Clinical application of tumour-in-normal contamination assessment from whole genome sequencing

Jonathan Mitchell, Salvatore Milite, Jack Bartram, Susan Walker, Nadezda Volkova, Olena Yavorska, Magdalena Zarowiecki, Jane Chalker, Rebecca Thomas, Luca Vago, Alona Sosinsky () and Giulio Caravagna ()
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Jonathan Mitchell: Genomics England
Salvatore Milite: Computational Biology Research Centre, Human Technopole
Jack Bartram: Great Ormond Street Hospital for Children
Susan Walker: Genomics England
Nadezda Volkova: Genomics England
Olena Yavorska: Genomics England
Magdalena Zarowiecki: Genomics England
Jane Chalker: Specialist Integrated Haematological Malignancy Diagnostic Service - Acquired Genomics
Rebecca Thomas: Great Ormond Street Hospital for Children
Luca Vago: Leukemia Genomics and Immunobiology, IRCCS Hospital San Raffaele
Alona Sosinsky: Genomics England
Giulio Caravagna: University of Trieste

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract The unexpected contamination of normal samples with tumour cells reduces variant detection sensitivity, compromising downstream analyses in canonical tumour-normal analyses. Leveraging whole-genome sequencing data available at Genomics England, we develop a tool for normal sample contamination assessment, which we validate in silico and against minimal residual disease testing. From a systematic review of $$771$$ 771 patients with haematological malignancies and sarcomas, we find contamination across a range of cancer clinical indications and DNA sources, with highest prevalence in saliva samples from acute myeloid leukaemia patients, and sorted CD3+ T-cells from myeloproliferative neoplasms. Further exploration reveals 108 hotspot mutations in genes associated with haematological cancers at risk of being subtracted by standard variant calling pipelines. Our work highlights the importance of contamination assessment for accurate somatic variants detection in research and clinical settings, especially with large-scale sequencing projects being utilised to deliver accurate data from which to make clinical decisions for patient care.

Date: 2024
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DOI: 10.1038/s41467-023-44158-2

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