One of the things that makes cancer cells so difficult to shut down are redundant regulatory mechanisms inside the cells. Targeting one gene with a pharmaceutical designed to kill the cell may not be enough to stop the cancer cell in its tracks, because another pathway may take over. Multiple redundant systems form complex networks.
“It may be necessary to perturb several interconnected cancer regulators in order to shut down the cancer cell,” says Han Xu, assistant professor in the Department of Epigenetics and Molecular Carcinogenesis at the University of Texas MD Anderson Cancer Center, Science Park campus, in Smithville. Xu was recruited from the Broad Institute of MIT and Harvard with the help of a First Time Tenure Track Award from CPRIT.
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One of the things that makes cancer cells so difficult to shut down are redundant regulatory mechanisms inside the cells. Targeting one gene with a pharmaceutical designed to kill the cell may not be enough to stop the cancer cell in its tracks, because another pathway may take over. Multiple redundant systems form complex networks.
“It may be necessary to perturb several interconnected cancer regulators in order to shut down the cancer cell,” says Han Xu, assistant professor in the Department of Epigenetics and Molecular Carcinogenesis at the University of Texas MD Anderson Cancer Center, Science Park campus, in Smithville. Xu was recruited from the Broad Institute of MIT and Harvard with the help of a First Time Tenure Track Award from CPRIT.
As a computational biologist, Xu is setting up a bioinformatics laboratory with a wet-lab component to work at the interface of machine learning, software engineering, genomics, epigenetics, and cancer.
Xu plans to use computational analysis of high-throughput genomics experiments to study gene regulatory networks in cancer.
“We measure thousands or even millions of data points,” Xu says, “so the mathematicians in my laboratory will develop computational tools to analyze the data and depict the whole network structure.”
A particular focus of Xu’s research will be regulatory networks in small cell lung cancer, a deadly cancer for which there are no good therapeutic targets.
As an expert in machine learning and data mining, Xu will fill a unique niche at MD Anderson. “My expertise will be complementary to many traditional disciplines,” Xu says, “and I will have many potential collaborators at MD Anderson and throughout Texas.”
The experimental, wet-lab component of his laboratory is something new for Xu, and is made possible by his CPRIT award. “As a computational scientist, I work a lot with data, but we are eager to generate the data ourselves with our own experiments,” he says.
Xu has hired a laboratory manager to help him set up the wet lab and recruited five postdocs to run experiments and analyze data. His current research interest is mainly on the optimization, design, and analysis of CRISPR-based genetic or epigenetic perturbation screens. His laboratory is developing machine-learning and statistical methods to boost the performance of CRISPR screens and is using these approaches for systematic functional characterization of coding and non-coding genomic regions. Working with molecular biologists and clinical researchers, his group combines their computational expertise with cutting-edge biotechnologies to address fundamental questions in cancer epigenetics and to decipher the genetic and epigenetic “codes” underlying various cancer phenotypes
Xu received his undergraduate training and master’s degree in computer science and information systems from Zhejiang University in China. He received his Ph.D. in bioinformatics from Nanyang Technical University in Singapore. He was a postdoc at the Dana Farber Cancer Institute at Harvard before moving to the Broad Institute as a research scientist in 2015.
Remarkably for a young researcher, Xu’s 26 peer-reviewed research papers have been cited more than 3,000 times by other researchers.
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