Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients

Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically defined tumor types, coupled with an expanding portfolio of molecularly targeted therapies, demands flexible and comprehensive approaches to profile clinically relevant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative using a comprehensive assay, MSK-IMPACT, through which we have compiled tumor and matched normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel noncoding alterations, and mutational signatures that were shared by common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.

Clinical utility of clonality testing by next generation sequencing in the monitoring of B-cell and T-cell malignancies

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Background: Clonality testing is an integral part of the initial assessment of B and T cell malignancies. The further use of conventional clonality assays for monitoring of disease post treatment is limited by low assay sensitivity and inability to track clones based on their unique sequences. Clonality assessment by next generation sequencing (NGS) provides increased diagnostic capabilities, allowing the tracking of disease specific clones as well as the full spectrum of clonal sequences that arise in response to therapy. Here we describe our initial experience using an NGS based assay for monitoring and our comparison to conventional clonality assays and flow cytometry. Methods: DNA was extracted from hematologic samples received for routine clonality assessment including diagnostic (DS) and follow-up post therapy (PT) samples. Clonality testing was performed by conventional PCR-based assays using biomed II primers and by NGS utilizing Lymphotrack IGH FR1 and TRG kits (Invivoscribe). The amplified products were sequenced on Illumina MiSeq. For MRD assessment, we created dedicated laboratory protocols as well as in-house developed software, MSK-LymphoClone (LC), containing a data analysis pipeline, analytical tools for clonality assessment and a signout portal for easy search and retrieval of pertinent clones. Results: Samples from 48 patients were included (48 DS and 60 PT) encompassing 12 plasma cell neoplasms, 11 acute lymphoblastic leukemias, 16 mature B-cell and 9 T-cell lymphomas. All diagnostic samples showed clonal rearrangement with 100% concordance between conventional and NGS assays. Residual disease was detected in 27/60 (45%) PT samples using conventional fragment size based assays, 27/57 (47%) using flow and 38/60 (63%) using NGS. Diagnostic clonal sequences were detected in as low as 0.0019% of total reads in PT samples tested by NGS. 18/60 (30.0%) PT samples (17 patients) were disease negative by all assays; 16 patients remained disease-free (median follow-up - 2.4 months). Conclusions: NGS clonality testing is a valuable tool for monitoring patients with B and T cell neoplasms showing higher sensitivity and specificity than conventional assays.

Metabolic environments and genomic features associated with pathogenic and mutualistic interactions between bacteria and plants

Genomic characteristics discriminating parasitic and mutualistic relationship of bacterial symbionts with plants are poorly understood. This study comparatively analyzed the genomes of 54 mutualists and pathogens to discover genomic markers associated with the different phenotypes. Using metabolic network models, we predict external environments associated with free-living and symbiotic lifestyles and quantify dependences of symbionts on the host in terms of the consumed metabolites. We show that specific differences between the phenotypes are pronounced at the levels of metabolic enzymes, especially carbohydrate active, and protein functions. Overall, biosynthetic functions are enriched and more diverse in plant mutualists whereas processes and functions involved in degradation and host invasion are enriched and more diverse in pathogens. A distinctive characteristic of plant pathogens is a putative novel secretion system with a circadian rhythm regulator. A specific marker of plant mutualists is the co-residence of genes encoding nitrogenase and ribulose bisphosphate carboxylase/oxygenase (RuBisCO). We predict that RuBisCO is likely used in a putative metabolic pathway to supplement carbon obtained heterotrophically with low-cost assimilation of carbon from CO2. We validate results of the comparative analysis by predicting correct phenotype, pathogenic or mutualistic, for 20 symbionts in an independent set of 30 pathogens, mutualists, and commensals.

Global transcriptome analysis of Clostridium thermocellum ATCC 27405 during growth on dilute acid pretreated Populus and switchgrass

BACKGROUND

The thermophilic anaerobe Clostridium thermocellum is a candidate consolidated bioprocessing (CBP) biocatalyst for cellulosic ethanol production. The aim of this study was to investigate C. thermocellum genes required to ferment biomass substrates and to conduct a robust comparison of DNA microarray and RNA sequencing (RNA-seq) analytical platforms.

RESULTS

C. thermocellum ATCC 27405 fermentations were conducted with a 5 g/L solid substrate loading of either pretreated switchgrass or Populus. Quantitative saccharification and inductively coupled plasma emission spectroscopy (ICP-ES) for elemental analysis revealed composition differences between biomass substrates, which may have influenced growth and transcriptomic profiles. High quality RNA was prepared for C. thermocellum grown on solid substrates and transcriptome profiles were obtained for two time points during active growth (12 hours and 37 hours postinoculation). A comparison of two transcriptomic analytical techniques, microarray and RNA-seq, was performed and the data analyzed for statistical significance. Large expression differences for cellulosomal genes were not observed. We updated gene predictions for the strain and a small novel gene, Cthe_3383, with a putative AgrD peptide quorum sensing function was among the most highly expressed genes. RNA-seq data also supported different small regulatory RNA predictions over others. The DNA microarray gave a greater number (2,351) of significant genes relative to RNA-seq (280 genes when normalized by the kernel density mean of M component (KDMM) method) in an analysis of variance (ANOVA) testing method with a 5% false discovery rate (FDR). When a 2-fold difference in expression threshold was applied, 73 genes were significantly differentially expressed in common between the two techniques. Sulfate and phosphate uptake/utilization genes, along with genes for a putative efflux pump system were some of the most differentially regulated transcripts when profiles for C. thermocellum grown on either pretreated switchgrass or Populus were compared.

CONCLUSIONS

Our results suggest that a high degree of agreement in differential gene expression measurements between transcriptomic platforms is possible, but choosing an appropriate normalization regime is essential.

Industrial robustness: understanding the mechanism of tolerance for the Populus hydrolysate-tolerant mutant strain of Clostridium thermocellum

BACKGROUND

An industrially robust microorganism that can efficiently degrade and convert lignocellulosic biomass into ethanol and next-generation fuels is required to economically produce future sustainable liquid transportation fuels. The anaerobic, thermophilic, cellulolytic bacterium Clostridium thermocellum is a candidate microorganism for such conversions but it, like many bacteria, is sensitive to potential toxic inhibitors developed in the liquid hydrolysate produced during biomass processing. Microbial processes leading to tolerance of these inhibitory compounds found in the pretreated biomass hydrolysate are likely complex and involve multiple genes.

METHODOLOGY/PRINCIPAL FINDINGS

In this study, we developed a 17.5% v/v Populus hydrolysate tolerant mutant strain of C. thermocellum by directed evolution. The genome of the wild type strain, six intermediate population samples and seven single colony isolates were sequenced to elucidate the mechanism of tolerance. Analysis of the 224 putative mutations revealed 73 high confidence mutations. A longitudinal analysis of the intermediate population samples, a pan-genomic analysis of the isolates, and a hotspot analysis revealed 24 core genes common to all seven isolates and 8 hotspots. Genetic mutations were matched with the observed phenotype through comparison of RNA expression levels during fermentation by the wild type strain and mutant isolate 6 in various concentrations of Populus hydrolysate (0%, 10%, and 17.5% v/v).

CONCLUSION/SIGNIFICANCE

The findings suggest that there are multiple mutations responsible for the Populus hydrolysate tolerant phenotype resulting in several simultaneous mechanisms of action, including increases in cellular repair, and altered energy metabolism. To date, this study provides the most comprehensive elucidation of the mechanism of tolerance to a pretreated biomass hydrolysate by C. thermocellum. These findings make important contributions to the development of industrially robust strains of consolidated bioprocessing microorganisms.

Binding Motifs in Bacterial Gene Promoters Modulate Transcriptional Effects of Global Regulators CRP and ArcA

Bacterial gene regulation involves transcription factors (TF) that bind to DNA recognition sequences in operon promoters. These recognition sequences, many of which are palindromic, are known as regulatory elements or transcription factor binding sites (TFBS). Some TFs are global regulators that can modulate the expression of hundreds of genes. In this study we examine global regulator half-sites, where a half-site, which we shall call a binding motif (BM), is one half of a palindromic TFBS. We explore the hypothesis that the number of BMs plays an important role in transcriptional regulation, examining empirical data from transcriptional profiling of the CRP and ArcA regulons. We compare the power of BM counts and of full TFBS characteristics to predict induced transcriptional activity. We find that CRP BM counts have a nonlinear effect on CRP-dependent transcriptional activity and predict this activity better than full TFBS quality or location.

BESC knowledgebase public portal

The BioEnergy Science Center (BESC) is undertaking large experimental campaigns to understand the biosynthesis and biodegradation of biomass and to develop biofuel solutions. BESC is generating large volumes of diverse data, including genome sequences, omics data and assay results. The purpose of the BESC Knowledgebase is to serve as a centralized repository for experimentally generated data and to provide an integrated, interactive and user-friendly analysis framework. The Portal makes available tools for visualization, integration and analysis of data either produced by BESC or obtained from external resources.

AVAILABILITY

http://besckb.ornl.gov.

CAZymes Analysis Toolkit (CAT): web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database

The Carbohydrate-Active Enzyme (CAZy) database provides a rich set of manually annotated enzymes that degrade, modify, or create glycosidic bonds. Despite rich and invaluable information stored in the database, software tools utilizing this information for annotation of newly sequenced genomes by CAZy families are limited. We have employed two annotation approaches to fill the gap between manually curated high-quality protein sequences collected in the CAZy database and the growing number of other protein sequences produced by genome or metagenome sequencing projects. The first approach is based on a similarity search against the entire nonredundant sequences of the CAZy database. The second approach performs annotation using links or correspondences between the CAZy families and protein family domains. The links were discovered using the association rule learning algorithm applied to sequences from the CAZy database. The approaches complement each other and in combination achieved high specificity and sensitivity when cross-evaluated with the manually curated genomes of Clostridium thermocellum ATCC 27405 and Saccharophagus degradans 2-40. The capability of the proposed framework to predict the function of unknown protein domains and of hypothetical proteins in the genome of Neurospora crassa is demonstrated. The framework is implemented as a Web service, the CAZymes Analysis Toolkit, and is available at http://cricket.ornl.gov/cgi-bin/cat.cgi.

Shewanella knowledgebase: integration of the experimental data and computational predictions suggests a biological role for transcription of intergenic regions

Shewanellae are facultative γ-proteobacteria whose remarkable respiratory versatility has resulted in interest in their utility for bioremediation of heavy metals and radionuclides and for energy generation in microbial fuel cells. Extensive experimental efforts over the last several years and the availability of 21 sequenced Shewanella genomes made it possible to collect and integrate a wealth of information on the genus into one public resource providing new avenues for making biological discoveries and for developing a system level understanding of the cellular processes. The Shewanella knowledgebase was established in 2005 to provide a framework for integrated genome-based studies on Shewanella ecophysiology. The present version of the knowledgebase provides access to a diverse set of experimental and genomic data along with tools for curation of genome annotations and visualization and integration of genomic data with experimental data. As a demonstration of the utility of this resource, we examined a single microarray data set from Shewanella oneidensis MR-1 for new insights into regulatory processes. The integrated analysis of the data predicted a new type of bacterial transcriptional regulation involving co-transcription of the intergenic region with the downstream gene and suggested a biological role for co-transcription that likely prevents the binding of a regulator of the upstream gene to the regulator binding site located in the intergenic region.

Database URL:http://shewanella-knowledgebase.org:8080/Shewanella/ or http://spruce.ornl.gov:8080/Shewanella/

Shewregdb: Database and visualization environment for experimental and predicted regulatory information in Shewanella oneidensis mr-1

Shewanella oneidensis MR-1 is an important model organism for environmental research as it has an exceptional metabolic and respiratory versatility regulated by a complex regulatory network. We have developed a database to collect experimental and computational data relating to regulation of gene and protein expression, and, a visualization environment that enables integration of these data types. The regulatory information in the database includes predictions of DNA regulator binding sites, sigma factor binding sites, transcription units, operons, promoters, and RNA regulators including non-coding RNAs, riboswitches, and different types of terminators.

Sentra: a database of signal transduction proteins for comparative genome analysis

Sentra (), a database of signal transduction proteins encoded in completely sequenced prokaryotic genomes, has been updated to reflect recent advances in understanding signal transduction events on a whole-genome scale. Sentra consists of two principal components, a manually curated list of signal transduction proteins in 202 completely sequenced prokaryotic genomes and an automatically generated listing of predicted signaling proteins in 235 sequenced genomes that are awaiting manual curation. In addition to two-component histidine kinases and response regulators, the database now lists manually curated Ser/Thr/Tyr protein kinases and protein phosphatases, as well as adenylate and diguanylate cyclases and c-di-GMP phosphodiesterases, as defined in several recent reviews. All entries in Sentra are extensively annotated with relevant information from public databases (e.g. UniProt, KEGG, PDB and NCBI). Sentra's infrastructure was redesigned to support interactive cross-genome comparisons of signal transduction capabilities of prokaryotic organisms from a taxonomic and phenotypic perspective and in the framework of signal transduction pathways from KEGG. Sentra leverages the PUMA2 system to support interactive analysis and annotation of signal transduction proteins by the users.

PUMA2--grid-based high-throughput analysis of genomes and metabolic pathways

The PUMA2 system (available at ) is an interactive, integrated bioinformatics environment for high-throughput genetic sequence analysis and metabolic reconstructions from sequence data. PUMA2 provides a framework for comparative and evolutionary analysis of genomic data and metabolic networks in the context of taxonomic and phenotypic information. Grid infrastructure is used to perform computationally intensive tasks. PUMA2 currently contains precomputed analysis of 213 prokaryotic, 22 eukaryotic, 650 mitochondrial and 1493 viral genomes and automated metabolic reconstructions for >200 organisms. Genomic data is annotated with information integrated from >20 sequence, structural and metabolic databases and ontologies. PUMA2 supports both automated and interactive expert-driven annotation of genomes, using a variety of publicly available bioinformatics tools. It also contains a suite of unique PUMA2 tools for automated assignment of gene function, evolutionary analysis of protein families and comparative analysis of metabolic pathways. PUMA2 allows users to submit batch sequence data for automated functional analysis and construction of metabolic models. The results of these analyses are made available to the users in the PUMA2 environment for further interactive sequence analysis and annotation.