Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge.

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Crowdsourced mapping of unexplored target space of kinase inhibitors.

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery.

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

CRI iAtlas: an interactive portal for immuno-oncology research.

The Cancer Research Institute (CRI) iAtlas is an interactive web platform for data exploration and discovery in the context of tumors and their interactions with the immune microenvironment. iAtlas allows researchers to study immune response characterizations and patterns for individual tumor types, tumor subtypes, and immune subtypes. iAtlas supports computation and visualization of correlations and statistics among features related to the tumor microenvironment, cell composition, immune expression signatures, tumor mutation burden, cancer driver mutations, adaptive cell clonality, patient survival, expression of key immunomodulators, and tumor infiltrating lymphocyte (TIL) spatial maps. iAtlas was launched to accompany the release of the TCGA PanCancer Atlas and has since been expanded to include new capabilities such as (1) user-defined loading of sample cohorts, (2) a tool for classifying expression data into immune subtypes, and (3) integration of TIL mapping from digital pathology images. We expect that the CRI iAtlas will accelerate discovery and improve patient outcomes by providing researchers access to standardized immunogenomics data to better understand the tumor immune microenvironment and its impact on patient responses to immunotherapy.

Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions.

The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction.

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

Linked Entity Attribute Pair (LEAP): A Harmonization Framework for Data Pooling.

PURPOSE: As data-sharing projects become increasingly frequent, so does the need to map data elements between multiple classification systems. A generic, robust, shareable architecture will result in increased efficiency and transparency of the mapping process, while upholding the integrity of the data. MATERIALS AND METHODS: The American Association for Cancer Research's Genomics Evidence Neoplasia Information Exchange (GENIE) collects clinical and genomic data for precision cancer medicine. As part of its commitment to open science, GENIE has partnered with the National Cancer Institute's Genomic Data Commons (GDC) as a secondary repository. After initial efforts to submit data from GENIE to GDC failed, we realized the need for a solution to allow for the iterative mapping of data elements between dynamic classification systems. We developed the Linked Entity Attribute Pair (LEAP) database framework to store and manage the term mappings used to submit data from GENIE to GDC. RESULTS: After creating and populating the LEAP framework, we identified 195 mappings from GENIE to GDC requiring remediation and observed a 28% reduction in effort to resolve these issues, as well as a reduction in inadvertent errors. These results led to a decrease in the time to map between OncoTree, the cancer type ontology used by GENIE, and International Classification of Disease for Oncology, 3rd Edition, used by GDC, from several months to less than 1 week. CONCLUSION: The LEAP framework provides a streamlined mapping process among various classification systems and allows for reusability so that efforts to create or adjust mappings are straightforward. The ability of the framework to track changes over time streamlines the process to map data elements across various dynamic classification systems.

Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.

We present a consensus atlas of the human brain transcriptome in Alzheimer's disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington's disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies.

Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection.

BACKGROUND: The phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information. RESULTS: To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches. CONCLUSIONS: The synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .

Molecular, phenotypic, and sample-associated data to describe pluripotent stem cell lines and derivatives.

The use of induced pluripotent stem cells (iPSC) derived from independent patients and sources holds considerable promise to improve the understanding of development and disease. However, optimized use of iPSC depends on our ability to develop methods to efficiently qualify cell lines and protocols, monitor genetic stability, and evaluate self-renewal and differentiation potential. To accomplish these goals, 57 stem cell lines from 10 laboratories were differentiated to 7 different states, resulting in 248 analyzed samples. Cell lines were differentiated and characterized at a central laboratory using standardized cell culture methodologies, protocols, and metadata descriptors. Stem cell and derived differentiated lines were characterized using RNA-seq, miRNA-seq, copy number arrays, DNA methylation arrays, flow cytometry, and molecular histology. All materials, including raw data, metadata, analysis and processing code, and methodological and provenance documentation are publicly available for re-use and interactive exploration at https://www.synapse.org/pcbc. The goal is to provide data that can improve our ability to robustly and reproducibly use human pluripotent stem cells to understand development and disease.

Practical Guidelines for High-Resolution Epigenomic Profiling of Nucleosomal Histones in Postmortem Human Brain Tissue.

BACKGROUND: The nervous system may include more than 100 residue-specific posttranslational modifications of histones forming the nucleosome core that are often regulated in cell-type-specific manner. On a genome-wide scale, some of the histone posttranslational modification landscapes show significant overlap with the genetic risk architecture for several psychiatric disorders, fueling PsychENCODE and other large-scale efforts to comprehensively map neuronal and nonneuronal epigenomes in hundreds of specimens. However, practical guidelines for efficient generation of histone chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) datasets from postmortem brains are needed. METHODS: Protocols and quality controls are given for the following: 1) extraction, purification, and NeuN neuronal marker immunotagging of nuclei from adult human cerebral cortex; 2) fluorescence-activated nuclei sorting; 3) preparation of chromatin by micrococcal nuclease digest; 4) ChIP for open chromatin-associated histone methylation and acetylation; and 5) generation and sequencing of ChIP-seq libraries. RESULTS: We present a ChIP-seq pipeline for epigenome mapping in the neuronal and nonneuronal nuclei from the postmortem brain. This includes a stepwise system of quality controls and user-friendly data presentation platforms. CONCLUSIONS: Our practical guidelines will be useful for projects aimed at histone posttranslational modification mapping in chromatin extracted from hundreds of postmortem brain samples in cell-type-specific manner.

Human whole genome genotype and transcriptome data for Alzheimer's and other neurodegenerative diseases.

Previous genome-wide association studies (GWAS), conducted by our group and others, have identified loci that harbor risk variants for neurodegenerative diseases, including Alzheimer's disease (AD). Human disease variants are enriched for polymorphisms that affect gene expression, including some that are known to associate with expression changes in the brain. Postulating that many variants confer risk to neurodegenerative disease via transcriptional regulatory mechanisms, we have analyzed gene expression levels in the brain tissue of subjects with AD and related diseases. Herein, we describe our collective datasets comprised of GWAS data from 2,099 subjects; microarray gene expression data from 773 brain samples, 186 of which also have RNAseq; and an independent cohort of 556 brain samples with RNAseq. We expect that these datasets, which are available to all qualified researchers, will enable investigators to explore and identify transcriptional mechanisms contributing to neurodegenerative diseases.

Gene expression elucidates functional impact of polygenic risk for schizophrenia.

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.

A narrow repertoire of transcriptional modules responsive to pyogenic bacteria is impaired in patients carrying loss-of-function mutations in MYD88 or IRAK4.

Loss of function of the kinase IRAK4 or the adaptor MyD88 in humans interrupts a pathway critical for pathogen sensing and ignition of inflammation. However, patients with loss-of-function mutations in the genes encoding these factors are, unexpectedly, susceptible to only a limited range of pathogens. We employed a systems approach to investigate transcriptome responses following in vitro exposure of patients' blood to agonists of Toll-like receptors (TLRs) and receptors for interleukin 1 (IL-1Rs) and to whole pathogens. Responses to purified agonists were globally abolished, but variable residual responses were present following exposure to whole pathogens. Further delineation of the latter responses identified a narrow repertoire of transcriptional programs affected by loss of MyD88 function or IRAK4 function. Our work introduces the use of a systems approach for the global assessment of innate immune responses and the characterization of human primary immunodeficiencies.

Modular transcriptional repertoire analyses of adults with systemic lupus erythematosus reveal distinct type I and type II interferon signatures.

OBJECTIVE: The role of interferon-α (IFNα) in the pathogenesis of systemic lupus erythematosus (SLE) is strongly supported by gene expression studies. The aim of this study was to improve characterization of the blood IFN signature in adult SLE patients. METHODS: Consecutive patients were enrolled and followed up prospectively. Microarray data were generated using Illumina BeadChips. A modular transcriptional repertoire was used as a framework for the analysis. RESULTS: Our repertoire of 260 modules, which consisted of coclustered gene sets, included 3 IFN-annotated modules (M1.2, M3.4, and M5.12) that were strongly up-regulated in SLE patients. A modular IFN signature was observed in 54 of 62 patients (87%) or 131 of all 157 samples (83%). The IFN signature was more complex than expected, with each module displaying a distinct activation threshold (M1.2 < M3.4 < M5.12), thus providing a modular score by which to stratify SLE patients based on the presence of 0, 1, 2, or 3 active IFN modules. A similar gradient in modular IFN signature was observed within patients with clinically quiescent disease, for whom moderate/strong modular scores (2 or 3 active IFN modules) were associated with higher anti-double-stranded DNA titers and lower lymphocyte counts than those in patients with absent/mild modular scores (0 or 1 active IFN modules). Longitudinal analyses revealed both stable (M1.2) and variable (M3.4 and M5.12) components of modular IFN signature over time in single patients. Interestingly, mining of other data sets suggested that M3.4 and M5.12 could also be driven by IFNß and IFNγ. CONCLUSION: Modular repertoire analysis reveals complex IFN signatures in SLE, which are not restricted to the previous IFNα signature, but which also involve IFNß and IFNγ.

Comparison of SIV and HIV-1 genomic RNA structures reveals impact of sequence evolution on conserved and non-conserved structural motifs.

RNA secondary structure plays a central role in the replication and metabolism of all RNA viruses, including retroviruses like HIV-1. However, structures with known function represent only a fraction of the secondary structure reported for HIV-1(NL4-3). One tool to assess the importance of RNA structures is to examine their conservation over evolutionary time. To this end, we used SHAPE to model the secondary structure of a second primate lentiviral genome, SIVmac239, which shares only 50% sequence identity at the nucleotide level with HIV-1NL4-3. Only about half of the paired nucleotides are paired in both genomic RNAs and, across the genome, just 71 base pairs form with the same pairing partner in both genomes. On average the RNA secondary structure is thus evolving at a much faster rate than the sequence. Structure at the Gag-Pro-Pol frameshift site is maintained but in a significantly altered form, while the impact of selection for maintaining a protein binding interaction can be seen in the conservation of pairing partners in the small RRE stems where Rev binds. Structures that are conserved between SIVmac239 and HIV-1(NL4-3) also occur at the 5' polyadenylation sequence, in the plus strand primer sites, PPT and cPPT, and in the stem-loop structure that includes the first splice acceptor site. The two genomes are adenosine-rich and cytidine-poor. The structured regions are enriched in guanosines, while unpaired regions are enriched in adenosines, and functionaly important structures have stronger base pairing than nonconserved structures. We conclude that much of the secondary structure is the result of fortuitous pairing in a metastable state that reforms during sequence evolution. However, secondary structure elements with important function are stabilized by higher guanosine content that allows regions of structure to persist as sequence evolution proceeds, and, within the confines of selective pressure, allows structures to evolve.

The transcription factor STAT5 is critical in dendritic cells for the development of TH2 but not TH1 responses.

Dendritic cells (DCs) are critical in immune responses, linking innate and adaptive immunity. We found here that DC-specific deletion of the transcription factor STAT5 was not critical for development but was required for T helper type 2 (TH2), but not TH1, allergic responses in both the skin and lungs. Loss of STAT5 in DCs led to the inability to respond to thymic stromal lymphopoietin (TSLP). STAT5 was required for TSLP-dependent DC activation, including upregulation of the expression of costimulatory molecules and chemokine production. Furthermore, TH2 responses in mice with DC-specific loss of STAT5 resembled those seen in mice deficient in the receptor for TSLP. Our results show that the TSLP-STAT5 axis in DCs is a critical component for the promotion of type 2 immunity at barrier surfaces.

Genome-wide mRNA expression correlates of viral control in CD4+ T-cells from HIV-1-infected individuals.

There is great interindividual variability in HIV-1 viral setpoint after seroconversion, some of which is known to be due to genetic differences among infected individuals. Here, our focus is on determining, genome-wide, the contribution of variable gene expression to viral control, and to relate it to genomic DNA polymorphism. RNA was extracted from purified CD4+ T-cells from 137 HIV-1 seroconverters, 16 elite controllers, and 3 healthy blood donors. Expression levels of more than 48,000 mRNA transcripts were assessed by the Human-6 v3 Expression BeadChips (Illumina). Genome-wide SNP data was generated from genomic DNA using the HumanHap550 Genotyping BeadChip (Illumina). We observed two distinct profiles with 260 genes differentially expressed depending on HIV-1 viral load. There was significant upregulation of expression of interferon stimulated genes with increasing viral load, including genes of the intrinsic antiretroviral defense. Upon successful antiretroviral treatment, the transcriptome profile of previously viremic individuals reverted to a pattern comparable to that of elite controllers and of uninfected individuals. Genome-wide evaluation of cis-acting SNPs identified genetic variants modulating expression of 190 genes. Those were compared to the genes whose expression was found associated with viral load: expression of one interferon stimulated gene, OAS1, was found to be regulated by a SNP (rs3177979, p = 4.9E-12); however, we could not detect an independent association of the SNP with viral setpoint. Thus, this study represents an attempt to integrate genome-wide SNP signals with genome-wide expression profiles in the search for biological correlates of HIV-1 control. It underscores the paradox of the association between increasing levels of viral load and greater expression of antiviral defense pathways. It also shows that elite controllers do not have a fully distinctive mRNA expression pattern in CD4+ T cells. Overall, changes in global RNA expression reflect responses to viral replication rather than a mechanism that might explain viral control.