Functional microRNA-targeting drug discovery by graph-based deep learning.

MicroRNAs are recognized as key drivers in many cancers but targeting them with small molecules remains a challenge. We present RiboStrike, a deep-learning framework that identifies small molecules against specific microRNAs. To demonstrate its capabilities, we applied it to microRNA-21 (miR-21), a known driver of breast cancer. To ensure selectivity toward miR-21, we performed counter-screens against miR-122 and DICER. Auxiliary models were used to evaluate toxicity and rank the candidates. Learning from various datasets, we screened a pool of nine million molecules and identified eight, three of which showed anti-miR-21 activity in both reporter assays and RNA sequencing experiments. Target selectivity of these compounds was assessed using microRNA profiling and RNA sequencing analysis. The top candidate was tested in a xenograft mouse model of breast cancer metastasis, demonstrating a significant reduction in lung metastases. These results demonstrate RiboStrike's ability to nominate compounds that target the activity of miRNAs in cancer.

Deriving Schwann cells from hPSCs enables disease modeling and drug discovery for diabetic peripheral neuropathy.

Schwann cells (SCs) are the primary glia of the peripheral nervous system. SCs are involved in many debilitating disorders, including diabetic peripheral neuropathy (DPN). Here, we present a strategy for deriving SCs from human pluripotent stem cells (hPSCs) that enables comprehensive studies of SC development, physiology, and disease. hPSC-derived SCs recapitulate the molecular features of primary SCs and are capable of in vitro and in vivo myelination. We established a model of DPN that revealed the selective vulnerability of SCs to high glucose. We performed a high-throughput screen and found that an antidepressant drug, bupropion, counteracts glucotoxicity in SCs. Treatment of hyperglycemic mice with bupropion prevents their sensory dysfunction, SC death, and myelin damage. Further, our retrospective analysis of health records revealed that bupropion treatment is associated with a lower incidence of neuropathy among diabetic patients. These results highlight the power of this approach for identifying therapeutic candidates for DPN.

Propagated Circulating Tumor Cells Uncover the Potential Role of NFκB, EMT, and TGFß Signaling Pathways and COP1 in Metastasis.

Circulating tumor cells (CTCs), a population of cancer cells that represent the seeds of metastatic nodules, are a promising model system for studying metastasis. However, the expansion of patient-derived CTCs ex vivo is challenging and dependent on the collection of high numbers of CTCs, which are ultra-rare. Here we report the development of a combined CTC and cultured CTC-derived xenograft (CDX) platform for expanding and studying patient-derived CTCs from metastatic colon, lung, and pancreatic cancers. The propagated CTCs yielded a highly aggressive population of cells that could be used to routinely and robustly establish primary tumors and metastatic lesions in CDXs. Differential gene analysis of the resultant CTC models emphasized a role for NF-κB, EMT, and TGFß signaling as pan-cancer signaling pathways involved in metastasis. Furthermore, metastatic CTCs were identified through a prospective five-gene signature (BCAR1, COL1A1, IGSF3, RRAD, and TFPI2). Whole-exome sequencing of CDX models and metastases further identified mutations in constitutive photomorphogenesis protein 1 (COP1) as a potential driver of metastasis. These findings illustrate the utility of the combined patient-derived CTC model and provide a glimpse of the promise of CTCs in identifying drivers of cancer metastasis.

Functional microRNA-Targeting Drug Discovery by Graph-Based Deep Learning.

MicroRNAs are recognized as key drivers in many cancers, but targeting them with small molecules remains a challenge. We present RiboStrike, a deep learning framework that identifies small molecules against specific microRNAs. To demonstrate its capabilities, we applied it to microRNA-21 (miR-21), a known driver of breast cancer. To ensure the selected molecules only targeted miR-21 and not other microRNAs, we also performed a counter-screen against DICER, an enzyme involved in microRNA biogenesis. Additionally, we used auxiliary models to evaluate toxicity and select the best candidates. Using datasets from various sources, we screened a pool of nine million molecules and identified eight, three of which showed anti-miR-21 activity in both reporter assays and RNA sequencing experiments. One of these was also tested in mouse models of breast cancer, resulting in a significant reduction of lung metastases. These results demonstrate RiboStrike’s ability to effectively screen for microRNA-targeting compounds in cancer.

PAI-1 is a potential transcriptional silencer that supports bladder cancer cell activity.

The extracellular activity of Plasminogen activator inhibitor-1 (PAI-1) is well described, acting as an inhibitor of tissue plasminogen activator and urokinase-type plasminogen activator, impacting fibrinolysis. Recent studies have revealed a pro-tumorigenic role of PAI-1 in human cancers, via the regulation of angiogenesis and tumor cell survival. In this study, immunohistochemical staining of 939 human bladder cancer specimens showed that PAI-1 expression levels correlated with tumor grade, tumor stage and overall survival. The typical subcellular localization of PAI-1 is cytoplasmic, but in approximately a quarter of the cases, PAI-1 was observed to be localized to both the tumor cell cytoplasm and the nucleus. To investigate the potential function of nuclear PAI-1 in tumor biology we applied chromatin immunoprecipitation (ChIP)-sequencing, gene expression profiling, and rapid immunoprecipitation mass spectrometry to a pair of bladder cancer cell lines. ChIP-sequencing revealed that PAI-1 can bind DNA at distal intergenic regions, suggesting a role as a transcriptional coregulator. The downregulation of PAI-1 in bladder cancer cell lines caused the upregulation of numerous genes, and the integration of ChIP-sequence and RNA-sequence data identified 57 candidate genes subject to PAI-1 regulation. Taken together, the data suggest that nuclear PAI-1 can influence gene expression programs and support malignancy.

A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy.

BACKGROUND: Advances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large-scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data. RESULTS: We have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Our approach, which we have named the cancer data integrator (CanDI), is straightforward to implement, is well documented, and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology. We show that CanDI empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer. CONCLUSIONS: CanDI provides a flexible approach for large-scale data integration in cancer research enabling rapid generation of hypotheses. The CanDI data integrator is available at https://github.com/GilbertLabUCSF/CanDI .

Profiling of novel circulating microRNAs as a non-invasive biomarker in diagnosis and follow-up of high and low-grade gliomas.

OBJECTIVE: Glioblastoma (GBM) is the most common primary malignant neoplasm of the central nervous system (CNS). Despite the progress in therapeutic strategies such as surgical techniques, radiotherapy, chemotherapy, and targeted therapy, prognosis and therapeutically convenient monitoring tools in patients with GBM has not improved significantly up to now.Therefore, exosomal miRNAs as novel non-invasive biomarkers having high sensitivity and specificity are required to improve diagnosis and to develop new targeted therapy strategies for GBM patients. The aim of the present study was to investigate a novel miRNA signature as a predictive biomarker for diagnosis and measurement of response to therapeutic interventions in plasma of GBM patients versus traumatic brain injury and diffuse low-grade astrocytoma (LGA) patients. PATIENTS AND METHODS: Plasma exosomal-microRNAs were isolated from GBM (n = 25), LGA (n = 25), and head trauma patients (n = 15) as non-glioma control from March 2017 to June 2018 in Department of Neurosurgery at Rasoul-e-Akram Hospital. Through a bioinformatics analysis, we used Miranda, TargetScan, mirBase, DIANA-microT-CDS, and KEGG database as well as microarray data analysis from GEO for microRNA candidates. Finally, miR-210, miR-185, miR-5194, and miR-449 were selected among those miRNAs because they were recorded to target the maximum number of genes in EGFR and c-MET signaling pathways. Then, exosomal microRNAs were extracted from plasma of patients and quantitated by locked nucleic acid real-time PCR in GBM, LGA, and trauma patients. RESULTS: This result is the first report on the role of circulating miR-185, miR-449, and miR-5194 in GBM compared to LGA and trauma. The plasma expression of miR-210 as an oncogenic miR was upregulated in GBM and LGA groups (P < 0.0001). Otherwise, miR-185, miR-5194, and miR-449 were significantly downregulated (P ≤ 0.05) in GBM and LGA compared to trauma patients. There was no significant downregulation in the expression of miR-185 between GBM and LGA, while the expression of miR-5194 (P ≤ 0.05) and miR-449 (P ≤ 0.05) was significantly decreased in GBM patients compared with LGA. CONCLUSIONS: These results indicate that the levels of miR-210, miR-449, and miR-5194 are a promising diagnostic and prognostic biomarker positively correlated with histopathological grade and invasiveness of GBM. These findings imply that circulating microRNA can be potentially used as novel biomarkers for glioma that might be beneficial in clinical management of glioma patients.