Stanley Chun-Wei Lee

Bio: Stanley Chun-Wei Lee is an academic researcher from Memorial Sloan Kettering Cancer Center. The author has contributed to research in topics: RNA splicing & Myeloid leukemia. The author has an hindex of 26, co-authored 44 publications receiving 3804 citations. Previous affiliations of Stanley Chun-Wei Lee include University of Melbourne & Walter and Eliza Hall Institute of Medical Research.

Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression

Abstract: One of the most common analysis tasks in genomic research is to identify genes that are differentially expressed (DE) between experimental conditions. Empirical Bayes (EB) statistical tests using moderated genewise variances have been very effective for this purpose, especially when the number of biological replicate samples is small. The EB procedures can however be heavily influenced by a small number of genes with very large or very small variances. This article improves the differential expression tests by robustifying the hyperparameter estimation procedure. The robust procedure has the effect of decreasing the informativeness of the prior distribution for outlier genes while increasing its informativeness for other genes. This effect has the double benefit of reducing the chance that hypervariable genes will be spuriously identified as DE while increasing statistical power for the main body of genes. The robust EB algorithm is fast and numerically stable. The procedure allows exact small-sample null distributions for the test statistics and reduces exactly to the original EB procedure when no outlier genes are present. Simulations show that the robustified tests have similar performance to the original tests in the absence of outlier genes but have greater power and robustness when outliers are present. The article includes case studies for which the robust method correctly identifies and downweights genes associated with hidden covariates and detects more genes likely to be scientifically relevant to the experimental conditions. The new procedure is implemented in the limma software package freely available from the Bioconductor repository.

Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression

Abstract: One of the most common analysis tasks in genomic research is to identify genes that are differentially expressed (DE) between experimental conditions. Empirical Bayes (EB) statistical tests using moderated genewise variances have been very effective for this purpose, especially when the number of biological replicate samples is small. The EB procedures can however be heavily influenced by a small number of genes with very large or very small variances. This article improves the differential expression tests by robustifying the hyperparameter estimation procedure. The robust procedure has the effect of decreasing the informativeness of the prior distribution for outlier genes while increasing its informativeness for other genes. This effect has the double benefit of reducing the chance that hypervariable genes will be spuriously identified as DE while increasing statistical power for the main body of genes. The robust EB algorithm is fast and numerically stable. The procedure allows exact small-sample null distributions for the test statistics and reduces exactly to the original EB procedure when no outlier genes are present. Simulations show that the robustified tests have similar performance to the original tests in the absence of outlier genes but have greater power and robustness when outliers are present. The article includes case studies for which the robust method correctly identifies and downweights genes associated with hidden covariates and detects more genes likely to be scientifically relevant to the experimental conditions. The new procedure is implemented in the limma software package freely available from the Bioconductor repository.

BET inhibitor resistance emerges from leukaemia stem cells

Abstract: Bromodomain and extra terminal protein (BET) inhibitors are first-in-class targeted therapies that deliver a new therapeutic opportunity by directly targeting bromodomain proteins that bind acetylated chromatin marks. Early clinical trials have shown promise, especially in acute myeloid leukaemia, and therefore the evaluation of resistance mechanisms is crucial to optimize the clinical efficacy of these drugs. Here we use primary mouse haematopoietic stem and progenitor cells immortalized with the fusion protein MLL-AF9 to generate several single-cell clones that demonstrate resistance, in vitro and in vivo, to the prototypical BET inhibitor, I-BET. Resistance to I-BET confers cross-resistance to chemically distinct BET inhibitors such as JQ1, as well as resistance to genetic knockdown of BET proteins. Resistance is not mediated through increased drug efflux or metabolism, but is shown to emerge from leukaemia stem cells both ex vivo and in vivo. Chromatin-bound BRD4 is globally reduced in resistant cells, whereas the expression of key target genes such as Myc remains unaltered, highlighting the existence of alternative mechanisms to regulate transcription. We demonstrate that resistance to BET inhibitors, in human and mouse leukaemia cells, is in part a consequence of increased Wnt/β-catenin signalling, and negative regulation of this pathway results in restoration of sensitivity to I-BET in vitro and in vivo. Together, these findings provide new insights into the biology of acute myeloid leukaemia, highlight potential therapeutic limitations of BET inhibitors, and identify strategies that may enhance the clinical utility of these unique targeted therapies.

Therapeutic targeting of splicing in cancer

Abstract: Recent studies have highlighted that splicing patterns are frequently altered in cancer and that mutations in genes encoding spliceosomal proteins, as well as mutations affecting the splicing of key cancer-associated genes, are enriched in cancer. In parallel, there is also accumulating evidence that several molecular subtypes of cancer are highly dependent on splicing function for cell survival. These findings have resulted in a growing interest in targeting splicing catalysis, splicing regulatory proteins, and/or specific key altered splicing events in the treatment of cancer. Here we present strategies that exist and that are in development to target altered dependency on the spliceosome, as well as aberrant splicing, in cancer. These include drugs to target global splicing in cancer subtypes that are preferentially dependent on wild-type splicing for survival, methods to alter post-translational modifications of splicing-regulating proteins, and strategies to modulate pathologic splicing events and protein-RNA interactions in cancer.

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

voom: precision weights unlock linear model analysis tools for RNA-seq read counts

Abstract: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

U1 snRNP regulates cancer cell migration and invasion in vitro

Abstract: Stimulated cells and cancer cells have widespread shortening of mRNA 3'-untranslated regions (3'UTRs) and switches to shorter mRNA isoforms due to usage of more proximal polyadenylation signals (PASs) in introns and last exons. U1 snRNP (U1), vertebrates' most abundant non-coding (spliceosomal) small nuclear RNA, silences proximal PASs and its inhibition with antisense morpholino oligonucleotides (U1 AMO) triggers widespread premature transcription termination and mRNA shortening. Here we show that low U1 AMO doses increase cancer cells' migration and invasion in vitro by up to 500%, whereas U1 over-expression has the opposite effect. In addition to 3'UTR length, numerous transcriptome changes that could contribute to this phenotype are observed, including alternative splicing, and mRNA expression levels of proto-oncogenes and tumor suppressors. These findings reveal an unexpected role for U1 homeostasis (available U1 relative to transcription) in oncogenic and activated cell states, and suggest U1 as a potential target for their modulation.

Integrative Genomics Viewer

Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis.

Abstract: Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.