Today's Clinical Lab - News, Editorial and Products for the Clinical Laboratory
DNA strand made of binary code
iStock, Nobi_Prizue

Using Machine Learning to Unlock the Genomic Code in Clinical Cancer Samples

This new tool could help unlock the medicinal value of millions of archived cancer samples

University of Helsinki
Published:Sep 07, 2022
|2 min read
Register for free to listen to this article
Listen with Speechify

A new paper from University of Helsinki, published today on Nature Communications, suggests a method for accurately analyzing genomics data in cancer archival biopsies. This tool uses machine learning methods to correct damaged DNA and unveil the true mutation processes in tumor samples. This helps to unlock tremendous medicine values in millions of archival cancer samples.

Molecular-based diagnosis helps to match the right patient with the right cancer treatment. Researchers took particular interest in DNA profiling in clinical cancer samples.

“This invaluable source is currently not being used for molecular diagnosis due to the poor DNA quality. Formalin causes severe damage to DNAs, which therefore place an inevitable challenge to analyze cancer genomes in preserved tissues,” says lead author Qingli Guo from University of Helsinki.

Analyzing mutation processes in cancer genomes can help early cancer detection, to accurately diagnose cancer, and reveal why some cancers become resistant to treatment. The new method can dramatically accelerate the development of clinical applications that can directly impact future cancer patient care.

The new method predicted more than 90 percent of developing cancer processes

Lead author Guo, working in close collaboration with scientists from The Institute of Cancer Research (ICR), London, and Queen Mary University of London, developed machine learning methods, named FFPEsig, to unravel exactly how formalin mutates DNA.

“Our results show that normally nearly half of the cancer processes will be missed without noise correction. However, using FFPEsig, more than 90 percent of them were accurately predicted.” says Qingli.

Cancer evolves gradually. Profiling mutational processes in longitudinal samples helps to identify clinical informative predictors and make diagnosis of each tumor stage.

“Our finding enables the characterization of clinically relevant signatures from the preserved tumors biopsies stored at room temperatures for decades. With a deep understanding of how formalin impacts cancer genome, our study opens a huge opportunity to transform the developed signature detection assays using the large cost-effective archival samples.”

The researchers pointed out the method currently does not completely remove artifacts that appeared in FFPE samples showing batch effects, and how well the tool performs varies by cancer type, so care must be taken to interpret any findings. We are also interested in further applying their methods in a much broader spectrum of archival samples in the future.

The research was funded by Cancer Research UK, the University of Helsinki, and in part by Academy of Finland. This project is co-led by senior authors Professor Ville Mustonen (University of Helsinki) and Professor Trevor Graham (the ICR).

- This press release was originally published on the University of Helsinki website