PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

The Hidden Pandemic of COVID-19-Induced Organizing Pneumonia.

Since the beginning of the COVID-19 pandemic, clinical, radiological, and histopathological studies have provided evidence that organizing pneumonia is a possible consequence of the SARS-CoV2 infection. This post-COVID-19 organizing pneumonia (PCOP) causes persisting dyspnea, impaired pulmonary function, and produces radiological abnormalities for at least 5 weeks after onset of symptoms. While most patients with PCOP recover within a year after acute COVID-19, 5-25% of cases need specialized treatment. However, despite substantial resources allocated worldwide to finding a solution to this problem, there are no approved treatments for PCOP. Oral corticosteroids produce a therapeutic response in a majority of such PCOP patients, but their application is limited by the anticipated high-relapse frequency and the risk of severe adverse effects. Herein, we conduct a systematic comparison of the epidemiology, pathogenesis, and clinical presentation of the organizing pneumonias caused by COVID-19 as well as other viral infections. We also use the clinical efficacy of corticosteroids in other postinfection OPs (PIOPs) to predict the therapeutic response in the treatment of PCOP. Finally, we discuss the potential application of a candidate anti-inflammatory and antifibrotic therapy for the treatment of PCOP based on the analysis of the latest clinical trials data.

Mapping of UK Biobank clinical codes: Challenges and possible solutions.

OBJECTIVE: The UK Biobank provides a rich collection of longitudinal clinical data coming from different healthcare providers and sources in England, Wales, and Scotland. Although extremely valuable and available to a wide research community, the heterogeneous dataset contains inconsistent medical terminology that is either aligned to several ontologies within the same category or unprocessed. To make these data useful to a research community, data cleaning, curation, and standardization are needed. Significant efforts to perform data reformatting, mapping to any selected ontologies (such as SNOMED-CT) and harmonization are required from any data user to integrate UK Biobank hospital inpatient and self-reported data, data from various registers with primary care (GP) data. The integrated clinical data would provide a more comprehensive picture of one's medical history. MATERIALS AND METHODS: We evaluated several approaches to map GP clinical Read codes to International Classification of Diseases (ICD) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) terminologies. The results were compared, mapping inconsistencies were flagged, a quality category was assigned to each mapping to evaluate overall mapping quality. RESULTS: We propose a curation and data integration pipeline for harmonizing diagnosis. We also report challenges identified in mapping Read codes from UK Biobank GP tables to ICD and SNOMED CT. DISCUSSION AND CONCLUSION: Some of the challenges-the lack of precise one-to-one mapping between ontologies or the need for additional ontology to fully map terms-are general reflecting trade-offs to be made at different steps. Other challenges are due to automatic mapping and can be overcome by leveraging existing mappings, supplemented with automated and manual curation.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

NCATS Inxight Drugs: a comprehensive and curated portal for translational research.

The United States has a complex regulatory scheme for marketing drugs. Understanding drug regulatory status is a daunting task that requires integrating data from many sources from the United States Food and Drug Administration (FDA), US government publications, and other processes related to drug development. At NCATS, we created Inxight Drugs (https://drugs.ncats.io), a web resource that attempts to address this challenge in a systematic manner. NCATS Inxight Drugs incorporates and unifies a wealth of data, including those supplied by the FDA and from independent public sources. The database offers a substantial amount of manually curated literature data unavailable from other sources. Currently, the database contains 125 036 product ingredients, including 2566 US approved drugs, 6242 marketed drugs, and 9684 investigational drugs. All substances are rigorously defined according to the ISO 11238 standard to comply with existing regulatory standards for unique drug substance identification. A special emphasis was placed on capturing manually curated and referenced data on treatment modalities and semantic relationships between substances. A supplementary resource 'Novel FDA Drug Approvals' features regulatory details of newly approved FDA drugs. The database is regularly updated using NCATS Stitcher data integration tool that automates data aggregation and supports full data access through a RESTful API.

Lung Fibrosis after COVID-19: Treatment Prospects.

At the end of 2019, a highly contagious infection began its ominous conquest of the world. It was soon discovered that the disease was caused by a novel coronavirus designated as SARS-CoV-2, and the disease was thus abbreviated to COVID-19 (COVID). The global medical community has directed its efforts not only to find effective therapies against the deadly pathogen but also to combat the concomitant complications. Two of the most common respiratory manifestations of COVID are a significant reduction in the diffusing capacity of the lungs (DLCO) and the associated pulmonary interstitial damage. One year after moderate COVID, the incidence rate of impaired DLCO and persistent lung damage still exceeds 30%, and one-third of the patients have severe DLCO impairment and fibrotic lung damage. The persistent respiratory complications may cause substantial population morbidity, long-term disability, and even death due to the lung fibrosis progression. The incidence of COVID-induced pulmonary fibrosis caused by COVID can be estimated based on a 15-year observational study of lung pathology after SARS. Most SARS patients with fibrotic lung damage recovered within the first year and then remained healthy; however, in 20% of the cases, significant fibrosis progression was found in 5-10 years. Based on these data, the incidence rate of post-COVID lung fibrosis can be estimated at 2-6% after moderate illness. What is worse, there are reasons to believe that fibrosis may become one of the major long-term complications of COVID, even in asymptomatic individuals. Currently, despite the best efforts of the global medical community, there are no treatments for COVID-induced pulmonary fibrosis. In this review, we analyze the latest data from ongoing clinical trials aimed at treating post-COVID lung fibrosis and analyze the rationale for the current drug candidates. We discuss the use of antifibrotic therapy for idiopathic pulmonary fibrosis, the IN01 vaccine, glucocorticosteroids as well as the stromal vascular fraction for the treatment and rehabilitation of patients with COVID-associated pulmonary damage.

An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD).

BACKGROUND: The Genetic and Rare Diseases (GARD) Information Center was established by the National Institutes of Health (NIH) to provide freely accessible consumer health information on over 6500 genetic and rare diseases. As the cumulative scientific understanding and underlying evidence for these diseases have expanded over time, existing practices to generate knowledge from these publications and resources have not been able to keep pace. Through determining the applicability of computational approaches to enhance or replace manual curation tasks, we aim to both improve the sustainability and relevance of consumer health information, but also to develop a foundational database, from which translational science researchers may start to unravel disease characteristics that are vital to the research process. RESULTS: We developed a meta-ontology based integrative knowledge graph for rare diseases in Neo4j. This integrative knowledge graph includes a total of 3,819,623 nodes and 84,223,681 relations from 34 different biomedical data resources, including curated drug and rare disease associations. Semi-automatic mappings were generated for 2154 unique FDA orphan designations to 776 unique GARD diseases, and 3322 unique FDA designated drugs to UNII, as well as 180,363 associations between drug and indication from Inxight Drugs, which were integrated into the knowledge graph. We conducted four case studies to demonstrate the capabilities of this integrative knowledge graph in accelerating the curation of scientific understanding on rare diseases through the generation of disease mappings/profiles and pathogenesis associations. CONCLUSIONS: By integrating well-established database resources, we developed an integrative knowledge graph containing a large volume of biomedical and research data. Demonstration of several immediate use cases and limitations of this process reveal both the potential feasibility and barriers of utilizing graph-based resources and approaches to support their use by providers of consumer health information, such as GARD, that may struggle with the needs of maintaining knowledge reliant on an evolving and growing evidence-base. Finally, the successful integration of these datasets into a freely accessible knowledge graph highlights an opportunity to take a translational science view on the field of rare diseases by enabling researchers to identify disease characteristics, which may play a role in the translation of discover across different research domains.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.

MOTIVATION: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. RESULTS: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. AVAILABILITY: https://neo4covid19.ncats.io.

Pathways, Processes, and Candidate Drugs Associated with a Hoxa Cluster-Dependency Model of Leukemia.

High expression of the HOXA cluster correlates with poor clinical outcome in acute myeloid leukemias, particularly those harboring rearrangements of the mixed-lineage-leukemia gene (MLLr). Whilst decreased HOXA expression acts as a readout for candidate experimental therapies, the necessity of the HOXA cluster for leukemia maintenance has not been fully explored. Primary leukemias were generated in hematopoietic stem/progenitor cells from Cre responsive transgenic mice for conditional deletion of the Hoxa locus. Hoxa deletion resulted in reduced proliferation and colony formation in which surviving leukemic cells retained at least one copy of the Hoxa cluster, indicating dependency. Comparative transcriptome analysis of Hoxa wild type and deleted leukemic cells identified a unique gene signature associated with key pathways including transcriptional mis-regulation in cancer, the Fanconi anemia pathway and cell cycle progression. Further bioinformatics analysis of the gene signature identified a number of candidate FDA-approved drugs for potential repurposing in high HOXA expressing cancers including MLLr leukemias. Together these findings support dependency for an MLLr leukemia on Hoxa expression and identified candidate drugs for further therapeutic evaluation.

The NCATS BioPlanet - An Integrated Platform for Exploring the Universe of Cellular Signaling Pathways for Toxicology, Systems Biology, and Chemical Genomics.

Chemical genomics aims to comprehensively define, and ultimately predict, the effects of small molecule compounds on biological systems. Chemical activity profiling approaches must consider chemical effects on all pathways operative in mammalian cells. To enable a strategic and maximally efficient chemical profiling of pathway space, we have created the NCATS BioPlanet, a comprehensive integrated pathway resource that incorporates the universe of 1,658 human pathways sourced from publicly available, manually curated sources, which have been subjected to thorough redundancy and consistency cross-evaluation. BioPlanet supports interactive browsing, retrieval, and analysis of pathways, exploration of pathway connections, and pathway search by gene targets, category, and availability of corresponding bioactivity assay, as well as visualization of pathways on a 3-dimensional globe, in which the distance between any two pathways is proportional to their degree of gene component overlap. Using this resource, we propose a strategy to identify a minimal set of 362 biological assays that can interrogate the universe of human pathways. The NCATS BioPlanet is a public resource, which will be continually expanded and updated, for systems biology, toxicology, and chemical genomics, available at http://tripod.nih.gov/bioplanet/.

Low-dose salinomycin induces anti-leukemic responses in AML and MLL.

Development of anti-cancer drugs towards clinical application is costly and inefficient. Large screens of drugs, efficacious for non-cancer disease, are currently being used to identify candidates for repurposing based on their anti-cancer properties. Here, we show that low-dose salinomycin, a coccidiostat ionophore previously identified in a breast cancer screen, has anti-leukemic efficacy. AML and MLLr cell lines, primary cells and patient samples were sensitive to submicromolar salinomycin. Most strikingly, colony formation of normal hematopoietic cells was unaffected by salinomycin, demonstrating a lack of hemotoxicity at the effective concentrations. Furthermore, salinomycin treatment of primary cells resulted in loss of leukemia repopulation ability following transplantation, as demonstrated by extended recipient survival compared to controls. Bioinformatic analysis of a 17-gene signature identified and validated in primary MLLr cells, uncovered immunomodulatory pathways, hubs and protein interactions as potential transducers of low dose salinomycin treatment. Additionally, increased protein expression of p62/Sqstm1, encoded for by one of the 17 signature genes, demonstrates a role for salinomycin in aggresome/vesicle formation indicative of an autophagic response.Together, the data support the efficacy of salinomycin as an anti-leukemic at non-hemotoxic concentrations. Further investigation alone or in combination with other therapies is warranted for future clinical trial.

Automatic cell counting with ImageJ.

Cell counting is an important routine procedure. However, to date there is no comprehensive, easy to use, and inexpensive solution for routine cell counting, and this procedure usually needs to be performed manually. Here, we report a complete solution for automatic cell counting in which a conventional light microscope is equipped with a web camera to obtain images of a suspension of mammalian cells in a hemocytometer assembly. Based on the ImageJ toolbox, we devised two algorithms to automatically count these cells. This approach is approximately 10 times faster and yields more reliable and consistent results compared with manual counting.

In vivo anticancer activity of rhomboidal Pt(II) metallacycles.

The development of novel antitumor agents that have high efficacy in suppressing tumor growth, have low toxicity to nontumor tissues, and exhibit rapid localization in the targeted tumor sites is an ongoing avenue of research at the interface of chemistry, cancer biology, and pharmacology. Supramolecular metal-based coordination complexes (SCCs) have well-defined shapes and geometries, and upon their internalization, SCCs could affect multiple oncogenic signaling pathways in cells and tissues. We investigated the uptake, intracellular localization, and antitumor activity of two rhomboidal Pt(II)-based SCCs. Laser-scanning confocal microscopy in A549 and HeLa cells was used to determine the uptake and localization of the assemblies within cells and their effect on tumor growth was investigated in mouse s.c. tumor xenograft models. The SCCs are soluble in cell culture media within the entire range of studied concentrations (1 nM-5 µM), are nontoxic, and showed efficacy in reducing the rate of tumor growth in s.c. mouse tumor xenografts. These properties reveal the potential of Pt(II)-based SCCs for future biomedical applications as therapeutic agents.

In vivo modulation of hypoxia-inducible signaling by topographical helix mimetics.

Development of small-molecule inhibitors of protein-protein interactions is a fundamental challenge at the interface of chemistry and cancer biology. Successful methods for design of protein-protein interaction inhibitors include computational and experimental high-throughput and fragment-based screening strategies to locate small-molecule fragments that bind protein surfaces. An alternative rational design approach seeks to mimic the orientation and disposition of critical binding residues at protein interfaces. We describe the design, synthesis, biochemical, and in vivo evaluation of a small-molecule scaffold that captures the topography of α-helices. We designed mimics of a key α-helical domain at the interface of hypoxia-inducible factor 1α and p300 to develop inhibitors of hypoxia-inducible signaling. The hypoxia-inducible factor/p300 interaction regulates the transcription of key genes, whose expression contributes to angiogenesis, metastasis, and altered energy metabolism in cancer. The designed compounds target the desired protein with high affinity and in a predetermined manner, with the optimal ligand providing effective reduction of tumor burden in experimental animal models.

Polyisobutylene-block-poly(methacrylic acid) diblock copolymers: self-assembly in aqueous media.

Ionic amphiphilic diblock copolymer polyisobutylene-block-poly(methacrylic acid) (PIBx-b-PMAAy), with various lengths of nonpolar (x=25-75) and polyelectrolyte (y=170-2600) blocks, spontaneously dissolve in aqueous media at pH>4, generating macromolecular assemblies, the aggregation number of which depends on external stimuli (pH and ionic strength). Spherical micellar morphology with a compact core formed by the PIB blocks and a swollen corona built up from the PMAA blocks was deduced by cryogenic transmission electron microscopy. The micelles were further characterized by means of dynamic and static light scattering as well as small-angle neutron scattering. The critical micellization concentration, estimated by means of fluorescence spectroscopy with the use of pyrene as a polarity probe, is decisively determined by the length of the PIB block and is insensitive to changes in the length of the PMAA block.