Lung squamous cell carcinoma mRNA expression subtypes are reproducible, clinically important, and correspond to normal cell types

Abstract

Purpose: Lung squamous cell carcinoma (SCC) is clinically and genetically heterogeneous, and current diagnostic practices do not adequately substratify this heterogeneity. A robust, biologically based SCC subclassification may describe this variability and lead to more precise patient prognosis and management. We sought to determine if SCC mRNA expression subtypes exist, are reproducible across multiple patient cohorts, and are clinically relevant.

Experimental design: Subtypes were detected by unsupervised consensus clustering in five published discovery cohorts of mRNA microarrays, totaling 382 SCC patients. An independent validation cohort of 56 SCC patients was collected and assayed by microarrays. A nearest-centroid subtype predictor was built using discovery cohorts. Validation cohort subtypes were predicted and evaluated for confirmation. Subtype survival outcome, clinical covariates, and biological processes were compared by statistical and bioinformatic methods.

Results: Four lung SCC mRNA expression subtypes, named primitive, classical, secretory, and basal, were detected and independently validated (P < 0.001). The primitive subtype had the worst survival outcome (P < 0.05) and is an independent predictor of survival (P < 0.05). Tumor differentiation and patient sex were associated with subtype. The expression profiles of the subtypes contained distinct biological processes (primitive: proliferation; classical: xenobiotic metabolism; secretory: immune response; basal: cell adhesion) and suggested distinct pharmacologic interventions. Comparison with lung model systems revealed distinct subtype to cell type correspondence.

Conclusions: Lung SCC consists of four mRNA expression subtypes that have different survival outcomes, patient populations, and biological processes. The subtypes stratify patients for more precise prognosis and targeted research.

Figure 1. Discovery cohort correlation matrix and dendrogram

Cells are labeled by discovery cohort and adjusted centroid where A-D are from Supplement Fig. 4. Cells in the matrix represent the 1 – Pearson correlation coefficient between two discovery cohort adjusted centroids by shading according to the scale above (B). For example, BildA and RoepmanA have highly similar expression profiles, have a large Pearson correlation coefficient, a small 1 – Pearson correlation coefficent value and have a cell darkly shaded. The matrix is ordered by columns and rows by the dendrogram at the top of the matrix (A). The dendrogram is the result of an agglomerative, average-linkage, hierarchical clustering using this correlation matrix. The four expression subtypes are marked (C). Statistical significance of the three binary divisions leading to the four subtypes is shown by Sigclust (31) p-values in the dendrogram at the corresponding binary split.

Figure 2. Independent validation of lung SCC expression subtypes

Heatmaps depict mRNA expression of discovery cohorts (A), the validation cohort (B), a normal lung centroid (C), and SCC cell lines (D). Microarrays are columns and are labeled with their class. Genes are rows and are ordered by a discovery cohort hierarchical clustering. The normal lung centroid is scaled to the validation cohort for visualization. Manually-selected, lung-relevant, validation genes are displayed separately for viewability.

Figure 3. Survival outcomes of SCC subtypes

Survival was estimated by the Kaplan-Meier method using all cohorts’ available data. The sample sizes (N) are different than the overall study sample size due to data availability (OS: Bild et al, Raponi et al, Roepman et al, and UNC cohorts; RFS: Lee et al, Roepman et al, and UNC cohorts). P-values are from log rank tests evaluating the independence of survival and subtype.

Figure 4. SCC subtypes compared to lung cell type models

The relationship of relative SCC subtype expression differences to relative expression differences of published lung model systems. The models “Mouse lung development” (21) and “Human bronchial epithelial cell air liquid interface culture (HBEC-ALIC)” (22) are microarray time series, where time is indicated on the horizontal axis (A,B). Points mark Pearson correlation coefficients of SCC subtype centroids to model time points using the top 1,000 genes having the greatest Pearson correlation coefficient with time. Bars represent 95% confidence intervals. Lines connect points corresponding to the same subtype. Large positive correlations indicate mRNA expression similarity while large negative correlations indicate dissimilarity. In (A), ‘e’ refers to embryonic day and ‘p’ refers to postnatal day. The model “Human microdissected lung cell compartments (HMLSCC)” (20) is compared to SCC subtypes via a heatmap of genes that are overexpressed in submucosal glands and in surface epithelium as rows and subtype centroids in columns (C).