Citations

Alpha‐1 antitrypsin deficiency (AATD) liver disease is characterized by marked heterogeneity in presentation and progression, despite a common underlying gene mutation, strongly suggesting the involvement of other genetic and/or epigenetic modifiers. Variation in clinical phenotype has added to the challenge of detection, diagnosis, and testing of new therapies in patients with AATD. We examined the contribution of DNA methylation (5‐methylcytosine [5mC]) to AATD liver disease heterogeneity because 5mC responds to environmental and genetic cues and its deregulation is a major driver of liver disease. Using liver biopsies from adults with early‐stage AATD and the ZZ genotype, genome‐wide 5mC patterns were interrogated. We compared DNA methylation among patients with early AATD, and among patients with normal liver, cirrhosis, and hepatocellular carcinoma derived from multiple etiologic exposures, and linked patient clinical/demographic features. Global analysis revealed significant genomic hypomethylation in AATD liver–impacting genes related to liver cancer, cell cycle, and fibrosis, as well as key regulatory molecules influencing growth, migration, and immune function. Further analysis indicated that 5mC changes are localized, with hypermethylation occurring within a background of genome‐wide 5mC loss and with patients with AATD manifesting distinct epigenetic landscapes despite their mutational homogeneity. By integrating clinical data with 5mC landscapes, we observed that CpGs differentially methylated among patients with AATD disease are linked to hallmark clinical features of AATD (e.g., hepatocyte degeneration and polymer accumulation) and further reveal links to well‐known sex‐specific effects of liver disease progression. Conclusion: Our data reveal molecular epigenetic signatures within this mutationally homogeneous group that point to ways to stratify patients for liver disease risk.

Abstract Motivation The significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging; it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations. Results Here we first characterize lncRNAs in contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between open reading frame length and guanine-cytosine (GC) content presents universally substantial divergence in lncRNAs and protein-coding RNAs, as observed in a broad variety of species. Based on the feature relationship, accordingly, we further present LGC, a novel algorithm for identifying lncRNAs that is able to accurately distinguish lncRNAs from protein-coding RNAs in a cross-species manner without any prior knowledge. As validated on large-scale empirical datasets, comparative results show that LGC outperforms existing algorithms by achieving higher accuracy, well-balanced sensitivity and specificity, and is robustly effective (>90% accuracy) in discriminating lncRNAs from protein-coding RNAs across diverse species that range from plants to mammals. To our knowledge, this study, for the first time, differentially characterizes lncRNAs and protein-coding RNAs based on feature relationship, which is further applied in computational identification of lncRNAs. Taken together, our study represents a significant advance in characterization and identification of lncRNAs and LGC thus bears broad potential utility for computational analysis of lncRNAs in a wide range of species. Availability and implementation LGC web server is publicly available at http://bigd.big.ac.cn/lgc/calculator. The scripts and data can be downloaded at http://bigd.big.ac.cn/biocode/tools/BT000004. Supplementary information Supplementary data are available at Bioinformatics online.

5058 Background: The detection of full length AR (AR-FL) or AR variants (AR-Vs) in blood and association with outcomes in mCRPC is unknown. We compared whole blood mRNA expression of AR-FL and AR-Vs to circulating tumor cells (CTCs) for predicting OS and time to treatment failure (TTF). Methods: We isolated RNA from whole blood collected in PAXgene RNA tubes and concurrent metastatic tissue biopsy from 51 men with mCRPC prior to initiation of abiraterone acetate in a prospective clinical trial (NCT#01953640). Whole transcriptome sequencing (RNAseq) was performed on blood samples and paired biopsies to detect AR-FL, ARV1, ARV3, ARV7, ARV8, AR12, ARV14, and ARV45. Reads were aligned to the GRCh38 reference genome with the spliced-alignment TopHat2 package. The Pearson correlation coefficient was calculated between AR-FL in blood and matched bone biopsy. CTCs were determined using the CELLSEARCH assay. Cox proportional hazard regression analysis was performed on AR-FL, each AR-V, and CTCs for association with OS and TTF. We compared the area under the curve (AUC) using CTCs alone to a multivariable model that included AR-Vs for predicting OS. Results: The median follow up was 3.0 years, (range 0.3-3.5); the median CTC count was 3 (range 0-372); 34/53 men were deceased. Blood based AR-FL or AR-Vs were detected in 50/53 patients with following distribution: AR-FL (41/53), ARV3 (9/53), ARV45 (8/53), ARV12 (4/53), ARV14 (4/53), ARV7 (2/53), and ARV8 (2/53). Whole blood AR-FL transcripts were highly correlated to paired bone biopsy (r2= 0.76). Elevated transcripts of either ARV12 or ARV14 were associated with decreased OS [hazard ratio (HR) 3.46, p = 0.006]. CTC count ≥5 was associated with poorer OS [HR 3.42, p = 0.02] and shorter TTF [HR 3.52, p = < 0.001]. Adjusting for CTC counts, in a multivariate model, blood AR12 expression was associated with poor OS [HR = 6.33, p = 0.009]. AR12 and CTCs trended toward improved AUC compared to CTC alone (0.78 vs 0.71, p = 0.07). Conclusions: AR-FL and AR-Vs are detectable in whole blood and are highly correlated with metastatic bone AR-FL expression. AR-Vs may add to prognostication in mCRPC and further validation is needed. Clinical trial information: NCT#01953640.

5053 Background: Genomic aberrations associated with resistance/response to AA/P are not known. In a prospective study we assessed whole-exome/RNA-seq based aberrations in CRPC metastatic biopsies for identifying molecular markers associated with primary resistance and response duration. Methods: Sequencing of metastatic biopsies was performed for analyzing molecular aberrations that predict primary resistance (defined as progression at 12-weeks of therapy (non-responders) using PSA, RECIST, bone scan criteria per PCWG2). Gene network analysis was performed in genes mutated more frequently in non-responders and in genes differentially expressed between non-responders and responders using a “risk ratio” (RR) of ≥2. Cox regression models with multiple gene network pathways were used for determining association with time to treatment change (TTTC). Results: Of 92 enrolled pts 82 had complete whole-exome, RNA-seq & 12-week outcome data available for analysis. At 12-weeks 33/82 had progressed. Using a RR of ≥2, 113 genes were more frequently mutated in non-responders & 292 in responders. In non-responders, gene network analysis revealed frequent mutations in Wnt/β-catenin pathway genes; frequent deletion of negative regulators of Wnt pathway ( DKK4, SFRP2, LRP6). Gene expression analyses revealed significantly reduced expression levels of Wnt/β-catenin pathway inhibitors and increased expression levels of cell cycle proliferation (CCP) genes in non-responders. Median study follow up was 32 months during which time 58/82 pts progressed and switched treatments. Median TTTC was 10.1 months (IQR:4.4-24.1). In multivariate analysis CCP scores of ≥50 predicted shorter TTTC (HR = 2.11, 95% CI: 1.17-3.80; p = 0.01). Conclusions: In metastases Wnt/β-catenin pathway activation is associated with primary AA/P resistance and increased CCP with acquired drug resistance. These findings offer molecular based predictive biomarkers in CRPC stage treatment. Clinical trial information: NCT#01953640.

5045 Background: Trophoblastic cell-surface antigen (Trop-2) is a transmembrane glycoprotein that is highly expressed in many solid tumors. Sacituzumab govitecan (IMMU-132) is an antibody-drug conjugate of an anti-Trop-2 humanized antibody with SN-38. Early clinical trials have shown high response rates in a broad range of diseases including triple negative breast and urothelial cancers. We evaluated Trop-2 expression in tumor biopsies and circulating tumor cells (CTCs) from men with mCRPC (metastatic castrate-resistant prostate cancer). Methods: Trop-2 expression was evaluated from mCRPC biopsies from patients (Pts) treated with abiraterone acetate (AA) on the PROMOTE clinical trial, CTCs from a separate cohort treated with either enzalutamide or AA. Trop-2 CTCs were compared with EpCAM captured CTCs using a microscale technology termed the VERSA (Vertical Exclusion-based Rare Sample Analysis) platform to compare protein and gene expression signatures of resistance to these agents. Results: RNA sequencing identified Trop-2 gene expression in > 70% of metastatic biopsies. The AR splice variant V7 was found in 48 biopsies that also expressed Trop-2. Trop-2 expression was not altered by treatment with AA at 12 weeks. The number of CTCs captured from 25 pts with Trop-2 or EpCAM were closely correlated (R2= 0.84). Gene expression analysis showed similar patterns of expression for the TROP-2 and EPCAM captured cells. AR splice variant expression (AR-V7, AR-V9) in Trop-2 and EpCAM CTCs was detected in 33% of patients. Expression of neuroendocrine markers was identified in 40% of Trop-2 CTCs. Conclusions: Trop-2 is frequently expressed in mCRPC and co-expressed in tumors that express AR splice variants. Trop-2 CTCs are detected in CRPC pts previously treated with AA or Enzalutamide that also express multiple AR splice variants and neuroendocrine markers. The results support Trop-2 expression as predictive biomarker of sensitivity to targeted therapies tumors resistant to AA or Enzalutamide. Men with mCRPC are being enrolled on a Phase I trial with IMMU-132, and multi-site Phase II clinical trial in men who have progressed on AA or Enzalutamide is being finalized.

A microfluidic device was designed to efficiently fragment chromatin for downstream epigenetic assays.

Background: Transcription factors are DNA-binding proteins that play key roles in many fundamental biological processes. Unraveling their interactions with DNA is essential to identify their target genes and understand the regulatory network. Genome-wide identification of their binding sites became feasible thanks to recent progress in experimental and computational approaches. ChIP-chip, ChIP-seq, and ChIP-exo are three widely used techniques to demarcate genome-wide transcription factor binding sites. Objective: This review aims to provide an overview of these three techniques including their experiment procedures, computational approaches, and popular analytic tools. Conclusion: ChIP-chip, ChIP-seq, and ChIP-exo have been the major techniques to study genome- wide in vivo protein-DNA interaction. Due to the rapid development of next-generation sequencing technology, array-based ChIP-chip is deprecated and ChIP-seq has become the most widely used technique to identify transcription factor binding sites in genome-wide. The newly developed ChIP-exo further improves the spatial resolution to single nucleotide. Numerous tools have been developed to analyze ChIP-chip, ChIP-seq and ChIP-exo data. However, different programs may employ different mechanisms or underlying algorithms thus each will inherently include its own set of statistical assumption and bias. So choosing the most appropriate analytic program for a given experiment needs careful considerations. Moreover, most programs only have command line interface so their installation and usage will require basic computation expertise in Unix/Linux.

Abstract Purpose: Deletions or mutations in PTEN and TP53 tumor suppressor genes have been linked to lineage plasticity in therapy-resistant prostate cancer. Fusion-driven overexpression of the oncogenic transcription factor ERG is observed in approximately 50% of all prostate cancers, many of which also harbor PTEN and TP53 alterations. However, the role of ERG in lineage plasticity of PTEN/TP53–altered tumors is unclear. Understanding the collective effect of multiple mutations within one tumor is essential to combat plasticity-driven therapy resistance. Experimental Design: We generated a Pten-negative/Trp53-mutated/ERG-overexpressing mouse model of prostate cancer and integrated RNA-sequencing with ERG chromatin immunoprecipitation-sequencing (ChIP-seq) to identify pathways regulated by ERG in the context of Pten/Trp53 alteration. We investigated ERG-dependent sensitivity to the antiandrogen enzalutamide and cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitor palbociclib in human prostate cancer cell lines, xenografts, and allografted mouse tumors. Trends were evaluated in TCGA, SU2C, and Beltran 2016 published patient cohorts and a human tissue microarray. Results: Transgenic ERG expression in mice blocked Pten/Trp53 alteration–induced decrease of AR expression and downstream luminal epithelial genes. ERG directly suppressed expression of cell cycle–related genes, which induced RB hypophosphorylation and repressed E2F1-mediated expression of mesenchymal lineage regulators, thereby restricting adenocarcinoma plasticity and maintaining antiandrogen sensitivity. In ERG-negative tumors, CDK4/6 inhibition delayed tumor growth. Conclusions: Our studies identify a previously undefined function of ERG to restrict lineage plasticity and maintain antiandrogen sensitivity in PTEN/TP53–altered prostate cancer. Our findings suggest ERG fusion as a biomarker to guide treatment of PTEN/TP53-altered, RB1-intact prostate cancer. Clin Cancer Res; 24(18); 4551–65. ©2018 AACR.