Project description:Metastatic, castration-resistant prostate cancer (mCRPC) is an advanced form of prostate cancer with a high mortality rate due to a current lack of treatment options. While much is already known about how mutations in protein-coding sequences across the genome affect prostate cancer, somatic mutations occurring in the 3’ untranslated regions (3’UTRs) of genes are largely unstudied. The 3’UTR is a genomic region that controls post-transcriptional gene expression through its recruitment of trans-acting factors such as RNA-binding proteins (RBPs) and microRNAs (miRNAs), which themselves are known to be oncogenes and tumor suppressors in many cases. To better understand the role of 3’UTR mutations across prostate cancer, we have created a database of 3’UTR somatic mutations in 185 patients with mCRPC, discovering 14,497 single-nucleotide mutations throughout the 3’UTRome. In order to functionally assay these variants, we have developed a novel pair of massively parallel reporter assays (MPRA) able to determine the effect of thousands of patient somatic mutations on post-transcriptional gene expression. In this two-pronged approach, we are able to measure whether each of 6,892 mutations found in recurrently mutated 3’UTRs affect mRNA stability, steady-state transcript level, and translation efficiency. This deep functional assessment of thousands of 3’UTR mutations allows us to uncover patterns in mutation functionality, including their association with RNA motifs and sequence conservation. Investigation into how the resultant gene expression changes from 3’UTR mutations affect prostate cancer pathogenesis, such as cancer growth or response to treatment, is also underway. This work represents an unprecedented view of the extent to which disease-relevant 3’UTR mutations affect mRNA stability, translation efficiency, and cancer phenotypes, expanding the boundaries of functional cancer genomics and potentially uncovering novel therapeutic targets in previously unexplored regulatory regions.

Project description:Our computational analyses of sequencing depth and the discovery rates of sequence elements in the 3’UTR strongly demonstrate that interrogation of the 3’UTRome in specific tissues and cell types across development will greatly expand the identification of new 3’UTR isoforms and the sequence elements therein. Therefore, we will generate and sequence the 3’UTRs from the cell types isolated from the developing germline, mature germ cells, and early embryogenesis. In addition, we will identify the 3’UTRs for the transcripts isolated from the major somatic tissues during late- and post-embryonic development. Based on our sequencing estimates, we anticipate that the tissue-specific interrogation of the 3’UTRome will reveal a far greater diversity of novel 3’UTR isoform expression that is masked in the whole-worm 3’UTRome sequence data. Using the transgenic myo-3::PABP strain, we have generated polyA-captured libraries for late embryos and across the major stages of post-embryonic development for the muscle transcriptome. We propose to generate these polyA-captured libraries for transcripts expressed in the other major tissues across development. PolyA-captured libraries will be generated for deep sequencing following the tissue-specific isolation of mRNAs by PABP pulldowns (the PABP immunoprecipitations will take advantage of a FLAG epitope which is fused to PABP in all of the transgenic strains). We propose to validate the expression of tissue- specific transcripts by qPCR for select transcripts known to be expressed in those particular tissues. In addition, following the strategy we have employed for the large-scale polyA-captured libraries from muscle, we will perform quality checks for 3ʼ end capture by manually sequencing ~60 clones isolated from each library. Once we have obtained the deep sequencing data, we will analyze all of the sequence reads using the bioinformatics pipeline that we established for the whole-worm polyA- captured sequences (Mangone et al., 2010).

Project description:Achieved biospecimens annotated with patient clinical characteristics are unique resources for translational research. However, RNA extracted from the achieved tissues is often degraded. RNA degradation can have a significant impact on the measure of transcript abundance that can lead to an increase rate of erroneous differentially expressed genes. Here, we are presenting the transcript integrity number (TIN) algorithm to measure the RNA degradation at transcript level. When applied to RNA-seq datasets generated from human brain Glioblastome cell line, human peripheral blood mononuclear cells, and metastatic castration resistant prostate cancer (mCRPC) clinical tissues, TIN provided a more reliable and more sensitive measure of RNA degradation than RIN, as demonstrated by much higher concordance with the RNA fragment size estimated from read pairs. More importantly, when comparing 10 mCRPC samples with lower RNA quality to another 10 samples with higher RNA quality, we demonstrated that calibrating gene quantification with TIN scores could mitigate RNA degradation effects and greatly improve gene expression analysis. The detected differentially expressed genes before TIN correction were predominantly ribosomal genes. However, when we adjusted gene quantifications with the corresponding TIN scores, we found differentially expressed genes were highly enriched in prostate cancer specific pathways. When further evaluating the performance of TIN correction using synthetic spike-in transcripts with predetermined abundance in RNA-seq data generated from Sequencing Control Consortium (SEQC), we found TIN adjustment had a better control of false positives and false negatives (sensitivity = 0.89, specificity = 0.91), as compared to gene expression analysis results without TIN correction (sensitivity =0.98, specificity = 0.50). RNA sequencing of 20 bone-metastatic castration resistant prostate cancer (mCRPC) using Illumina HiSeq 2500. Out of 20 mCRPC samples, 10 samples have relative low RNA integrity and another 10 samples have relative higher RNA integrity as measured by Agilent RIN score.

Dataset Information

A genome-wide functional study of 3’UTR mutations in advanced prostate cancer [RNA-seq]

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