Dataset Information

Inferences of Individual Drug Response-Related Long Non-coding RNAs Based on Integrating Multi-omics Data in Breast Cancer.

ABSTRACT: Differences in individual drug responses are obstacles in breast cancer (BRCA) treatment, so predicting responses would help to plan treatment strategies. The accumulation of cancer molecular profiling and drug response data provide opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs in BRCA. This study evaluated drug responses with a multi-omics integrated system that depended on long non-coding RNAs (lncRNAs). We identified drug response-related lncRNAs (DRlncs) by combining expression data of lncRNA, microRNA, messenger RNA, methylation levels, somatic mutations, and the survival data of cancer patients treated with drugs. We constructed an integrated and computational multi-omics approach to identify DRlncs for diverse chemotherapeutic drugs in BRCA. Some DRlncs were identified with Adriamycin, Cytoxan, Tamoxifen, and all samples for BRCA patients. These DRlncs showed specific features regarding both expression and computational accuracies. The DRlnc-gene co-expression networks were constructed and analyzed. Key DRlncs, such as HOXA-AS2 (Ensembl: ENSG00000253552), in the drug Adriamycin were characterized. The experimental analysis also suggested that HOXA-AS2 (Ensembl: ENSG00000253552) was a key DRlnc in Adriamycin drug resistance in BRCA patients. Some DRlncs were associated with survival and some specific functions. A possible mechanism of DRlnc HOXA-AS2 (Ensembl: ENSG00000253552) in the Adriamycin drug response for BRCA resistance was inferred. In summary, this study provides a framework for lncRNA-based evaluation of clinical drug responses in BRCA. Understanding the underlying molecular mechanisms of drug responses will facilitate improved responses to chemotherapy and outcomes of BRCA treatment.

SUBMITTER: Cui H

PROVIDER: S-EPMC7066040 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Inferences of Individual Drug Response-Related Long Non-coding RNAs Based on Integrating Multi-omics Data in Breast Cancer.

Cui Hao H Kong Hanqing H Peng Fuhui F Wang Chunjing C Zhang Dandan D Tian Jiawei J Zhang Lei L

Molecular therapy. Nucleic acids 20200215

Differences in individual drug responses are obstacles in breast cancer (BRCA) treatment, so predicting responses would help to plan treatment strategies. The accumulation of cancer molecular profiling and drug response data provide opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs in BRCA. This study evaluated drug responses with a multi-omics integrated system that depended on long non-coding RNAs (lncRNAs). We identified drug r ...[more]

PMID: 32163894

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Project description:BackgroundIndividualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy.MethodsIn this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations.ResultsTotally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP ( http://bio-bigdata.hrbmu.edu.cn/CancerDAP/ ) available to explore 2751 subpathways relevant with 191 anticancer drugs response.ConclusionsTaken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions.

Dataset Information

Inferences of Individual Drug Response-Related Long Non-coding RNAs Based on Integrating Multi-omics Data in Breast Cancer.

Publications

Inferences of Individual Drug Response-Related Long Non-coding RNAs Based on Integrating Multi-omics Data in Breast Cancer.

Similar Datasets

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