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A combined gene expression tool for parallel histological prediction and gene fusion detection in non-small cell lung cancer
Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.
Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.
Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden; Department of Pathology, Regional Laboratories Region Skåne, Lund, Sweden.
Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden; Department of Pathology, Regional Laboratories Region Skåne, Lund, Sweden.
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2019 (engelsk)Inngår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, artikkel-id 5207Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Accurate histological classification and identification of fusion genes represent two cornerstones of clinical diagnostics in non-small cell lung cancer (NSCLC). Here, we present a NanoString gene expression platform and a novel platform-independent, single sample predictor (SSP) of NSCLC histology for combined, simultaneous, histological classification and fusion gene detection in minimal formalin fixed paraffin embedded (FFPE) tissue. The SSP was developed in 68 NSCLC tumors of adenocarcinoma (AC), squamous cell carcinoma (SqCC) and large-cell neuroendocrine carcinoma (LCNEC) histology, based on NanoString expression of 11 (CHGA, SYP, CD56, SFTPG, NAPSA, TTF-1, TP73L, KRT6A, KRT5, KRT40, KRT16) relevant genes for IHC-based NSCLC histology classification. The SSP was combined with a gene fusion detection module (analyzing ALK, RET, ROS1, MET, NRG1, and NTRK1) into a multicomponent NanoString assay. The histological SSP was validated in six cohorts varying in size (n = 11-199), tissue origin (early or advanced disease), histological composition (including undifferentiated cancer), and gene expression platform. Fusion gene detection revealed five EML4-ALK fusions, four KIF5B-RET fusions, two CD74-NRG1 fusion and three MET exon 14 skipping events among 131 tested cases. The histological SSP was successfully trained and tested in the development cohort (mean AUC = 0.96 in iterated test sets). The SSP proved successful in predicting histology of NSCLC tumors of well-defined subgroups and difficult undifferentiated morphology irrespective of gene expression data platform. Discrepancies between gene expression prediction and histologic diagnosis included cases with mixed histologies, true large cell carcinomas, or poorly differentiated adenocarcinomas with mucin expression. In summary, we present a proof-of-concept multicomponent assay for parallel histological classification and multiplexed fusion gene detection in archival tissue, including a novel platform-independent histological SSP classifier. The assay and SSP could serve as a promising complement in the routine evaluation of diagnostic lung cancer biopsies.

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Nature Publishing Group, 2019. Vol. 9, artikkel-id 5207
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Identifikatorer
URN: urn:nbn:se:liu:diva-156391DOI: 10.1038/s41598-019-41585-4ISI: 000462298600094PubMedID: 30914778Scopus ID: 2-s2.0-85063519807OAI: oai:DiVA.org:liu-156391DiVA, id: diva2:1305727
Merknad

Funding Agencies|Swedish Cancer Society; Sjoberg Foundation; Mrs Berta Kamprad Foundation; Gunnar Nilsson Cancer Foundation; Crafoord Foundation; BioCARE a Strategic Research Program at Lund University; Gustav V:s Jubilee Foundation; Skane University Hospital Foundation; National Health Services (Region Skane/ALF)

Tilgjengelig fra: 2019-04-18 Laget: 2019-04-18 Sist oppdatert: 2019-10-22bibliografisk kontrollert

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