Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS.
Nat Commun
; 15(1): 6112, 2024 Jul 20.
Article
in En
| MEDLINE
| ID: mdl-39030176
ABSTRACT
Ductal carcinoma in situ (DCIS) is a pre-invasive tumor that can progress to invasive breast cancer, a leading cause of cancer death. We generate a large-scale tissue microarray dataset of chromatin images, from 560 samples from 122 female patients in 3 disease stages and 11 phenotypic categories. Using representation learning on chromatin images alone, without multiplexed staining or high-throughput sequencing, we identify eight morphological cell states and tissue features marking DCIS. All cell states are observed in all disease stages with different proportions, indicating that cell states enriched in invasive cancer exist in small fractions in normal breast tissue. Tissue-level analysis reveals significant changes in the spatial organization of cell states across disease stages, which is predictive of disease stage and phenotypic category. Taken together, we show that chromatin imaging represents a powerful measure of cell state and disease stage of DCIS, providing a simple and effective tumor biomarker.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Breast Neoplasms
/
Chromatin
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Carcinoma, Intraductal, Noninfiltrating
Limits:
Female
/
Humans
Language:
En
Journal:
Nat Commun
/
Nature communications
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: