Browsing by Subject "Imaging genomics"

Now showing 1 - 5 of 5
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    Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer
    (American Society for Biochemistry and Molecular Biology, 2019-08-09) Zhan, Xiaohui; Cheng, Jun; Huang, Zhi; Han, Zhi; Helm, Bryan; Liu, Xiaowen; Zhang, Jie; Wang, Tian-Fu; Ni, Dong; Huang, Kun; Medicine, School of Medicine
    Tumors are heterogeneous tissues with different types of cells such as cancer cells, fibroblasts, and lymphocytes. Although the morphological features of tumors are critical for cancer diagnosis and prognosis, the underlying molecular events and genes for tumor morphology are far from being clear. With the advancement in computational pathology and accumulation of large amount of cancer samples with matched molecular and histopathology data, researchers can carry out integrative analysis to investigate this issue. In this study, we systematically examine the relationships between morphological features and various molecular data in breast cancers. Specifically, we identified 73 breast cancer patients from the TCGA and CPTAC projects matched whole slide images, RNA-seq, and proteomic data. By calculating 100 different morphological features and correlating them with the transcriptomic and proteomic data, we inferred four major biological processes associated with various interpretable morphological features. These processes include metabolism, cell cycle, immune response, and extracellular matrix development, which are all hallmarks of cancers and the associated morphological features are related to area, density, and shapes of epithelial cells, fibroblasts, and lymphocytes. In addition, protein specific biological processes were inferred solely from proteomic data, suggesting the importance of proteomic data in obtaining a holistic understanding of the molecular basis for tumor tissue morphology. Furthermore, survival analysis yielded specific morphological features related to patient prognosis, which have a strong association with important molecular events based on our analysis. Overall, our study demonstrated the power for integrating multiple types of biological data for cancer samples in generating new hypothesis as well as identifying potential biomarkers predicting patient outcome. Future work includes causal analysis to identify key regulators for cancer tissue development and validating the findings using more independent data sets.
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    Identification of discriminative imaging proteomics associations in Alzheimer's Disease via a novel sparse correlation model
    (World Scientific, 2016-12) Yan, Jingwen; Risacher, Shannon L.; Nho, Kwangsik; Saykin, Andrew J.; Shen, Li; Department of Biohealth Informatics, School of Informatics and Computing
    Brain imaging and protein expression, from both cerebrospinal fluid and blood plasma, have been found to provide complementary information in predicting the clinical outcomes of Alzheimer's disease (AD). But the underlying associations that contribute to such a complementary relationship have not been previously studied yet. In this work, we will perform an imaging proteomics association analysis to explore how they are related with each other. While traditional association models, such as Sparse Canonical Correlation Analysis (SCCA), can not guarantee the selection of only disease-relevant biomarkers and associations, we propose a novel discriminative SCCA (denoted as DSCCA) model with new penalty terms to account for the disease status information. Given brain imaging, proteomic and diagnostic data, the proposed model can perform a joint association and multi-class discrimination analysis, such that we can not only identify disease-relevant multimodal biomarkers, but also reveal strong associations between them. Based on a real imaging proteomic data set, the empirical results show that DSCCA and traditional SCCA have comparable association performances. But in a further classification analysis, canonical variables of imaging and proteomic data obtained in DSCCA demonstrate much more discrimination power toward multiple pairs of diagnosis groups than those obtained in SCCA.
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    IMAGING GENOMICS
    (2017) Shen, L. I.; Cooper, Lee A. D.; Radiology and Imaging Sciences, School of Medicine
    Imaging genomics is an emerging research field, where integrative analysis of imaging and omics data is performed to provide new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and functions, and to impact the development of new diagnostic, therapeutic and preventive approaches. The Imaging Genomics Session at PSB 2017 aims to encourage discussion on fundamental concepts, new methods and innovative applications in this young and rapidly evolving field.
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    Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer's disease
    (BioMed Central, 2017-05) Kim, Dokyoon; Basile, Anna O.; Bang, Lisa; Horgusluoglu, Emrin; Lee, Seunggeun; Ritchie, Marylyn D.; Saykin, Andrew J.; Nho, Kwangsik; Medicine, School of Medicine
    BACKGROUND: Rapid advancement of next generation sequencing technologies such as whole genome sequencing (WGS) has facilitated the search for genetic factors that influence disease risk in the field of human genetics. To identify rare variants associated with human diseases or traits, an efficient genome-wide binning approach is needed. In this study we developed a novel biological knowledge-based binning approach for rare-variant association analysis and then applied the approach to structural neuroimaging endophenotypes related to late-onset Alzheimer's disease (LOAD). METHODS: For rare-variant analysis, we used the knowledge-driven binning approach implemented in Bin-KAT, an automated tool, that provides 1) binning/collapsing methods for multi-level variant aggregation with a flexible, biologically informed binning strategy and 2) an option of performing unified collapsing and statistical rare variant analyses in one tool. A total of 750 non-Hispanic Caucasian participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort who had both WGS data and magnetic resonance imaging (MRI) scans were used in this study. Mean bilateral cortical thickness of the entorhinal cortex extracted from MRI scans was used as an AD-related neuroimaging endophenotype. SKAT was used for a genome-wide gene- and region-based association analysis of rare variants (MAF (minor allele frequency) < 0.05) and potential confounding factors (age, gender, years of education, intracranial volume (ICV) and MRI field strength) for entorhinal cortex thickness were used as covariates. Significant associations were determined using FDR adjustment for multiple comparisons. RESULTS: Our knowledge-driven binning approach identified 16 functional exonic rare variants in FANCC significantly associated with entorhinal cortex thickness (FDR-corrected p-value < 0.05). In addition, the approach identified 7 evolutionary conserved regions, which were mapped to FAF1, RFX7, LYPLAL1 and GOLGA3, significantly associated with entorhinal cortex thickness (FDR-corrected p-value < 0.05). In further analysis, the functional exonic rare variants in FANCC were also significantly associated with hippocampal volume and cerebrospinal fluid (CSF) Aβ1-42 (p-value < 0.05). CONCLUSIONS: Our novel binning approach identified rare variants in FANCC as well as 7 evolutionary conserved regions significantly associated with a LOAD-related neuroimaging endophenotype. FANCC (fanconi anemia complementation group C) has been shown to modulate TLR and p38 MAPK-dependent expression of IL-1β in macrophages. Our results warrant further investigation in a larger independent cohort and demonstrate that the biological knowledge-driven binning approach is a powerful strategy to identify rare variants associated with AD and other complex disease.
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    Rare variants in the splicing regulatory elements of EXOC3L4 are associated with brain glucose metabolism in Alzheimer's disease
    (Biomed Central, 2018-09-14) Miller, Jason E.; Shivakumar, Manu K.; Lee, Younghee; Han, Seonggyun; Horgousluoglu, Emrin; Risacher, Shannon L.; Saykin, Andrew J.; Nho, Kwangsik; Kim, Dokyoon; Radiology and Imaging Sciences, School of Medicine
    BACKGROUND: Alzheimer's disease (AD) is one of the most common neurodegenerative diseases that causes problems related to brain function. To some extent it is understood on a molecular level how AD arises, however there are a lack of biomarkers that can be used for early diagnosis. Two popular methods to identify AD-related biomarkers use genetics and neuroimaging. Genes and neuroimaging phenotypes have provided some insights as to the potential for AD biomarkers. While the field of imaging-genomics has identified genetic features associated with structural and functional neuroimaging phenotypes, it remains unclear how variants that affect splicing could be important for understanding the genetic etiology of AD. METHODS: In this study, rare variants (minor allele frequency < 0.01) in splicing regulatory element (SRE) loci from whole genome sequencing (WGS) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, were used to identify genes that are associated with global brain cortical glucose metabolism in AD measured by FDG PET-scans. Gene-based associated analyses of rare variants were performed using the program BioBin and the optimal Sequence Kernel Association Test (SKAT-O). RESULTS: The gene, EXOC3L4, was identified as significantly associated with global cortical glucose metabolism (FDR (false discovery rate) corrected p < 0.05) using SRE coding variants only. Three loci that may affect splicing within EXOC3L4 contribute to the association. CONCLUSION: Based on sequence homology, EXOC3L4 is likely a part of the exocyst complex. Our results suggest the possibility that variants which affect proper splicing of EXOC3L4 via SREs may impact vesicle transport, giving rise to AD related phenotypes. Overall, by utilizing WGS and functional neuroimaging we have identified a gene significantly associated with an AD related endophenotype, potentially through a mechanism that involves splicing.