Sequencing large-scale changes in DNA to detect cancer earlier

Mutations in the genome significantly impact disease development — from a single base pair to large structural changes, such as insertions or deletions of chunks of DNA. Next-generation sequencing is used in patient care to detect many of these mutations. However, sequencing errors can lead to incorrect conclusions or miss mutations present in only a few cells. Failing to correctly detect such mutations can have negative consequences, such as not identifying a small number of remaining tumor cells (minimal residual disease) during cancer treatment.

Work by Xiaotu Ma, PhD, Department of Computational Biology, is addressing these issues by improving DNA sequencing data analysis. Ma’s team found they could improve detection of minimal residual disease from 51% to 61% in patient samples of relapsed leukemia. The improvements were based on rigorous analysis of insertions, deletions and other structural variants, which proved to be superior biomarkers with lower error rates compared to single nucleotide changes. The findings, which may improve future clinical diagnostics, were published in Cell Genomics.

“These large structural changes offer us great power to detect these cancers early and enable earlier interventions,” Ma said. “However, because this is the first time where large structural changes demonstrate their unique power for early cancer detection, we have a lot to learn about their potential impact in a clinical setting.”

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