Stallings-Mann, Melody L

Abstract Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses.

Abstract Purpose: Little is known about the role of the immune system in the earliest stages of breast carcinogenesis. We studied quantitative differences in immune cell types between breast tissues from normal donors and those from women with benign breast disease (BBD). Experimental Design: A breast tissue matched case–control study was created from donors to the Susan G. Komen for the Cure Tissue Bank (KTB) and from women diagnosed with BBD at Mayo Clinic (Rochester, MN) who either subsequently developed cancer (BBD cases) or remained cancer-free (BBD controls). Serial tissue sections underwent immunostaining and digital quantification of cell number per mm2 for CD4+ T cells, CD8+ T cells, CD20+ B cells, and CD68+ macrophages and quantification of positive pixel measure for CD11c (dendritic cells). Results: In 94 age-matched triplets, BBD lobules showed greater densities of CD8+ T cells, CD11c+ dendritic cells, CD20+ B cells, and CD68+ macrophages compared with KTB normals. Relative to BBD controls, BBD cases had lower CD20+ cell density (P = 0.04). Nearly 42% of BBD cases had no CD20+ B cells in evaluated lobules compared with 28% of BBD controls (P = 0.02). The absence of CD20+ cells versus the presence in all lobules showed an adjusted OR of 5.7 (95% confidence interval, 1.4–23.1) for subsequent breast cancer risk. Conclusions: Elevated infiltration of both innate and adaptive immune effectors in BBD tissues suggests an immunogenic microenvironment. The reduced B-cell infiltration in women with later breast cancer suggests a role for B cells in preventing disease progression and as a possible biomarker for breast cancer risk. Clin Cancer Res; 23(14); 3945–52. ©2017 AACR.