Venetoclax triggers sublethal apoptotic signaling in venetoclax-resistant acute myeloid leukemia cells and induces vulnerability to PARP inhibition and azacitidine.

Venetoclax plus azacitidine treatment is clinically beneficial for elderly and unfit acute myeloid leukemia (AML) patients. However, the treatment is rarely curative, and relapse due to resistant disease eventually emerges. Since no current clinically feasible treatments are known to be effective at the state of acquired venetoclax resistance, this is becoming a major challenge in AML treatment.

Monosomy 7/del(7q) cause sensitivity to inhibitors of nicotinamide phosphoribosyltransferase in acute myeloid leukemia.

Monosomy 7 and del(7q) (-7/-7q) are frequent chromosomal abnormalities detected in up to 10% of patients with acute myeloid leukemia (AML). Despite unfavorable treatment outcomes, no approved targeted therapies exist for patients with -7/-7q. Therefore, we aimed to identify novel vulnerabilities.

Erythroid/megakaryocytic differentiation confers BCL-XL dependency and venetoclax resistance in acute myeloid leukemia.

Myeloid neoplasms with erythroid or megakaryocytic differentiation include pure erythroid leukemia, myelodysplastic syndrome with erythroid features, and acute megakaryoblastic leukemia (FAB M7) and are characterized by poor prognosis and limited treatment options. Here, we investigate the drug sensitivity landscape of these rare malignancies. We show that acute myeloid leukemia (AML) cells with erythroid or megakaryocytic differentiation depend on the antiapoptotic protein B-cell lymphoma (BCL)-XL, rather than BCL-2, using combined ex vivo drug sensitivity testing, genetic perturbation, and transcriptomic profiling.

DrugRepo: a novel approach to repurposing drugs based on chemical and genomic features.

The drug development process consumes 9-12 years and approximately one billion US dollars in costs. Due to the high finances and time costs required by the traditional drug discovery paradigm, repurposing old drugs to treat cancer and rare diseases is becoming popular. Computational approaches are mainly data-driven and involve a systematic analysis of different data types leading to the formulation of repurposing hypotheses.

Using BERT to identify drug-target interactions from whole PubMed.

Background: Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of curated articles likely constitutes only a fraction of all the articles that contain experimentally determined DTIs. Finding such articles and extracting the experimental information is a challenging task, and there is a pressing need for systematic approaches to assist the curation of DTIs.

Pancreatic Cancer Organoids in the Field of Precision Medicine: A Review of Literature and Experience on Drug Sensitivity Testing with Multiple Readouts and Synergy Scoring.

Pancreatic ductal adenocarcinoma (PDAC) is a silent killer, often diagnosed late. However, it is also dishearteningly resistant to nearly all forms of treatment. New therapies are urgently needed, and with the advent of organoid culture for pancreatic cancer, an increasing number of innovative approaches are being tested.

Patient-tailored design for selective co-inhibition of leukemic cell subpopulations.

The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens.

Artificial intelligence, machine learning, and drug repurposing in cancer.

: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means.: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources.

Pancreatic cancer is associated with aberrant monocyte function and successive differentiation into macrophages with inferior anti-tumour characteristics.

Background/objectives: Inflammation is related to the development and progression of pancreatic cancer (PC). Locally, anti-inflammatory macrophages (M2), and systemically, high levels of certain inflammation-modulating cytokines associate with poor prognosis in PC. The detailed effects of systemic inflammation on circulating monocytes and macrophage polarisation remain unknown.

Pan-RAF inhibition induces apoptosis in acute myeloid leukemia cells and synergizes with BCL2 inhibition.

Pan-RAF inhibitors have shown promise as antitumor agents in RAS and RAF mutated solid cancers. However, the efficacy of pan-RAF inhibitors in acute myeloid leukemia (AML) has not previously been explored. In AML, the RAS-RAF-MEK-ERK (MAPK) pathway is one of the most aberrantly activated oncogenic pathways, but previous targeting of this pathway by MEK inhibitors has not proven effective in clinical trials.

Immunogenomic Landscape of Hematological Malignancies.

Understanding factors that shape the immune landscape across hematological malignancies is essential for immunotherapy development. We integrated over 8,000 transcriptomes and 2,000 samples with multilevel genomics of hematological cancers to investigate how immunological features are linked to cancer subtypes, genetic and epigenetic alterations, and patient survival, and validated key findings experimentally. Infiltration of cytotoxic lymphocytes was associated with TP53 and myelodysplasia-related changes in acute myeloid leukemia, and activated B cell-like phenotype and interferon-γ response in lymphoma.

Exploration of databases and methods supporting drug repurposing: a comprehensive survey.

Drug development involves a deep understanding of the mechanisms of action and possible side effects of each drug, and sometimes results in the identification of new and unexpected uses for drugs, termed as drug repurposing. Both in case of serendipitous observations and systematic mechanistic explorations, confirmation of new indications for a drug requires hypothesis building around relevant drug-related data, such as molecular targets involved, and patient and cellular responses. These datasets are available in public repositories, but apart from sifting through the sheer amount of data imposing computational bottleneck, a major challenge is the difficulty in selecting which databases to use from an increasingly large number of available databases.

Interactive visual analysis of drug-target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing.

Knowledge of the full target space of drugs (or drug-like compounds) provides important insights into the potential therapeutic use of the agents to modulate or avoid their various on- and off-targets in drug discovery and precision medicine. However, there is a lack of consolidated databases and associated data exploration tools that allow for systematic profiling of drug target-binding potencies of both approved and investigational agents using a network-centric approach. We recently initiated a community-driven platform, Drug Target Commons (DTC), which is an open-data crowdsourcing platform designed to improve the management, reproducibility and extended use of compound-target bioactivity data for drug discovery and repurposing, as well as target identification applications.

PROX1 is a transcriptional regulator of MMP14.

The transcription factor PROX1 is essential for development and cell fate specification. Its function in cancer is context-dependent since PROX1 has been shown to play both oncogenic and tumour suppressive roles. Here, we show that PROX1 suppresses the transcription of MMP14, a metalloprotease involved in angiogenesis and cancer invasion, by binding and suppressing the activity of MMP14 promoter.

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration.

Antiviral Properties of Chemical Inhibitors of Cellular Anti-Apoptotic Bcl-2 Proteins.

Viral diseases remain serious threats to public health because of the shortage of effective means of control. To combat the surge of viral diseases, new treatments are urgently needed. Here we show that small-molecules, which inhibit cellular anti-apoptotic Bcl-2 proteins (Bcl-2i), induced the premature death of cells infected with different RNA or DNA viruses, whereas, at the same concentrations, no toxicity was observed in mock-infected cells.

Immunohistochemical Characterization and Sensitivity to Human Adenovirus Serotypes 3, 5, and 11p of New Cell Lines Derived from Human Diffuse Grade II to IV Gliomas.

Background: Oncolytic adenoviruses show promise in targeting gliomas because they do not replicate in normal brain cells. However, clinical responses occur only in a subset of patients. One explanation could be the heterogenic expression level of virus receptors.

Cancer-Targeted Oncolytic Adenoviruses for Modulation of the Immune System.

Adenovirus is one of the most commonly used vectors for gene therapy and it is the first approved virus-derived drug for treatment of cancer. As an oncolytic agent, it can induce lysis of infected cells, but it can also engage the immune system, promoting activation and maturation of antigen- presenting cells (APCs). In essence, oncolysis combined with the associated immunostimulatory actions result in a "personalized in situ vaccine" for each patient.

Dasatinib Changes Immune Cell Profiles Concomitant with Reduced Tumor Growth in Several Murine Solid Tumor Models.

Dasatinib, a broad-range tyrosine kinase inhibitor, induces rapid mobilization of lymphocytes and clonal expansion of cytotoxic cells in leukemia patients. Here, we investigated whether dasatinib could induce beneficial immunomodulatory effects in solid tumor models. The effects on tumor growth and on the immune system were studied in four different syngeneic mouse models (B16.