Targeting low levels of MIF expression as a potential therapeutic strategy for ALS.

Mutations in SOD1 cause amyotrophic lateral sclerosis (ALS), a neurodegenerative disease characterized by motor neuron (MN) loss. We previously discovered that macrophage migration inhibitory factor (MIF), whose levels are extremely low in spinal MNs, inhibits mutant SOD1 misfolding and toxicity. In this study, we show that a single peripheral injection of adeno-associated virus (AAV) delivering MIF into adult SOD1G37R mice significantly improves their motor function, delays disease progression, and extends survival. Moreover, MIF treatment reduces neuroinflammation and misfolded SOD1 accumulation, rescues MNs, and corrects dysregulated pathways as observed by proteomics and transcriptomics. Furthermore, we reveal low MIF levels in human induced pluripotent stem cell-derived MNs from familial ALS patients with different genetic mutations, as well as in post mortem tissues of sporadic ALS patients. Our findings indicate that peripheral MIF administration may provide a potential therapeutic mechanism for modulating misfolded SOD1 in vivo and disease outcome in ALS patients.

Selective CDK7 Inhibition Suppresses Cell Cycle Progression and MYC Signaling While Enhancing Apoptosis in Therapy-resistant Estrogen Receptor-positive Breast Cancer.

PURPOSE: Resistance to endocrine therapy (ET) and CDK4/6 inhibitors (CDK4/6i) is a clinical challenge in estrogen receptor (ER)-positive (ER+) breast cancer. Cyclin-dependent kinase 7 (CDK7) is a candidate target in endocrine-resistant ER+ breast cancer models and selective CDK7 inhibitors (CDK7i) are in clinical development for the treatment of ER+ breast cancer. Nonetheless, the precise mechanisms responsible for the activity of CDK7i in ER+ breast cancer remain elusive. Herein, we sought to unravel these mechanisms. EXPERIMENTAL DESIGN: We conducted multi-omic analyses in ER+ breast cancer models in vitro and in vivo, including models with different genetic backgrounds. We also performed genome-wide CRISPR/Cas9 knockout screens to identify potential therapeutic vulnerabilities in CDK4/6i-resistant models. RESULTS: We found that the on-target antitumor effects of CDK7 inhibition in ER+ breast cancer are in part p53 dependent, and involve cell cycle inhibition and suppression of c-Myc. Moreover, CDK7 inhibition exhibited cytotoxic effects, distinctive from the cytostatic nature of ET and CDK4/6i. CDK7 inhibition resulted in suppression of ER phosphorylation at S118; however, long-term CDK7 inhibition resulted in increased ER signaling, supporting the combination of ET with a CDK7i. Finally, genome-wide CRISPR/Cas9 knockout screens identified CDK7 and MYC signaling as putative vulnerabilities in CDK4/6i resistance, and CDK7 inhibition effectively inhibited CDK4/6i-resistant models. CONCLUSIONS: Taken together, these findings support the clinical investigation of selective CDK7 inhibition combined with ET to overcome treatment resistance in ER+ breast cancer. In addition, our study highlights the potential of increased c-Myc activity and intact p53 as predictors of sensitivity to CDK7i-based treatments.

Identification of gene fusions associated with amyotrophic lateral sclerosis.

INTRODUCTION/AIMS: Genetics is an important risk factor for amyotrophic lateral sclerosis (ALS), a neurodegenerative disease affecting motor neurons. Recent findings demonstrate that in addition to specific genetic mutations, structural variants caused by genetic instability can also play a causative role in ALS. Genomic instability can lead to deletions, duplications, insertions, inversions, and translocations in the genome, and these changes can sometimes lead to fusion of distinct genes into a single transcript. Gene fusion events have been studied extensively in cancer; however, they have not been thoroughly investigated in ALS. The aim of this study was to determine whether gene fusions are present in ALS. METHODS: Gene fusions were identified using STAR Fusion v1.10.0 software in bulk RNA-Seq data from human postmortem samples from publicly available data sets from Target ALS and the New York Genome Center ALS Consortium. RESULTS: We report the presence of gene fusion events in several brain regions as well as in spinal cord samples in ALS. Although most gene fusions were intra-chromosomal events between neighboring genes and present in both ALS and control samples, there was a significantly greater number of unique gene fusions in ALS compared to controls. Lastly, we identified specific gene fusions with a significant burden in ALS, that were absent from both control samples and known cancer gene fusion databases. DISCUSSION: Collectively, our findings reveal an enrichment of gene fusions in ALS and suggest that these events may be an additional genetic cause linked to ALS pathogenesis.

Developmental basis of SHH medulloblastoma heterogeneity.

Many genes that drive normal cellular development also contribute to oncogenesis. Medulloblastoma (MB) tumors likely arise from neuronal progenitors in the cerebellum, and we hypothesized that the heterogeneity observed in MBs with sonic hedgehog (SHH) activation could be due to differences in developmental pathways. To investigate this question, here we perform single-nucleus RNA sequencing on highly differentiated SHH MBs with extensively nodular histology and observed malignant cells resembling each stage of canonical granule neuron development. Through innovative computational approaches, we connect these results to published datasets and find that some established molecular subtypes of SHH MB appear arrested at different developmental stages. Additionally, using multiplexed proteomic imaging and MALDI imaging mass spectrometry, we identify distinct histological and metabolic profiles for highly differentiated tumors. Our approaches are applicable to understanding the interplay between heterogeneity and differentiation in other cancers and can provide important insights for the design of targeted therapies.

Compartments in medulloblastoma with extensive nodularity are connected through differentiation along the granular precursor lineage.

Medulloblastomas with extensive nodularity are cerebellar tumors characterized by two distinct compartments and variable disease progression. The mechanisms governing the balance between proliferation and differentiation in MBEN remain poorly understood. Here, we employ a multi-modal single cell transcriptome analysis to dissect this process. In the internodular compartment, we identify proliferating cerebellar granular neuronal precursor-like malignant cells, along with stromal, vascular, and immune cells. In contrast, the nodular compartment comprises postmitotic, neuronally differentiated malignant cells. Both compartments are connected through an intermediate cell stage resembling actively migrating CGNPs. Notably, we also discover astrocytic-like malignant cells, found in proximity to migrating and differentiated cells at the transition zone between the two compartments. Our study sheds light on the spatial tissue organization and its link to the developmental trajectory, resulting in a more benign tumor phenotype. This integrative approach holds promise to explore intercompartmental interactions in other cancers with varying histology.

Dictionary of immune responses to cytokines at single-cell resolution.

Cytokines mediate cell-cell communication in the immune system and represent important therapeutic targets1-3. A myriad of studies have highlighted their central role in immune function4-13, yet we lack a global view of the cellular responses of each immune cell type to each cytokine. To address this gap, we created the Immune Dictionary, a compendium of single-cell transcriptomic profiles of more than 17 immune cell types in response to each of 86 cytokines (>1,400 cytokine-cell type combinations) in mouse lymph nodes in vivo. A cytokine-centric view of the dictionary revealed that most cytokines induce highly cell-type-specific responses. For example, the inflammatory cytokine interleukin-1ß induces distinct gene programmes in almost every cell type. A cell-type-centric view of the dictionary identified more than 66 cytokine-driven cellular polarization states across immune cell types, including previously uncharacterized states such as an interleukin-18-induced polyfunctional natural killer cell state. Based on this dictionary, we developed companion software, Immune Response Enrichment Analysis, for assessing cytokine activities and immune cell polarization from gene expression data, and applied it to reveal cytokine networks in tumours following immune checkpoint blockade therapy. Our dictionary generates new hypotheses for cytokine functions, illuminates pleiotropic effects of cytokines, expands our knowledge of activation states of each immune cell type, and provides a framework to deduce the roles of specific cytokines and cell-cell communication networks in any immune response.

Circular extrachromosomal DNA promotes tumor heterogeneity in high-risk medulloblastoma.

Circular extrachromosomal DNA (ecDNA) in patient tumors is an important driver of oncogenic gene expression, evolution of drug resistance and poor patient outcomes. Applying computational methods for the detection and reconstruction of ecDNA across a retrospective cohort of 481 medulloblastoma tumors from 465 patients, we identify circular ecDNA in 82 patients (18%). Patients with ecDNA-positive medulloblastoma were more than twice as likely to relapse and three times as likely to die within 5 years of diagnosis. A subset of tumors harbored multiple ecDNA lineages, each containing distinct amplified oncogenes. Multimodal sequencing, imaging and CRISPR inhibition experiments in medulloblastoma models reveal intratumoral heterogeneity of ecDNA copy number per cell and frequent putative 'enhancer rewiring' events on ecDNA. This study reveals the frequency and diversity of ecDNA in medulloblastoma, stratified into molecular subgroups, and suggests copy number heterogeneity and enhancer rewiring as oncogenic features of ecDNA.

Intratumoral drug-releasing microdevices allow in situ high-throughput pharmaco phenotyping in patients with gliomas.

The lack of reliable predictive biomarkers to guide effective therapy is a major obstacle to the advancement of therapy for high-grade gliomas, particularly glioblastoma (GBM), one of the few cancers whose prognosis has not improved over the past several decades. With this pilot clinical trial (number NCT04135807), we provide first-in-human evidence that drug-releasing intratumoral microdevices (IMDs) can be safely and effectively used to obtain patient-specific, high-throughput molecular and histopathological drug response profiling. These data can complement other strategies to inform the selection of drugs based on their observed antitumor effect in situ. IMDs are integrated into surgical practice during tumor resection and remain in situ only for the duration of the otherwise standard operation (2 to 3 hours). None of the six enrolled patients experienced adverse events related to the IMD, and the exposed tissue was usable for downstream analysis for 11 out of 12 retrieved specimens. Analysis of the specimens provided preliminary evidence of the robustness of the readout, compatibility with a wide array of techniques for molecular tissue interrogation, and promising similarities with the available observed clinical-radiological responses to temozolomide. From an investigational aspect, the amount of information obtained with IMDs allows characterization of tissue effects of any drugs of interest, within the physiological context of the intact tumor, and without affecting the standard surgical workflow.

Genome-scale screens identify factors regulating tumor cell responses to natural killer cells.

To systematically define molecular features in human tumor cells that determine their degree of sensitivity to human allogeneic natural killer (NK) cells, we quantified the NK cell responsiveness of hundreds of molecularly annotated 'DNA-barcoded' solid tumor cell lines in multiplexed format and applied genome-scale CRISPR-based gene-editing screens in several solid tumor cell lines, to functionally interrogate which genes in tumor cells regulate the response to NK cells. In these orthogonal studies, NK cell-sensitive tumor cells tend to exhibit 'mesenchymal-like' transcriptional programs; high transcriptional signature for chromatin remodeling complexes; high levels of B7-H6 (NCR3LG1); and low levels of HLA-E/antigen presentation genes. Importantly, transcriptional signatures of NK cell-sensitive tumor cells correlate with immune checkpoint inhibitor (ICI) resistance in clinical samples. This study provides a comprehensive map of mechanisms regulating tumor cell responses to NK cells, with implications for future biomarker-driven applications of NK cell immunotherapies.

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.

High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with beneficial effects in models of Huntington's Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these effects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specific assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases.

Treatment with JQ1, a BET bromodomain inhibitor, is selectively detrimental to R6/2 Huntington's disease mice.

Transcriptional and epigenetic alterations occur early in Huntington's disease (HD), and treatment with epigenetic modulators is beneficial in several HD animal models. The drug JQ1, which inhibits histone acetyl-lysine reader bromodomains, has shown promise for multiple cancers and neurodegenerative disease. We tested whether JQ1 could improve behavioral phenotypes in the R6/2 mouse model of HD and modulate HD-associated changes in transcription and epigenomics. R6/2 and non-transgenic (NT) mice were treated with JQ1 daily from 5 to 11 weeks of age and behavioral phenotypes evaluated over this period. Following the trial, cortex and striatum were isolated and subjected to mRNA-seq and ChIP-seq for the histone marks H3K4me3 and H3K27ac. Initially, JQ1 enhanced motor performance in NT mice. In R6/2 mice, however, JQ1 had no effect on rotarod or grip strength but exacerbated weight loss and worsened performance on the pole test. JQ1-induced gene expression changes in NT mice were distinct from those in R6/2 and primarily involved protein translation and bioenergetics pathways. Dysregulation of HD-related pathways in striatum was exacerbated by JQ1 in R6/2 mice, but not in NTs, and JQ1 caused a corresponding increase in the formation of a mutant huntingtin protein-dependent high molecular weight species associated with pathogenesis. This study suggests that drugs predicted to be beneficial based on their mode of action and effects in wild-type or in other neurodegenerative disease models may have an altered impact in the HD context. These observations have important implications in the development of epigenetic modulators as therapies for HD.

Early epigenomic and transcriptional changes reveal Elk-1 transcription factor as a therapeutic target in Huntington's disease.

Huntington's disease (HD) is a chronic neurodegenerative disorder characterized by a late clinical onset despite ubiquitous expression of the mutant Huntingtin gene (HTT) from birth. Transcriptional dysregulation is a pivotal feature of HD. Yet, the genes that are altered in the prodromal period and their regulators, which present opportunities for therapeutic intervention, remain to be elucidated. Using transcriptional and chromatin profiling, we found aberrant transcription and changes in histone H3K27acetylation in the striatum of R6/1 mice during the presymptomatic disease stages. Integrating these data, we identified the Elk-1 transcription factor as a candidate regulator of prodromal changes in HD. Exogenous expression of Elk-1 exerted beneficial effects in a primary striatal cell culture model of HD, and adeno-associated virus-mediated Elk-1 overexpression alleviated transcriptional dysregulation in R6/1 mice. Collectively, our work demonstrates that aberrant gene expression precedes overt disease onset in HD, identifies the Elk-1 transcription factor as a key regulator linked to early epigenetic and transcriptional changes in HD, and presents evidence for Elk-1 as a target for alleviating molecular pathology in HD.

Zfp281 (ZBP-99) plays a functionally redundant role with Zfp148 (ZBP-89) during erythroid development.

Erythroid maturation requires the concerted action of a core set of transcription factors. We previously identified the Krüppel-type zinc finger transcription factor Zfp148 (also called ZBP-89) as an interacting partner of the master erythroid transcription factor GATA1. Here we report the conditional knockout of Zfp148 in mice. Global loss of Zfp148 results in perinatal lethality from nonhematologic causes. Selective Zfp148 loss within the hematopoietic system results in a mild microcytic and hypochromic anemia, mildly impaired erythroid maturation, and delayed recovery from phenylhydrazine-induced hemolysis. Based on the mild erythroid phenotype of these mice compared with GATA1-deficient mice, we hypothesized that additional factor(s) may complement Zfp148 function during erythropoiesis. We show that Zfp281 (also called ZBP-99), another member of the Zfp148 transcription factor family, is highly expressed in murine and human erythroid cells. Zfp281 knockdown by itself results in partial erythroid defects. However, combined deficiency of Zfp148 and Zfp281 causes a marked erythroid maturation block. Zfp281 physically associates with GATA1, occupies many common chromatin sites with GATA1 and Zfp148, and regulates a common set of genes required for erythroid cell differentiation. These findings uncover a previously unknown role for Zfp281 in erythroid development and suggest that it functionally overlaps with that of Zfp148 during erythropoiesis.

Shallow Sparsely-Connected Autoencoders for Gene Set Projection.

When analyzing biological data, it can be helpful to consider gene sets, or predefined groups of biologically related genes. Methods exist for identifying gene sets that are differential between conditions, but large public datasets from consortium projects and single-cell RNA-Sequencing have opened the door for gene set analysis using more sophisticated machine learning techniques, such as autoencoders and variational autoencoders. We present shallow sparsely-connected autoencoders (SSCAs) and variational autoencoders (SSCVAs) as tools for projecting gene-level data onto gene sets. We tested these approaches on single-cell RNA-Sequencing data from blood cells and on RNA-Sequencing data from breast cancer patients. Both SSCA and SSCVA can recover known biological features from these datasets and the SSCVA method often outperforms SSCA (and six existing gene set scoring algorithms) on classification and prediction tasks.

Characteristics of myeloproliferative neoplasms in patients exposed to ionizing radiation following the Chernobyl nuclear accident.

Myeloproliferative neoplasms (MPNs) driver mutations are usually found in JAK2, MPL, and CALR genes; however, 10%-15% of cases are triple negative (TN). A previous study showed lower rate of JAK2 V617F in primary myelofibrosis patients exposed to low doses of ionizing radiation (IR) from Chernobyl accident. To examine distinct driver mutations, we enrolled 281 Ukrainian IR-exposed and unexposed MPN patients. Genomic DNA was obtained from peripheral blood leukocytes. JAK2 V617F, MPL W515, types 1- and 2-like CALR mutations were identified by Sanger Sequencing and real time polymerase chain reaction. Chromosomal alterations were assessed by oligo-SNP microarray platform. Additional genetic variants were identified by whole exome and targeted sequencing. Statistical significance was evaluated by Fisher's exact test and Wilcoxon's rank sum test (R, version 3.4.2). IR-exposed MPN patients exhibited a different genetic profile vs unexposed: lower rate of JAK2 V617F (58.4% vs 75.4%, P = .0077), higher rate of type 1-like CALR mutation (12.2% vs 3.1%, P = .0056), higher rate of TN cases (27.8% vs 16.2%, P = .0366), higher rate of potentially pathogenic sequence variants (mean numbers: 4.8 vs 3.1, P = .0242). Furthermore, we identified several potential drivers specific to IR-exposed TN MPN patients: ATM p.S1691R with copy-neutral loss of heterozygosity at 11q; EZH2 p.D659G at 7q and SUZ12 p.V71 M at 17q with copy number loss. Thus, IR-exposed MPN patients represent a group with distinct genomic characteristics worthy of further study.

Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups.

There is a pressing need to identify therapeutic targets in tumors with low mutation rates such as the malignant pediatric brain tumor medulloblastoma. To address this challenge, we quantitatively profiled global proteomes and phospho-proteomes of 45 medulloblastoma samples. Integrated analyses revealed that tumors with similar RNA expression vary extensively at the post-transcriptional and post-translational levels. We identified distinct pathways associated with two subsets of SHH tumors, and found post-translational modifications of MYC that are associated with poor outcomes in group 3 tumors. We found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. Our study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies.

Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data.

With the extraordinary rise in available biological data, biologists and clinicians need unbiased tools for data integration in order to reach accurate, succinct conclusions. Network biology provides one such method for high-throughput data integration, but comes with its own set of algorithmic problems and needed expertise. We provide a step-by-step guide for using Omics Integrator, a software package designed for the integration of transcriptomic, epigenomic, and proteomic data. Omics Integrator can be found at http://fraenkel.mit.edu/omicsintegrator .

Functional Genomics Approach Identifies Novel Signaling Regulators of TGFα Ectodomain Shedding.

Ectodomain shedding of cell-surface precursor proteins by metalloproteases generates important cellular signaling molecules. Of importance for disease is the release of ligands that activate the EGFR, such as TGFα, which is mostly carried out by ADAM17 [a member of the A-disintegrin and metalloprotease (ADAM) domain family]. EGFR ligand shedding has been linked to many diseases, in particular cancer development, growth and metastasis, as well as resistance to cancer therapeutics. Excessive EGFR ligand release can outcompete therapeutic EGFR inhibition or the inhibition of other growth factor pathways by providing bypass signaling via EGFR activation. Drugging metalloproteases directly have failed clinically because it indiscriminately affected shedding of numerous substrates. It is therefore essential to identify regulators for EGFR ligand cleavage. Here, integration of a functional shRNA genomic screen, computational network analysis, and dedicated validation tests succeeded in identifying several key signaling pathways as novel regulators of TGFα shedding in cancer cells. Most notably, a cluster of genes with NFκB pathway regulatory functions was found to strongly influence TGFα release, albeit independent of their NFκB regulatory functions. Inflammatory regulators thus also govern cancer cell growth-promoting ectodomain cleavage, lending mechanistic understanding to the well-known connection between inflammation and cancer.Implications: Using genomic screens and network analysis, this study defines targets that regulate ectodomain shedding and suggests new treatment opportunities for EGFR-driven cancers. Mol Cancer Res; 16(1); 147-61. ©2017 AACR.

Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens.

Small molecule screens are widely used to prioritize pharmaceutical development. However, determining the pathways targeted by these molecules is challenging, since the compounds are often promiscuous. We present a network strategy that takes into account the polypharmacology of small molecules in order to generate hypotheses for their broader mode of action. We report a screen for kinase inhibitors that increase the efficacy of gemcitabine, the first-line chemotherapy for pancreatic cancer. Eight kinase inhibitors emerge that are known to affect 201 kinases, of which only three kinases have been previously identified as modifiers of gemcitabine toxicity. In this work, we use the SAMNet algorithm to identify pathways linking these kinases and genetic modifiers of gemcitabine toxicity with transcriptional and epigenetic changes induced by gemcitabine that we measure using DNaseI-seq and RNA-seq. SAMNet uses a constrained optimization algorithm to connect genes from these complementary datasets through a small set of protein-protein and protein-DNA interactions. The resulting network recapitulates known pathways including DNA repair, cell proliferation and the epithelial-to-mesenchymal transition. We use the network to predict genes with important roles in the gemcitabine response, including six that have already been shown to modify gemcitabine efficacy in pancreatic cancer and ten novel candidates. Our work reveals the important role of polypharmacology in the activity of these chemosensitizing agents.

Network Modeling Identifies Patient-specific Pathways in Glioblastoma.

Glioblastoma is the most aggressive type of malignant human brain tumor. Molecular profiling experiments have revealed that these tumors are extremely heterogeneous. This heterogeneity is one of the principal challenges for developing targeted therapies. We hypothesize that despite the diverse molecular profiles, it might still be possible to identify common signaling changes that could be targeted in some or all tumors. Using a network modeling approach, we reconstruct the altered signaling pathways from tumor-specific phosphoproteomic data and known protein-protein interactions. We then develop a network-based strategy for identifying tumor specific proteins and pathways that were predicted by the models but not directly observed in the experiments. Among these hidden targets, we show that the ERK activator kinase1 (MEK1) displays increased phosphorylation in all tumors. By contrast, protein numb homolog (NUMB) is present only in the subset of the tumors that are the most invasive. Additionally, increased S100A4 is associated with only one of the tumors. Overall, our results demonstrate that despite the heterogeneity of the proteomic data, network models can identify common or tumor specific pathway-level changes. These results represent an important proof of principle that can improve the target selection process for tumor specific treatments.