Yang Shi

The lncRNA PCAT29 Inhibits Oncogenic Phenotypes in Prostate Cancer

Long noncoding RNAs (lncRNA) have recently been associated with the development and progression of a variety of human cancers. However, to date, the interplay between known oncogenic or tumor-suppressive events and lncRNAs has not been well described. Here, the novel lncRNA, prostate cancer–associated transcript 29 (PCAT29), is characterized along with its relationship to the androgen receptor. PCAT29 is suppressed by DHT and upregulated upon castration therapy in a prostate cancer xenograft model. PCAT29 knockdown significantly increased proliferation and migration of prostate cancer cells, whereas PCAT29 overexpression conferred the opposite effect and suppressed growth and metastases of prostate tumors in chick chorioallantoic membrane assays. Finally, in prostate cancer patient specimens, low PCAT29 expression correlated with poor prognostic outcomes. Taken together, these data expose PCAT29 as an androgen-regulated tumor suppressor in prostate cancer. Implications: This study identifies PCAT29 as the first androgen receptor–repressed lncRNA that functions as a tumor suppressor and that its loss may identify a subset of patients at higher risk for disease recurrence. Visual Overview: http://mcr.aacrjournals.org/content/early/2014/07/31/1541-7786.MCR-14-0257/F1.large.jpg. Mol Cancer Res; 12(8); 1081–7. ©2014 AACR. Visual Overview

A Novel RNA In Situ Hybridization Assay for the Long Noncoding RNA SChLAP1 Predicts Poor Clinical Outcome After Radical Prostatectomy in Clinically Localized Prostate Cancer

Long noncoding RNAs (lncRNAs) are an emerging class of oncogenic molecules implicated in a diverse range of human malignancies. We recently identified SChLAP1 as a novel lncRNA that demonstrates outlier expression in a subset of prostate cancers, promotes tumor cell invasion and metastasis, and associates with lethal disease. Based on these findings, we sought to develop an RNA in situ hybridization (ISH) assay for SChLAP1 to 1) investigate the spectrum of SChLAP1 expression from benign prostatic tissue to metastatic castration-resistant prostate cancer and 2) to determine whether SChLAP1 expression by ISH is associated with outcome after radical prostatectomy in patients with clinically localized disease. The results from our current study demonstrate that SChLAP1 expression increases with prostate cancer progression, and high SChLAP1 expression by ISH is associated with poor outcome after radical prostatectomy in patients with clinically localized prostate cancer by both univariate (hazard ratio = 2.343, P = .005) and multivariate (hazard ratio = 1.99, P = .032) Cox regression analyses. This study highlights a potential clinical utility for SChLAP1 ISH as a novel tissue-based biomarker assay for outcome prognostication after radical prostatectomy.

Frequent discordance between ERG gene rearrangement and ERG protein expression in a rapid autopsy cohort of patients with lethal, metastatic, castration-resistant prostate cancer.

ERG rearrangements in localized prostate cancer can be detected with high sensitivity and specificity by immunohistochemistry (IHC). However, recent data suggest that ERG IHC may be less sensitive for ERG rearrangements in castration‐resistant prostate cancer (CRPC). Thus, we sought to examine ERG protein expression in a cohort of rapid autopsy patients with lethal metastatic CRPC (mCRPC).

rSeqDiff: detecting differential isoform expression from RNA-Seq data using hierarchical likelihood ratio test.

High-throughput sequencing of transcriptomes (RNA-Seq) has recently become a powerful tool for the study of gene expression. We present rSeqDiff, an efficient algorithm for the detection of differential expression and differential splicing of genes from RNA-Seq experiments across multiple conditions. Unlike existing approaches which detect differential expression of transcripts, our approach considers three cases for each gene: 1) no differential expression, 2) differential expression without differential splicing and 3) differential splicing. We specify statistical models characterizing each of these three cases and use hierarchical likelihood ratio test for model selection. Simulation studies show that our approach achieves good power for detecting differentially expressed or differentially spliced genes. Comparisons with competing methods on two real RNA-Seq datasets demonstrate that our approach provides accurate estimates of isoform abundances and biological meaningful rankings of differentially spliced genes. The proposed approach is implemented as an R package named rSeqDiff.

RNA-Seq accurately identifies cancer biomarker signatures to distinguish tissue of origin.

Metastatic cancer of unknown primary (CUP) accounts for up to 5% of all new cancer cases, with a 5-year survival rate of only 10%. Accurate identification of tissue of origin would allow for directed, personalized therapies to improve clinical outcomes. Our objective was to use transcriptome sequencing (RNA-Seq) to identify lineage-specific biomarker signatures for the cancer types that most commonly metastasize as CUP (colorectum, kidney, liver, lung, ovary, pancreas, prostate, and stomach). RNA-Seq data of 17,471 transcripts from a total of 3,244 cancer samples across 26 different tissue types were compiled from in-house sequencing data and publically available International Cancer Genome Consortium and The Cancer Genome Atlas datasets. Robust cancer biomarker signatures were extracted using a 10-fold cross-validation method of log transformation, quantile normalization, transcript ranking by area under the receiver operating characteristic curve, and stepwise logistic regression. The entire algorithm was then repeated with a new set of randomly generated training and test sets, yielding highly concordant biomarker signatures. External validation of the cancer-specific signatures yielded high sensitivity (92.0% ± 3.15%; mean ± standard deviation) and specificity (97.7% ± 2.99%) for each cancer biomarker signature. The overall performance of this RNA-Seq biomarker-generating algorithm yielded an accuracy of 90.5%. In conclusion, we demonstrate a computational model for producing highly sensitive and specific cancer biomarker signatures from RNA-Seq data, generating signatures for the top eight cancer types responsible for CUP to accurately identify tumor origin.

Concurrent nuclear ERG and MYC protein overexpression defines a subset of locally advanced prostate cancer: Potential opportunities for synergistic targeted therapeutics

Recurrent ERG gene fusions, the most common genetic alterations in prostate cancer, drive overexpression of the nuclear transcription factor ERG, and are early clonal events in prostate cancer progression. The nuclear transcription factor MYC is also frequently overexpressed in prostate cancer and may play a role in tumor initiation and/or progression. The relationship between nuclear ERG and MYC protein overexpression in prostate cancer, as well as the clinicopathologic characteristics and prognosis of ERG‐positive/MYC high tumors, is not well understood.

RSeqNP: A non-parametric approach for detecting differential expression and splicing from RNA-Seq data

UNLABELLEDnHigh-throughput sequencing of transcriptomes (RNA-Seq) has become a powerful tool to study gene expression. Here we present an R package, rSeqNP, which implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data. rSeqNP uses permutation tests to access statistical significance and can be applied to a variety of experimental designs. By combining information across isoforms, rSeqNP is able to detect more differentially expressed or spliced genes from RNA-Seq data.nnnAVAILABILITY AND IMPLEMENTATIONnThe R package with its source code and documentation are freely available at http://www-personal.umich.edu/∼jianghui/rseqnp/.nnnCONTACTnjianghui@umich.edunnnSUPPLEMENTARY INFORMATIONnSupplementary data are available at Bioinformatics online.

Abstract 1398: Targeting novel co-activators of androgen receptor in castration resistant prostate cancer

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CAnnResistance to androgen deprivation therapies and increased androgen receptor (AR) activity are major drivers of castrate resistant prostate cancer (CRPC), an advanced and frequently lethal form of this disease. Substantial prior work has focused on targeting AR directly; however, the identification and therapeutic targeting of co-activators of AR signaling remains an underexplored area of potential clinical significance. Here we demonstrate that the MLL complex acts as a co-activator of AR signaling. AR directly interacts with the sub-unit menin to recruit MLL and its complex to AR target genes. Inhibition of the menin-MLL interaction can block AR signaling and inhibit the formation of castration resistant tumors in vivo. Furthermore, we find that menin is up-regulated in localized and metastatic prostate cancer and high menin expression correlates with poor overall survival. Taken together our study identifies a novel co-activator complex of AR that can be targeted in CRPCs.nnCitation Format: Rohit Malik, Amjad P. Khan, John R. Prensner, Matthew K. Iyer, Dmitry Borkin, Xiaoju Wang, Xia Jiang, Shruthi Subramaniam, Yang Shi, Rachell Stender, Yi-Mi Wu, Xuhong Cao, Jolanta Grembecka, Tomasz Cierpicki, Arul Chinnaiyan. Targeting novel co-activators of androgen receptor in castration resistant prostate cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1398. doi:10.1158/1538-7445.AM2014-1398