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

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 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.

KRT6A Promotes EMT and Cancer Stem Cell Transformation in Lung Adenocarcinoma

“…Keratin 6A is a family member of type II keratin proteins, biologically KRT6A could lead to epidermalization of squamous epithelium, 6,7 and KRT6A plays an important role in EMT in nasopharyngeal carcinoma, 6 KRT6A mutation plays an important role in pachyonychia congenita and could serve as a diagnosis marker. [7][8][9] In present study, the in vitro KRT6A silencing model was constructed to investigate the biological role of KRT6A. Our data indicate that KRT6A knockdown could significantly undermine the cancer cells proliferation and migration; moreover, KRT6A was involved with CSC transformation and EMT in LUAD.…”

Methylation Patterns and Chromatin Accessibility in Neuroendocrine Lung Cancer

“…After initial processing and filtering, a total of 108,973 regions (peaks) of open chromatin remained, and of these peaks, 13,987 peaks showed differential presence in NE vs. NSCLC cell lines (FDR adjusted Wald test, p < 0.05, Supplementary Table S3). [20], two tumors with mixed histological subtypes where only the adenocarcinoma component was RNA sequenced, and one original adenocarcinoma case (L504) with immunohistochemistry expression of a diagnostic NE marker (CHGA) (see [20]…”

“…The expression data (FPKM) were offset by 0.5 and log2 transformed prior to module score calculations. Original pathological assessments for the dataset were updated with data from Karlsson et al [20] who reclassified two NE samples (L480/L834) as mixed histology and one adenocarcinoma (L504) as having a NE component.…”

“…(A) Sorted gene module scores in the dataset. Legend shows sample annotations for large cell neuroendocrine carcinoma (LCNEC) tumors confirmed as LCNEC in re-review[20], two tumors with mixed histological subtypes where only the adenocarcinoma component was RNA sequenced, and one original adenocarcinoma case (L504) with immunohistochemistry expression of a diagnostic NE marker (CHGA) (see[20] for details); (B) Gene module scores stratified by tumor histological subtypes. LCC, large cell carcinoma; AC_SqCC, adenosquamous; SARC, sarcomatoid; (C) ROC curve showing classification results for the gene module when not including tumors L480 and L834 of mixed NE/non-NE histology; (D) Expression of the neural GTEx gene module in normal brain tissue (n = 5), low-grade glioma (LGG, n = 25), and high-grade (n = 25) glioblastoma multiforme (GBM).…”

RNA-Based Multiplexing Assay for Routine Testing of Fusion and Splicing Variants in Cytological Samples of NSCLC Patients

“…However, other methodologies have recently been employed, including mass array and direct hibrydization techniques such as nCounter and the Nanostring platform, which has the advantages of not depending on enzymatic amplification reactions, being an easy technology, and allowing the analysis of low quantity samples. Although several reports have described the use of nCounter for the detection of ALK, ROS1, and RET fusions in FFPE samples [ 6 , 7 , 8 ], only one of them specifically described results of fusion testing in cytological samples, while MET alterations were not assayed [ 8 ].…”