New Genomic Tool to Identify Growth in Rare Cancer Cells

Novel genomics tool enables more accurate identification of rare mutations in cancer cells. This new computational method allows scientists to identify rare gene mutations in cancer cells with greater accuracy and sensitivity than currently available approaches.

Highlights:

A new tool to identify rare mutations in cancer cells has been developed.
This tool is called Lancet.
Lancet reduces errors and enhances correct identification of mutations in cancer cells.

Rare gene mutations in cancer cells can be identified with greater accuracy with a new tool called Lancet, which is developed by a research team at the New York Genome Center (NYGC).

Lancet allows scientists to identify rare gene mutations in cancer cells more accurately than currently available approaches do.

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‘This study presents a new tool called Lancet that can identify rare mutations in cancer cells. Lancet uses colored de Bruijn graphs to jointly analyze tumor and normal reads. This strategy substantially reduces errors and increases the accuracy of identifying mutations.’

The new tool – Lancet
The development of this new tool is a major advancement in the identification of tumor cell mutations, a process known as somatic variant calling.

"With its unique ability to jointly analyze the whole genome of tumor and matched normal cells, Lancet provides a useful tool for researchers to conduct more accurate genome-wide somatic variant calling," notes first author Giuseppe Narzisi, PhD, Senior Bioinformatics Scientist, NYGC.

"Reliable detection of somatic variations is of critical importance in cancer research and increasingly in the clinical setting, where identification of somatic mutations forms the basis for personalized medicine," said Michael Zody, PhD, Senior Director, Computational Biology, NYGC, and senior author of the study. "Lancet will be an important addition to the toolkit of both clinicians and researchers working to advance the field of cancer genomics and improve care for cancer patients."

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How it works
Unlike current computational methods, Lancet instead uses an approach called micro-assembly to reconstruct the complete sequences of small regions of the genome without relying on a reference.

Lancet uses a data structure called a colored de Bruijn graph to jointly analyze the tumor and normal DNA, and provides greater sensitivity to find rare variants unique to the tumor while also providing greater accuracy of differentiating tumor variants from those present in healthy tissue in that individual. Using Lancet to combine the sequencing data from the normal and tumor cells represents a more powerful way of identifying mutations, Dr. Narzisi said, since users are no longer dependent on analyzing sequence data from tumor and normal cells separately.

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Lancet performed better and had higher accuracy and better sensitivity to detect somatic variants compared to the most widely-used somatic variant callers. The researchers demonstrated this, through extensive experimental comparison on synthetic and real whole-genome sequencing datasets.

"In our study, we show that existing tools are not that precise in scoring mutations, so that some candidate variants which were highly scored by some tools ended up being false positives," Dr. Narzisi said. "That becomes a problem when you want to prioritize which variants to validate using other technologies or you want to move forward with a clinical study. You may end up focusing on variants that do not exist."

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References:

Giuseppe Narzisi, André Corvelo et al. Genome-Wide Somatic Variant Calling Using Localized Colored De Bruijn Graphs, Communications Biology doi:10.1038/s42003-018-0023-9

Source-Medindia