Turku, Finland


© BioMed Central 2005