4.7 Article

Genomic profiling reveals heterogeneous populations of ductal carcinoma in situ of the breast

Journal

COMMUNICATIONS BIOLOGY
Volume 4, Issue 1, Pages -

Publisher

NATURE RESEARCH
DOI: 10.1038/s42003-021-01959-9

Keywords

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Funding

  1. JSPS Fujita Memorial Fund for Medical Research
  2. Japan Society for the Promotion of Science [16H06279]

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The study conducted a multi-omic analysis of ductal carcinoma in situ (DCIS) and identified age, gene amplification, and gene mutation as possible indicators of relapse. The results revealed heterogeneous cell populations in DCIS, providing predictive markers for classifying and optimizing treatment.
Satoi Nagasawa and Yuta Kuze et al. report a multi-omic analysis of ductal carcinoma in situ (DCIS) of the breast, including whole-exome, single-cell, and spatial transcriptome sequencing. They find that for patients under 45 years of age, HER2 amplification and GATA3 mutation are associated with higher risk of relapse, suggesting they could be used as predictive markers when deciding on a treatment course. In a substantial number of patients, ductal carcinoma in situ (DCIS) of the breast will never progress to invasive ductal carcinoma, and these patients are often overtreated under the current clinical criteria. Although various candidate markers are available, relevant markers for delineating risk categories have not yet been established. In this study, we analyzed the clinical characteristics of 431 patients with DCIS and performed whole-exome sequencing analysis in a 21-patient discovery cohort and targeted deep sequencing analysis in a 72-patient validation cohort. We determined that age <45 years, HER2 amplification, and GATA3 mutation are possible indicators of relapse. PIK3CA mutation negativity and PgR negativity were also suggested to be risk factors. Spatial transcriptome analysis further revealed that GATA3 dysfunction upregulates epithelial-to-mesenchymal transition and angiogenesis, followed by PgR downregulation. These results reveal the existence of heterogeneous cell populations in DCIS and provide predictive markers for classifying DCIS and optimizing treatment.

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