Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer

• Published in: BMC medical genomics 2020-12, Vol.13 (Suppl 11), p.195-195, Article 195
• Main Authors: Xu, Siwen, Lu, Zixiao, Shao, Wei, Yu, Christina Y, Reiter, Jill L, Feng, Qianjin, Feng, Weixing, Huang, Kun, Liu, Yunlong
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Analysis; ATAC-seq; Bioinformatics; Biomarkers, Tumor - genetics; Biomarkers, Tumor - metabolism; Breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Chromatin; Chromatin - genetics; Chromatin accessibility data; Computational biology; Computational Biology - methods; Computer programs; Computer-aided medical diagnosis; Correlation analysis; Cytology; Deep learning; Estrogen Receptor alpha - genetics; Estrogen Receptor alpha - metabolism; Estrogen receptors; Estrogens; Female; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Genomes; Genomics; Health aspects; Histology, Pathological; Histopathological images; Histopathology; Humans; Image processing; Integrative analysis; Medical prognosis; Methods; Middle Aged; Molecular Imaging - methods; Molecular modelling; Morphology; Patients; Phenotypes; Pipelines; Prognosis; Promoter Regions, Genetic; Regulatory Elements, Transcriptional; Regulatory sequences; Ribonucleic acid; RNA; Segmentation; Structure; Survival Rate; Therapeutic targets; Tumors
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-020-00828-4

Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer. An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis. Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR