Yuan-Ming Yeh

Enhanced Interferon Signaling Pathway in Oral Cancer Revealed by Quantitative Proteome Analysis of Microdissected Specimens Using 16O/18O Labeling and Integrated Two-dimensional LC-ESI-MALDI Tandem MS

Oral squamous cell carcinoma (OSCC) remains one of the most common cancers worldwide, and the mortality rate of this disease has increased in recent years. No molecular markers are available to assist with the early detection and therapeutic evaluation of OSCC; thus, identification of differentially expressed proteins may assist with the detection of potential disease markers and shed light on the molecular mechanisms of OSCC pathogenesis. We performed a multidimensional 16O/18O proteomics analysis using an integrated ESI-ion trap and MALDI-TOF/TOF MS system and a computational data analysis pipeline to identify proteins that are differentially expressed in microdissected OSCC tumor cells relative to adjacent non-tumor epithelia. We identified 1233 unique proteins in microdissected oral squamous epithelia obtained from three pairs of OSCC specimens with a false discovery rate of <3%. Among these, 977 proteins were quantified between tumor and non-tumor cells. Our data revealed 80 dysregulated proteins (53 up-regulated and 27 down-regulated) when a 2.5-fold change was used as the threshold. Immunohistochemical staining and Western blot analyses were performed to confirm the overexpression of 12 up-regulated proteins in OSCC tissues. When the biological roles of 80 differentially expressed proteins were assessed via MetaCore™ analysis, the interferon (IFN) signaling pathway emerged as one of the most significantly altered pathways in OSCC. As many as 20% (10 of 53) of the up-regulated proteins belonged to the IFN-stimulated gene (ISG) family, including ubiquitin cross-reactive protein (UCRP)/ISG15. Using head-and-neck cancer tissue microarrays, we determined that UCRP is overexpressed in the majority of cheek and tongue cancers and in several cases of larynx cancer. In addition, we found that IFN-β stimulates UCRP expression in oral cancer cells and enhances their motility in vitro. Our findings shed new light on OSCC pathogenesis and provide a basis for the future development of novel biomarkers.

APOBEC3A is an oral cancer prognostic biomarker in Taiwanese carriers of an APOBEC deletion polymorphism.

Oral squamous cell carcinoma is a prominent cancer worldwide, particularly in Taiwan. By integrating omics analyses in 50 matched samples, we uncover in Taiwanese patients a predominant mutation signature associated with cytidine deaminase APOBEC, which correlates with the upregulation of APOBEC3A expression in the APOBEC3 gene cluster at 22q13. APOBEC3A expression is significantly higher in tumors carrying APOBEC3B-deletion allele(s). High-level APOBEC3A expression is associated with better overall survival, especially among patients carrying APOBEC3B-deletion alleles, as examined in a second cohort (n = 188; p = 0.004). The frequency of APOBEC3B-deletion alleles is ~50% in 143 genotyped oral squamous cell carcinoma -Taiwan samples (27A3B−/−:89A3B+/−:27A3B+/+), compared to the 5.8% found in 314 OSCC-TCGA samples. We thus report a frequent APOBEC mutational profile, which relates to a APOBEC3B-deletion germline polymorphism in Taiwanese oral squamous cell carcinoma that impacts expression of APOBEC3A, and is shown to be of clinical prognostic relevance. Our finding might be recapitulated by genomic studies in other cancer types.Oral squamous cell carcinoma is a prevalent malignancy in Taiwan. Here, the authors show that OSCC in Taiwanese show a frequent deletion polymorphism in the cytidine deaminases gene cluster APOBEC3 resulting in increased expression of A3A, which is shown to be of clinical prognostic relevance.

Phosphoproteome profiling of the sexually transmitted pathogen Trichomonas vaginalis

BACKGROUND/PURPOSE(S) Trichomoniasis caused by Trichomonas vaginalis is the most common non-viral sexually transmitted infection. Morphological transformation from the trophozoite stage to the amoeboid or pseudocyst stage is crucial for T. vaginalis infection and survival. Protein phosphorylation is a key post-translational modification involved in the regulation of several biological processes in various prokaryotes and eukaryotes. More than 880 protein kinases have been identified in the T. vaginalis genome. However, little is known about the phosphorylation of specific proteins and the distribution of phosphorylated proteins in different stages of the morphological transformation of T. vaginalis. METHODS To obtain a more comprehensive understanding of the T. vaginalis phosphoproteome, we analyzed phosphorylated proteins in the three morphological stages using titanium dioxide combined with LC-MS/MS. RESULTS A total of 93 phosphopeptides originating from 82 unique proteins were identified. Among these proteins, 21 were detected in all stages, 29 were identified in two different stages, and 32 were stage specific. CONCLUSION Identification of stage-specific phosphorylated proteins indicates that phosphorylation of these proteins may play a key role in the morphological transformation of T. vaginalis.

Proteomic analyses of genes regulated by heterogeneous nuclear ribonucleoproteins A/B in Jurkat cells

Several lines of evidence suggest that hnRNPs A/B (hnRNPs A1, A2/B1, and A3) play an important role in proliferation, although the functional overlap among members of hnRNPs A/B remains largely unknown. In this study, we have employed RNAi knockdown and proteomic approaches to investigate the biological functions of hnRNPs A/B. Depletion of hnRNP A2, but not A1 or A3, produced a significant inhibition of cellular proliferation in Jurkat cells. Analysis of the proteomes in the cells depleted for hnRNP A1, A2, or A3 has identified a total of 167 differentially expressed proteins in the depleted cells. Network analysis of the proteins altered in the cells depleted for hnRNP A2 revealed that the biological processes likely affected by these proteins are related to cell cycle, cytoskeleton rearrangement, and transcription regulation. Indeed, we have confirmed that the level of RhoA and CrkL was selectively reduced in the cells depleted of hnRNP A2, but not in the cells depleted for hnRNP A1 or A3. Therefore, we suggest that the reduced proliferation observed in the cells depleted of hnRNP A2 may result from its effects on cell adhesion processes in the Jurkat cells.

CMPD: cancer mutant proteome database

Whole-exome sequencing, which centres on the protein coding regions of disease/cancer associated genes, represents the most cost-effective method to-date for deciphering the association between genetic alterations and diseases. Large-scale whole exome/genome sequencing projects have been launched by various institutions, such as NCI, Broad Institute and TCGA, to provide a comprehensive catalogue of coding variants in diverse tissue samples and cell lines. Further functional and clinical interrogation of these sequence variations must rely on extensive cross-platforms integration of sequencing information and a proteome database that explicitly and comprehensively archives the corresponding mutated peptide sequences. While such data resource is a critical for the mass spectrometry-based proteomic analysis of exomic variants, no database is currently available for the collection of mutant protein sequences that correspond to recent large-scale genomic data. To address this issue and serve as bridge to integrate genomic and proteomics datasets, CMPD (http://cgbc.cgu.edu.tw/cmpd) collected over 2 millions genetic alterations, which not only facilitates the confirmation and examination of potential cancer biomarkers but also provides an invaluable resource for translational medicine research and opportunities to identify mutated proteins encoded by mutated genes.

Vanno: a visualization-aided variant annotation tool.

Next‐generation sequencing (NGS) technologies have revolutionized the field of genetics and are trending toward clinical diagnostics. Exome and targeted sequencing in a disease context represent a major NGS clinical application, considering its utility and cost‐effectiveness. With the ongoing discovery of disease‐associated genes, various gene panels have been launched for both basic research and diagnostic tests. However, the fundamental inconsistencies among the diverse annotation sources, software packages, and data formats have complicated the subsequent analysis. To manage disease‐associated NGS data, we developed Vanno, a Web‐based application for in‐depth analysis and rapid evaluation of disease‐causative genome sequence alterations. Vanno integrates information from biomedical databases, functional predictions from available evaluation models, and mutation landscapes from TCGA cancer types. A highly integrated framework that incorporates filtering, sorting, clustering, and visual analytic modules is provided to facilitate exploration of oncogenomics datasets at different levels, such as gene, variant, protein domain, or three‐dimensional structure. Such design is crucial for the extraction of knowledge from sequence alterations and translating biological insights into clinical applications. Taken together, Vanno supports almost all disease‐associated gene tests and exome sequencing panels designed for NGS, providing a complete solution for targeted and exome sequencing analysis. Vanno is freely available at http://cgts.cgu.edu.tw/vanno.

CPAP: Cancer Panel Analysis Pipeline

Targeted sequencing using next‐generation sequencing technologies is currently being rapidly adopted for clinical sequencing and cancer marker tests. However, no existing bioinformatics tool is available for the analysis and visualization of multiple targeted sequencing datasets. In the present study, we use cancer panel targeted sequencing datasets generated by the Life Technologies Ion Personal Genome Machine Sequencer as an example to illustrate how to develop an automated pipeline for the comparative analyses of multiple datasets. Cancer Panel Analysis Pipeline (CPAP) uses standard output files from variant calling software to generate a distribution map of SNPs among all of the samples in a circular diagram generated by Circos. The diagram is hyperlinked to a dynamic HTML table that allows the users to identify target SNPs by using different filters. CPAP also integrates additional information about the identified SNPs by linking to an integrated SQL database compiled from SNP‐related databases, including dbSNP, 1000 Genomes Project, COSMIC, and dbNSFP. CPAP only takes 17 min to complete a comparative analysis of 500 datasets. CPAP not only provides an automated platform for the analysis of multiple cancer panel datasets but can also serve as a model for any customized targeted sequencing project.

mSignatureDB: a database for deciphering mutational signatures in human cancers

Abstract Cancer is a genetic disease caused by somatic mutations; however, the understanding of the causative biological processes generating these mutations is limited. A cancer genome bears the cumulative effects of mutational processes during tumor development. Deciphering mutational signatures in cancer is a new topic in cancer research. The Wellcome Trust Sanger Institute (WTSI) has categorized 30 reference signatures in the COSMIC database based on the analyses of ∼10 000 sequencing datasets from TCGA and ICGC. Large cohorts and bioinformatics skills are required to perform the same analysis as WTSI. The quantification of known signatures in custom cohorts is not possible under the current framework of the COSMIC database, which motivates us to construct a database for mutational signatures in cancers and make such analyses more accessible to general researchers. mSignatureDB (http://tardis.cgu.edu.tw/msignaturedb) integrates R packages and in-house scripts to determine the contributions of the published signatures in 15 780 individual tumors from 73 TCGA/ICGC cancer projects, making comparison of signature patterns within and between projects become possible. mSignatureDB also allows users to perform signature analysis on their own datasets, quantifying contributions of signatures at sample resolution, which is a unique feature of mSignatureDB not available in other related databases.

PARRoT - a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms

BackgroundNext-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared.ResultsHere, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours.ConclusionsIn this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw.

Integrated genomic analyses in PDX model reveal a cyclin-dependent kinase inhibitor Palbociclib as a novel candidate drug for nasopharyngeal carcinoma

BackgroundPatient-derived xenograft (PDX) tumor model has become a new approach in identifying druggable tumor mutations, screening and evaluating personalized cancer drugs based on the mutated targets.MethodsWe established five nasopharyngeal carcinoma (NPC) PDXs in mouse model. Subsequently, whole-exome sequencing (WES) and genomic mutation analyses were performed to search for genetic alterations for new drug targets. Potential drugs were applied in two NPC PDX mice model to assess their anti-cancer activities. RNA sequencing and transcriptomic analysis were performed in one NPC PDX mice to correlate with the efficacy of the anti-cancer drugs.ResultsA relative high incident rate of copy number variations (CNVs) of cell cycle-associated genes. Among the five NPC-PDXs, three had cyclin D1 (CCND1) amplification while four had cyclin-dependent kinase inhibitor CDKN2A deletion. Furthermore, CCND1 overexpression was observed in > 90% FFPE clinical metastatic NPC tumors (87/91) and was associated with poor outcomes. CNV analysis disclosed that plasma CCND1/CDKN2A ratio is correlated with EBV DNA load in NPC patients’ plasma and could serve as a screening test to select potential CDK4/6 inhibitor treatment candidates. Based on our NPC PDX model and RNA sequencing, Palbociclib, a cyclin-dependent kinase inhibitor, proved to have anti-tumor effects by inducing G1 arrest. One NPC patient with liver metastatic was treated with Palbociclib, had stable disease response and a drop in Epstein Barr virus (EBV) EBV titer.ConclusionsOur integrated information of sequencing-based genomic studies and tumor transcriptomes with drug treatment in NPC-PDX models provided guidelines for personalized precision treatments and revealed a cyclin-dependent kinase inhibitor Palbociclib as a novel candidate drug for NPC.