Shuang Zhao

The Landscape of Long Noncoding RNAs in the Human Transcriptome

Long noncoding RNAs (lncRNAs) are emerging as important regulators of tissue physiology and disease processes including cancer. To delineate genome-wide lncRNA expression, we curated 7,256 RNA sequencing (RNA-seq) libraries from tumors, normal tissues and cell lines comprising over 43 Tb of sequence from 25 independent studies. We applied ab initio assembly methodology to this data set, yielding a consensus human transcriptome of 91,013 expressed genes. Over 68% (58,648) of genes were classified as lncRNAs, of which 79% were previously unannotated. About 1% (597) of the lncRNAs harbored ultraconserved elements, and 7% (3,900) overlapped disease-associated SNPs. To prioritize lineage-specific, disease-associated lncRNA expression, we employed non-parametric differential expression testing and nominated 7,942 lineage- or cancer-associated lncRNA genes. The lncRNA landscape characterized here may shed light on normal biology and cancer pathogenesis and may be valuable for future biomarker development.

RNA biomarkers associated with metastatic progression in prostate cancer: a multi-institutional high-throughput analysis of SChLAP1.

BACKGROUNDnImproved clinical predictors for disease progression are needed for localised prostate cancer, since only a subset of patients develop recurrent or refractory disease after first-line treatment. Therefore, we undertook an unbiased analysis to identify RNA biomarkers associated with metastatic progression after prostatectomy.nnnMETHODSnProstate cancer samples from patients treated with radical prostatectomy at three academic institutions were analysed for gene expression by a high-density Affymetrix GeneChip platform, encompassing more than 1 million genomic loci. In a discovery cohort, all protein-coding genes and known long non-coding RNAs were ranked by fold change in expression between tumours that subsequently metastasised versus those that did not. The top ranked gene was then validated for its prognostic value for metastatic progression in three additional independent cohorts. 95% of the gene expression assays were done in a Clinical Laboratory Improvements Amendments certified laboratory facility. All genes were assessed for their ability to predict metastatic progression by receiver-operating-curve area-under-the-curve analyses. Multivariate analyses were done for the primary endpoint of metastatic progression, with variables including Gleason score, preoperative prostate-specific antigen concentration, seminal vesicle invasion, surgical margin status, extracapsular extension, lymph node invasion, and expression of the highest ranked gene.nnnFINDINGSn1008 patients were included in the study: 545 in the discovery cohort and 463 in the validation cohorts. The long non-coding RNA SChLAP1 was identified as the highest-ranked overexpressed gene in cancers with metastatic progression. Validation in three independent cohorts confirmed the prognostic value of SChLAP1 for metastatic progression. On multivariate modelling, SChLAP1 expression (high vs low) independently predicted metastasis within 10 years (odds ratio [OR] 2·45, 95% CI 1·70-3·53; p<0·0001). The only other variable that independently predicted metastasis within 10 years was Gleason score (8-10 vs 5-7; OR 2·14, 95% CI 1·77-2·58; p<0·0001).nnnINTERPRETATIONnWe identified and validated high SChLAP1 expression as significantly prognostic for metastatic disease progression of prostate cancer. Our findings suggest that further development of SChLAP1 as a potential biomarker, for treatment intensification in aggressive prostate cancer, warrants future study.nnnFUNDINGnProstate Cancer Foundation, National Institutes of Health, Department of Defense, Early Detection Research Network, Doris Duke Charitable Foundation, and Howard Hughes Medical Institute.

Characterization of 1577 primary prostate cancers reveals novel biological and clinicopathologic insights into molecular subtypes

BACKGROUNDnProstate cancer (PCa) molecular subtypes have been defined by essentially mutually exclusive events, including ETS gene fusions (most commonly involving ERG) and SPINK1 overexpression. Clinical assessment may aid in disease stratification, complementing available prognostic tests.nnnOBJECTIVEnTo determine the analytical validity and clinicopatholgic associations of microarray-based molecular subtyping.nnnDESIGN, SETTING, AND PARTICIPANTSnWe analyzed Affymetrix GeneChip expression profiles for 1577 patients from eight radical prostatectomy cohorts, including 1351 cases assessed using the Decipher prognostic assay (GenomeDx Biosciences, San Diego, CA, USA) performed in a laboratory with Clinical Laboratory Improvements Amendment certification. A microarray-based (m-) random forest ERG classification model was trained and validated. Outlier expression analysis was used to predict other mutually exclusive non-ERG ETS gene rearrangements (ETS(+)) or SPINK1 overexpression (SPINK1(+)).nnnOUTCOME MEASUREMENTSnAssociations with clinical features and outcomes by multivariate logistic regression analysis and receiver operating curves.nnnRESULTS AND LIMITATIONSnThe m-ERG classifier showed 95% accuracy in an independent validation subset (155 samples). Across cohorts, 45% of PCas were classified as m-ERG(+), 9% as m-ETS(+), 8% as m-SPINK1(+), and 38% as triple negative (m-ERG(-)/m-ETS(-)/m-SPINK1(-)). Gene expression profiling supports three underlying molecularly defined groups: m-ERG(+), m-ETS(+), and m-SPINK1(+)/triple negative. On multivariate analysis, m-ERG(+) tumors were associated with lower preoperative serum prostate-specific antigen and Gleason scores, but greater extraprostatic extension (p<0.001). m-ETS(+) tumors were associated with seminal vesicle invasion (p=0.01), while m-SPINK1(+)/triple negative tumors had higher Gleason scores and were more frequent in Black/African American patients (p<0.001). Clinical outcomes were not significantly different among subtypes.nnnCONCLUSIONSnA clinically available prognostic test (Decipher) can also assess PCa molecular subtypes, obviating the need for additional testing. Clinicopathologic differences were found among subtypes based on global expression patterns.nnnPATIENT SUMMARYnMolecular subtyping of prostate cancer can be achieved using extra data generated from a clinical-grade, genome-wide expression-profiling prognostic assay (Decipher). Transcriptomic and clinical analysis support three distinct molecular subtypes: (1) m-ERG(+), (2) m-ETS(+), and (3) m-SPINK1(+)/triple negative (m-ERG(-)/m-ETS(-)/m-SPINK1(-)). Incorporation of subtyping into a clinically available assay may facilitate additional applications beyond routine prognosis.

DNA-PKcs-Mediated Transcriptional Regulation Drives Prostate Cancer Progression and Metastasis

Emerging evidence demonstrates that the DNA repair kinase DNA-PKcs exerts divergent roles in transcriptional regulation of unsolved consequence. Here, inxa0vitro and inxa0vivo interrogation demonstrate that DNA-PKcs functions as a selective modulator of transcriptional networks that induce cell migration, invasion, and metastasis. Accordingly, suppression of DNA-PKcs inhibits tumor metastases. Clinical assessment revealed that DNA-PKcs is significantly elevated in advanced disease and independently predicts for metastases, recurrence, and reduced overall survival. Further investigation demonstrated that DNA-PKcs in advanced tumors is highly activated, independent of DNA damage indicators. Combined, these findings reveal unexpected DNA-PKcs functions, identify DNA-PKcs as a potent driver of tumor progression and metastases, and nominate DNA-PKcs as a therapeutic target for advanced malignancies.

Patient-reported quality of life after stereotactic body radiotherapy (SBRT), intensity modulated radiotherapy (IMRT), and brachytherapy

BACKGROUND AND PURPOSEnStereotactic body radiotherapy (SBRT) is being used for prostate cancer, but concerns persist about toxicity compared to other radiotherapy options.nnnMATERIALS AND METHODSnWe conducted a multi-institutional pooled cohort analysis of patient-reported quality of life (QOL) [EPIC-26] before and after intensity-modulated radiotherapy (IMRT), brachytherapy, or SBRT for localized prostate cancer. Data were analyzed by mean domain score, minimal clinically detectable difference (MCD) in domain score, and multivariate analyses to determine factors associated with domain scores at 2-years.nnnRESULTSnData were analyzed from 803 patients at baseline and 645 at 2-years. Mean declines at 2-years across all patients were -1.9, -4.8, -4.9, and -13.3 points for urinary obstructive, urinary incontinence, bowel, and sexual symptom domains, respectively, corresponding to MCD in 29%, 20%, and 28% of patients. On multivariate analysis (vs. IMRT), brachytherapy had worse urinary irritation at 2-years (-6.8 points, p<0.0001) but no differences in other domains (p>0.15). QOL after SBRT was similar for urinary (p>0.5) and sexual domains (p=0.57), but was associated with better bowel score (+6.7 points, p<0.0002).nnnCONCLUSIONSnQOL 2-years after brachytherapy, IMRT, or SBRT is very good and largely similar, with small differences in urinary and bowel QOL that are likely minimized by modern techniques.

A Comprehensive Analysis of CXCL12 Isoforms in Breast Cancer1,2

CXCL12-CXCR4-CXCR7 signaling promotes tumor growth and metastasis in breast cancer. Alternative splicing of CXCL12 produces isoforms with distinct structural and biochemical properties, but little is known about isoform-specific differences in breast cancer subtypes and patient outcomes. We investigated global expression profiles of the six CXCL12 isoforms, CXCR4, and CXCR7 in The Cancer Genome Atlas breast cancer cohort using next-generation RNA sequencing in 948 breast cancer and benign samples and seven breast cancer cell lines. We compared expression levels with several clinical parameters, as well as metastasis, recurrence, and overall survival (OS). CXCL12-α, -β, and -γ are highly co-expressed, with low expression correlating with more aggressive subtypes, higher stage disease, and worse clinical outcomes. CXCL12-δ did not correlate with other isoforms but was prognostic for OS and showed the same trend for metastasis and recurrence-free survival. Effects of CXCL12-δ remained independently prognostic when taking into account expression of CXCL12,CXCR4, and CXCR7. These results were also reflected when comparing CXCL12-α, -β, and -γ in breast cancer cell lines. We summarized expression of all CXCL12 isoforms in an important chemokine signaling pathway in breast cancer in a large clinical cohort and common breast cancer cell lines, establishing differences among isoforms in multiple clinical, pathologic, and molecular subgroups. We identified for the first time the clinical importance of a previously unstudied isoform, CXCL12-δ.

Abstract B39: Long noncoding RNA PCAT14/PRCAT104; A prognostic biomarker in prostate cancer

Prostate cancer is the second most common epithelial cancer and the second leading cause of cancer-related death for men in the United States. While majority of prostate cancer cases are indolent and cause minimal morbidity and mortality, a subset of men progress to a hormone-refractory aggressive disease with high mortality. Markers such as PSA and PCA3 perform well in diagnosis of disease; however these biomarkers are unable to predict disease progression. Therefore, there is an urgent need to identify clinical predictors of disease progression. Long non-coding RNAs (lncRNAs) are emerging as an important class of biomolecules that exhibit significant lineage- and cancer-specificity making them ideal biomarker candidates. Using RNA-Seq data from The Cancer Genome Atlas (TCGA) and as well as from libraries generated in our laboratory, we identified PCAT14/PRCAT104 as a marker of low Gleason disease. PCAT14 was shown to be highly expressed in prostate cancer compared to benign tissue; however, its expression in prostate cancer was limited to low Gleason disease. To directly evaluate the relationship between PCAT14 levels and clinical outcome, we assessed its expression in more that 1600 Prostate cancer samples by a high-density Affymetrix GeneChip platform. In all 5 cohorts analyzed, PCAT14 was shown to be a strong prognostic marker in its ability to predict biochemical recurrence, clinical progression to systemic disease and prostate cancer–specific mortality. Furthermore, in a multivariate analysis, PCAT14 expression also predict resistance to androgen deprivation therapy (ADT) (p=0.012). More interestingly, PCAT14 was able to add to prognostic value of SCHLAP1, a lncRNA that we previously showed to be an excellent prognostic marker in prostate cancer. PCAT14 in combination with SCHLAP1 was able better in predicting 10-year metastasis free survival (AUC: 0.64) than SCHLAP1 alone (AUC=0.58). Taken together, we identified a novel marker of low grade prostate cancer that in combination with SCHALP1, a marker of aggressive cancer, can predict disease progression and hence can be of immense value for treatment individualization in prostate cancer Citation Format: Rohit Malik, Xiang Zhang, Sudhanshu Shukla, Yashar Y. Niknafs, Shuang Zhao, Felix Y. Feng, Arul M. Chinnaiyan. Long noncoding RNA PCAT14/PRCAT104; A prognostic biomarker in prostate cancer. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr B39.

Abstract PD6-1: The long noncoding RNA M41 promotes aggressiveness and tamoxifen resistance in ER-positive breast cancers

Background: Long noncoding RNAs (lncRNAs) have recently been associated with the development and progression of a variety of human cancers. To date, the interplay between known oncogenic drivers, such as estrogen receptor (ER), and lncRNAs has not been well described. In this study, we identify M41 as the top outlier lncRNA in ER-positive vs ER-negative breast cancer and investigate its role in preclinical cancer phenotypes and clinical outcomes. Methods and Materials: RNA sequencing was performed on 89 breast cancer samples and cell lines, including 42 ER+ cases, and a modified cancer outlier analysis was used to identify lncRNAs enriched in ER-positive disease. To assess ER regulation of the top enriched lncRNA (M41), ChIP-Seq and ChIP-PCR was used to detect binding of ER to M41 promoter and qPCR was used to determine changes in M41 expression following 10 nM estradiol treatment in MCF7 and T47D cells. Following knockdown via siRNA, the impact of M41 expression was assessed on cell invasion, migration, proliferation, and anchorage-independent growth. The impact of M41 knockdown on tamoxifen sensitivity was assessed by cell proliferation studies in MCF7 cells with acquired tamoxifen resistance. Lastly, clinical associations between M41 expression and grade/node status, as well as event-free survival (EFS), was determined using ANOVA and Kaplan-Meier analyses of TCGA samples. Results: M41, an uncharacterized lncRNA located on chr21q22.2, was identified as the top outlier lncRNA in ER-positive vs ER-negative breast cancer. M41 demonstrated outlier expression (RPKM values>50) in 15% of ER-positive cancers, and was not significantly expressed in normal breast tissue. ChIP studies show that ER robustly binds to the M41 promoter. Estradiol stimulation significantly increased M41 expression in a time-dependent manner. Knockdown of M41 significantly inhibited all assessed oncogenic phenotypes in the ER-positive MCF7 and T47D cells, with a 60-80% decrease in both invasion and anchorage-independent growth, but had no effect in the ER-negative MDA-MB-231 cell line (which has minimal M41 expression). M41 expression was greater than 10-fold higher in tamoxifen-resistant MCF7 cells compared to parental controls (p Conclusion: We have identified M41 as an ER-associated oncogenic lncRNA that contributes to preclinical cancer phenotype, promotes tamoxifen resistance in cell line models, and associates with poor outcomes in clinical samples. We suggest that M41 represents a novel biomarker candidate for the prognosis of ER-positive breast cancers and provides new insight into the biological complexity of breast tumor biology. Citation Format: Felix Y Feng, Teng Ma, Corey Speers, Matthew K Iyer, Shuang Zhao, John R Prensner, James M Rae, Lori J Pierce, Arul M Chinnaiyan. The long noncoding RNA M41 promotes aggressiveness and tamoxifen resistance in ER-positive breast cancers [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr PD6-1.

Abstract A08: PARP1-mediated E2F1 regulation of DNA repair capacity

PARP1 holds two major functions on chromatin, DNA damage repair and transcriptional regulation, both of which are relevant in the context of cancer. Notably, PARP1 has been found to be a key modulator of androgen receptor (AR) function and AR-dependent phenotypes, which is a driving factor in prostate cancer (PCa) biology and therapeutic management. Recent studies indicate an unanticipated prevalence of DNA repair alterations in advanced PCa and showed that PARP1 inhibitors (PARPi) can effectively manage of a subset of these tumors. Despite the functions of PARP1 in DNA repair having been exploited as a therapeutic target for tumors with BRCA1/2 aberrations, factors beyond DNA repair alterations clearly play a role in the response to PARPi. Notably, in the TO-PARP trial, not all patients with DNA repair aberrations responded to PARPi; conversely, tumors lacking BRCA1/2 or other DNA repair alterations show objective response to PARPi in PCa and other tumor types. These clinical data suggest that the genetic (e.g. BRCA-ness) and pharmacologic interplay is complex in the context of PARPi. Given the preclinical and clinical data, pursuing a deeper understanding of the molecular underpinnings of PARPi action in PCa may yield significant benefit. Genome-wide transcriptional profiling in response to PARPi was performed and the PARP1-regulated transcriptome was identified. Both the PARP1-regulated transcriptome, as well as PARP1 enzymatic activity were found to be elevated as a function of PCa progression. Further interrogation of the PARP1-regulated transcriptome revealed a major impact on E2F1-regulated genes, and chromatin immunoprecipitation analyses indicated that PARP1 functions to regulate the chromatin architecture and E2F1 occupancy at E2F1 target gene loci. Most prominent among the E2F1-regulated genes responsive to PARPi were genes associated with DNA damage repair, with a particular enrichment for genes involved in homologous recombination (HR). In sum, these data indicate PARP1 regulates function of key oncogenic transcription factors (AR and E2F1) in PCa, and part of the effect of PARPi may be through down-regulation of DNA repair factors. Citation Format: Matthew J. Schiewer, Amy C. Mandigo, Nicholas Gordon, Sumin Han, Shuang Zhao, Joseph Evans, Theodore Parsons, Ruth Birbe, Peter McCue, Tapio Visakorpi, Ganesh Raj, Mark Rubin, Johann de Bono, Costas Lallas, Edouard Trabulsi, Leonard G. Gomella, Adam P. Dicker, Wm. Kevin Kelly, Felix Y. Feng, Karen E. Knudsen. PARP1-mediated E2F1 regulation of DNA repair capacity [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr A08.

Abstract A1-64: Molecular and clinical characterization of 1,577 primary prostate cancer tumors reveals novel clinical and biological insights into its subtypes

Background: Prostate cancer molecular subtypes based on ETS gene fusions and SPINK1 were originally identified through distinct gene expression profiles. Such molecular subtypes may have utility in disease stratification and clonality assessment, complementing available purely prognostic tests. Hence, we determined the analytical validity of molecular subtyping and explored clinical associations using global gene expression profiles in a large cohort of PCa. Methods: We analyzed 1,577 patient Affymetrix Human Exon 1.0ST GeneChip expression profiles from 8 radical prostatectomy (RP) cohorts; 5 of them generated as part of the Decipher® platform for the Decipher® discovery or validation. Multi-feature random forest classifiers and outlier analysis were used to define microarray-based molecular subtypes and characterize clinical associations. Results: A random forest (RF) classifier (m-ERG) was trained and validated to predict ERG fusion status using separate subsets of a single-institution RP cohort (total n=407) with known ERG rearrangement status defined by FISH, achieving >95% sensitivity and specificity in the validation subset. The model was then applied to 7 independent RP cohorts to predict ERG rearrangement status. Less frequent rearrangements involving other ETS genes (ETV1, ETV4, ETV5, FLI1) or SPINK1 over-expression were predicted based on gene expression outlier analysis. Across cohorts, 45%, 9% 8% and 38% of PCa were classified as ERG+, ERG—ETS+, ERG—SPINK+, and Triple Negative (ERG—/ETS—/SPINK1—), respectively. Global gene expression analysis shows that the four subtypes could be collapsed into three entities (ERG+, ERG—ETS+ and SPINK+/Triple Negative) based on expression patterns and clinical characteristics similarity. Based on multivariable analysis, ERG+ is significantly associated with lower pre-PSA (p Conclusions: The Decipher® platform can accurately determine ERG rearrangement status and PCa molecular subtypes. Inclusion of molecular subtyping, such as m-ERG status, may enable additional precision medicine opportunities in prognostic tests and provides insights into the development of novel therapeutic approaches. Citation Format: Mohammed Alshalalfa, Scott A. Tomlins, Nicholas Erho, Kasra Yousefi, Shuang Zhao, Robert B. Den, Adam P. Dicker, Bruce Trock, Angelo DeMarzo, Edward M. Schaeffer, Ashley Ross, Eric A. Klein, Cristina Magi-Galluzzi, Jeffery R. Karnes, Robert B Jenkins, Elai Davicioni, Felix Feng. Molecular and clinical characterization of 1,577 primary prostate cancer tumors reveals novel clinical and biological insights into its subtypes. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-64.