A new paper from University of Helsinki, published today in 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.

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