key: cord-0301312-1qpqrrzt authors: Troulé, Kevin; López-Fernández, Hugo; García-Martín, Santiago; Reboiro-Jato, Miguel; Carretero-Puche, Carlos; Martorell-Marugán, Jordi; Martín-Serrano, Guillermo; Carmona-Sáez, Pedro; González-Peña, Daniel; Al-Shahrour, Fátima; Gómez-López, Gonzalo title: DREIMT: a drug repositioning database and prioritization tool for immunomodulation date: 2020-06-24 journal: bioRxiv DOI: 10.1101/2020.06.24.168468 sha: 25012758dbec126f3de94e9a99017c55f2da2f64 doc_id: 301312 cord_uid: 1qpqrrzt Motivation Drug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases. Results DREIMT is a new hypothesis-generation web tool which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4,960 drug profiles and ~2,6K immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug-signature association scores, FDRs and downloadable plots and results tables. Availability http://www.dreimt.org Contact falshahrour@cnio.es; ggomez@cnio.es Immune system dysregulations have been related to a wide spectrum of complex diseases such as autoimmune disorders and cancer. Autoimmunity processes are clinically diverse and they are distinguished by an immune-mediated attack on the body's own tissues led by self-reactive B and T cells (Rose et al. 2016) . In cancer, the intercellular signalling between the malignant cells and certain immune cell subpopulations (e.g. regulatory T cells (Tregs) and tumor-associated macrophages (TAMs)) contributes to tumor microenvironment immunosuppression fostering cell proliferation and tumor evasion (Stockis et al. 2019; Linde et al. 2018) . The presence of Tregs and TAMs subpopulations in tumor microenvironment has been also correlated with cancer poor prognosis in contrast to better prognosis shown by those tumors with high rate of tumor-infiltrated CD8+ T cells (Fridman et al. 2017) . This has led to propose immune cells targeting as a therapeutic strategy. For instance, some studies have shown that activity of specific immune cell populations can be targeted using systemic or non-systemic approaches (e.g. drugconjugated nanoparticles) thus enhancing the outcome of some tumors and autoimmune diseases (Lu et al. 2020; Riley et al., 2019; Genovese et al., 2016) . Recently, drugmediated immunomodulation has been proposed as a therapeutic approach in patients with COVID-19 associated cytokine storm (Richardson P et al., 2020) . This highlights the interest in developing new methodologies to propose immunomodulatory therapeutic strategies capable of selectively affect the function of specific immune cells. Here we introduce DREIMT (Drug REpositioning for IMmune Transcriptome), a tool for hypothesis generation of drugs capable of modulating (boosting or inhibiting) the immune cells activity. DREIMT integrates ~2.6k immune gene expression signatures with a collection of 4690 consensus drug profiles (Perales-Patón et al., 2019) from the catalog of drug perturbations expression profiles in cancer cell lines from the LINCS L1000 dataset (Subramanian et al., 2017) (Figure 1 ). Immune signatures were obtained from published studies and public databases covering up to 70 immune cell subtypes (Suppl. Materials S1-3, T1). DREIMT performs a fgsea to test the significance of the immune signature enrichment across the ranked genes of each drug profile (Korotkevich et al., 2020) . The fgsea enrichment scores are used to calculate the Drug Prioritization score (τ) as described elsewhere (Subramanian et al., 2017) , comparing the obtained enrichment score for a particular signature to the rest of immune signatures. The τ score allows the prioritization of drugs to target immune cells. DREIMT also includes a Drug Specificity Score (DSS) that summarises the cell-specificity of a given drug across multiple cancer cell lines (Hodos et al., 2018; Suppl. Materials S4-6) . Significant drug-immune signature associations (|τ|>80) are stored in the DREIMT database (DREIMTdb) accessible through a RESTful API. We have validated >20 DREIMT hypothesis by scientific literature (Suppl. Materials S7, T2). Materials S10. Drugs affect the biology and activity of the immune system, however such interactions are often poorly understood. We have developed DREIMT, a tool to allow users to generate hypotheses and explore novel druggable targets across the immune system, thus supporting drug repositioning leveraging transcriptomics data. DREIMT is fully accessible at http://www.dreimt.org. RETOS (RTI2018-097596-B-I00) T is supported by Severo Ochoa FPI Universidades e Formación Profesional (Xunta de Galicia) ED431C2018/55-GRC Competitive Reference Group. P.C-S is supported by Junta de Andalucía Nanoparticle-Based modulation of the immune system The immune contexture in cancer prognosis and treatment Baricitinib in patients with refractory rheumatoid arthritis Cell-specific prediction and application of drug-induced gene expression profiles Fast gene set enrichment analysis Macrophages orchestrate breast cancer early dissemination and metastasis Complement signals determine opposite effects of B cells in chemotherapy-induced immunity vulcanSpot: a tool to prioritize therapeutic vulnerabilities in cancer Baricitinib as potential treatment for 2019-nCoV acute respiratory disease Delivery technologies for cancer immunotherapy Prediction and prevention of autoimmune disease in the 21st Century: A review and preview Regulation of regulatory T cells in cancer A next generation connectivity map: L1000 platform and the first 1,000,000 profiles José Antonio López for the fruitful discussions for the development of the DREIMT concept.BU staff for beta testing and DREIMTdb curation.