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Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer

Journal Article (2022)
Author(s)

Esther H. Lips (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)

Tapsi Kumar (The University of Texas MD Anderson Cancer Center, MD Anderson UTHealth Graduate School of Biomedical Sciences)

Anargyros Megalios (King’s College London)

Lindy L. Visser (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)

Michael Sheinman (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)

Angelo Fortunato (Arizona State University)

Vandna Shah (King’s College London)

Marlous Hoogstraat (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)

Lodewyk F.A. Wessels (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis, TU Delft - Pattern Recognition and Bioinformatics)

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Research Group
Pattern Recognition and Bioinformatics
DOI related publication
https://doi.org/10.1038/s41588-022-01082-3
More Info
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Publication Year
2022
Language
English
Research Group
Pattern Recognition and Bioinformatics
Journal title
Nature Genetics
Issue number
6
Volume number
54
Pages (from-to)
850-860
Downloads counter
302
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Institutional Repository
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Abstract

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5–10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.