Silvia Liu

Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data

Background: Fusion transcripts are formed by either fusion genes (DNA level) or trans-splicing events (RNA level). They have been recognized as a promising tool for diagnosing, subtyping and treating cancers. RNA-seq has become a precise and efficient standard for genome-wide screening of such aberration events. Many fusion transcript detection algorithms have been developed for paired-end RNA-seq data but their performance has not been comprehensively evaluated to guide practitioners. In this paper, we evaluated 15 popular algorithms by their precision and recall trade-off, accuracy of supporting reads and computational cost. We further combine top-performing methods for improved ensemble detection. Results: Fifteen fusion transcript detection tools were compared using three synthetic data sets under different coverage, read length, insert size and background noise, and three real data sets with selected experimental validations. No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA. We further demonstrated the potential of a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation. Conclusion: Our result provides insightful recommendations when applying individual tool or combining top performers to identify fusion transcript candidates.

Combined systemic elimination of MET and epidermal growth factor receptor signaling completely abolishes liver regeneration and leads to liver decompensation

Receptor tyrosine kinases MET and epidermal growth factor receptor (EGFR) are critically involved in initiation of liver regeneration. Other cytokines and signaling molecules also participate in the early part of the process. Regeneration employs effective redundancy schemes to compensate for the missing signals. Elimination of any single extracellular signaling pathway only delays but does not abolish the process. Our present study, however, shows that combined systemic elimination of MET and EGFR signaling (MET knockout + EGFR‐inhibited mice) abolishes liver regeneration, prevents restoration of liver mass, and leads to liver decompensation. MET knockout or simply EGFR‐inhibited mice had distinct and signaling‐specific alterations in Ser/Thr phosphorylation of mammalian target of rapamycin, AKT, extracellular signal–regulated kinases 1/2, phosphatase and tensin homolog, adenosine monophosphate–activated protein kinase α, etc. In the combined MET and EGFR signaling elimination of MET knockout + EGFR‐inhibited mice, however, alterations dependent on either MET or EGFR combined to create shutdown of many programs vital to hepatocytes. These included decrease in expression of enzymes related to fatty acid metabolism, urea cycle, cell replication, and mitochondrial functions and increase in expression of glycolysis enzymes. There was, however, increased expression of genes of plasma proteins. Hepatocyte average volume decreased to 35% of control, with a proportional decrease in the dimensions of the hepatic lobules. Mice died at 15‐18 days after hepatectomy with ascites, increased plasma ammonia, and very small livers. Conclusion: MET and EGFR separately control many nonoverlapping signaling endpoints, allowing for compensation when only one of the signals is blocked, though the combined elimination of the signals is not tolerated; the results provide critical new information on interactive MET and EGFR signaling and the contribution of their combined absence to regeneration arrest and liver decompensation. (Hepatology 2016;64:1711‐1724)

Targeting genomic rearrangements in tumor cells through Cas9-mediated insertion of a suicide gene

Specifically targeting genomic rearrangements and mutations in tumor cells remains an elusive goal in cancer therapy. Here, we used Cas9-based genome editing to introduce the gene encoding the prodrug-converting enzyme herpes simplex virus type 1 thymidine kinase (HSV1-tk) into the genomes of cancer cells carrying unique sequences resulting from genome rearrangements. Specifically, we targeted the breakpoints of TMEM135–CCDC67 and MAN2A1–FER fusions in human prostate cancer or hepatocellular carcinoma cells in vitro and in mouse xenografts. We designed one adenovirus to deliver the nickase Cas9D10A and guide RNAs targeting the breakpoint sequences, and another to deliver an EGFP-HSV1-tk construct flanked by sequences homologous to those surrounding the breakpoint. Infection with both viruses resulted in breakpoint-dependent expression of EGFP-tk and ganciclovir-mediated apoptosis. When mouse xenografts were treated with adenoviruses and ganciclovir, all animals showed decreased tumor burden and no mortality during the study. Thus, Cas9-mediated suicide-gene insertion may be a viable genotype-specific cancer therapy.

Novel Fusion Transcripts Associate with Progressive Prostate Cancer

The mechanisms underlying the potential for aggressive behavior of prostate cancer (PCa) remain elusive. In this study, whole genome and/or transcriptome sequencing was performed on 19 specimens of PCa, matched adjacent benign prostate tissues, matched blood specimens, and organ donor prostates. A set of novel fusion transcripts was discovered in PCa. Eight of these fusion transcripts were validated through multiple approaches. The occurrence of these fusion transcripts was then analyzed in 289 prostate samples from three institutes, with clinical follow-up ranging from 1 to 15 years. The analyses indicated that most patients [69 (91%) of 76] positive for any of these fusion transcripts (TRMT11-GRIK2, SLC45A2-AMACR, MTOR-TP53BP1, LRRC59-FLJ60017, TMEM135-CCDC67, KDM4-AC011523.2, MAN2A1-FER, and CCNH-C5orf30) experienced PCa recurrence, metastases, and/or PCa-specific death after radical prostatectomy. These outcomes occurred in only 37% (58/157) of patients without carrying those fusion transcripts. Three fusion transcripts occurred exclusively in PCa samples from patients who experienced recurrence or PCaerelated death. The formation of these fusion transcripts may be the result of genome recombination. A combination of these fusion transcripts in PCa with Gleasons grading or with nomogram significantly improves the prediction rate of PCa recurrence. Our analyses suggest that formation of these fusion transcripts may underlie the aggressive behavior of PCa.

Discovery and Classification of Fusion Transcripts in Prostate Cancer and Normal Prostate Tissue.

Fusion transcript formation is one of the fundamental mechanisms that drives the development of prostate cancer. Because of the advance of high-throughput parallel sequencing, many fusion transcripts have been discovered. However, the discovery rate of fusion transcripts specific for prostate cancer is lagging behind the discoveries made on chromosome abnormalities of prostate cancer. Recent analyses suggest that many fusion transcripts are present in both benign and cancerous tissues. Some of these fusion transcripts likely represent important components of normal gene expression in cells. It is necessary to identify the criteria and features of fusion transcripts that are specific for cancer. In this review, we discuss optimization of RNA sequencing depth for fusion transcript discovery and the characteristics of fusion transcripts in normal prostate tissues and prostate cancer. We also propose a new classification of cancer-specific fusion transcripts on the basis of their tail gene fusion protein product and the roles that these fusions may play in cancer development.

Meta-Analytic Framework for Sparse K-Means to Identify Disease Subtypes in Multiple Transcriptomic Studies

Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step toward this goal. In this article, we extend a sparse K-means method toward a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer datasets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups. Supplementary materials for this article are available online.

Meta-analytic framework for liquid association

Motivation: Although coexpression analysis via pair‐wise expression correlation is popularly used to elucidate gene‐gene interactions at the whole‐genome scale, many complicated multi‐gene regulations require more advanced detection methods. Liquid association (LA) is a powerful tool to detect the dynamic correlation of two gene variables depending on the expression level of a third variable (LA scouting gene). LA detection from single transcriptomic study, however, is often unstable and not generalizable due to cohort bias, biological variation and limited sample size. With the rapid development of microarray and NGS technology, LA analysis combining multiple gene expression studies can provide more accurate and stable results. Results: In this article, we proposed two meta‐analytic approaches for LA analysis (MetaLA and MetaMLA) to combine multiple transcriptomic studies. To compensate demanding computing, we also proposed a two‐step fast screening algorithm for more efficient genome‐wide screening: bootstrap filtering and sign filtering. We applied the methods to five Saccharomyces cerevisiae datasets related to environmental changes. The fast screening algorithm reduced 98% of running time. When compared with single study analysis, MetaLA and MetaMLA provided stronger detection signal and more consistent and stable results. The top triplets are highly enriched in fundamental biological processes related to environmental changes. Our method can help biologists understand underlying regulatory mechanisms under different environmental exposure or disease states. Availability and Implementation: A MetaLA R package, data and code for this article are available at http://tsenglab.biostat.pitt.edu/software.htm Contact: ctseng@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Oncogenic activity of amplified miniature chromosome maintenance 8 in human malignancies

Miniature chromosome maintenance (MCM) proteins play critical roles in DNA replication licensing, initiation and elongation. MCM8, one of the MCM proteins playing a critical role in DNA repairing and recombination, was found to have overexpression and increased DNA copy number in a variety of human malignancies. The gain of MCM8 is associated with aggressive clinical features of several human cancers. Increased expression of MCM8 in prostate cancer is associated with cancer recurrence. Forced expression of MCM8 in RWPE1 cells, the immortalized but non-transformed prostate epithelial cell line, exhibited fast cell growth and transformation, while knock down of MCM8 in PC3, DU145 and LNCaP cells induced cell growth arrest, and decreased tumour volumes and mortality of severe combined immunodeficiency mice xenografted with PC3 and DU145 cells. MCM8 bound cyclin D1 and activated Rb protein phosphorylation by cyclin-dependent kinase 4 in vitro and in vivo. The cyclin D1/MCM8 interaction is required for Rb phosphorylation and S-phase entry in cancer cells. As a result, our study showed that copy number increase and overexpression of MCM8 may play critical roles in human cancer development.

Genomic Copy Number Variations in the Genomes of Leukocytes Predict Prostate Cancer Clinical Outcomes

Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV) were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason’s grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.

FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods

Comprehensive and accurate identification of structural variations (SVs) from next generation sequencing data remains a major challenge. We develop FusorSV, which uses a data mining approach to assess performance and merge callsets from an ensemble of SV-calling algorithms. It includes a fusion model built using analysis of 27 deep-coverage human genomes from the 1000 Genomes Project. We identify 843 novel SV calls that were not reported by the 1000 Genomes Project for these 27 samples. Experimental validation of a subset of these calls yields a validation rate of 86.7%. FusorSV is available at https://github.com/TheJacksonLaboratory/SVE.