Publications by authors named "Benjamin J Ainscough"

15 Publications

  • Page 1 of 1

Author Correction: The prognostic effects of somatic mutations in ER-positive breast cancer.

Nat Commun 2018 11 14;9(1):4850. Epub 2018 Nov 14.

McDonnell Genome Institute, Washington University School of Medicine, St. Louis 63108, MO, USA.

The original version of this Article contained errors in the depiction of confidence intervals in the NF1 BCSS data illustrated in Figure 3b. These have now been corrected in both the PDF and HTML versions of the Article. The incorrect version of Figure 3b is presented in the associated Author Correction.
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http://dx.doi.org/10.1038/s41467-018-07407-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6235964PMC
November 2018

A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data.

Nat Genet 2018 12 5;50(12):1735-1743. Epub 2018 Nov 5.

McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.

Cancer genomic analysis requires accurate identification of somatic variants in sequencing data. Manual review to refine somatic variant calls is required as a final step after automated processing. However, manual variant refinement is time-consuming, costly, poorly standardized, and non-reproducible. Here, we systematized and standardized somatic variant refinement using a machine learning approach. The final model incorporates 41,000 variants from 440 sequencing cases. This model accurately recapitulated manual refinement labels for three independent testing sets (13,579 variants) and accurately predicted somatic variants confirmed by orthogonal validation sequencing data (212,158 variants). The model improves on manual somatic refinement by reducing bias on calls otherwise subject to high inter-reviewer variability.
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http://dx.doi.org/10.1038/s41588-018-0257-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428590PMC
December 2018

Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples.

Genet Med 2019 04 5;21(4):972-981. Epub 2018 Oct 5.

McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.

Purpose: Following automated variant calling, manual review of aligned read sequences is required to identify a high-quality list of somatic variants. Despite widespread use in analyzing sequence data, methods to standardize manual review have not been described, resulting in high inter- and intralab variability.

Methods: This manual review standard operating procedure (SOP) consists of methods to annotate variants with four different calls and 19 tags. The calls indicate a reviewer's confidence in each variant and the tags indicate commonly observed sequencing patterns and artifacts that inform the manual review call. Four individuals were asked to classify variants prior to, and after, reading the SOP and accuracy was assessed by comparing reviewer calls with orthogonal validation sequencing.

Results: After reading the SOP, average accuracy in somatic variant identification increased by 16.7% (p value = 0.0298) and average interreviewer agreement increased by 12.7% (p value < 0.001). Manual review conducted after reading the SOP did not significantly increase reviewer time.

Conclusion: This SOP supports and enhances manual somatic variant detection by improving reviewer accuracy while reducing the interreviewer variability for variant calling and annotation.
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http://dx.doi.org/10.1038/s41436-018-0278-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6450397PMC
April 2019

The prognostic effects of somatic mutations in ER-positive breast cancer.

Nat Commun 2018 09 4;9(1):3476. Epub 2018 Sep 4.

McDonnell Genome Institute, Washington University School of Medicine, St. Louis, 63108, MO, USA.

Here we report targeted sequencing of 83 genes using DNA from primary breast cancer samples from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients to determine interactions between somatic mutation and prognosis. Independent validation of prognostic interactions was achieved using data from the METABRIC study. Previously established associations between MAP3K1 and PIK3CA mutations with luminal A status/favorable prognosis and TP53 mutations with Luminal B/non-luminal tumors/poor prognosis were observed, validating the methodological approach. In UBC-TAM, NF1 frame-shift nonsense (FS/NS) mutations were also a poor outcome driver that was validated in METABRIC. For MA12, poor outcome associated with PIK3R1 mutation was also reproducible. DDR1 mutations were strongly associated with poor prognosis in UBC-TAM despite stringent false discovery correction (q = 0.0003). In conclusion, uncommon recurrent somatic mutations should be further explored to create a more complete explanation of the highly variable outcomes that typifies ER+ breast cancer.
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http://dx.doi.org/10.1038/s41467-018-05914-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123466PMC
September 2018

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

High-performance web services for querying gene and variant annotation.

Genome Biol 2016 05 6;17(1):91. Epub 2016 May 6.

Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, 92037, USA.

Efficient tools for data management and integration are essential for many aspects of high-throughput biology. In particular, annotations of genes and human genetic variants are commonly used but highly fragmented across many resources. Here, we describe MyGene.info and MyVariant.info, high-performance web services for querying gene and variant annotation information. These web services are currently accessed more than three million times permonth. They also demonstrate a generalizable cloud-based model for organizing and querying biological annotation information. MyGene.info and MyVariant.info are provided as high-performance web services, accessible at http://mygene.info and http://myvariant.info . Both are offered free of charge to the research community.
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http://dx.doi.org/10.1186/s13059-016-0953-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4858870PMC
May 2016

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

Convergent loss of PTEN leads to clinical resistance to a PI(3)Kα inhibitor.

Nature 2015 Feb 17;518(7538):240-4. Epub 2014 Nov 17.

Human Oncology and Pathogenesis Program (HOPP), Memorial Sloan Kettering Cancer Center, 1275 York Avenue, Box 20, New York, New York 10065, USA.

Broad and deep tumour genome sequencing has shed new light on tumour heterogeneity and provided important insights into the evolution of metastases arising from different clones. There is an additional layer of complexity, in that tumour evolution may be influenced by selective pressure provided by therapy, in a similar fashion to that occurring in infectious diseases. Here we studied tumour genomic evolution in a patient (index patient) with metastatic breast cancer bearing an activating PIK3CA (phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha, PI(3)Kα) mutation. The patient was treated with the PI(3)Kα inhibitor BYL719, which achieved a lasting clinical response, but the patient eventually became resistant to this drug (emergence of lung metastases) and died shortly thereafter. A rapid autopsy was performed and material from a total of 14 metastatic sites was collected and sequenced. All metastatic lesions, when compared to the pre-treatment tumour, had a copy loss of PTEN (phosphatase and tensin homolog) and those lesions that became refractory to BYL719 had additional and different PTEN genetic alterations, resulting in the loss of PTEN expression. To put these results in context, we examined six other patients also treated with BYL719. Acquired bi-allelic loss of PTEN was found in one of these patients, whereas in two others PIK3CA mutations present in the primary tumour were no longer detected at the time of progression. To characterize our findings functionally, we examined the effects of PTEN knockdown in several preclinical models (both in cell lines intrinsically sensitive to BYL719 and in PTEN-null xenografts derived from our index patient), which we found resulted in resistance to BYL719, whereas simultaneous PI(3)K p110β blockade reverted this resistance phenotype. We conclude that parallel genetic evolution of separate metastatic sites with different PTEN genomic alterations leads to a convergent PTEN-null phenotype resistant to PI(3)Kα inhibition.
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http://dx.doi.org/10.1038/nature13948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4326538PMC
February 2015

Genome-wide association study of CSF levels of 59 alzheimer's disease candidate proteins: significant associations with proteins involved in amyloid processing and inflammation.

PLoS Genet 2014 Oct 23;10(10):e1004758. Epub 2014 Oct 23.

Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri, United States of America; Hope Center for Neurological Disorders, Washington University School of Medicine, St Louis, Missouri, United States of America; Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St Louis, Missouri, United States of America; Department of Neurology, 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.

Cerebrospinal fluid (CSF) 42 amino acid species of amyloid beta (Aβ42) and tau levels are strongly correlated with the presence of Alzheimer's disease (AD) neuropathology including amyloid plaques and neurodegeneration and have been successfully used as endophenotypes for genetic studies of AD. Additional CSF analytes may also serve as useful endophenotypes that capture other aspects of AD pathophysiology. Here we have conducted a genome-wide association study of CSF levels of 59 AD-related analytes. All analytes were measured using the Rules Based Medicine Human DiscoveryMAP Panel, which includes analytes relevant to several disease-related processes. Data from two independently collected and measured datasets, the Knight Alzheimer's Disease Research Center (ADRC) and Alzheimer's Disease Neuroimaging Initiative (ADNI), were analyzed separately, and combined results were obtained using meta-analysis. We identified genetic associations with CSF levels of 5 proteins (Angiotensin-converting enzyme (ACE), Chemokine (C-C motif) ligand 2 (CCL2), Chemokine (C-C motif) ligand 4 (CCL4), Interleukin 6 receptor (IL6R) and Matrix metalloproteinase-3 (MMP3)) with study-wide significant p-values (p<1.46×10-10) and significant, consistent evidence for association in both the Knight ADRC and the ADNI samples. These proteins are involved in amyloid processing and pro-inflammatory signaling. SNPs associated with ACE, IL6R and MMP3 protein levels are located within the coding regions of the corresponding structural gene. The SNPs associated with CSF levels of CCL4 and CCL2 are located in known chemokine binding proteins. The genetic associations reported here are novel and suggest mechanisms for genetic control of CSF and plasma levels of these disease-related proteins. Significant SNPs in ACE and MMP3 also showed association with AD risk. Our findings suggest that these proteins/pathways may be valuable therapeutic targets for AD. Robust associations in cognitively normal individuals suggest that these SNPs also influence regulation of these proteins more generally and may therefore be relevant to other diseases.
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http://dx.doi.org/10.1371/journal.pgen.1004758DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4207667PMC
October 2014

Organizing knowledge to enable personalization of medicine in cancer.

Genome Biol 2014 Aug 27;15(8):438. Epub 2014 Aug 27.

Interpretation of the clinical significance of genomic alterations remains the most severe bottleneck preventing the realization of personalized medicine in cancer. We propose a knowledge commons to facilitate collaborative contributions and open discussion of clinical decision-making based on genomic events in cancer.
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http://dx.doi.org/10.1186/s13059-014-0438-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4281950PMC
August 2014

Phosphorylated tau-Aβ42 ratio as a continuous trait for biomarker discovery for early-stage Alzheimer's disease in multiplex immunoassay panels of cerebrospinal fluid.

Biol Psychiatry 2014 May 19;75(9):723-31. Epub 2014 Jan 19.

Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri; Department of Neurology, Washington University School of Medicine, St. Louis, Missouri; Department of Genetics, Washington University School of Medicine, St. Louis, Missouri; Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, Missouri; Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, Missouri. Electronic address:

Background: Identification of the physiologic changes that occur during the early stages of Alzheimer's disease (AD) may provide critical insights for the diagnosis, prognosis, and treatment of disease. Cerebrospinal fluid (CSF) biomarkers are a rich source of information that reflect the brain proteome.

Methods: A novel approach was applied to screen a panel of ~190 CSF analytes quantified by multiplex immunoassay, and common associations were detected in the Knight Alzheimer's Disease Research Center (N = 311) and the Alzheimer's Disease Neuroimaging Initiative (N = 293) cohorts. Rather than case-control status, the ratio of CSF levels of tau phosphorylated at threonine 181 (ptau181) and Aβ42 was used as a continuous trait in these analyses.

Results: The ptau181-Aβ42 ratio has more statistical power than traditional modeling approaches, and the levels of CSF heart-type fatty acid binding protein (FABP) and 12 other correlated analytes increase as AD progresses. These results were validated using the traditional case-control status model. Stratification of the dataset demonstrated that increases in these analytes occur very early in the disease course and were apparent even in nondemented individuals with AD pathology (low ptau181, low Aβ42) compared with elderly control subjects with no pathology (low ptau181, high Aβ42). The FABP-Aβ42 ratio demonstrates a similar hazard ratio for disease conversion to ptau181-Aβ42 even though the overlap in classification is incomplete suggesting that FABP contributes independent information as a predictor of AD.

Conclusions: Our results indicate that the approach presented here can be used to identify novel biomarkers for AD correctly and that CSF heart FABP levels start to increase at very early stages of AD.
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http://dx.doi.org/10.1016/j.biopsych.2013.11.032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4007142PMC
May 2014
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