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.
Highlights
Background corrected raw counts corresponding to11 genes (LCNEC: CHGA, SYP, CD56, AC: SFTPG, napsin A (NAPSA), TTF-1, squamous cell carcinoma (SqCC): TP73L, KRT6A, KRT5, KRT40, KRT16) used in clinical diagnostics or shown to form specific gene expression modules related to lung tumor histologies[25] were extracted from the NanoString data
All fusion positive and MET exon 14 skipping cases were found in the development cohort and validation cohort I
We set out to test the novel concept of establishing a multipurpose assay for histological classification and parallel gene fusion detection[11,24] based on analysis of gene expression patterns in archival tissue
Summary
Background corrected raw counts corresponding to11 genes (LCNEC: CHGA, SYP, CD56, AC: SFTPG, NAPSA, TTF-1, SqCC: TP73L, KRT6A, KRT5, KRT40, KRT16) used in clinical diagnostics or shown to form specific gene expression modules related to lung tumor histologies[25] were extracted from the NanoString data. A single sample predictor (SSP) was built using the Absolute Intrinsic Molecular Subtyping (AIMS) model[26] (available as scripts from the original authors’ GitHub account) in the training set and evaluated in the test set using accuracy, balanced accuracy and area under curve (AUC) as performance metrics. We iterated this process 10 times to assure that sample partitioning did not greatly influence results, creating 10 different models with 10 respective metric values (accuracy, balanced accuracy, and AUC), using a mean value over the iterations for final SSP performance evaluation. The SSP model and exemplary datasets for NSCLC histology prediction is available as an R package (SSP_NSCLC_histology.zip)
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