Development and enhancement of bioinformatic tools to integrate and understand aberrant epigenomic and genomic changes associated with cancer: methods, development and analysis

• Grant number: 16/10436-9
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Effective date (Start): September 01, 2016
• Effective date (End): August 31, 2017
• Field of knowledge: Health Sciences - Medicine - Medical Clinics
• Principal Investigator: Houtan Noushmehr
• Grantee: Tiago Chedraoui Silva
• Supervisor: Benjamin P. Berman
• Host Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Research place: Cedars-Sinai Medical Center, United States
• Associated to the scholarship: 16/01389-7 - Bioinformatic tool to integrate and understand aberrant epigenomic and genomic changes associated with cancer: methods, development and analysis, BP.DD

Abstract

Cancer, which is one of the major causes of mortality worldwide, is a complex disease orchestrated by aberrant genomic and epigenomic changes that can modify gene regulatory circuits and cellular identity. Emerging evidence obtained through high-throughput genomic data deposited within the public TCGA international consortium suggests that one in ten cancer patients would be more accurately classified by molecular taxonomy versus histology. Therefore, we have hypothesized that the establishment of genome-wide maps of the de novo DNA binding motifs localization coupled with differentially methylated regions and gene expression changes might help to characterize and exploit cancer phenotypes at the molecular level.Technological advances and public databases like The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (roadmap) have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high resolution. Markedly however, biological information is stored in different formats and there is no current tool to integrate the data, highlighting an urgent need to develop bioinformatic tools and/or computational softwares to overcome this challenge. In this context, the main purpose of this study is the development of biOMICs, a package coded in the GNU GPL (General Public License) R programming language that will be submitted to the larger open-source Bioconductor community project. Also, we will help our collaborators improve of the R/Bioconductor ELMER package that identifies regulatory enchancers using gene expression, DNA methylation data and motif analysis.Our expectation is that biOMICs can effectively automate search, retrieve, and analyze the vast (epi)genomic data currently available from TCGA, ENCODE, and Roadmap, and integrate genomics and epigenomics features with researchers own high-throughput data. Furthermore, we will also navigate through these data manually in order to validate the capacity of biOMICs in discovering epigenomic signatures able to redefine subtypes of cancer. Finally, we will use biOMICs and ELMER to investigate the molecular differences between two subgroups of gliomas, one of the most aggressive primary brain cancer, recently discovered by our laboratory. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

MALTA, TATHIANE M.; DE SOUZA, CAMILA F.; SABEDOT, THAIS S.; SILVA, TIAGO C.; MOSELLA, MARITZA S.; KALKANIS, STEVEN N.; SNYDER, JAMES; CASTRO, ANA VALERIA B.; NOUSHMEHR, HOUTAN. Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications. NEURO-ONCOLOGY, v. 20, n. 5, p. 608-620, MAY 2018. (16/06488-3, 16/15485-8, 14/08321-3, 16/12329-5, 16/10436-9, 15/07925-5, 16/01975-3, 14/02245-3, 16/01389-7, 16/11039-3)

MALTA, TATHIANE M.; SOKOLOV, ARTEM; GENTLES, ANDREW J.; BURZYKOWSKI, TOMASZ; POISSON, LAILA; WEINSTEIN, JOHN N.; KAMINSKA, BOZENA; HUELSKEN, JOERG; OMBERG, LARSSON; GEVAERT, OLIVIER; et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell, v. 173, n. 2, p. 338+, APR 5 2018. (16/06488-3, 14/08321-3, 15/07925-5, 16/01975-3, 16/01389-7, 16/15485-8, 14/02245-3, 16/10436-9, 16/12329-5)