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• W2891851777 abstract "The outcome of patients with malignant pleural mesothelioma (MPM) is poor, and diagnosis is complicated by a lack of biomarkers capable of distinguishing primary MPM from cancers that have metastasized to the pleura. Clinical diagnosis and tissue of origin is currently assessed through the use of a panel of positive and negative markers; however, there remains a subset of cases that are not identifiable by current clinical biomarkers. Recent studies suggest that the human genome encodes more miRNAs than are currently annotated, and that the novel miRNAs may display enhanced tissue and lineage specificity. We conducted a de novo search for novel miRNAs by applying a prediction algorithm to the small RNA-sequence data in a cohort of MPM tumors (n=87) from The Cancer Genome Atlas (TCGA). This analysis yielded 424 predicted novel miRNA-like sequences, which were subsequently filtered by RNA structure, abundance, and genomic location to identify 154 previously unannotated miRNA sequences. This represents a significant increase to the repertoire of 1,597 annotated miRNAs in MPM. Protein-coding genes predicted to be targeted by these novel miRNAs, using the miRanda algorithm, include genes involved in MPM biology. One of the most highly expressed novel miRNAs identified targets the Ataxia Telangiectasia Mutated (ATM) gene. Another target gene, BRCA1 Associated Protein 1 (BAP1), is also in the DNA damage response pathway. To investigate the ability of these 154 novel miRNAs to distinguish MPM from other thoracic cancers, we assessed their expression in 1,093 lung tumors from four independent cohorts from TCGA and the BC Cancer Agency (BCCA): two adenocarcinoma (LUAD) cohorts (TCGA n=497, BCCA n=94) and two squamous cell carcinoma (LUSC) cohorts (TCGA n=467, BCCA n=35). Principal component analyses revealed that novel miRNA expression was able to unambiguously distinguish MPM from LUAD and LUSC. Furthermore, we developed an miRNA-based classifier model using the weighted voting class prediction method. A 10 novel miRNAs classifier was deduced by comparing MPM and LUAD cases from TCGA and validated by comparing MPM against LUAD cases from the BCCA cohort. Remarkably, this classifier successfully identified 86 out of the 87 MPM cases (98.8%) and 100% of LUAD cases (true positive rate = 98.85%, false positive rate = 1.15%). The strikingly high sensitivity and specificity in distinguishing MPM from LUAD illustrates the potential of using novel miRNAs to supplement current clinical markers to define MPM. Citation Format: Erin A. Marshall, Christine Anderson, Kevin W. Ng, Brenda C. Minatel, Katey S.S. Enfield, Adam P. Sage, Zhaolin Xu, Wan L. Lam, Victor D. Martinez. Novel miRNAs as tissue-of-origin markers for distinguishing malignant pleural mesothelioma from lung adenocarcinoma [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr B36." @default.
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• W2891851777 date "2018-09-01" @default.
• W2891851777 modified "2023-10-14" @default.
• W2891851777 title "Abstract B36: Novel miRNAs as tissue-of-origin markers for distinguishing malignant pleural mesothelioma from lung adenocarcinoma" @default.
• W2891851777 doi "https://doi.org/10.1158/1557-3265.aacriaslc18-b36" @default.
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