Eric Wang

Bio: Eric Wang is an academic researcher from New York University. The author has contributed to research in topics: Myeloid leukemia & Leukemia. The author has an hindex of 19, co-authored 27 publications receiving 4342 citations. Previous affiliations of Eric Wang include Memorial Sloan Kettering Cancer Center & Cold Spring Harbor Laboratory.

RNAi screen identifies Brd4 as a therapeutic target in acute myeloid leukaemia

Abstract: Epigenetic pathways can regulate gene expression by controlling and interpreting chromatin modifications. Cancer cells are characterized by altered epigenetic landscapes, and commonly exploit the chromatin regulatory machinery to enforce oncogenic gene expression programs. Although chromatin alterations are, in principle, reversible and often amenable to drug intervention, the promise of targeting such pathways therapeutically has been limited by an incomplete understanding of cancer-specific dependencies on epigenetic regulators. Here we describe a non-biased approach to probe epigenetic vulnerabilities in acute myeloid leukaemia (AML), an aggressive haematopoietic malignancy that is often associated with aberrant chromatin states. By screening a custom library of small hairpin RNAs (shRNAs) targeting known chromatin regulators in a genetically defined AML mouse model, we identify the protein bromodomain-containing 4 (Brd4) as being critically required for disease maintenance. Suppression of Brd4 using shRNAs or the small-molecule inhibitor JQ1 led to robust antileukaemic effects in vitro and in vivo, accompanied by terminal myeloid differentiation and elimination of leukaemia stem cells. Similar sensitivities were observed in a variety of human AML cell lines and primary patient samples, revealing that JQ1 has broad activity in diverse AML subtypes. The effects of Brd4 suppression are, at least in part, due to its role in sustaining Myc expression to promote aberrant self-renewal, which implicates JQ1 as a pharmacological means to suppress MYC in cancer. Our results establish small-molecule inhibition of Brd4 as a promising therapeutic strategy in AML and, potentially, other cancers, and highlight the utility of RNA interference (RNAi) screening for revealing epigenetic vulnerabilities that can be exploited for direct pharmacological intervention.

The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Abstract: Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains

Abstract: CRISPR-Cas9 genome editing technology holds great promise for discovering therapeutic targets in cancer and other diseases Current screening strategies target CRISPR-Cas9-induced mutations to the 5' exons of candidate genes, but this approach often produces in-frame variants that retain functionality, which can obscure even strong genetic dependencies Here we overcome this limitation by targeting CRISPR-Cas9 mutagenesis to exons encoding functional protein domains This generates a higher proportion of null mutations and substantially increases the potency of negative selection We also show that the magnitude of negative selection can be used to infer the functional importance of individual protein domains of interest A screen of 192 chromatin regulatory domains in murine acute myeloid leukemia cells identifies six known drug targets and 19 additional dependencies A broader application of this approach may allow comprehensive identification of protein domains that sustain cancer cells and are suitable for drug targeting

Role of SWI/SNF in acute leukemia maintenance and enhancer-mediated Myc regulation

Abstract: .Cancer cells frequently depend on chromatin regulatory activities to maintain a malignant phenotype. Here, we show that leukemia cells require the mammalian SWI/SNF chromatin remodeling complex for their survival and aberrant self-renewal potential. While Brg1, an ATPase subunit of SWI/SNF, is known to suppress tumor formation in several cell types, we found that leukemia cells instead rely on Brg1 to support their oncogenic transcriptional program, which includes Myc as one of its key targets. To account for this context-specific function, we identify a cluster of lineage-specific enhancers located 1.7 Mb downstream from Myc that are occupied by SWI/SNF as well as the BET protein Brd4. Brg1 is required at these distal elements to maintain transcription factor occupancy and for long-range chromatin looping interactions with the Myc promoter. Notably, these distal Myc enhancers coincide with a region that is focally amplified in ~3% of acute myeloid leukemias. Together, these findings define a leukemia maintenance function for SWI/SNF that is linked to enhancer-mediated gene regulation, providing general insights into how cancer cells exploit transcriptional coactivators to maintain oncogenic gene expression programs.

An integrated approach to dissecting oncogene addiction implicates a Myb-coordinated self-renewal program as essential for leukemia maintenance

Abstract: Although human cancers have complex genotypes and are genomically unstable, they often remain dependent on the continued presence of single-driver mutations—a phenomenon dubbed ‘‘oncogene addiction.’’ Such dependencies have been demonstrated in mouse models, where conditional expression systems have revealed that oncogenes able to initiate cancer are often required for tumor maintenance and progression, thus validating the pathways they control as therapeutic targets. Here, we implement an integrative approach that combines genetically defined mouse models, transcriptional profiling, and a novel inducible RNAi platform to characterize cellular programs that underlie addiction to MLL-AF9—a fusion oncoprotein involved in aggressive forms of acute myeloid leukemia (AML). We show that MLL-AF9 contributes to leukemia maintenance by enforcing a Mybcoordinated program of aberrant self-renewal involving genes linked to leukemia stem cell potential and poor prognosis in human AML. Accordingly, partial and transient Myb suppression precisely phenocopies MLL-AF9 withdrawal and eradicates aggressive AML in vivo without preventing normal myelopoiesis, indicating that strategies to inhibit Myb-dependent aberrant self-renewal programs hold promise as effective and cancer-specific therapeutics. Together, our results identify Myb as a critical mediator of oncogene addiction in AML, delineate relevant Myb target genes that are amenable to pharmacologic inhibition, and establish a general approach for dissecting oncogene addiction in vivo.

Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9

Abstract: CRISPR-Cas9-based genetic screens are a powerful new tool in biology. By simply altering the sequence of the single-guide RNA (sgRNA), one can reprogram Cas9 to target different sites in the genome with relative ease, but the on-target activity and off-target effects of individual sgRNAs can vary widely. Here, we use recently devised sgRNA design rules to create human and mouse genome-wide libraries, perform positive and negative selection screens and observe that the use of these rules produced improved results. Additionally, we profile the off-target activity of thousands of sgRNAs and develop a metric to predict off-target sites. We incorporate these findings from large-scale, empirical data to improve our computational design rules and create optimized sgRNA libraries that maximize on-target activity and minimize off-target effects to enable more effective and efficient genetic screens and genome engineering.

A decade of exploring the cancer epigenome — biological and translational implications

Abstract: The past decade has highlighted the central role of epigenetic processes in cancer causation, progression and treatment. Next-generation sequencing is providing a window for visualizing the human epigenome and how it is altered in cancer. This view provides many surprises, including linking epigenetic abnormalities to mutations in genes that control DNA methylation, the packaging and the function of DNA in chromatin, and metabolism. Epigenetic alterations are leading candidates for the development of specific markers for cancer detection, diagnosis and prognosis. The enzymatic processes that control the epigenome present new opportunities for deriving therapeutic strategies designed to reverse transcriptional abnormalities that are inherent to the cancer epigenome.

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

Abstract: To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.