Smart online app to better diagnose cancer

New York, Sep 8 (IANS) Scientists have developed an online tool that can help clinicians better differentiate the defects in individual tumour cells that appear to be similar.

The open-source software, which is freely available online, is expected to help scientists better define the nature of a cancer and other diseases and improve their treatment.

Seemingly similar cells, especially cancer cells, often have significantly different genetic mutations and therefore detailed knowledge of these mutations, called copy number variations (CNVs), in individual cells can point to specific treatment regimens.

“You may think that every cell in a tumour would be the same, but that is actually not the case,” said Michael Schatz, associate professor at Cold Spring Harbor Laboratory (CSHL) in New York, US.

“We are realising that there can be a lot of changes inside even a single tumour,” Schatz noted.

“If you are going to treat cancer, you need to diagnose exactly what subclass of cancer you have,” Schatz explained.

The new interactive online programme called Gingko reduces the uncertainty of single-cell analysis and provides a simple way to visualise patterns in copy number mutations across populations of cells, the study said.

Copy number variation is a common mutation in which large chunks of DNA are either deleted from or added to the genome.

One powerful single-cell analytic technique for exploring CNV is whole genome sequencing. The challenge is that, before sequencing can be done, the cell’s DNA has to be amplified many times over.

This process is rife with errors, with some arbitrary chunks of DNA being amplified more than others. In addition, because many labs use their own software to examine CNVs, there is little consistency in how researchers analyse their results.

To address these two challenges, Schatz and his colleagues created Gingko.

The interactive, web-based programme automatically processes sequence data, maps the sequences to a reference genome, and creates CNV profiles for every cell that can then be viewed with a user-friendly graphical interface.

The software was described online in the journal Nature Methods.

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