Cancer target discovery using SAGE

Cancer reduces transcriptome specialization

A central goal of cancer biology is to understand how cells from this family of genetic diseases undergo specific morphological and physiological changes and regress to a de-regulated state of the cell cycle. The fact that tumors are unable to perform most of the specific functions of the original tissue led us to hypothesize that the degree of specialization of the transcriptome of cancerous tissues must be less than their normal counterparts. With the aid of information theory tools, we analyzed four datasets derived from transcriptomes of normal and tumor tissues to quantitatively test the hypothesis that cancer reduces transcriptome specialization. Here, we show that the transcriptional specialization of a tumor is significantly less than the corresponding normal tissue and comparable with the specialization of dedifferentiated embryonic stem cells. Furthermore, we demonstrate that the drop in specialization in cancerous tissues is largely due to a decrease in expression of genes that are highly specific to the normal organ. This approach gives us a better understanding of carcinogenesis and offers new tools for the identification of genes that are highly influential in cancer progression.

Identification of DEIN, a novel gene with high expression levels in stage IVS neuroblastoma

Neuroblastoma at stage IVS, defined by dissemination to specific tissues and age <1 year at diagnosis, regularly follows spontaneous regression without cytotoxic treatment. To uncover the molecular characteristics of this subtype, Serial Analysis of Gene Expression (SAGE) profiles from stage IVS and fatal stage IV tumors were compared. A SAGE tag (GCAACTTAAC) was detected that was overrepresented in stage IVS disease and that had no reliable match in current National Center for Biotechnology Information databases of SAGE profiles, thus pointing to a novel gene. The corresponding gene, which maps to chromosome 4q33-34, was identified using a modified 3'- and 5'-rapid amplification of cDNA ends PCR and was designated as DEIN (differentially expressed in neuroblastoma). The gene comprises five transcript variants and its sequence overlaps with expressed sequences of the yet uncharacterized UniGene cluster Hs.61435. DEIN exhibits nucleotide sequence conservation over a broad range of species with an overall homology of 65% between human and mouse. As none of the predicted amino acid sequences is homologous to known proteins, it remains to be determined whether DEIN represents a coding or noncoding RNA. Northern blot analysis and semiquantitative reverse transcription-PCR showed high DEIN expression in neuroblastoma, whereas expression was absent or weak in most normal adult tissues. Analysis of 121 primary neuroblastomas by real-time reverse transcription-PCR revealed a strong association with age at diagnosis <1 year and particularly with stage IVS disease (both P < 0.001). The characteristic expression pattern of DEIN suggests a specific role of this gene in the unique biology of stage IVS tumors and may help to molecularly define this special subtype of neuroblastoma.

Genomic approaches in the management and treatment of breast cancer

Breast cancer is the most common malignancy afflicting women from Western cultures. It has been estimated that approximately 211 000 women will be diagnosed with breast cancer in 2003 in the United States alone, and each year over 40 000 women will die of this disease. Developments in breast cancer molecular and cellular biology research have brought us closer to understanding the genetic basis of this disease. Unfortunately, this information has not yet been incorporated into the routine diagnosis and treatment of breast cancer in the clinic. Recent advancements in microarray technology hold the promise of further increasing our understanding of the complexity and heterogeneity of this disease, and providing new avenues for the prognostication and prediction of breast cancer outcomes. The most recent application of microarray genomic technologies to studying breast cancer will be the focus of this review.

Bioinformatics and cancer target discovery

The convergence of genomic technologies and the development of drugs designed against specific molecular targets provides many opportunities for using bioinformatics to bridge the gap between biological knowledge and clinical therapy. Identifying genes that have properties similar to known targets is conceptually straightforward. Additionally, genes can be linked to cancer via recurrent genomic or genetic abnormalities. Finally, by integrating large and disparate datasets, gene-level distinctions can be made between the different biological states that the data represents. These bioinformatics approaches and their associated methodologies, which can be applied across a range of technologies, facilitate the rapid identification of new target leads for further experimental validation.