The goal of this application is to develop methods to deconvolute the computational complexity of tissue based molecular signatures. In this proposal we will concentrate on the human breast as a model organ and as a methodological proof-of-principle. However, we expect that the approaches we develop will be widely applicable to all tissue and tumor types. B. SIGNIFICANCE: We discovered that the human breast is composed of 11 cell types and 4 hormonal states (HR0-3). Importantly, there was up to 7-fold survival difference in the outcome of patients with HR0 vs. HR3 breast tumors. Our preliminary work suggests that much of the phenotypic differences between HR groups are epigenetic. Thus, a comprehensive characterization of HR0-3 specific epigenetic signatures may have great clinical significance. C. CHALLENGE: The vast majority of existing high-throughput molecular signatures of normal and malignant human tissues are derived from unfractionated tissue fragments. Thus, the existing data is a composite reflecting a tissue mosaic that is composed of many cell types. The question we are tackling here is how to deconvolute the existing Epigenomics and TCGA tissue-specific signatures into their single cell-type-specific components (HR0-3). This is responsive to RFA-RM-14-001 stated purposes: (1) "Analyses that use reference epigenomic data to identify specific features that distinguish cell types" and (2) "Integrative analyses that combine reference epigenomic maps with other public or investigator-generated data sets". C. HYPOTHESIS: (1) identification of lineage specific breast cell-surface markers would permit the isolation of specific cell lineages, facilitating downstream research;and (2) identification of lineage specific epigenetic markers will facilitate development of computational techniques that are necessary for deconvolution of complex epigenomic signatures.
AIM 1 : Isolation and profiling of HR0-3 cell lineage from normal and malignant human breast 1 A. We will isolate HR0-3 cell subtypes from normal and malignant breast tissues using intracellular markers ER, AR, VDR. 1 B. We will identify cell surface markers that correspond to HR0-3 cell subtypes in normal breast and breast cancers, permitting isolation of viable ER+, vs. AR+ vs. VDR+ vs. HR0 vs. HR3 cell types.
AIM 2. Computational determination of lineage-specific and tumor-specific DNA methylation markers in human breast tissue. 2 A. We will characterize tumor-specific and lineage-specific DNA methylation markers. 2 B. We will validate the lineage and tumor specificity of DNA methylation markers by analyzing methylation data from normal breast tissue samples. 2 C. We will investigate the feasibility of computational determination of lineage-specific and tumor-specific DNA methylation markers by integration with other genomic types.
Based on our recent published results we hypothesize that some of the major prognostic differences among breast cancers are inherited epigenetically from their normal cell-of-origin. These epigenetic signatures can be very useful in accurately classifying and treating cancers. However, existing molecular data in public databases is not cell-of-origin specific. In this application we propose to develop methods that will take advantage of the data generated through the NIH Roadmap Epigenomics Program and combine them with other public data sets in order to aid in interpretation of the results of previous studies and identify specific features that distinguish cell types, biomarkers, and therapeutic targets.
|Koru-Sengul, Tulay; Santander, Ana M; Miao, Feng et al. (2016) Breast cancers from black women exhibit higher numbers of immunosuppressive macrophages with proliferative activity and of crown-like structures associated with lower survival compared to non-black Latinas and Caucasians. Breast Cancer Res Treat 158:113-26|
|Thakkar, A; Wang, B; Picon-Ruiz, M et al. (2016) Vitamin D and androgen receptor-targeted therapy for triple-negative breast cancer. Breast Cancer Res Treat 157:77-90|
|Houseman, E Andres; Kile, Molly L; Christiani, David C et al. (2016) Reference-free deconvolution of DNA methylation data and mediation by cell composition effects. BMC Bioinformatics 17:259|
|Wang, Bin; Lee, Chung-Wei; Witt, Abigail et al. (2015) Heat shock factor 1 induces cancer stem cell phenotype in breast cancer cell lines. Breast Cancer Res Treat 153:57-66|
|Houseman, Eugene AndrÃ©s; Ince, Tan A (2014) Normal cell-type epigenetics and breast cancer classification: a case study of cell mixture-adjusted analysis of DNA methylation data from tumors. Cancer Inform 13:53-64|