在癌细胞中检测DNA变异的计算机程序

Researchers at The University of Texas MD Anderson Cancer Center have announced a new method for detecting DNA¹ mutations in a single cancer cell versus² current technology that analyzes³ millions of cells which they believe could have important applications for cancer diagnosis⁴ and treatment. The results are published in the April 18 online issue of Nature Methods. Existing technology, known as next-generation sequencing (NGS), measures genomes derived⁵ from millions of cells versus the newer method for single-cell sequencing, called Monovar. Developed by MD Anderson researchers, Monovar allows scientists to examine data from multiple single cells. The study was, in part, funded by MD Anderson's Moon Shots Program, an unprecedented⁶ effort to significantly reduce deaths from cancer. 

 

"NGS technologies have vastly improved our understanding of the human genome and its variation in diseases such as cancer," said Ken⁷ Chen, Ph.D., assistant professor of Bioinformatics and Computational Biology and co-author of the Nature Methods article. "However, because NGS measures large numbers of cells, genomic variations within tissue samples are often masked."

 

This led to development of newer technology, called single cell sequencing (SCS), that has had a major impact in many areas of biology, including cancer research, neurobiology, microbiology, and immunology, and has greatly improved understanding of certain tumor⁸ characteristics in cancer. Monovar improves further on the new SCS's computational tools which scientists found "lacking" by more accurately⁹ detecting slight alterations¹⁰ in DNA makeup¹¹ known as single nucleotide variants¹² (SNVs).

 

"To improve the SNVs in SCS datasets, we developed Monovar," said Nicholas Navin, Ph.D., assistant professor of Genetics and co-author of the paper. "Monovar is a novel statistical¹⁴ method able to leverage¹⁵ data from multiple single cells to discover SNVs and provides highly detailed¹⁶ genetic¹³ data."

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