Hung I Harry Chen

Evidence for an Unanticipated Relationship between Undifferentiated Pleomorphic Sarcoma and Embryonal Rhabdomyosarcoma

Embryonal rhabdomyosarcoma (eRMS) shows the most myodifferentiation among sarcomas, yet the precise cell of origin remains undefined. Using Ptch1, p53 and/or Rb1 conditional mouse models and controlling prenatal or postnatal myogenic cell of origin, we demonstrate that eRMS and undifferentiated pleomorphic sarcoma (UPS) lie in a continuum, with satellite cells predisposed to giving rise to UPS. Conversely, p53 loss in maturing myoblasts gives rise to eRMS, which have the highest myodifferentiation potential. Regardless of origin, Rb1 loss modifies tumor phenotype to mimic UPS. In human sarcomas that lack pathognomic chromosomal translocations, p53 loss of function is prevalent, whereas Shh or Rb1 alterations likely act primarily as modifiers. Thus, sarcoma phenotype is strongly influenced by cell of origin and mutational profile.

Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads

BackgroundRNA sequencing (RNA-seq) is a powerful tool for genome-wide expression profiling of biological samples with the advantage of high-throughput and high resolution. There are many existing algorithms nowadays for quantifying expression levels and detecting differential gene expression, but none of them takes the misaligned reads that are mapped to non-exonic regions into account. We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.ResultsWe implemented our novel XBSeq algorithm and evaluated it by using a set of simulated expression datasets under different conditions, using a combination of negative binomial and Poisson distributions with parameters derived from real RNA-seq data. We compared the performance of our method with other commonly used differential expression analysis algorithms. We also evaluated the changes in true and false positive rates with variations in biological replicates, differential fold changes, and expression levels in non-exonic regions. We also tested the algorithm on a set of real RNA-seq data where the common and different detection results from different algorithms were reported.ConclusionsIn this paper, we proposed a novel XBSeq, a differential expression analysis algorithm for RNA-seq data that takes non-exonic mapped reads into consideration. When background noise is at baseline level, the performance of XBSeq and DESeq are mostly equivalent. However, our method surpasses DESeq and other algorithms with the increase of non-exonic mapped reads. Only in very low read count condition XBSeq had a slightly higher false discovery rate, which may be improved by adjusting the background noise effect in this situation. Taken together, by considering non-exonic mapped reads, XBSeq can provide accurate expression measurement and thus detect differential expressed genes even in noisy conditions.

De-Regulated MicroRNAs in Pediatric Cancer Stem Cells Target Pathways Involved in Cell Proliferation, Cell Cycle and Development

Background microRNAs (miRNAs) have been implicated in the control of many biological processes and their deregulation has been associated with many cancers. In recent years, the cancer stem cell (CSC) concept has been applied to many cancers including pediatric. We hypothesized that a common signature of deregulated miRNAs in the CSCs fraction may explain the disrupted signaling pathways in CSCs. Methodology/Results Using a high throughput qPCR approach we identified 26 CSC associated differentially expressed miRNAs (DEmiRs). Using BCmicrO algorithm 865 potential CSC associated DEmiR targets were obtained. These potential targets were subjected to KEGG, Biocarta and Gene Ontology pathway and biological processes analysis. Four annotated pathways were enriched: cell cycle, cell proliferation, p53 and TGF-beta/BMP. Knocking down hsa-miR-21-5p, hsa-miR-181c-5p and hsa-miR-135b-5p using antisense oligonucleotides and small interfering RNA in cell lines led to the depletion of the CSC fraction and impairment of sphere formation (CSC surrogate assays). Conclusion Our findings indicated that CSC associated DEmiRs and the putative pathways they regulate may have potential therapeutic applications in pediatric cancers.

Lineage of origin in rhabdomyosarcoma informs pharmacological response

Lineage or cell of origin of cancers is often unknown and thus is not a consideration in therapeutic approaches. Alveolar rhabdomyosarcoma (aRMS) is an aggressive childhood cancer for which the cell of origin remains debated. We used conditional genetic mouse models of aRMS to activate the pathognomonic Pax3:Foxo1 fusion oncogene and inactivate p53 in several stages of prenatal and postnatal muscle development. We reveal that lineage of origin significantly influences tumor histomorphology and sensitivity to targeted therapeutics. Furthermore, we uncovered differential transcriptional regulation of the Pax3:Foxo1 locus by tumor lineage of origin, which led us to identify the histone deacetylase inhibitor entinostat as a pharmacological agent for the potential conversion of Pax3:Foxo1-positive aRMS to a state akin to fusion-negative RMS through direct transcriptional suppression of Pax3:Foxo1.

A probe-density-based analysis method for array CGH data: simulation, normalization and centralization

MOTIVATION Genomic instability is one of the fundamental factors in tumorigenesis and tumor progression. Many studies have shown that copy-number abnormalities at the DNA level are important in the pathogenesis of cancer. Array comparative genomic hybridization (aCGH), developed based on expression microarray technology, can reveal the chromosomal aberrations in segmental copies at a high resolution. However, due to the nature of aCGH, many standard expression data processing tools, such as data normalization, often fail to yield satisfactory results. RESULTS We demonstrated a novel aCGH normalization algorithm, which provides an accurate aCGH data normalization by utilizing the dependency of neighboring probe measurements in aCGH experiments. To facilitate the study, we have developed a hidden Markov model (HMM) to simulate a series of aCGH experiments with random DNA copy number alterations that are used to validate the performance of our normalization. In addition, we applied the proposed normalization algorithm to an aCGH study of lung cancer cell lines. By using the proposed algorithm, data quality and the reliability of experimental results are significantly improved, and the distinct patterns of DNA copy number alternations are observed among those lung cancer cell lines. SUPPLEMENTARY INFORMATION Source codes and.gures may be found at http://ntumaps.cgm.ntu.edu.tw/aCGH_supplementary.

ZBTB7A Suppresses Melanoma Metastasis by Transcriptionally Repressing MCAM.

The excessive metastatic propensity of melanoma makes it the most deadly form of skin cancer, yet the underlying mechanism of metastasis remains elusive. Here, mining of cancer genome datasets discovered a frequent loss of chromosome 19p13.3 and associated downregulation of the zinc finger transcription factor ZBTB7A in metastatic melanoma. Functional assessment of ZBTB7A-regulated genes identified MCAM, which encodes an adhesion protein key to melanoma metastasis. Using an integrated approach, it is demonstrated that ZBTB7A directly binds to the promoter and transcriptionally represses the expression of MCAM, establishing ZBTB7A as a bona fide transcriptional repressor of MCAM. Consistently, downregulation of ZBTB7A results in marked upregulation of MCAM and enhanced melanoma cell invasion and metastasis. An inverse correlation of ZBTB7A and MCAM expression in association with melanoma metastasis is further validated with data from analysis of human melanoma specimens. Implications: Together, these results uncover a previously unrecognized role of ZBTB7A in negative regulation of melanoma metastasis and have important clinical implications. Mol Cancer Res; 13(8); 1206–17. ©2015 AACR.

Detection of high variability in gene expression from single-cell RNA-seq profiling

BackgroundThe advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs).ResultsWe have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. We implemented our GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. We evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. We also applied the GEVM on real scRNA-seq data to test the performance under distinct cases.ConclusionsIn this paper, we proposed a gene expression variation model that can be used to determine significant variably expressed genes. Applying the model to the simulated single-cell data, we observed robust parameter estimation under different conditions with minimal root mean square errors. We also examined the model on two distinct scRNA-seq data sets using different single-cell protocols and determined the VEGs. Obtaining VEGs allowed us to observe possible subpopulations, providing further evidences of cell heterogeneity. With the GEVM, we can easily find out significant variably expressed genes in different scRNA-seq data sets.

Constructing a compound mode-of-action network for personalized drug effectiveness prediction

The problem of large scale computational drug screening is considered in this paper. A new concept of Mode-of-Action (MoA) network, or MoNet, is introduced to model the relationship of therapeutic effectiveness between different drugs. A new algorithm for constructing Mode-of-Action groups and subsequently MoNet based on gene expression profile of drug treatment is proposed. The algorithm has been applied to the data from the Connectivity Map (cMap) project and a cMap MoNet was obtained. A new drug effectiveness prediction algorithm based on MoNet is also developed and applied to an independent breast cancer data set. The prediction result shows improvement in precision over cMap.

Identification of functional genomic regions with copy number alteration in liver cancer

Copy number alterations (CNAs) happen frequently in solid tumors. Several oncogenes and tumor suppressor genes have been identified with CNAs. However, the systematic survey of CNA regulated functions is still lack. By employing systems biology approaches, instead of examining individual genes, we directly identified the spatial functional hotspots in human genome. Total 249 genomic regions, or segments, with 410 enriched biological functions were identified. An aCGH data set of hepatocellular carcinoma (HCC) tumors was employed in this study and several putative affected functions of CNAs were identified. Our results indicate that 4 immune related segments were lost in most of patients. In addition, our data implied these immune related segments might be involved in HCC oncogenesis. The result further demonstrated that our method enables researchers to survey biological functions of CNAs and to construct regulation hypothesis at pathway and functional levels.

Mapping miRNA Regulation to Functional Gene Sets

Distinct cellular functions and biological process of cells will likely be reflected in alteration in levels of genes as well as their regulatory components, such as the level of miRNAs. By employing systems biology approaches, we will be able to unambiguously identify the regulatory pathways and biological processes that are unique to specific disease states or responses to treatment. In this study, we propose an additive and weighted model to combine all possible miRNA target predictions to study the regulation change at a specific gene set, pathway or interaction network, rather than individual miRNA, enabling researchers to construct regulation hypothesis at pathway or interaction network level.