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Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap

Journal of Genetics and genomics 2018年第7期45卷 361-371页

作者： Christopher J.Conley Umut Ozbek Pei Wang Jie Peng Department of Statistics University of California Davis Department of Population Health Science and Policy Icahn School of Medicine at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai

We propose a novel conditional graphical model -- spaceMap -- to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations （CNAs） on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network, spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast （n = 77） and ovarian （n = 174） cancer studies. Each cancer exhibits disruption of＇ion transmembrane transport＇ and ＇regulation from RNA polymerase lI promoter＇ by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers.

关键词： integrative genomics Proteogenomics Conditional graphical models Network analysis

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BrcaSeg:A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images

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genomics, Proteomics & Bioinformatics 2021年第6期19卷 1032-1042页

作者： Zixiao Lu Xiaohui Zhan Yi Wu Jun Cheng Wei Shao Dong Ni Zhi Han Jie Zhang Qianjin Feng Kun Huang Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical EngineeringSouthern Medical UniversityGuangzhou 510515China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical EngineeringHealth Science CenterShenzhen UniversityShenzhen 518060China Department of Medicine Indiana University School of MedicineIndianapolisIN 46202USA Department of Medical and Molecular Genetics Indiana University School of MedicineIndianapolisIN 46202USA Department of Biostatistics and Health Data Science Indiana University School of MedicineIndianapolisIN 46202USA Regenstrief Institute IndianapolisIN 46202USA

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and *** stroma from epithelial tissues is critically important for spatial characterization of the tumor ***,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological *** CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)*** achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art *** this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression *** subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue *** Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these *** all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid *** can be accessed at https://***/Serian1992/ImgBio.

关键词： Whole-slide tissue image Computational pathology Deep learning integrative genomics Breast cancer

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