Our Science – Lee Website

Maxwell P. Lee, Ph.D.

Laboratory of Population Genetics Investigator Building 41, Room D702C 8424 Helgerman Court Bethesda, MD 20892 Phone:   301-435-8956 Fax:   301-435-8963 E-Mail:   leemax@mail.nih.gov

Biography

Dr. Lee received his B.S. in biology from the University of Science and Technology of China in 1982 and his Ph.D. in biochemistry from Duke University in 1989. He carried out postdoctoral work at Duke from 1990 to 1994 and joined the National Institute on Aging (NIA) as a senior staff fellow in 1994. Dr. Lee became a junior faculty member at Johns Hopkins Medical School between 1995 and 1999, then joined IBM as a software engineer in 1999. He came to the NCI in 2000 as a tenure track investigator.

Research

Genetics and Epigenetics of Cancer and Informatics Solutions to Biomedical Research

Both genetic and epigenetic mutations contribute to human cancers. Mutations in oncogenes, tumor suppressor genes, and DNA repair genes have been identified in human cancers. Epigenetic mutations such as methylation of tumor suppressor genes and DNA repair genes and loss of imprinting of the IGF-II gene are also associated with cancers. The major goal of our research is to identify cancer genes and epigenetic markers for human cancers. Rapid progress in the human genome project has significantly speeded up discovery in genetic research and disease research. Sequence analysis and bioinformatics play a vital role in understanding the genetic basis of human diseases. One of Dr. Lee's major interests is to apply bioinformatics to genetic research.

Positional cloning and candidate cloning of cancer genes. We have generated transcript maps for regions showing loss of heterozygosity (LOH) at 13q11 in esophageal cancer. Mutational analysis of candidate genes in the human chromosome 13q11 identified RNF6 as a candidate tumor suppressor genes. We are also taking a candidate gene cloning approach to analyze several hundreds of genes involved in cancer such as oncogenes, tumor suppressor genes, and DNA repair genes. We are performing mutational analysis as well as methylation of CpG islands in the promoter region of these cancer genes. In addition, altered gene expression in tumors will be analyzed by methods such as Affymetrix expression chips.

Genome-wide search of imprinted genes. We have used transcribed single nucleotide polymorphisms (SNPs) to isolate several imprinted genes. We are using Affymetrix chips to genotype DNA and to quantitatively analyze the allele-specific gene expression. Similarly, we will systematically isolate epigenetic markers. We will examine both the imprinted genes and epigenetic markers in tumors and their matched normal tissues, in populations with high and low risk of cancer. The alteration in imprinting will be analyzed for its association with other genetic changes such as genomic instability, mutations in cancer genes, and genotype.

Genome-wide search of cancer-associated alternative RNA splicing. We identified 26,258 alternative splicing isoforms of which 845 were significantly associated with human cancer by mining EST database. We found that canonical GT-AG splice junctions were used significantly less frequently in the alternative splicing isoforms in tumors. We validated these cancer-associated alternative splicing isoforms with experiments. These results suggest that alternative splicing may have potential as a diagnostic marker for cancer.

Bioinformatics approach to cancer gene discovery. We have undertaken a computational approach to systematically search for all human imprinted genes. We have decided to search for imprinting genes from a single nucleotide polymorphism database containing all SNPs in expressed sequence tags (ESTs). The Bayesian statistics were used to estimate the genotype frequency. Significant reduction in the frequency of these libraries expressing both alleles suggests that the SNP is located in an imprinted gene. We have identified about 100 candidate imprinted genes and are validating these results by experiments. A similar approach is also used to identify both mutations in cancer cells and SNPs associated with cancers. We are interested in identifying genetic elements important for genomic imprinting and genomic instability. We are taking a number of approaches including comparative genome analysis and motif analysis to search for genetic elements.

Informatics solutions for research. We are also developing several tools for automating positional cloning and mutational analysis. We have developed programs to parse genes into exons and generated database for exons and their flanking intron sequences and mapped SNPs to these genetic elements. We are also developing tools for analyzing gene expression and constructing comprehensive genetic networks. We developed a computational approach to analyze coherence of gene expression in pathways. We tested the hypothesis that genes in the same pathway are more likely to be coordinately regulated than a randomly selected gene set. We defined coherence indicator as the ratio of the number of gene pairs in the pathway, whose correlation coefficients are significant, divided by the total number of gene pairs in the pathway using microarray data. Our analysis indicated that the mean coherence indicator of pathways is significantly larger than the mean coherence indicator of random gene sets drawn from the reference gene set. We also found that more pathways had a significant coherence indicator in tumor than in normal tissue. The increase in the number of pathways with significant coherence indicators may reflect the fact that tumor cells have a higher rate of metabolism than normal cells.

Our collaborators include Ken Buetow, Ying Hu, Nan Hu, Philip Taylor, Kevin Howcroft, Dinah Singer, Joseph Riss, Carl Barrett, Neil Caporaso, Lynn Goldin in NIH.

This page was last updated on 6/11/2008.