bioRxivCancer BiologyPreprint
Spatial Proteomics of the Normal Breast Collagen Stroma: Links to BI-RADS Categories and Body Mass Index
Jaclyn DunneiD, Denys RujchanarongiD, Yeonhee ParkiD, Jade K. MacdonaldiD, Taylor S. HulahaniD, Harrison B. TayloriD, Laura SpruilliD, Heather C. Jensen‐SmithiD, Michael Anthony HollingsworthiD, George Earl SanduskyiD, Anand S. MehtaiD, Richard R. DrakeiD, Marvella Elizabeth FordiD, Harikrishna NakshatriiD, Peggi M. AngeliD
Abstract
Collagen breast stroma is the basis of increased breast density and a well-established breast cancer risk factor, yet proteomic regulation of normal breast stroma remains poorly defined. This study reports spatial regulation of the collagen proteome in normal breast tissue sections annotated by clinical characteristics. Normal breast samples from the Susan G. Komen tissue bank included data on genetic ancestry (n=40 total; n=20 African ancestry; n=20 European ancestry), body-mass-index (BMI), age, and mammogram density by the Breast Imaging Reporting and Data System (BI-RADS). Multiplexed cell marker staining showed CD44 and COL1A1 markers modulated with BMI. Collagen fiber widths by second harmonic generation (SHG) showed potential contrasts in BMI categories by genetic ancestry. Targeted extracellular matrix proteomics mass spectrometry imaging showed collagen alpha-1(I) chain domain proteome was spatially heterogenous across the normal breast microenvironment with site specific post-translational modification of proline hydroxylation. Signatures computationally extracted from breast stroma reported that 47 collagen peptides distinguished BI-RADS categories (area under the receiver operating curve>0.7; p-value>0.05). Proteomic alterations were found between overweight to obese categories with strong positive associations to BMI by multivariate analysis. This study provides the first spatial analysis of the collagen proteome in normal breast within contexts of cellular markers and clinical characteristics.
- organism: human
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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