Lynette H. Grouse

Generation and initial analysis of more than 15,000 full-length human and mouse cDNA sequences.

The National Institutes of Health Mammalian Gene Collection (MGC) Program is a multiinstitutional effort to identify and sequence a cDNA clone containing a complete ORF for each human and mouse gene. ESTs were generated from libraries enriched for full-length cDNAs and analyzed to identify candidate full-ORF clones, which then were sequenced to high accuracy. The MGC has currently sequenced and verified the full ORF for a nonredundant set of >9,000 human and >6,000 mouse genes. Candidate full-ORF clones for an additional 7,800 human and 3,500 mouse genes also have been identified. All MGC sequences and clones are available without restriction through public databases and clone distribution networks (see http://mgc.nci.nih.gov).

Molecular profiling of clinical tissue specimens: feasibility and applications.

The relationship between gene expression profiles and cellular behavior in humans is largely unknown. Expression patterns of individual cell types have yet to be precisely measured, and, at present, we know or can predict the function of a relatively small percentage of genes. However, biomedical research is in the midst of an informational and technological revolution with the potential to increase dramatically our understanding of how expression modulates cellular phenotype and response to the environment. The entire sequence of the human genome will be known by the year 2003 or earlier. 1,2 In concert, the pace of efforts to complete identification and full-length cDNA sequencing of all genes has accelerated, and these goals will be attained within the next few years. 3-7 Accompanying the expanding base of genetic information are several new technologies capable of global gene expression measurements. 8-16 Taken together, the expanding genetic database and developing expression technologies are leading to an exciting new paradigm in biomedical research known as molecular profiling.

Determination of a minimal deletion interval on chromosome band 8p21 in sporadic prostate cancer

Loss of the short arm of chromosome 8 is a common event in prostatic neoplasms. Previous studies indicate that there may be up to three separate tumor suppressor genes on chromosome arm 8p, based on patterns of allelic loss. The responsible gene or genes have yet to be identified. In the present study, we used laser‐capture microdissection of primary human prostate tumors and 17 microsatellite markers across chromosome band 8p21 to determine a minimal deletion interval. From an initial set of 120 cases, three tumors contained overlapping interstitial deletions on chromosome band 8p21. The three cases define an internally consistent minimal candidate tumor suppressor gene interval of approximately two megabases. Published 2002 Wiley‐Liss, Inc.

Molecular Profiling of Clinical Tissue Specimens : Feasibility and Applications

The relationship between gene expression profiles and cellular behavior in humans is largely unknown. Expression patterns of individual cell types have yet to be precisely measured, and, at present, we know or can predict the function of a relatively small percentage of genes. However, biomedical research is in the midst of an informational and technological revolution with the potential to increase dramatically our understanding of how expression modulates cellular phenotype and response to the environment. The entire sequence of the human genome will be known by the year 2003 or earlier. 1, 2 In concert, the pace of efforts to complete identification and full-length cDNA sequencing of all genes has accelerated, and these goals will be attained within the next few years. 3, 4, 5, 6, 7 Accompanying the expanding base of genetic information are several new technologies capable of global gene expression measurements. 8, 9, 10, 11, 12, 13, 14, 15, 16 Taken together, the expanding genetic database and developing expression technologies are leading to an exciting new paradigm in biomedical research known as molecular profiling.

In silico analysis of cancer through the Cancer Genome Anatomy Project

The Cancer Genome Anatomy Project (CGAP) was designed and implemented to provide public datasets, material resources and informatics tools to serve as a platform to support the elucidation of the molecular signatures of cancer. This overview of CGAP describes the status of this effort to develop resources based on gene expression, polymorphism identification and chromosome aberrations, and we describe a variety of analytical tools designed to facilitate in silico analysis of these datasets.

The cancer genome anatomy project: Online resources to reveal the molecular signatures of cancer

Four years ago, the National Cancer Institute implemented the Cancer Genome Anatomy Project (CGAP), which was designed to build an interface between genomics and cancer research.1 It was evident that new approaches to science, based on comprehensive molecular analysis, promised remarkable new opportunities to enhance our fundamental understanding of cancer. New technologies offered the potential to delineate specific types of genetic changes, including patterns of altered gene expression and function that could be used to define any cancer in the context of, but not strictly dependent upon, its site of origin. Therefore, we anticipated that these new technologies would elucidate the molecular features of an individual tumor, and profile progression and response to therapy. The molecular information generated could be used by basic and clinical researchers to define the molecular signatures that distinguish different cancers. Pivotal to this approach is the availability of a robust database and analysis tools. The goal of such an electronic database is to seamlessly integrate molecular and clinical data. Essential to the utility of such a database is the availability of analysis tools that allow researchers to perform in silico analysis to correlate alterations in genes and their expression products with clinical data about the tumor. The challenge would be to extract and integrate all of the relevant information from those dataset to enrich our understanding of cancer. Here, we describe the progress CGAP has made toward building such a database and designing analysis tools.