Jason Walker

Recurring Mutations Found by Sequencing an Acute Myeloid Leukemia Genome

BACKGROUND The full complement of DNA mutations that are responsible for the pathogenesis of acute myeloid leukemia (AML) is not yet known. METHODS We used massively parallel DNA sequencing to obtain a very high level of coverage (approximately 98%) of a primary, cytogenetically normal, de novo genome for AML with minimal maturation (AML-M1) and a matched normal skin genome. RESULTS We identified 12 acquired (somatic) mutations within the coding sequences of genes and 52 somatic point mutations in conserved or regulatory portions of the genome. All mutations appeared to be heterozygous and present in nearly all cells in the tumor sample. Four of the 64 mutations occurred in at least 1 additional AML sample in 188 samples that were tested. Mutations in NRAS and NPM1 had been identified previously in patients with AML, but two other mutations had not been identified. One of these mutations, in the IDH1 gene, was present in 15 of 187 additional AML genomes tested and was strongly associated with normal cytogenetic status; it was present in 13 of 80 cytogenetically normal samples (16%). The other was a nongenic mutation in a genomic region with regulatory potential and conservation in higher mammals; we detected it in one additional AML tumor. The AML genome that we sequenced contains approximately 750 point mutations, of which only a small fraction are likely to be relevant to pathogenesis. CONCLUSIONS By comparing the sequences of tumor and skin genomes of a patient with AML-M1, we have identified recurring mutations that may be relevant for pathogenesis.

GENOMIC LANDSCAPE OF NON-SMALL CELL LUNG CANCER IN SMOKERS AND NEVER SMOKERS

We report the results of whole-genome and transcriptome sequencing of tumor and adjacent normal tissue samples from 17 patients with non-small cell lung carcinoma (NSCLC). We identified 3,726 point mutations and more than 90 indels in the coding sequence, with an average mutation frequency more than 10-fold higher in smokers than in never-smokers. Novel alterations in genes involved in chromatin modification and DNA repair pathways were identified, along with DACH1, CFTR, RELN, ABCB5, and HGF. Deep digital sequencing revealed diverse clonality patterns in both never-smokers and smokers. All validated EFGR and KRAS mutations were present in the founder clones, suggesting possible roles in cancer initiation. Analysis revealed 14 fusions, including ROS1 and ALK, as well as novel metabolic enzymes. Cell-cycle and JAK-STAT pathways are significantly altered in lung cancer, along with perturbations in 54 genes that are potentially targetable with currently available drugs.

A physical map of the chicken genome

Strategies for assembling large, complex genomes have evolved to include a combination of whole-genome shotgun sequencing and hierarchal map-assisted sequencing. Whole-genome maps of all types can aid genome assemblies, generally starting with low-resolution cytogenetic maps and ending with the highest resolution of sequence. Fingerprint clone maps are based upon complete restriction enzyme digests of clones representative of the target genome, and ultimately comprise a near-contiguous path of clones across the genome. Such clone-based maps are used to validate sequence assembly order, supply long-range linking information for assembled sequences, anchor sequences to the genetic map and provide templates for closing gaps. Fingerprint maps are also a critical resource for subsequent functional genomic studies, because they provide a redundant and ordered sampling of the genome with clones. In an accompanying paper we describe the draft genome sequence of the chicken, Gallus gallus, the first species sequenced that is both a model organism and a global food source. Here we present a clone-based physical map of the chicken genome at 20-fold coverage, containing 260 contigs of overlapping clones. This map represents approximately 91% of the chicken genome and enables identification of chicken clones aligned to positions in other sequenced genomes.

DGIdb 2.0: mining clinically relevant drug–gene interactions

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.

Genomic impact of transient low-dose decitabine treatment on primary AML cells.

Acute myeloid leukemia (AML) is characterized by dysregulated gene expression and abnormal patterns of DNA methylation; the relationship between these events is unclear. Many AML patients are now being treated with hypomethylating agents, such as decitabine (DAC), although the mechanisms by which it induces remissions remain unknown. The goal of this study was to use a novel stromal coculture assay that can expand primary AML cells to identify the immediate changes induced by DAC with a dose (100nM) that decreases total 5-methylcytosine content and reactivates imprinted genes (without causing myeloid differentiation, which would confound downstream genomic analyses). Using array-based technologies, we found that DAC treatment caused global hypomethylation in all samples (with a preference for regions with higher levels of baseline methylation), yet there was limited correlation between changes in methylation and gene expression. Moreover, the patterns of methylation and gene expression across the samples were primarily determined by the intrinsic properties of the primary cells, rather than DAC treatment. Although DAC induces hypomethylation, we could not identify canonical target genes that are altered by DAC in primary AML cells, suggesting that the mechanism of action of DAC is more complex than previously recognized.

The PRO-ACT database Design, initial analyses, and predictive features

Objective: To pool data from completed amyotrophic lateral sclerosis (ALS) clinical trials and create an open-access resource that enables greater understanding of the phenotype and biology of ALS. Methods: Clinical trials data were pooled from 16 completed phase II/III ALS clinical trials and one observational study. Over 8 million de-identified longitudinally collected data points from over 8,600 individuals with ALS were standardized across trials and merged to create the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database. This database includes demographics, family histories, and longitudinal clinical and laboratory data. Mixed effects models were used to describe the rate of disease progression measured by the Revised ALS Functional Rating Scale (ALSFRS-R) and vital capacity (VC). Cox regression models were used to describe survival data. Implementing Bonferroni correction, the critical p value for 15 different tests was p = 0.003. Results: The ALSFRS-R rate of decline was 1.02 (±2.3) points per month and the VC rate of decline was 2.24% of predicted (±6.9) per month. Higher levels of uric acid at trial entry were predictive of a slower drop in ALSFRS-R (p = 0.01) and VC (p < 0.0001), and longer survival (p = 0.02). Higher levels of creatinine at baseline were predictive of a slower drop in ALSFRS-R (p = 0.01) and VC (p < 0.0001), and longer survival (p = 0.01). Finally, higher body mass index (BMI) at baseline was associated with longer survival (p < 0.0001). Conclusion: The PRO-ACT database is the largest publicly available repository of merged ALS clinical trials data. We report that baseline levels of creatinine and uric acid, as well as baseline BMI, are strong predictors of disease progression and survival.

CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer

CIViC is an expert-crowdsourced knowledgebase for Clinical Interpretation of Variants in Cancer describing the therapeutic, prognostic, diagnostic and predisposing relevance of inherited and somatic variants of all types. CIViC is committed to open-source code, open-access content, public application programming interfaces (APIs) and provenance of supporting evidence to allow for the transparent creation of current and accurate variant interpretations for use in cancer precision medicine.

Genome Modeling System: A Knowledge Management Platform for Genomics

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.

RNA Sequencing of Tumor-Associated Microglia Reveals Ccl5 as a Stromal Chemokine Critical for Neurofibromatosis-1 Glioma Growth

Solid cancers develop within a supportive microenvironment that promotes tumor formation and growth through the elaboration of mitogens and chemokines. Within these tumors, monocytes (macrophages and microglia) represent rich sources of these stromal factors. Leveraging a genetically engineered mouse model of neurofibromatosis type 1 (NF1) low-grade brain tumor (optic glioma), we have previously demonstrated that microglia are essential for glioma formation and maintenance. To identify potential tumor-associated microglial factors that support glioma growth (gliomagens), we initiated a comprehensive large-scale discovery effort using optimized RNA-sequencing methods focused specifically on glioma-associated microglia. Candidate microglial gliomagens were prioritized to identify potential secreted or membrane-bound proteins, which were next validated by quantitative real-time polymerase chain reaction as well as by RNA fluorescence in situ hybridization following minocycline-mediated microglial inactivation in vivo. Using these selection criteria, chemokine (C-C motif) ligand 5 (Ccl5) was identified as a chemokine highly expressed in genetically engineered Nf1 mouse optic gliomas relative to nonneoplastic optic nerves. As a candidate gliomagen, recombinant Ccl5 increased Nf1-deficient optic nerve astrocyte growth in vitro. Importantly, consistent with its critical role in maintaining tumor growth, treatment with Ccl5 neutralizing antibodies reduced Nf1 mouse optic glioma growth and improved retinal dysfunction in vivo. Collectively, these findings establish Ccl5 as an important microglial growth factor for low-grade glioma maintenance relevant to the development of future stroma-targeted brain tumor therapies.

cDNA Hybrid Capture Improves Transcriptome Analysis on Low-Input and Archived Samples

The use of massively parallel sequencing for studying RNA expression has greatly enhanced our understanding of the transcriptome through the myriad ways these data can be characterized. In particular, clinical samples provide important insights about RNA expression in health and disease, yet these studies can be complicated by RNA degradation that results from the use of formalin as a clinical preservative and by the limited amounts of RNA often available from these precious samples. In this study we describe the combined use of RNA sequencing with an exome capture selection step to enhance the yield of on-exon sequencing read data when compared with RNA sequencing alone. In particular, the exome capture step preserves the dynamic range of expression, permitting differential comparisons and validation of expressed mutations from limited and FFPE preserved samples, while reducing the data generation requirement. We conclude that cDNA hybrid capture has the potential to significantly improve transcriptome analysis from low-yield FFPE material.