Publications by authors named "Jason R Walker"

11 Publications

  • Page 1 of 1

Unraveling the chaotic genomic landscape of primary and metastatic canine appendicular osteosarcoma with current sequencing technologies and bioinformatic approaches.

PLoS One 2021 8;16(2):e0246443. Epub 2021 Feb 8.

Department of Veterinary Medicine and Surgery, University of Missouri, Columbia, MO, United States of America.

Osteosarcoma is a rare disease in children but is one of the most common cancers in adult large breed dogs. The mutational landscape of both the primary and pulmonary metastatic tumor in two dogs with appendicular osteosarcoma (OSA) was comprehensively evaluated using an automated whole genome sequencing, exome and RNA-seq pipeline that was adapted for this study for use in dogs. Chromosomal lesions were the most common type of mutation. The mutational landscape varied substantially between dogs but the lesions within the same patient were similar. Copy number neutral loss of heterozygosity in mutant TP53 was the most significant driver mutation and involved a large region in the middle of chromosome 5. Canine and human OSA is characterized by loss of cell cycle checkpoint integrity and DNA damage response pathways. Mutational profiling of individual patients with canine OSA would be recommended prior to targeted therapy, given the heterogeneity seen in our study and previous studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0246443PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870011PMC
February 2021

The clonal evolution of metastatic colorectal cancer.

Sci Adv 2020 Jun 10;6(24):eaay9691. Epub 2020 Jun 10.

The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA.

Tumor heterogeneity and evolution drive treatment resistance in metastatic colorectal cancer (mCRC). Patient-derived xenografts (PDXs) can model mCRC biology; however, their ability to accurately mimic human tumor heterogeneity is unclear. Current genomic studies in mCRC have limited scope and lack matched PDXs. Therefore, the landscape of tumor heterogeneity and its impact on the evolution of metastasis and PDXs remain undefined. We performed whole-genome, deep exome, and targeted validation sequencing of multiple primary regions, matched distant metastases, and PDXs from 11 patients with mCRC. We observed intricate clonal heterogeneity and evolution affecting metastasis dissemination and PDX clonal selection. Metastasis formation followed both monoclonal and polyclonal seeding models. In four cases, metastasis-seeding clones were not identified in any primary region, consistent with a metastasis-seeding-metastasis model. PDXs underrepresented the subclonal heterogeneity of parental tumors. These suggest that single sample tumor sequencing and current PDX models may be insufficient to guide precision medicine.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1126/sciadv.aay9691DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286679PMC
June 2020

Comprehensive discovery of noncoding RNAs in acute myeloid leukemia cell transcriptomes.

Exp Hematol 2017 11 28;55:19-33. Epub 2017 Jul 28.

The McDonnell Genome Institute, Washington University, St. Louis, MO; Department of Medicine, Washington University, St. Louis, MO; Siteman Cancer Center, Washington University, St. Louis, MO; Department of Biomedical Engineering, Washington University, St. Louis, MO. Electronic address:

To detect diverse and novel RNA species comprehensively, we compared deep small RNA and RNA sequencing (RNA-seq) methods applied to a primary acute myeloid leukemia (AML) sample. We were able to discover previously unannotated small RNAs using deep sequencing of a library method using broader insert size selection. We analyzed the long noncoding RNA (lncRNA) landscape in AML by comparing deep sequencing from multiple RNA-seq library construction methods for the sample that we studied and then integrating RNA-seq data from 179 AML cases. This identified lncRNAs that are completely novel, differentially expressed, and associated with specific AML subtypes. Our study revealed the complexity of the noncoding RNA transcriptome through a combined strategy of strand-specific small RNA and total RNA-seq. This dataset will serve as an invaluable resource for future RNA-based analyses.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.exphem.2017.07.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5772960PMC
November 2017

Comprehensive genomic analysis reveals FLT3 activation and a therapeutic strategy for a patient with relapsed adult B-lymphoblastic leukemia.

Exp Hematol 2016 Jul 13;44(7):603-13. Epub 2016 May 13.

Siteman Cancer Center, Washington University, St. Louis, MO, USA; Department of Medicine, Washington University, St. Louis, MO, USA.

The genomic events responsible for the pathogenesis of relapsed adult B-lymphoblastic leukemia (B-ALL) are not yet clear. We performed integrative analysis of whole-genome, whole-exome, custom capture, whole-transcriptome (RNA-seq), and locus-specific genomic assays across nine time points from a patient with primary de novo B-ALL. Comprehensive genome and transcriptome characterization revealed a dramatic tumor evolution during progression, yielding a tumor with complex clonal architecture at second relapse. We observed and validated point mutations in EP300 and NF1, a highly expressed EP300-ZNF384 gene fusion, a microdeletion in IKZF1, a focal deletion affecting SETD2, and large deletions affecting RB1, PAX5, NF1, and ETV6. Although the genome analysis revealed events of potential biological relevance, no clinically actionable treatment options were evident at the time of the second relapse. However, transcriptome analysis identified aberrant overexpression of the targetable protein kinase encoded by the FLT3 gene. Although the patient had refractory disease after salvage therapy for the second relapse, treatment with the FLT3 inhibitor sunitinib rapidly induced a near complete molecular response, permitting the patient to proceed to a matched-unrelated donor stem cell transplantation. The patient remains in complete remission more than 4 years later. Analysis of this patient's relapse genome revealed an unexpected, actionable therapeutic target that led to a specific therapy associated with a rapid clinical response. For some patients with relapsed or refractory cancers, this approach may indicate a novel therapeutic intervention that could alter outcome.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.exphem.2016.04.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4914477PMC
July 2016

Optimizing cancer genome sequencing and analysis.

Cell Syst 2015 Sep;1(3):210-223

The McDonnell Genome Institute, Washington University, St. Louis, MO, USA, 63108 ; Department of Genetics, Washington University, St. Louis, MO, USA, 63108 ; Siteman Cancer Center, Washington University, St. Louis, MO, USA, 63108 ; Department of Medicine, Washington University, St. Louis, MO, USA, 63108.

Tumors are typically sequenced to depths of 75-100× (exome) or 30-50× (whole genome). We demonstrate that current sequencing paradigms are inadequate for tumors that are impure, aneuploid or clonally heterogeneous. To reassess optimal sequencing strategies, we performed ultra-deep (up to ~312×) whole genome sequencing (WGS) and exome capture (up to ~433×) of a primary acute myeloid leukemia, its subsequent relapse, and a matched normal skin sample. We tested multiple alignment and variant calling algorithms and validated ~200,000 putative SNVs by sequencing them to depths of ~1,000×. Additional targeted sequencing provided over 10,000× coverage and ddPCR assays provided up to ~250,000× sampling of selected sites. We evaluated the effects of different library generation approaches, depth of sequencing, and analysis strategies on the ability to effectively characterize a complex tumor. This dataset, representing the most comprehensively sequenced tumor described to date, will serve as an invaluable community resource (dbGaP accession id phs000159).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cels.2015.08.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4669575PMC
September 2015

DGIdb 2.0: mining clinically relevant drug-gene interactions.

Nucleic Acids Res 2016 Jan 3;44(D1):D1036-44. Epub 2015 Nov 3.

McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that consolidates disparate data sources describing drug-gene interactions and gene druggability. It provides an intuitive graphical user interface and a documented application programming interface (API) for querying these data. DGIdb was assembled through an extensive manual curation effort, reflecting the combined information of twenty-seven sources. For DGIdb 2.0, substantial updates have been made to increase content and improve its usefulness as a resource for mining clinically actionable drug targets. Specifically, nine new sources of drug-gene interactions have been added, including seven resources specifically focused on interactions linked to clinical trials. These additions have more than doubled the overall count of drug-gene interactions. The total number of druggable gene claims has also increased by 30%. Importantly, a majority of the unrestricted, publicly-accessible sources used in DGIdb are now automatically updated on a weekly basis, providing the most current information for these sources. Finally, a new web view and API have been developed to allow searching for interactions by drug identifiers to complement existing gene-based search functionality. With these updates, DGIdb represents a comprehensive and user friendly tool for mining the druggable genome for precision medicine hypothesis generation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkv1165DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702839PMC
January 2016

Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud.

PLoS Comput Biol 2015 Aug 6;11(8):e1004393. Epub 2015 Aug 6.

McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America; Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri, United States of America; Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America; Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America.

Massively parallel RNA sequencing (RNA-seq) has rapidly become the assay of choice for interrogating RNA transcript abundance and diversity. This article provides a detailed introduction to fundamental RNA-seq molecular biology and informatics concepts. We make available open-access RNA-seq tutorials that cover cloud computing, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality-control strategies, expression, differential expression, and alternative splicing analysis methods. These tutorials and additional training resources are accompanied by complete analysis pipelines and test datasets made available without encumbrance at www.rnaseq.wiki.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1004393DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527835PMC
August 2015

Genome Modeling System: A Knowledge Management Platform for Genomics.

PLoS Comput Biol 2015 Jul 9;11(7):e1004274. Epub 2015 Jul 9.

The Genome Institute, Washington University in St. Louis, St. Louis, Missouri, United States of America.

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1004274DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4497734PMC
July 2015

DGIdb: mining the druggable genome.

Nat Methods 2013 Dec 13;10(12):1209-10. Epub 2013 Oct 13.

1] The Genome Institute, Washington University School of Medicine, St. Louis, Missouri, USA. [2] Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA. [3].

The Drug-Gene Interaction database (DGIdb) mines existing resources that generate hypotheses about how mutated genes might be targeted therapeutically or prioritized for drug development. It provides an interface for searching lists of genes against a compendium of drug-gene interactions and potentially 'druggable' genes. DGIdb can be accessed at http://dgidb.org/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/nmeth.2689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3851581PMC
December 2013

The origin and evolution of mutations in acute myeloid leukemia.

Cell 2012 Jul;150(2):264-78

Department of Medicine, Washington University, St. Louis, MO 63110, USA.

Most mutations in cancer genomes are thought to be acquired after the initiating event, which may cause genomic instability and drive clonal evolution. However, for acute myeloid leukemia (AML), normal karyotypes are common, and genomic instability is unusual. To better understand clonal evolution in AML, we sequenced the genomes of M3-AML samples with a known initiating event (PML-RARA) versus the genomes of normal karyotype M1-AML samples and the exomes of hematopoietic stem/progenitor cells (HSPCs) from healthy people. Collectively, the data suggest that most of the mutations found in AML genomes are actually random events that occurred in HSPCs before they acquired the initiating mutation; the mutational history of that cell is "captured" as the clone expands. In many cases, only one or two additional, cooperating mutations are needed to generate the malignant founding clone. Cells from the founding clone can acquire additional cooperating mutations, yielding subclones that can contribute to disease progression and/or relapse.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cell.2012.06.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3407563PMC
July 2012