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Approved Anti-cancer Drugs Target Oncogenic Non-coding RNAs.

ABSTRACT: Potential RNA drug targets for small molecules are found throughout the human transcriptome, yet small molecules known to elicit a pharmacological response by directly targeting RNA are limited to antibacterials. Herein, we describe AbsorbArray, a small molecule microarray-based approach that allows for unmodified compounds, including FDA-approved drugs, to be probed for binding to RNA motif libraries in a massively parallel format. Several drug classes bind RNA including kinase and topoisomerase inhibitors. The latter avidly bound the motif found in the Dicer site of oncogenic microRNA (miR)-21 and inhibited its processing both in vitro and in cells. The most potent compound de-repressed a downstream protein target and inhibited a miR-21-mediated invasive phenotype. The compound's activity was ablated upon overexpression of pre-miR-21. Target validation via chemical crosslinking and isolation by pull-down showed direct engagement of pre-miR-21 by the small molecule in cells, demonstrating that RNAs should indeed be considered druggable.

SUBMITTER: Velagapudi SP

PROVIDER: S-EPMC6334646 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Approved Anti-cancer Drugs Target Oncogenic Non-coding RNAs.

Velagapudi Sai Pradeep SP Costales Matthew G MG Vummidi Balayeshwanth R BR Nakai Yoshio Y Angelbello Alicia J AJ Tran Tuan T Haniff Hafeez S HS Matsumoto Yasumasa Y Wang Zi Fu ZF Chatterjee Arnab K AK Childs-Disney Jessica L JL Disney Matthew D MD

Cell chemical biology 20180628 9

Potential RNA drug targets for small molecules are found throughout the human transcriptome, yet small molecules known to elicit a pharmacological response by directly targeting RNA are limited to antibacterials. Herein, we describe AbsorbArray, a small molecule microarray-based approach that allows for unmodified compounds, including FDA-approved drugs, to be probed for binding to RNA motif libraries in a massively parallel format. Several drug classes bind RNA including kinase and topoisomeras ...[more]

PMID: 30251629

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Project description:BackgroundThe clinical outcomes of breast cancer (BC) are unpredictable due to the high level of heterogeneity and complex immune status of the tumor microenvironment (TME). When set up, multiple long non-coding RNA (lncRNA) signatures tended to be employed to appraise the prognosis of BC. Nevertheless, predicting immunotherapy responses in BC is still essential. LncRNAs play pivotal roles in cancer development through diverse oncogenic signal pathways. Hence, we attempted to construct an oncogenic signal pathway-based lncRNA signature for forecasting prognosis and immunotherapy response by providing reliable signatures.MethodsWe preliminarily retrieved RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) database and extracted lncRNA profiles by matching them with GENCODE. Following this, Gene Set Variation Analysis (GSVA) was used to identify the lncRNAs closely associated with 10 oncogenic signaling pathways from the TCGA-BRCA (breast-invasive carcinoma) cohort and was further screened by the least absolute shrinkage and selection operator Cox regression model. Next, an lncRNA signature (OncoSig) was established through the expression level of the final 29 selected lncRNAs. To examine survival differences in the stratification described by the OncoSig, the Kaplan-Meier (KM) survival curve with the log-rank test was operated on four independent cohorts (n = 936). Subsequently, multiple Cox regression was used to investigate the independence of the OncoSig as a prognostic factor. With the concordance index (C-index), the time-dependent receiver operating characteristic was employed to assess the performance of the OncoSig compared to other publicly available lncRNA signatures for BC. In addition, biological differences between the high- and low-risk groups, as portrayed by the OncoSig, were analyzed on the basis of statistical tests. Immune cell infiltration was investigated using gene set enrichment analysis (GSEA) and deconvolution tools (including CIBERSORT and ESTIMATE). The combined effect of the Oncosig and immune checkpoint genes on prognosis and immunotherapy was elucidated through the KM survival curve. Ultimately, a pan-cancer analysis was conducted to attest to the prevalence of the OncoSig.ResultsThe OncoSig score stratified BC patients into high- and low-risk groups, where the latter manifested a significantly higher survival rate and immune cell infiltration when compared to the former. A multivariate analysis suggested that OncoSig is an independent prognosis predictor for BC patients. In addition, compared to the other four publicly available lncRNA signatures, OncoSig exhibited superior predictive performance (AUC = 0.787, mean C-index = 0.714). The analyses of the OncoSig and immune checkpoint genes clarified that a lower OncoSig score meant significantly longer survival and improved response to immunotherapy. In addition to BC, a high OncoSig score in several other cancers was negatively correlated with survival and immune cell infiltration.ConclusionsOur study established a trustworthy and discriminable prognostic signature for BC patients with similar clinical profiles, thus providing a new perspective in the evaluation of immunotherapy responses. More importantly, this finding can be generalized to be applicable to the vast majority of human cancers.

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Approved Anti-cancer Drugs Target Oncogenic Non-coding RNAs.

Publications

Approved Anti-cancer Drugs Target Oncogenic Non-coding RNAs.

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