Preeclampsia is the medical condition characterized by hypertension and proteinuria in pregnant women. It occurs in 5 to 8% of all pregnancies (as common as breast cancer) and is the leading cause of morbidity and mortality for both the mother and the unborn child. Surprisingly, epidemiological studies have shown strong evidence that women associated with preeclamptic pregnancies have almost 50% reduced rate of breast cancers decades later. However, due to ethical and practical reasons, direct evidence from long-term follow up in human population is lacking. Moreover, the underlying mechanism to explain the lasting effect of preeclampsia remains unknown. Here we propose an alternative and integrative omics approach to investigate the molecular links between preeclampsia and breast cancer risks later in life. We will conduct a nested case control study to investigate the epigenome, RNA-Seq transcriptome and proteomics differences in the placenta and matched maternal blood DNA samples associated with preeclampsia. The de-identified samples will be collected through the Hawaii Biorepository (HiBR), and they reflect the unique multi-ethnic population of Hawaii. We will construct bioinformatics pipelines to analyze all three types of omics data individually, and develop new computational methods for omics data integration. We will identify coherent genes, modules and biological pathways as the biomarkers of preeclampsia. Moreover, we will compare our data with the DNA methylome, RNA-Seq transcriptome, and proteomics data of the breast cancer samples from The Cancer Genome Atlas. Through such comparison, we will uncover the cancer-related genes, modules and biological pathways within the preeclampsia samples. This will provide direct molecular-level evidence to link preeclampsia and breast cancer risks later in life, which is practically impossible using the approach of population follow-up. It will also identify biomarkers of breast cancer susceptibility as early as a child's birth, for the purpose of cancer prevention.
The goal of this proposal is to develop an integrative omic approach to identify biomarkers for preeclampsia and investigate the molecular links between preeclampsia and breast cancer risks. Through the integration of epigenome, transcriptome and proteome of the placenta and matched maternal blood DNA samples from a nested case control study, coherent biomarkers related to preeclampsia will be identified. Moreover, molecular features that are cancer relevant will be revealed, by comparing the preeclmpsia data to the breast cancer data from the Cancer Genome Atlas (TCGA).
|Ching, Travers; Garmire, Lana X (2018) Pan-cancer analysis of expressed somatic nucleotide variants in long intergenic non-coding RNA. Pac Symp Biocomput 23:512-523|
|Chaudhary, Kumardeep; Poirion, Olivier B; Lu, Liangqun et al. (2018) Multimodal Meta-Analysis of 1,494 Hepatocellular Carcinoma Samples Reveals Significant Impact of Consensus Driver Genes on Phenotypes. Clin Cancer Res :|
|Poirion, Olivier; Zhu, Xun; Ching, Travers et al. (2018) Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage. Nat Commun 9:4892|
|Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X (2018) Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data. J Proteome Res 17:337-347|
|Chaudhary, Kumardeep; Poirion, Olivier B; Lu, Liangqun et al. (2018) Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin Cancer Res 24:1248-1259|
|Poirion, Olivier B; Chaudhary, Kumardeep; Garmire, Lana X (2018) Deep Learning data integration for better risk stratification models of bladder cancer. AMIA Jt Summits Transl Sci Proc 2017:197-206|
|Ching, Travers; Zhu, Xun; Garmire, Lana X (2018) Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput Biol 14:e1006076|
|Ortega, Michael A; Poirion, Olivier; Zhu, Xun et al. (2017) Using single-cell multiple omics approaches to resolve tumor heterogeneity. Clin Transl Med 6:46|
|Garmire, Lana X; Gliske, Stephen; Nguyen, Quynh C et al. (2017) THE TRAINING OF NEXT GENERATION DATA SCIENTISTS IN BIOMEDICINE. Pac Symp Biocomput 22:640-645|
|Zhu, Xun; Wolfgruber, Thomas K; Tasato, Austin et al. (2017) Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists. Genome Med 9:108|
Showing the most recent 10 out of 16 publications