Jennifer Yen

A systematic genome-wide analysis of zebrafish protein-coding gene function

Since the publication of the human reference genome, the identities of specific genes associated with human diseases are being discovered at a rapid rate. A central problem is that the biological activity of these genes is often unclear. Detailed investigations in model vertebrate organisms, typically mice, have been essential for understanding the activities of many orthologues of these disease-associated genes. Although gene-targeting approaches and phenotype analysis have led to a detailed understanding of nearly 6,000 protein-coding genes, this number falls considerably short of the more than 22,000 mouse protein-coding genes. Similarly, in zebrafish genetics, one-by-one gene studies using positional cloning, insertional mutagenesis, antisense morpholino oligonucleotides, targeted re-sequencing, and zinc finger and TAL endonucleases have made substantial contributions to our understanding of the biological activity of vertebrate genes, but again the number of genes studied falls well short of the more than 26,000 zebrafish protein-coding genes. Importantly, for both mice and zebrafish, none of these strategies are particularly suited to the rapid generation of knockouts in thousands of genes and the assessment of their biological activity. Here we describe an active project that aims to identify and phenotype the disruptive mutations in every zebrafish protein-coding gene, using a well-annotated zebrafish reference genome sequence, high-throughput sequencing and efficient chemical mutagenesis. So far we have identified potentially disruptive mutations in more than 38% of all known zebrafish protein-coding genes. We have developed a multi-allelic phenotyping scheme to efficiently assess the effects of each allele during embryogenesis and have analysed the phenotypic consequences of over 1,000 alleles. All mutant alleles and data are available to the community and our phenotyping scheme is adaptable to phenotypic analysis beyond embryogenesis.

Psychopathology, Families, and Culture: Autism

Autism spectrum disorders (ASDs) are now considered to be the most common of the developmental disorders, although the effect of cultural influences on the diagnosis and treatment of ASDs has received limited attention. The existing literature on this topic suggests that both macro-level and microlevel cultural factors can affect the characterization, diagnosis, and treatment of ASDs. As a result, it is important for clinicians to consider cultural factors throughout the diagnostic, treatment planning, and intervention implementation processes. In this article, cultural influences on the prevalence of autism and the diagnostic and treatment processes are reviewed and synthesized through a consideration of the developmental context and through clinical practice suggestions.

The genetic heterogeneity and mutational burden of engineered melanomas in zebrafish models

BackgroundMelanoma is the most deadly form of skin cancer. Expression of oncogenic BRAF or NRAS, which are frequently mutated in human melanomas, promote the formation of nevi but are not sufficient for tumorigenesis. Even with germline mutated p53, these engineered melanomas present with variable onset and pathology, implicating additional somatic mutations in a multi-hit tumorigenic process.ResultsTo decipher the genetics of these melanomas, we sequence the protein coding exons of 53 primary melanomas generated from several BRAFV600E or NRASQ61K driven transgenic zebrafish lines. We find that engineered zebrafish melanomas show an overall low mutation burden, which has a strong, inverse association with the number of initiating germline drivers. Although tumors reveal distinct mutation spectrums, they show mostly C > T transitions without UV light exposure, and enrichment of mutations in melanogenesis, p53 and MAPK signaling. Importantly, a recurrent amplification occurring with pre-configured drivers BRAFV600E and p53-/- suggests a novel path of BRAF cooperativity through the protein kinase A pathway.ConclusionThis is the first analysis of a melanoma mutational landscape in the absence of UV light, where tumors manifest with remarkably low mutation burden and high heterogeneity. Genotype specific amplification of protein kinase A in cooperation with BRAF and p53 mutation suggests the involvement of melanogenesis in these tumors. This work is important for defining the spectrum of events in BRAF or NRAS driven melanoma in the absence of UV light, and for informed exploitation of models such as transgenic zebrafish to better understand mechanisms leading to human melanoma formation.

Zebrafish models of cancer: progress and future challenges.

The need for scalable strategies to probe the biological consequences of candidate cancer genes has never been more pressing. The zebrafish, with its capacity for high-throughput transgenesis, in vivo imaging and chemical/genetic screening, has ideal features for undertaking this task. Unique biological insights from zebrafish have already led to the identification of novel oncogenic drivers and small molecules being used to treat the human cancer. This review summarizes the recent main findings and describes pertinent areas where the zebrafish can greatly contribute to our understanding of cancer biology and treatment.

A variant by any name: quantifying annotation discordance across tools and clinical databases

BackgroundClinical genomic testing is dependent on the robust identification and reporting of variant-level information in relation to disease. With the shift to high-throughput sequencing, a major challenge for clinical diagnostics is the cross-identification of variants called on their genomic position to resources that rely on transcript- or protein-based descriptions.MethodsWe evaluated the accuracy of three tools (SnpEff, Variant Effect Predictor, and Variation Reporter) that generate transcript and protein-based variant nomenclature from genomic coordinates according to guidelines by the Human Genome Variation Society (HGVS). Our evaluation was based on transcript-controlled comparisons to a manually curated set of 126 test variants of various types drawn from data sources, each with HGVS-compliant transcript and protein descriptors. We further evaluated the concordance between annotations generated by Snpeff and Variant Effect Predictor and those in major germline and cancer databases: ClinVar and COSMIC, respectively.ResultsWe find that there is substantial discordance between the annotation tools and databases in the description of insertions and/or deletions. Using our ground truth set of variants, constructed specifically to identify challenging events, accuracy was between 80 and 90% for coding and 50 and 70% for protein changes for 114 to 126 variants. Exact concordance for SNV syntax was over 99.5% between ClinVar and Variant Effect Predictor and SnpEff, but less than 90% for non-SNV variants. For COSMIC, exact concordance for coding and protein SNVs was between 65 and 88% and less than 15% for insertions. Across the tools and datasets, there was a wide range of different but equivalent expressions describing protein variants.ConclusionsOur results reveal significant inconsistency in variant representation across tools and databases. While some of these syntax differences may be clear to a clinician, they can confound variant matching, an important step in variant classification. These results highlight the urgent need for the adoption and adherence to uniform standards in variant annotation, with consistent reporting on the genomic reference, to enable accurate and efficient data-driven clinical care.

Abstract 546: Supporting neoantigen identification for personalized cancer vaccines trough analytical validation of an augmented content enhanced (ACE) transcriptome

The identification of neoantigens has become a critical step in the development of neoantigen-based personalized cancer vaccines and other immunotherapy applications. Since neoantigens can be generated from tumor specific mutations in any expressed gene, the first step in identification of neoantigens typically involves deep exome and transcriptome sequencing on the tumor and exome sequencing of the matched normal. As personalized vaccines enter clinical trials with the potential for clinical use, there is a growing need for strong analytical validation of these platforms. To address this we have developed our ACE Exome (~200X) and Transcriptome platforms for neoantigen identification which utilitize an augmented exome approach designed to increase sensitivity for neoantigens in low complexity, traditionally hard to sequencing regions. To enable this platform for neoantigen based personalized cancer vaccines, we have performed a validation of both our ACE Exome (tumor and normal) and ACE transcriptome platforms for detecting DNA-based SNVs and Indels, as well as for RNA based small variant and fusion calls. These are variant types are especially important for neoantigen identification. In this abstract we describe the ACE Exome validation. We used 11 cancer cell lines and their matched normals to assess analytical sensitivity and limits of detection (LOD) for small variant (SNV and Indel) detection using our ACE exome and Tumor Normal bioinformatics pipeline. We identified a gold set of variants, 875 SNVs and 19 Indels that were previously validated in these 11 cell lines (COSMIC, CCLE and Sanger Sequencing confirmed variants). These gold set variants were used to calculate our analytical sensitivity (percent of gold variants detected across the 11 cell line pairs using our assay). To determine our LOD, we chose 3 of the 11 cancer cell lines and created 6 dilutions (5%, 10%, 20%, 30%, 50% and 80% tumor purity) with their matched normal. We then determined Positive Predictive Agreement (PPA, percent of pure cell line variants detected in a diluted samples) and False Discovery Rate (FDR, percent of erroneously detected variants in the diluted sample that were not detected in the pure cell lines) metrics for variants across different minor allelic frequencies (MAF) in the diluted samples. The ACE “Tumor Normal” Exome assay had a high sensitivity of 98% for SNVs and 95% for Indels. The assay also showed robust PPA (sensitivity) of 97% and FDR (specificity) of 2% for SNVs with MAF g= 10% and PPA of 87% and FDR of 3% for Indels with MAF g= 10%. We demonstrate that the ACE “Tumor Normal” Exome assay is highly accurate for identification of SNVs and Indels in cancer exomes. With high analytical sensitivity, PPA and low FDR we believe this assay provides augmented ability to detect cancer driver and potential neoantigen generating mutations across various tumor types. Citation Format: Ravi K. Alla, Jennifer Yen, Sean M. Boyle, Richard Chen. Supporting neoantigen identification for personalized cancer vaccines through analytical validation of an augmented content enhanced (ACE) exome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 428. doi:10.1158/1538-7445.AM2017-428

Abstract 3612: Challenges in variant searching and annotation for clinical cancer testing

Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA Cancer testing is undergoing a revolution. While small gene panels have previously dominated the landscape, whole exome (WES) based strategies have recently emerged as valuable tools for identifying mutations with clinical significance and are being rapidly adopted into routine clinical cancer care. Assessing the significance and actionability of these variants is largely dependent on the growth of public repositories of germline and somatic variants, such as ExAC, COSMIC, TCGA and ClinVar. However, efforts to integrate data from these various sources have revealed numerous challenges. One of these challenges is the generation and use of variant syntax in a standardized, unambiguous format, which is paramount for variant search for both the clinical and scientific community. Variants can be represented in many different ways: in the VCF file format, which is the de facto standard for sequencing data; in genomic or transcript-based coordinates using HGVS nomenclature; as amino acid alterations in three- or single-letter codes according to transcript and protein definitions that can vary based on the database used. Analyses of dbSNP and COSMIC identified at least 350,000 and 27,000 variants respectively that, without “normalization”, have ambiguous VCF representations (using software described by Tan et al., 2015). This indicates that one-to-one searches and annotations in VCF files may not identify an exact match in the absence of normalization. At the coding and protein level, variants are typically reported according to syntax recommendations by the Human Genome Variation Society (HGVS). In evaluating a number of tools for generating HGVS nomenclature (snpEff, VEP, and Variation Reporter), we found challenges in reconciling syntax representation across these tools and databases. We demonstrate that variant annotation is dependent on both transcript and version, complicating comparisons between NCBI and Ensembl-based systems, such as ClinVar (NCBI) and COSMIC (Ensembl). Even given the same transcript, variants can be represented differently. Over 20% of variants output by the tools in our comparison reported different nomenclature for the same exact variant reported by ClinVar. Using a manually curated ‘gold truth’ set of variants, we found that as many as 75% of non-missense variants are called incorrectly by these tools. The results of our tests have significant implications for the search and annotation of variants during cancer analyses and interpretation, and serve to inform the ongoing adoption and refinement of available resources. Citation Format: Jennifer Yen, Sarah Garcia, Michael Clark, Steve Chervitz, Brian Linebaugh, Aldrin Montana, John West, Richard Chen, Deanna Church. Challenges in variant searching and annotation for clinical cancer testing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3612.

Abstract 3234: Comparative oncogenomics: identifying melanoma drivers from zebrafish to humans.

The elucidation of core networks driving tumorigenesis is greatly enhanced by studying tumors from multiple species. A comparison of tumors between evolutionarily distant organisms such as humans and fish offers a particularly powerful method to identify such core functionalities. In the past several years, the zebrafish has emerged as an important vertebrate model of cancer because it develops tumors that are histologically and functionally similar to the human disease. Overexpression of the BRAFV600E oncogene under the melanocyte-specific mitf promoter leads to a 100% penetrant melanoma in the context of p53 loss of function. We have previously shown that these zebrafish tumors exhibit characteristics of neural crest progenitors, rather than differentiated melanocytes, similar to many human melanomas which also overexpress the neural crest markers sox10 and ednrb. To identify the genomic lesions that are common to both species, we are performing RNA and DNA-sequencing of the zebrafish tumors and comparing them to human melanoma resequencing samples to identify common driver events. To facilitate this, we have developed technologies for both short and long term culture of zebrafish melanoma cell lines, along with normal tissue from each individual animal. One such melanoma line, ZMEL1, has been comprehensively characterized. RNA-seq demonstrated upregulation of neural crest genes such as ednrb, and pathway analysis indicated enrichment for ERK/MAP kinase and PI3K/AKT signaling pathways, and these tumors are sensitive to inhibition by the BRAF inhibitor PLX4720. Whole-genome sequencing (WGS) was performed on tumor cell line and matched normal tissue to a depth of 39X coverage on the Illumina platform. A total of 5193 single basepair mutations were identified, of which 2% were found in the coding region, 41% in intronic regions and 56% in the intergenic DNA. Within the coding region, 25% were G>C and 18% T>C substitutions, with approximately equal number of non-synonymous to silent mutations. Coding mutations in several genes such as rps6kb1b and plb1 are of particular interest because of their reported mutation in human cancer as well. 35 regions of copy number changes were found, comprising over 600 genes, and these include amplification of known oncogenes such as SETDB1. Functional analysis of the genomic changes common to both zebrafish and human tumors is now being undertaken using in vitro and in vivo melanoma models. In summary, comparative oncogenomics between zebrafish and human melanoma provides a powerful method for filtering the vast number of changes down to a list of manageable, testable hypotheses. This method can be broadly applied to other tumors types. Citation Format: Richard M. White, Erin Langdon, Charles Kaufman, Ellen van Rooijen, Jennifer Yen, Peter Van Loo, Leonard Zon. Comparative oncogenomics: identifying melanoma drivers from zebrafish to humans. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3234. doi:10.1158/1538-7445.AM2013-3234 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.