Chris Tsz-Kwong Man, PhD
- Texas Medical Center
Associate Professor, Department of Pediatrics, Section of Hematology-Oncology, Baylor College of Medicine
Faculty Affiliate, Keck Center of Computational Biology
|University of Texas Health Science Center||PhD||Doctor of Philosophy||1997|
|Washington University School of Medicine||post-doctoral fellow||2001|
|Hong Kong Baptist College||bachelors||1986|
Dr. Chris Man is an Associate Professor of Pediatrics at Baylor College of Medicine where he participates in the Bone Tumor Program and the Cancer Genetics and Genomics Program at Texas Children's Cancer Center.
|American Association of Cancer Research (AACR)||Member|
|Children’s Oncology Group (COG)||Associate Member|
|Gulf Coast Consortium of Bioinformatics||Member|
|International Society of Computational Biology||Member|
Dr. Chris Man’s laboratory is interested in applying computational and OMICS approaches to improve both the diagnosis and prognosis of pediatric cancer patients. The main focus of his research is to identify circulating and tumor biomarkers for early detection of chemoresistance and metastasis in pediatric osteosarcoma. Dr. Man and his lab are also interested in using bioinformatic and meta-analysis approaches to identify novel drug targets and pathways in pediatric cancers. The current projects include:
- molecular classification of pediatric cancers based on genomic and proteomic profiling;
- development of computational tools to integrate and analyze large-scale datasets; and
- biomarker and target discovery in pediatric cancers using novel technologies.
Molecular classification of osteosarcoma
Osteosarcoma is the most common malignant bone tumor among children in United States. Despite the advancement in experimental therapy, the survival of patients who respond poorly in pre-operative chemotherapy remains low. There are no reliable prognostic or diagnostic markers for this disease at the time of diagnosis. To address this problem, we take advantage of the high throughput technologies, such as microarray and ProteinChip to generate expression and proteomic profiles of clinical specimens obtained from osteosarcoma patients. These profiles are used to perform class prediction and generate various cancer signatures by statistical and bioinformatic approaches. The results of these computational analyses are further validated by other experimental techniques, such as quantitative real-time PCR and immunochemical staining. Genes that have been validated will be selected for detailed functional study to elucidate the molecular mechanisms of these genes in relation to the cancer. By combining these powerful molecular and computational methods, we are working towards improving the diagnosis and prognosis of osteosarcoma patients.
Integration of data from various genomic sources
With the increasing amounts of different types of genomic data (SNP, BAC arrays, cytogenetics, and expression), the need to integrate these data sets is becoming more and more important. By integrating results from various data sources, we are now able to discover new genes and pathways that may have been neglected before. For instances, genes that may not be significant by expression analysis alone are now significant by correlating them to known cytogenetic or genomic lesions. On the other hand, we can also quickly identify the genes that may be responsible for the recurrent cytogenetic lesions based on the expression profiles.
NIDDK Short Course on Statistical Genetics For Obesity and Nutrition Researchers - The University of Alabama at Birmingham (2005)
BioConductor Short Course - Fred Hutchinson Cancer Research Center (2005)
Bioinformatics Workshop in Genome and Microarray Analysis -National Center of Genome Research (2002)
Design and Analysis of DNA Microarray Studies - Rockefeller University (2002)
Circulating and tumor biomarkers
Early detection of chemoresistance and metastasis in pediatric osteosarcoma.
Identification of novel drug targets and pathways
Molecular classification of pediatric cancers based on genomic and proteomic profiling
Development of computational tools to integrate and analyze large-scale datasets
Biomarker and target discovery in pediatric cancers using novel technologies