Identification of somatic mutations in human prostate cancer by RNA-Seq

Photothermal therapy of papillary thyroid cancer tumor xenografts with targeted thyroid stimulating hormone receptor antibody functionalized multiwalled carbon nanotubes

Multiwalled carbon nanotubes (MWCNTs) are being widely investigated in multiple biomedical applications including, and not limited to, drug delivery, gene therapy, imaging, biosensing, and tissue engineering. Their large surface area and aspect ratio in addition to their unique structural, optical properties, and thermal conductivity also make them potent candidates for novel hyperthermia therapy. Here we introduce thyroid hormone stimulating receptor (TSHR) antibody–conjugate–MWCNT formulation as an enhanced tumor targeting and light-absorbing device for the photoablation of xenografted BCPAP papillary thyroid cancer tumors. To ensure successful photothermal tumor ablation, we determined three key criteria that needed to be addressed: (1) predictive pre-operational modeling; (2) real-time monitoring of the tumor ablation process; and (3) post-operational follow-up to assess the efficacy and ensure complete response with minimal side effects. A COMSOL-based model of spatial temperature distributions of MWCNTs upon selected laser irradiation of the tumor was prepared to accurately predict the internal tumor temperature. This modeling ensured that 4.5W of total laser power delivered over 2 min, would cause an increase of tumor temperature above 45 ℃, and be needed to completely ablate the tumor while minimizing the damage to neighboring tissues. Experimentally, our temperature monitoring results were in line with our predictive modeling, with effective tumor photoablation leading to a significantly reduced post 5-week tumor recurrence using the TSHR-targeted MWCNTs. Ultimately, the results from this study support a utility for photosensitive biologically modified MWCNTs as a cancer therapeutic modality. Further studies will assist with the transition of photothermal therapy from preclinical studies to clinical evaluations.

Fibrosis in Pathology of Heart and Kidney: From Deep RNA-Sequencing to Novel Molecular Targets

Diseases of the heart and the kidney, including heart failure and chronic kidney disease, can dramatically impair life expectancy and the quality of life of patients. The heart and kidney form a functional axis; therefore, functional impairment of 1 organ will inevitably affect the function of the other. Fibrosis represents the common final pathway of diseases of both organs, regardless of the disease entity. Thus, inhibition of fibrosis represents a promising therapeutic approach to treat diseases of both organs and to resolve functional impairment. However, despite the growing knowledge in this field, the exact pathomechanisms that drive fibrosis remain elusive. RNA-sequencing approaches, particularly single-cell RNA-sequencing, have revolutionized the investigation of pathomechanisms at a molecular level and facilitated the discovery of disease-associated cell types and mechanisms. In this review, we give a brief overview over the evolution of RNA-sequencing techniques, summarize most recent insights into the pathogenesis of heart and kidney fibrosis, and discuss how transcriptomic data can be used, to identify new drug targets and to develop novel therapeutic strategies.

Genome-wide Identification and Analysis of Splicing QTLs in Multiple Sclerosis by RNA-Seq Data

Multiple sclerosis (MS) is an autoimmune disease characterized by inflammatory demyelinating lesions in the central nervous system. Recently, the dysregulation of alternative splicing (AS) in the brain has been found to significantly influence the progression of MS. Moreover, previous studies demonstrate that many MS-related variants in the genome act as the important regulation factors of AS events and contribute to the pathogenesis of MS. However, by far, no genome-wide research about the effect of genomic variants on AS events in MS has been reported. Here, we first implemented a strategy to obtain genomic variant genotype and AS isoform average percentage spliced-in values from RNA-seq data of 142 individuals (51 MS patients and 91 controls). Then, combing the two sets of data, we performed a cis-splicing quantitative trait loci (sQTLs) analysis to identify the cis-acting loci and the affected differential AS events in MS and further explored the characteristics of these cis-sQTLs. Finally, the weighted gene coexpression network and gene set enrichment analyses were used to investigate gene interaction pattern and functions of the affected AS events in MS. In total, we identified 5835 variants affecting 672 differential AS events. The cis-sQTLs tend to be distributed in proximity of the gene transcription initiation site, and the intronic variants of them are more capable of regulating AS events. The retained intron AS events are more susceptible to influence of genome variants, and their functions are involved in protein kinase and phosphorylation modification. In summary, these findings provide an insight into the mechanism of MS.

Search for potential reading frameshifts in cds from Arabidopsis thaliana and other genomes

A new mathematical method for potential reading frameshift detection in protein-coding sequences (cds) was developed. The algorithm is adjusted to the triplet periodicity of each analysed sequence using dynamic programming and a genetic algorithm. This does not require any preliminary training. Using the developed method, cds from the Arabidopsis thaliana genome were analysed. In total, the algorithm found 9,930 sequences containing one or more potential reading frameshift(s). This is ∼21% of all analysed sequences of the genome. The Type I and Type II error rates were estimated as 11% and 30%, respectively. Similar results were obtained for the genomes of Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Rattus norvegicus and Xenopus tropicalis. Also, the developed algorithm was tested on 17 bacterial genomes. We compared our results with the previously obtained data on the search for potential reading frameshifts in these genomes. This study discussed the possibility that the reading frameshift seems like a relatively frequently encountered mutation; and this mutation could participate in the creation of new genes and proteins.

Genome-wide identification and analysis of the eQTL lncRNAs in multiple sclerosis based on RNA-seq data

The pathogenesis of multiple sclerosis (MS) is significantly regulated by long noncoding RNAs (lncRNAs), the expression of which is substantially influenced by a number of MS-associated risk single nucleotide polymorphisms (SNPs). It is thus hypothesized that the dysregulation of lncRNA induced by genomic variants may be one of the key molecular mechanisms for the pathology of MS. However, due to the lack of sufficient data on lncRNA expression and SNP genotypes of the same MS patients, such molecular mechanisms underlying the pathology of MS remain elusive. In this study, a bioinformatics strategy was applied to obtain lncRNA expression and SNP genotype data simultaneously from 142 samples (51 MS patients and 91 controls) based on RNA-seq data, and an expression quantitative trait loci (eQTL) analysis was conducted. In total, 2383 differentially expressed lncRNAs were identified as specifically expressing in brain-related tissues, and 517 of them were affected by SNPs. Then, the functional characterization, secondary structure changes and tissue and disease specificity of the cis-eQTL SNPs and lncRNA were assessed. The cis-eQTL SNPs were substantially and specifically enriched in neurological disease and intergenic region, and the secondary structure was altered in 17.6% of all lncRNAs in MS. Finally, the weighted gene coexpression network and gene set enrichment analyses were used to investigate how the influence of SNPs on lncRNAs contributed to the pathogenesis of MS. As a result, the regulation of lncRNAs by SNPs was found to mainly influence the antigen processing/presentation and mitogen-activated protein kinases (MAPK) signaling pathway in MS. These results revealed the effectiveness of the strategy proposed in this study and give insight into the mechanism (SNP-mediated modulation of lncRNAs) underlying the pathology of MS.

Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer

Prostate cancer is one of the most common types of cancer among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts of data that may reveal novel and informative biomarkers. We introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have tremendous value in guiding treatment decisions. Analysis of normal versus malignant prostate cancer data sets indicates differential expression of the genes HEATR5B, DDC, and GABPB1-AS1 as potential prostate cancer biomarkers. Our study also supports PTGFR, NREP, SCARNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease.

Single-nucleotide variants in human RNA: RNA editing and beyond

Through analysis of paired high-throughput DNA-Seq and RNA-Seq data, researchers quickly recognized that RNA-Seq can be used for more than just gene expression quantification. The alternative applications of RNA-Seq data are abundant, and we are particularly interested in its usefulness for detecting single-nucleotide variants, which arise from RNA editing, genomic variants and other RNA modifications. A stunning discovery made from RNA-Seq analyses is the unexpectedly high prevalence of RNA-editing events, many of which cannot be explained by known RNA-editing mechanisms. Over the past 6-7 years, substantial efforts have been made to maximize the potential of RNA-Seq data. In this review we describe the controversial history of mining RNA-editing events from RNA-Seq data and the corresponding development of methodologies to identify, predict, assess the quality of and catalog RNA-editing events as well as genomic variants.

Identification of potential key genes and high-frequency mutant genes in prostate cancer by using RNA-Seq data

The aim of the present study was to identify potential key genes and single nucleotide variations (SNVs) in prostate cancer. RNA sequencing (RNA-seq) data, GSE22260, were downloaded from the Gene Expression Omnibus database, including 4 prostate cancer samples and 4 normal tissues samples. RNA-Seq reads were processed using Tophat and differentially-expressed genes (DEGs) were identified using the Cufflinks package. Gene Ontology enrichment analysis of DEGs was performed. Subsequently, Seqpos was used to identify the potential upstream regulatory elements of DEGs. SNV was analyzed using Genome Analysis Toolkit. In addition, the frequency and risk-level of mutant genes were calculated using VarioWatch. A total of 150 upregulated and 211 downregulated DEGs were selected and 25 upregulated and 17 downregulated potential upstream regulatory elements were identified, respectively. The SNV annotations of somatic mutations revealed that 65% were base transition and 35% were base transversion. At frequencies ≥2, a total of 17 mutation sites were identified. The mutation site with the highest frequency was located in the folate hydrolase 1B (FOLH1B) gene. Furthermore, 20 high-risk mutant genes with high frequency were identified using VarioWatch, including ribosomal protein S4 Y-linked 2 (RPS4Y2), polycystin 1 transient receptor potential channel interacting (PKD1) and FOLH1B. In addition, kallikrein 1 (KLK1) and PKD1 are known tumor suppressor genes. The potential regulatory elements and high-frequency mutant genes (RPS4Y2, KLK1, PKD1 and FOLH1B) may have key functions in prostate cancer. The results of the present study may provide novel information for the understanding of prostate cancer development.

Prostate-specific membrane antigen-directed nanoparticle targeting for extreme nearfield ablation of prostate cancer cells

Almost all biological therapeutic interventions cannot overcome neoplastic heterogeneity. Physical ablation therapy is immune to tumor heterogeneity, but nearby tissue damage is the limiting factor in delivering lethal doses. Multi-walled carbon nanotubes offer a number of unique properties: chemical stability, photonic properties including efficient light absorption, thermal conductivity, and extensive surface area availability for covalent chemical ligation. When combined together with a targeting moiety such as an antibody or small molecule, one can deliver highly localized temperature increases and cause extensive cellular damage. We have functionalized multi-walled carbon nanotubes by conjugating an antibody against prostate-specific membrane antigen. In our in vitro studies using prostate-specific membrane antigen-positive LNCaP prostate cancer cells, we have effectively demonstrated cell ablation of >80% with a single 30-s exposure to a 2.7-W, 532-nm laser for the first time without bulk heating. We also confirmed the specificity and selectivity of prostate-specific membrane antigen targeting by assessing prostate-specific membrane antigen-null PC3 cell lines under the same conditions (<10% cell ablation). This suggests that we can achieve an extreme nearfield cell ablation effect, thus restricting potential tissue damage when transferred to in vivo clinical applications. Developing this new platform will introduce novel approaches toward current therapeutic modalities and will usher in a new age of effective cancer treatment squarely addressing tumoral heterogeneity.

Transcriptome analysis revealed chimeric RNAs, single nucleotide polymorphisms and allele-specific expression in porcine prenatal skeletal muscle

Prenatal skeletal muscle development genetically determines postnatal muscle characteristics such as growth and meat quality in pigs. However, the molecular mechanisms underlying prenatal skeletal muscle development remain unclear. Here, we performed the first genome-wide analysis of chimeric RNAs, single nuclear polymorphisms (SNPs) and allele-specific expression (ASE) in prenatal skeletal muscle in pigs. We identified 14,810 protein coding genes and 163 high-confidence chimeric RNAs expressed in prenatal skeletal muscle. More than 94.5% of the chimeric RNAs obeyed the canonical GT/AG splice rule and were trans-splicing events. Ten and two RNAs were aligned to human and mouse chimeric transcripts, respectively. We detected 106,457 high-quality SNPs (6,955 novel), which were mostly (89.09%) located within QTLs for production traits. The high proportion of non-exonic SNPs revealed the incomplete annotation status of the current swine reference genome. ASE analysis revealed that 11,300 heterozygous SNPs showed allelic imbalance, whereas 131 ASE variants were located in the chimeric RNAs. Moreover, 4 ASE variants were associated with various economically relevant traits of pigs. Taken together, our data provide a source for studies of chimeric RNAs and biomarkers for pig breeding, while illuminating the complex transcriptional events underlying prenatal skeletal muscle development in mammals.

Practicability of detecting somatic point mutation from RNA high throughput sequencing data

Traditionally, somatic mutations are detected by examining DNA sequence. The maturity of sequencing technology has allowed researchers to screen for somatic mutations in the whole genome. Increasingly, researchers have become interested in identifying somatic mutations through RNAseq data. With this motivation, we evaluated the practicability of detecting somatic mutations from RNAseq data. Current somatic mutation calling tools were designed for DNA sequencing data. To increase performance on RNAseq data, we developed a somatic mutation caller GLMVC based on bias reduced generalized linear model for both DNA and RNA sequencing data. Through comparison with MuTect and Varscan we showed that GLMVC performed better for somatic mutation detection using exome sequencing or RNAseq data. GLMVC is freely available for download at the following website: https://github.com/shengqh/GLMVC/wiki.

A novel model used to detect differential splice junctions as biomarkers in prostate cancer from RNA-Seq data

BACKGROUND

In cancer alternative RNA splicing represents one mechanism for flexible gene regulation, whereby protein isoforms can be created to promote cell growth, division and survival. Detecting novel splice junctions in the cancer transcriptome may reveal pathways driving tumorigenic events. In this regard, RNA-Seq, a high-throughput sequencing technology, has expanded the study of cancer transcriptomics in the areas of gene expression, chimeric events and alternative splicing in search of novel biomarkers for the disease.

RESULTS

In this study, we propose a new two-dimensional peak finding method for detecting differential splice junctions in prostate cancer using RNA-Seq data. We have designed an integrative process that involves a new two-dimensional peak finding algorithm to combine junctions and then remove irrelevant introns across different samples within a population. We have also designed a scoring mechanism to select the most common junctions.

CONCLUSIONS

Our computational analysis on three independent datasets collected from patients diagnosed with prostate cancer reveals a small subset of junctions that may potentially serve as biomarkers for prostate cancer.

AVAILABILITY

The pipeline, along with their corresponding algorithms, are available upon request.

Using high-throughput sequencing transcriptome data for INDEL detection: challenges for cancer drug discovery

INTRODUCTION

A cancer cell is a mosaic of genomic and epigenomic alterations. Distinct cancer molecular signatures can be observed depending on tumor type or patient genetic background. One type of genomic alteration is the insertion and/or deletion (INDEL) of nucleotides in the DNA sequence, which may vary in length, and may change the encoded protein or modify protein domains. INDELs are associated to a large number of diseases and their detection is done based on low-throughput techniques. However, high-throughput sequencing has also started to be used for detection of novel disease-causing INDELs. This search may identify novel drug targets.

AREAS COVERED

This review presents examples of using high-throughput sequencing (DNA-Seq and RNA-Seq) to investigate the incidence of INDELs in coding regions of human genes. Some of these examples successfully utilized RNA-Seq to identify INDELs associated to diseases. In addition, other studies have described small INDELs related to chemo-resistance or poor outcome of patients, while structural variants were associated with a better clinical outcome.

EXPERT OPINION

On average, there is twice as much RNA-Seq data available at the most used repositories for such data compared to DNA-Seq. Therefore, using RNA-Seq data is a promising strategy for studying cancer samples with unknown mechanisms of drug resistance, aiming at the discovery of proteins with potential as novel drug targets.

Gene profiling of bone around orthodontic mini-implants by RNA-sequencing analysis

This study aimed to evaluate the genes that were expressed in the healing bones around SLA-treated titanium orthodontic mini-implants in a beagle at early (1-week) and late (4-week) stages with RNA-sequencing (RNA-Seq). Samples from sites of surgical defects were used as controls. Total RNA was extracted from the tissue around the implants, and an RNA-Seq analysis was performed with Illumina TruSeq. In the 1-week group, genes in the gene ontology (GO) categories of cell growth and the extracellular matrix (ECM) were upregulated, while genes in the categories of the oxidation-reduction process, intermediate filaments, and structural molecule activity were downregulated. In the 4-week group, the genes upregulated included ECM binding, stem cell fate specification, and intramembranous ossification, while genes in the oxidation-reduction process category were downregulated. GO analysis revealed an upregulation of genes that were related to significant mechanisms, including those with roles in cell proliferation, the ECM, growth factors, and osteogenic-related pathways, which are associated with bone formation. From these results, implant-induced bone formation progressed considerably during the times examined in this study. The upregulation or downregulation of selected genes was confirmed with real-time reverse transcription polymerase chain reaction. The RNA-Seq strategy was useful for defining the biological responses to orthodontic mini-implants and identifying the specific genetic networks for targeted evaluations of successful peri-implant bone remodeling.

Proteomic-coupled-network analysis of T877A-androgen receptor interactomes can predict clinical prostate cancer outcomes between White (non-Hispanic) and African-American groups

The androgen receptor (AR) remains an important contributor to the neoplastic evolution of prostate cancer (CaP). CaP progression is linked to several somatic AR mutational changes that endow upon the AR dramatic gain-of-function properties. One of the most common somatic mutations identified is Thr877-to-Ala (T877A), located in the ligand-binding domain, that results in a receptor capable of promiscuous binding and activation by a variety of steroid hormones and ligands including estrogens, progestins, glucocorticoids, and several anti-androgens. In an attempt to further define somatic mutated AR gain-of-function properties, as a consequence of its promiscuous ligand binding, we undertook a proteomic/network analysis approach to characterize the protein interactome of the mutant T877A-AR in LNCaP cells under eight different ligand-specific treatments (dihydrotestosterone, mibolerone, R1881, testosterone, estradiol, progesterone, dexamethasone, and cyproterone acetate). In extending the analysis of our multi-ligand complexes of the mutant T877A-AR we observed significant enrichment of specific complexes between normal and primary prostatic tumors, which were furthermore correlated with known clinical outcomes. Further analysis of certain mutant T877A-AR complexes showed specific population preferences distinguishing primary prostatic disease between white (non-Hispanic) vs. African-American males. Moreover, these cancer-related AR-protein complexes demonstrated predictive survival outcomes specific to CaP, and not for breast, lung, lymphoma or medulloblastoma cancers. Our study, by coupling data generated by our proteomics to network analysis of clinical samples, has helped to define real and novel biological pathways in complicated gain-of-function AR complex systems.

Alternative applications for distinct RNA sequencing strategies

Recent advances in RNA library preparation methods, platform accessibility and cost efficiency have allowed high-throughput RNA sequencing (RNAseq) to replace conventional hybridization microarray platforms as the method of choice for mRNA profiling and transcriptome analyses. RNAseq is a powerful technique to profile both long and short RNA expression, and the depth of information gained from distinct RNAseq methods is striking and facilitates discovery. In addition to expression analysis, distinct RNAseq approaches also allow investigators the ability to assess transcriptional elongation, DNA variance and exogenous RNA content. Here we review the current state of the art in transcriptome sequencing and address epigenetic regulation, quantification of transcription activation, RNAseq output and a diverse set of applications for RNAseq data. We detail how RNAseq can be used to identify allele-specific expression, single-nucleotide polymorphisms and somatic mutations and discuss the benefits and limitations of using RNAseq to monitor DNA characteristics. Moreover, we highlight the power of combining RNA- and DNAseq methods for genomic analysis. In summary, RNAseq provides the opportunity to gain greater insight into transcriptional regulation and output than simply miRNA and mRNA profiling.

Integrated RNA and DNA sequencing improves mutation detection in low purity tumors

Identifying somatic mutations is critical for cancer genome characterization and for prioritizing patient treatment. DNA whole exome sequencing (DNA-WES) is currently the most popular technology; however, this yields low sensitivity in low purity tumors. RNA sequencing (RNA-seq) covers the expressed exome with depth proportional to expression. We hypothesized that integrating DNA-WES and RNA-seq would enable superior mutation detection versus DNA-WES alone. We developed a first-of-its-kind method, called UNCeqR, that detects somatic mutations by integrating patient-matched RNA-seq and DNA-WES. In simulation, the integrated DNA and RNA model outperformed the DNA-WES only model. Validation by patient-matched whole genome sequencing demonstrated superior performance of the integrated model over DNA-WES only models, including a published method and published mutation profiles. Genome-wide mutational analysis of breast and lung cancer cohorts (n = 871) revealed remarkable tumor genomics properties. Low purity tumors experienced the largest gains in mutation detection by integrating RNA-seq and DNA-WES. RNA provided greater mutation signal than DNA in expressed mutations. Compared to earlier studies on this cohort, UNCeqR increased mutation rates of driver and therapeutically targeted genes (e.g. PIK3CA, ERBB2 and FGFR2). In summary, integrating RNA-seq with DNA-WES increases mutation detection performance, especially for low purity tumors.

Wavelet analysis in current cancer genome research: a survey

With the rapid development of next generation sequencing technology, the amount of biological sequence data of the cancer genome increases exponentially, which calls for efficient and effective algorithms that may identify patterns hidden underneath the raw data that may distinguish cancer Achilles' heels. From a signal processing point of view, biological units of information, including DNA and protein sequences, have been viewed as one-dimensional signals. Therefore, researchers have been applying signal processing techniques to mine the potentially significant patterns within these sequences. More specifically, in recent years, wavelet transforms have become an important mathematical analysis tool, with a wide and ever increasing range of applications. The versatility of wavelet analytic techniques has forged new interdisciplinary bounds by offering common solutions to apparently diverse problems and providing a new unifying perspective on problems of cancer genome research. In this paper, we provide a survey of how wavelet analysis has been applied to cancer bioinformatics questions. Specifically, we discuss several approaches of representing the biological sequence data numerically and methods of using wavelet analysis on the numerical sequences.