Flaherty, Patrick
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Flaherty
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Patrick
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Publication Ultrasensitive Detection of Rare Mutations using Next-Generation Targeted Resequencing(2011-01-01) Flaherty, Patrick; Natsoulis, Georges; Muralidharan, Omkar; Winters, Mark; Buenrostro, Jason; Bell, John; Brown, Sheldon; Holodniy, Mark; Zhang, Nancy; Ji, Hanlee P.With next-generation DNA sequencing technologies, one can interrogate a specific genomic region of interest at very high depth of coverage and identify less prevalent, rare mutations in heterogeneous clinical samples. However, the mutation detection levels are limited by the error rate of the sequencing technology as well as by the availability of variant-calling algorithms with high statistical power and low false positive rates. We demonstrate that we can robustly detect mutations at 0.1% fractional representation. This represents accurate detection of one mutant per every 1000 wild-type alleles. To achieve this sensitive level of mutation detection, we integrate a high accuracy indexing strategy and reference replication for estimating sequencing error variance. We employ a statistical model to estimate the error rate at each position of the reference and to quantify the fraction of variant base in the sample. Our method is highly specific (99%) and sensitive (100%) when applied to a known 0.1% sample fraction admixture of two synthetic DNA samples to validate our method. As a clinical application of this method, we analyzed nine clinical samples of H1N1 influenza A and detected an oseltamivir (antiviral therapy) resistance mutation in the H1N1 neuraminidase gene at a sample fraction of 0.18%.Publication Functional Profiling of the Saccharomyces cerevisiae Genome(2002-01-01) Flaherty, PatrickDetermining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.Publication Identification of a new class of antifungals targeting the synthesis of fungal sphingolipids(2015-01-01) Flaherty, PatrickRecent estimates suggest that>300 million people are afflicted by serious fungal infections worldwide. Current antifungal drugs are static and toxic and/or have a narrow spectrum of activity. Thus, there is an urgent need for the development of new antifungal drugs. The fungal sphingolipid glucosylceramide (GlcCer) is critical in promoting virulence of a variety of human-pathogenic fungi. In this study, we screened a synthetic drug library for compounds that target the synthesis of fungal, but not mammalian, GlcCer and found two compounds [N=-(3-bromo-4-hydroxybenzylidene)-2-methylbenzohydrazide (BHBM) and its derivative, 3-bromo-N=-(3-bromo-4-hydroxybenzylidene) benzohydrazide (D0)] that were highly effective in vitro and in vivo against several pathogenic fungi. BHBM and D0 were well tolerated in animals and are highly synergistic or additive to current antifungals. BHBM and D0 significantly affected fungal cell morphology and resulted in the accumulation of intracellular vesicles. Deep-sequencing analysis of drug-resistant mutants revealed that four protein products, encoded by genes APL5, COS111, MKK1, and STE2, which are involved in vesicular transport and cell cycle progression, are targeted by BHBM.Publication RVD: A command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing(2013-01-01) Cushing, Anna; Flaherty, Patrick; Hopmans, Erik; Bell, John M.; Ji, Hanlee P.Background: Rare single nucleotide variants play an important role in genetic diversity and heterogeneity of specific human disease. For example, an individual clinical sample can harbor rare mutations at minor frequencies. Genetic diversity within an individual clinical sample is oftentimes reflected in rare mutations. Therefore, detecting rare variants prior to treatment may prove to be a useful predictor for therapeutic response. Current rare variant detection algorithms using next generation DNA sequencing are limited by inherent sequencing error rate and platform availability. Findings: Here we describe an optimized implementation of a rare variant detection algorithm called RVD for use in targeted gene resequencing. RVD is available both as a command-line program and for use in MATLAB and estimates context-specific error using a beta-binomial model to call variants with minor allele frequency (MAF) as low as 0.1%. We show that RVD accepts standard BAM formatted sequence files. We tested RVD analysis on multiple Illumina sequencing platforms, among the most widely used DNA sequencing platforms. Conclusions: RVD meets a growing need for highly sensitive and specific tools for variant detection. To demonstrate the usefulness of RVD, we carried out a thorough analysis of the software’s performance on synthetic and clinical virus samples sequenced on both an Illumina GAIIx and a MiSeq. We expect RVD can improve understanding the genetics and treatment of common viral diseases including influenza. RVD is available at the following URL:http://dna-discovery.stanford.edu/software/rvd/.Publication Metastatic Tumor Evolution in Diuse Gastric Cancer and Cancer Organoid Modeling Implicate TGFBR2 as a Potential Driver(2014-01-01) Flaherty, PatrickBackground: Gastric cancer is the second-leading cause of global cancer deaths, with metastatic disease representing the primary cause of mortality. To identify candidate drivers involved in oncogenesis and tumor evolution, we conduct an extensive genome sequencing analysis of metastatic progression in a diffuse gastric cancer. This involves a comparison between a primary tumor from a hereditary diffuse gastric cancer syndrome proband and its recurrence as an ovarian metastasis. Results: Both the primary tumor and ovarian metastasis have common biallelic loss-of-function of both the CDH1 and TP53 tumor suppressors, indicating a common genetic origin. While the primary tumor exhibits amplification of the Fibroblast growth factor receptor 2 (FGFR2) gene, the metastasis notably lacks FGFR2 amplification but rather possesses unique biallelic alterations of Transforming growth factor-beta receptor 2 (TGFBR2), indicating the divergent in vivo evolution of a TGFBR2-mutant metastatic clonal population in this patient. As TGFBR2 mutations have not previously been functionally validated in gastric cancer, we modeled the metastatic potential of TGFBR2 loss in a murine three-dimensional primary gastric organoid culture. The Tgfbr2 shRNA knockdown within Cdh1-/-; Tp53-/- organoids generates invasion in vitro and robust metastatic tumorigenicity in vivo, confirming Tgfbr2 metastasis suppressor activity. Conclusions: We document the metastatic differentiation and genetic heterogeneity of diffuse gastric cancer and reveal the potential metastatic role of TGFBR2 loss-of-function. In support of this study, we apply a murine primary organoid culture method capable of recapitulating in vivo metastatic gastric cancer. Overall, we describe an integrated approach to identify and functionally validate putative cancer drivers involved in metastasis.Publication Chemogenomic profiling: Identifying the functional interactions of small molecules in yeast(2004-01-01) Flaherty, PatrickWe demonstrate the efficacy of a genome-wide protocol in yeast that allows the identification of those gene products that functionally interact with small molecules and result in the inhibition of cellular proliferation. Here we present results from screening 10 diverse compounds in 80 genome-wide experiments against the complete collection of heterozygous yeast deletion strains. These compounds include anticancer and antifungal agents, statins, alverine citrate, and dyclonine. In several cases, we identified previously known interactions; furthermore, in each case, our analysis revealed novel cellular interactions, even when the relationship between a compound and its cellular target had been well established. In addition, we identified a chemical core structure shared among three therapeutically distinct compounds that inhibit the ERG24 heterozygous deletion strain, demonstrating that cells may respond similarly to compounds of related structure. The ability to identify on-and-off target effects in vivo is fundamental to understanding the cellular response to small-molecule perturbants.Publication RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data(2015-01-01) He, Yuting; Zhang, Fan; Flaherty, PatrickMOTIVATION: Next-generation sequencing technology is increasingly being used for clinical diagnostic tests. Clinical samples are often genomically heterogeneous due to low sample purity or the presence of genetic subpopulations. Therefore, a variant calling algorithm for calling low-frequency polymorphisms in heterogeneous samples is needed. RESULTS: We present a novel variant calling algorithm that uses a hierarchical Bayesian model to estimate allele frequency and call variants in heterogeneous samples. We show that our algorithm improves upon current classifiers and has higher sensitivity and specificity over a wide range of median read depth and minor allele fraction. We apply our model and identify 15 mutated loci in the PAXP1 gene in a matched clinical breast ductal carcinoma tumor sample; two of which are likely loss-of-heterozygosity events.Publication Systematic genomic identification of colorectal cancer genes delineating advanced from early clinical stage(2013-01-01) Lee, HoJoon; Flaherty, Patrick; Ji, Hanlee P.Background: Colorectal cancer is the third leading cause of cancer deaths in the United States. The initial assessment of colorectal cancer involves clinical staging that takes into account the extent of primary tumor invasion, determining the number of lymph nodes with metastatic cancer and the identification of metastatic sites in other organs. Advanced clinical stage indicates metastatic cancer, either in regional lymph nodes or in distant organs. While the genomic and genetic basis of colorectal cancer has been elucidated to some degree, less is known about the identity of specific cancer genes that are associated with advanced clinical stage and metastasis. Methods: We compiled multiple genomic data types (mutations, copy number alterations, gene expression and methylation status) as well as clinical meta-data from The Cancer Genome Atlas (TCGA). We used an elastic-net regularized regression method on the combined genomic data to identify genetic aberrations and their associated cancer genes that are indicators of clinical stage. We ranked candidate genes by their regression coefficient and level of support from multiple assay modalities. Results: A fit of the elastic-net regularized regression to 197 samples and integrated analysis of four genomic platforms identified the set of top gene predictors of advanced clinical stage, including: WRN, SYK, DDX5 and ADRA2C. These genetic features were identified robustly in bootstrap resampling analysis. Conclusions: We conducted an analysis integrating multiple genomic features including mutations, copy number alterations, gene expression and methylation. This integrated approach in which one considers all of these genomic features performs better than any individual genomic assay. We identified multiple genes that robustly delineate advanced clinical stage, suggesting their possible role in colorectal cancer metastatic progression.Publication GEMINI: a computationally-efficient search engine for large gene expression datasets(2016-01-01) DeFreitas, Timothy; Saddiki, Hachem; Flaherty, PatrickBackground Low-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query – a text-based string – is mismatched with the form of the target – a genomic profile. Results To improve access to massive genomic data resources, we have developed a fast search engine, GEMINI, that uses a genomic profile as a query to search for similar genomic profiles. GEMINI implements a nearest-neighbor search algorithm using a vantage-point tree to store a database of n profiles and in certain circumstances achieves an O(log n) expected query time in the limit. We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and show that it achieves a query time that scales as the logarithm of the number of records in practice on genomic data. In a database with 105samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec. Conclusions GEMINI is a fast search engine that uses a query genomic profile to search for similar profiles in a very large genomic database. It enables users to identify similar profiles independent of sample label, data origin or other meta-data information.Publication Genome-Wide Requirements for Resistance to Functionally Distinct DNA-Damaging Agents(2005-01-01) Flaherty, PatrickThe mechanistic and therapeutic differences in the cellular response to DNA-damaging compounds are not completely understood, despite intense study. To expand our knowledge of DNA damage, we assayed the effects of 12 closely related DNA-damaging agents on the complete pool of ;4,700 barcoded homozygous deletion strains of Saccharomyces cerevisiae. In our protocol, deletion strains are pooled together and grown competitively in the presence of compound. Relative strain sensitivity is determined by hybridization of PCR-amplified barcodes to an oligonucleotide array carrying the barcode complements. These screens identified genes in well-characterized DNAdamage-response pathways as well as genes whose role in the DNA-damage response had not been previously established. High-throughput individual growth analysis was used to independently confirm microarray results. Each compound produced a unique genome-wide profile. Analysis of these data allowed us to determine the relative importance of DNA-repair modules for resistance to each of the 12 profiled compounds. Clustering the data for 12 distinct compounds uncovered both known and novel functional interactions that comprise the DNA-damage response and allowed us to define the genetic determinants required for repair of interstrand cross-links. Further genetic analysis allowed determination of epistasis for one of these functional groups.