Cytotoxic T cell depletion with increasing epithelial abnormality in women with benign breast disease.

PURPOSE: We quantified cytotoxic T cells in nonmalignant breast tissues from women with and without subsequent breast cancer to assess evidence of whether immunosurveillance may be suppressed prior to tumor development. METHODS: We used an age-matched set of breast tissues from women with benign breast disease (BBD) who subsequently developed breast cancer (BBD with later BC), women with BBD who remained cancer free (BBD cancer-free), and normal Komen Tissue Bank (KTB) tissue donors (KTB controls). We evaluated terminal duct lobular units (lobules) for degree of epithelial abnormality and density of dual-positive CD8/CD103 T cells, as CD103+ cells are thought to be a subset of CD8+ cytotoxic T cells located primarily in the intraepithelial compartment. RESULTS: In 10 sets of age-matched women, 256 breast lobules were studied: 85 in BBD women with later BC, 85 in BBD cancer-free women, and 86 in KTB donors. The majority of all lobules were histologically normal (N = 143, 56%), with 65 (25%) nonproliferative fibrocystic change, and 48 (19%) proliferative epithelial change (with or without atypia). In BBD women with later BC, median CD8+/CD103+ cell density was 39.6, 31.7, and 10.5 cells/mm CONCLUSION: In women with BBD, breast lobules with increasing epithelial abnormality show significant decreases in cytotoxic T cells as measured by CD8/CD103 staining, suggesting that impaired immunosurveillance may be a component of the earliest stages of breast cancer development

Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning

Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management. However, assessment of TDLU involution is time-consuming and difficult to standardize and quantitate. Accordingly, we developed a CNN to enable automated quantitative measurement of TDLU involution and tested its performance in 174 specimens selected from the pathology archives at Mayo Clinic, Rochester, MN. The CNN was trained and tested on a subset of 33 biopsies, delineating important tissue types. Nine quantitative features were extracted from delineated TDLU regions. Our CNN reached an overall dice-score of 0.871 (+/- 0.049) for tissue classes versus reference standard annotation. Consensus of four reviewers scoring 705 images for TDLU involution demonstrated substantial agreement with the CNN method (unweighted kappa = 0.747 +/- 0.01). Quantitative involution measures showed anticipated associations with BBD histology, breast cancer risk, breast density, menopausal status, and breast cancer risk prediction scores (p < 0.05). Our work demonstrates the potential to improve risk prediction for women with BBD biopsies by applying CNN approaches to generate automated quantitative evaluation of TDLU involution

Additional file 7 of Quality control recommendations for RNASeq using FFPE samples based on pre-sequencing lab metrics and post-sequencing bioinformatics metrics

Additional file 7. A decision tree model to predict QC pass/fail based on DV50

Additional file 2 of Quality control recommendations for RNASeq using FFPE samples based on pre-sequencing lab metrics and post-sequencing bioinformatics metrics

Additional file 2. Evaluation of sample size on the calculation of sample wise median correlation

Additional file 1 of Quality control recommendations for RNASeq using FFPE samples based on pre-sequencing lab metrics and post-sequencing bioinformatics metrics

Additional file 1. Bioinformatics evaluation of library preparation protocols using FFPE and FFzn pairs