Predicting Transcriptional and Epigenetic Networks in Cancer from Sequencing Data

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DESCRIPTION (provided by applicant): This project aims to develop bioinformatic resources for processing and integrating the large-scale sequencing data that are rapidly emerging for studying oncogenic transcription factors (TFs) in cancer research. While our methods will be applicable to general TFs, we will develop our tools by focusing on microphthalmia-associated transcription factor (MITF), a key onco-protein frequently amplified in melanoma. MITF is perhaps the most intensely studied TF in melanoma, being responsible for turning multiple signals into a transcriptional control of proliferation, survival, and invasion. Studying the mechanisms of an oncogenic TF, such as MITF, and comprehensively identifying its direct target genes thus remain important unsolved problems in cancer research. Cancer genomics based on high-throughput DNA sequencing is now rapidly generating enormous amounts of genetic and epigenetic data that can collectively reveal how MITF functions as a potent regulator of melanoma progression. Analyzing such massive heterogeneous datasets is frequently challenged by both sequencing failures and the lack of analysis methods for integrating and interpreting the resulting information. The proposed tools will address these urgent problems: (1) We will develop a stand-alone platform- independent quality control visualization software for ChIP-seq and RNA-seq data. Our software package will automatically test and graphically summarize the quality of data and also suggest potential sources of failure; (2) We will develop and apply computational tools for discovering cooperating TFs of MITF. TF binding activity in itself is often insufficient to regulate gene expression, suggesting that specific combinations of cooperating factors crucially determine MITF's ability to transcribe key oncogenes in melanoma. We will thus computationally identify and experimentally validate cooperating factors of MITF by combining ChIP-seq data with DNA sequence analysis; (3) We will develop and apply statistical methods for inferring the epigenetic changes that are both controlled by and guiding MITF and, as a result, identify aberrant epigenetic modifications that disrupt normal MITF functions; (4) As aberrant expression of non-coding RNAs (ncRNAs) and retrotransposons can critically alter cell cycle, apoptosis and proliferation, we will identify active ncRNAs and retrotransposons in melanoma and discover their transcriptional regulators. These results will help reveal the transcriptional and epigenetic network of MITF in melanoma and produce valuable resources applicable to other cancers.