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A combined gene expression tool for parallel histological prediction and gene fusion detection in non-small cell lung cancer

Karlsson, Anna LU ; Cirenajwis, Helena LU ; Ericson-Lindquist, Kajsa LU ; Brunnström, Hans LU orcid ; Reuterswärd, Christel LU ; Jönsson, Mats LU ; Ortiz-Villalón, Cristian ; Hussein, Aziz ; Bergman, Bengt and Vikström, Anders , et al. (2019) In Scientific Reports 9(1).
Abstract

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... (More)

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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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
9
issue
1
article number
5207
publisher
Nature Publishing Group
external identifiers
  • pmid:30914778
  • scopus:85063519807
ISSN
2045-2322
DOI
10.1038/s41598-019-41585-4
language
English
LU publication?
yes
id
91158fcf-f0b0-4ab8-ae0a-57efcd9c78d4
date added to LUP
2019-04-05 08:00:16
date last changed
2024-07-09 09:20:11
@article{91158fcf-f0b0-4ab8-ae0a-57efcd9c78d4,
  abstract     = {{<p>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.</p>}},
  author       = {{Karlsson, Anna and Cirenajwis, Helena and Ericson-Lindquist, Kajsa and Brunnström, Hans and Reuterswärd, Christel and Jönsson, Mats and Ortiz-Villalón, Cristian and Hussein, Aziz and Bergman, Bengt and Vikström, Anders and Monsef, Nastaran and Branden, Eva and Koyi, Hirsh and de Petris, Luigi and Micke, Patrick and Patthey, Annika and Behndig, Annelie F. and Johansson, Mikael and Planck, Maria and Staaf, Johan}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  month        = {{03}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{A combined gene expression tool for parallel histological prediction and gene fusion detection in non-small cell lung cancer}},
  url          = {{http://dx.doi.org/10.1038/s41598-019-41585-4}},
  doi          = {{10.1038/s41598-019-41585-4}},
  volume       = {{9}},
  year         = {{2019}},
}