Leming Shi

Next-generation sequencing in the clinic: Promises and challenges

The advent of next generation sequencing (NGS) technologies has revolutionized the field of genomics, enabling fast and cost-effective generation of genome-scale sequence data with exquisite resolution and accuracy. Over the past years, rapid technological advances led by academic institutions and companies have continued to broaden NGS applications from research to the clinic. A recent crop of discoveries have highlighted the medical impact of NGS technologies on Mendelian and complex diseases, particularly cancer. However, the ever-increasing pace of NGS adoption presents enormous challenges in terms of data processing, storage, management and interpretation as well as sequencing quality control, which hinder the translation from sequence data into clinical practice. In this review, we first summarize the technical characteristics and performance of current NGS platforms. We further highlight advances in the applications of NGS technologies towards the development of clinical diagnostics and therapeutics. Common issues in NGS workflows are also discussed to guide the selection of NGS platforms and pipelines for specific research purposes.

Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard dashboard of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.

An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era

BackgroundGene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment?ResultsWe systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined.ConclusionsSignature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.

Enhancing Reproducibility in Cancer Drug Screening: How Do We Move Forward?

Large-scale pharmacogenomic high-throughput screening (HTS) studies hold great potential for generating robust genomic predictors of drug response. Two recent large-scale HTS studies have reported results of such screens, revealing several known and novel drug sensitivities and biomarkers. Subsequent evaluation, however, found only moderate interlaboratory concordance in the drug response phenotypes, possibly due to differences in the experimental protocols used in the two studies. This highlights the need for community-wide implementation of standardized assays for measuring drug response phenotypes so that the full potential of HTS is realized. We suggest that the path forward is to establish best practices and standardization of the critical steps in these assays through a collective effort to ensure that the data produced from large-scale screens would not only be of high intrastudy consistency, so that they could be replicated and compared successfully across multiple laboratories.

Transcriptional profiling of Chinese medicinal formula Si-Wu-Tang on breast cancer cells reveals phytoestrogenic activity

BackgroundSi-Wu-Tang (SWT), comprising the combination of four herbs, Paeoniae, Angelicae, Chuanxiong and Rehmanniae, is one of the most popular traditional oriental medicines for women’s diseases. In our previous study, the microarray gene expression profiles of SWT on breast cancer cell line MCF-7 were found similar to the effect of β-estradiol (E2) on MCF-7 cells in the Connectivity Map database.MethodsFurther data analysis was conducted to find the main similarities and differences between the effects of SWT and E2 on MCF-7 gene expression. The cell proliferation assay on MCF-7 (ER-positive) and MDA-MB-231 (ER-negative) cells were used to examine such estrogenic activity. The estrogenic potency of SWT was further confirmed by estrogen-responsive element (ERE) luciferase reporter assay in MCF-7 cells.ResultsMany estrogen regulated genes strongly up-regulated by E2 were similarly up-regulated by SWT, e.g., GREB1, PGR and EGR3. Of interest with regard to safety of SWT, the oncogenes MYBL1 and RET were strongly induced by E2 but not by SWT. Quantitative RT-PCR analysis revealed a highly concordant expression change in selected genes with data obtained by microarrays. Further supporting SWT’s estrogenic activity, in MCF-7 but not in MDA-MB-231 cells, SWT stimulated cell growth at lower concentrations (< 3.0 mg/ml), while at high concentrations, it inhibits the growth of both cell lines. The growth inhibitory potency of SWT was significantly higher in MDA-MB-231 than in MCF-7 cells. The SWT-induced cell growth of MCF-7 could be blocked by addition of the estrogen receptor antagonist tamoxifen. In addition, SWT was able to activate the ERE activity at lower concentrations. The herbal components Angelicae, Chuanxiong and Rehmanniae at lower concentrations (< 3.0 mg/ml) also showed growth-inducing and ERE-activating activity in MCF-7 cells.ConclusionsThese results revealed a new mechanism to support the clinical use of SWT for estrogen related diseases and possibly for cancer prevention. This study also demonstrated the feasibility of using microarray transcriptional profiling to discover phytoestrogenic components that are present in natural products.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis

BackgroundAs the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding.MethodsQualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network.ResultsNine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature.ConclusionsNetwork analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.

Activation of the Nrf2 signaling pathway in usnic acid-induced toxicity in HepG2 cells

Many usnic acid-containing dietary supplements have been marketed as weight loss agents, although severe hepatotoxicity and acute liver failure have been associated with their overuse. Our previous mechanistic studies revealed that autophagy, disturbance of calcium homeostasis, and ER stress are involved in usnic acid-induced toxicity. In this study, we investigated the role of oxidative stress and the Nrf2 signaling pathway in usnic acid-induced toxicity in HepG2 cells. We found that a 24-h treatment with usnic acid caused DNA damage and S-phase cell cycle arrest in a concentration-dependent manner. Usnic acid also triggered oxidative stress as demonstrated by increased reactive oxygen species generation and glutathione depletion. Short-term treatment (6xa0h) with usnic acid significantly increased the protein level for Nrf2 (nuclear factor erythroid 2-related factor 2), promoted Nrf2 translocation to the nucleus, up-regulated antioxidant response element (ARE)-luciferase reporter activity, and induced the expression of Nrf2-regulated targets, including glutathione reductase, glutathione S-transferase, and NAD(P)H quinone oxidoreductase-1 (NQO1). Furthermore, knockdown of Nrf2 with shRNA potentiated usnic acid-induced DNA damage and cytotoxicity. Taken together, our results show that usnic acid causes cell cycle dysregulation, DNA damage, and oxidative stress and that the Nrf2 signaling pathway is activated in usnic acid-induced cytotoxicity.

Assessment of pharmacogenomic agreement.

In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were inconsistent. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response, seemingly contradicting our previous findings. Reanalyzing the authors’ published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

Revisiting inconsistency in large pharmacogenomic studies

In 2013, we published a comparative analysis mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines, and that the software analysis tools we provided should have been easier to run, particularly as the GDSC and CCLE released additional data. xa0xa0xa0xa0xa0xa0xa0xa0xa0xa0xa0 Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. xa0xa0xa0xa0xa0xa0xa0xa0xa0xa0xa0 Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

Endoplasmic reticulum stress and MAPK signaling pathway activation underlie leflunomide-induced toxicity in HepG2 Cells

Leflunomide, used for the treatment of rheumatoid arthritis, has been reported to cause severe liver problems and liver failure; however, the underlying mechanisms are not clear. In this study, we used multiple approaches including genomic analysis to investigate and characterize the possible molecular mechanisms of the cytotoxicity of leflunomide in hepatic cells. We found that leflunomide caused endoplasmic reticulum (ER) stress and activated an unfolded protein response, as evidenced by increased expression of related genes including CHOP and GADD34; and elevated protein levels of typical ER stress markers including CHOP, ATF-4, p-eIF2α, and spliced XBP1. The secretion of Gaussia luciferase was suppressed in cells treated with leflunomide in an ER stress reporter assay. Inhibition of ER stress with an ER stress inhibitor 4-phenylbutyrate, and knockdown of ATF-4 and CHOP genes partially protected cells upon leflunomide exposure. In addition, both genomic and biochemical analyses revealed that JNK and ERK1/2 of MAPK signaling pathways were activated, and both contributed to the leflunomide-induced cytotoxicity. Inhibiting JNK activation using a JNK inhibitor attenuated the ER stress and cytotoxicity of leflunomide, whereas inhibiting ERK1/2 using an ERK1/2 inhibitor or ERK1/2 siRNA increased the adverse effect caused by leflunomide, suggesting opposite roles for the two pathways. In summary, our data indicate that both ER stress and the activation of JNK and ERK1/2 contribute to leflunomide-induced cytotoxicity.