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. Clin Cancer Res; 16(19); 4864–75. ©2010 AACR.
This article is featured in Highlights of This Issue, p. 4683
Lung squamous cell carcinoma (SCC) has broad clinical, genetic, and morphologic heterogeneity. Currently, there is no subclassification that adequately describes this variability and SCC patients are basically treated as though they have the same disease. One explanation for SCC variability is that SCC is not a singular disease but a mixture of multiple discrete diseases or subtypes defined by innate biological differences. Using five discovery cohorts and an independent validation cohort totaling 438 patients, we show that SCC is composed of four robust mRNA expression subtypes (named primitive, classical, secretory, and basal). The subtypes have significantly different survival outcomes, patient populations, and biological processes. Using these subtypes as a basis for a future clinical diagnostic assay, patients could receive a more precise prognosis. Additionally, we described model system partners for the subtypes that can be used for targeted basic research.
Lung cancer is the leading cause of cancer-related death worldwide (1). Squamous cell carcinoma (SCC) is a major histologic type and comprises ∼30% of all pulmonary tumors (2, 3). SCC is defined by the presence of cytoplasmic keratinization and/or desmosomes (intracellular bridges; ref. 4). Clinically, SCC tumors occur more often in smokers and males compared with the other histologic types (2, 5). Patients affected with SCC tumors show a wide range of clinical outcomes. For instance, 83% of autopsied SCC patients had regional metastases (5) and 68% of SCC stage I patients survived beyond 5 years (6). Within SCC, there is noticeable morphologic variability, especially among poorly differentiated tumors (4, 7). The WHO SCC type includes a stratification of this variability with four variants (papillary, small cell, clear cell, and basaloid; ref. 4), but their prevalence and clinical and biological significance remain unclear. Because there is significant pathologic and clinical outcome variability within the SCC histologic type, a robust, biologically derived subclassification may be valuable.
Recent years have seen progress in classification of a variety of malignancies using full-genome molecular assays, primarily those directed at mRNA expression [e.g., leukemia (8), breast (9), and lung adenocarcinoma (10)]. A successful approach is unsupervised class discovery, which detects naturally occurring tumor classes (“mRNA expression subtypes”) without prespecified characteristics such as patient survival (8). Preliminary efforts have been made in SCC, suggesting the existence of SCC mRNA expression subtypes. In independent analyses, investigators (11–13) discovered two mRNA expression subtypes with intriguing biological profiles and a corresponding patient survival difference. These studies show that SCC might be subclassified using mRNA expression into groups with clinical relevance; however, the studies were not done in a manner that validated either the number or the nature of these intriguing classes. A validated mRNA expression classification could substantially progress patient care and research in lung SCC. In this study, we describe four novel reproducible expression subtypes (primitive, classical, secretory, and basal) of lung SCC. The SCC subtypes have different survival outcomes, patient demographics, physical characteristics, biological processes, and correspondence to normal lung cell types.
Materials and Methods
Frozen, surgically extracted, macrodissected, primary tumors from treatment-naive patients at the University of North Carolina with a lung SCC diagnosis were collected under Institutional Review Board approved protocols 90-0573 and 07-0120. Morphologic quality control was based on a review of a representative H&E-stained section from paraffin-embedded tissue immediately adjacent to the frozen tissue for confirmation of squamous histology by four pathologists (Supplementary Fig. S1) and for quantification of tumor content. Tumor RNA was extracted (14) and assayed for mRNA expression using Agilent 44,000 probe microarrays for a total of 56 microarrays. Microarrays were processed by normexp background correction and loess normalization (15). This data set is referred to as the “validation cohort” and was deposited at National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17710).
Published data sets
A structured search for publicly available SCC mRNA expression microarray data sets was conducted via Gene Expression Omnibus and PubMed and manually selecting data sets that have a large number of lung SCC samples to permit subtype analysis and that have significant cross–data set gene reliability, as measured by integrative correlations (16). This search yielded five data sets (referred to as the “discovery cohorts”) from the following studies: Bild et al. (17), Expression Project for Oncology (Expo; http://www.intgen.org/expo/), Lee et al. (18), Raponi et al. (13), and Roepman et al. (19). Published cohorts contained surgical resections from treatment-naive patients if indicated. Clinical data and raw or processed microarray data were obtained. Only microarrays with SCC histology were retained. Raw microarrays or gene lists from lung model systems were obtained (20–23). Microarrays were subjected to standard quality assessments, mapped to a common transcript database, and processed into gene-level expression values (Supplementary Table S1).
Unsupervised subtype discovery
The subtype discovery and validation procedure is depicted in a flowchart (Supplementary Fig. S2). Genes with high reliability and variability were selected similar to previously described methods (9, 10, 12, 13, 16). Gene reliability was measured by integrative correlations, and genes having an estimated false discovery rate (FDR) of 0.1% were retained (16). To select variable genes, genes in each discovery cohort were ranked by median absolute deviation in decreasing order. These ranks were averaged and reranked to make a single, ranked gene list. The top 25% of this ranked list, totaling 2,307 genes, was used for clustering. Before clustering, each data set was gene median centered (24, 25). Subtypes were determined in each discovery cohort by the Consensus Clustering algorithm via ConsensusClusterPlus (26, 27). This algorithm completed 1,000 microarray subsamples at a proportion of 80% and clustered these subsamples by an agglomerative average-linkage hierarchical algorithm using 1-Pearson correlation coefficient distance. Consensus values, the proportion that two microarrays occupy the same cluster, were calculated and then clustered by an agglomerative average-linkage hierarchical algorithm using Euclidean distance.
Subtype summarization by centroids
Centroids are median expression profiles of a group of arrays and were prepared using methods previously described (25, 28). Centroids were determined by taking a group of microarrays from a gene median centered cohort and obtaining the median of each gene. Multicohort centroids are determined by taking a group of centroids and taking the median of each gene.
Differentially expressed genes
Differentially expressed genes were determined by a standardized mean difference procedure that considers between cohort and within-cohort variation (29) using the GeneMeta Bioconductor library (http://bioconductor.org/packages/2.2/bioc/html/GeneMeta.html) and a random effects option. Gene set enrichment analysis (GSEA) was used to determine gene sets significantly enriched in ranked gene lists (30).
Validation cohort subtype prediction
Subtype status of the validation cohort was predicted by a nearest-centroid classification algorithm following previously published methods (28). In brief, the predictor was built, using only the discovery cohorts, by adding genes to a balanced centroid, assessing subtype prediction error rates by leave-one-out cross-validation, adding genes differentially expressed from the most mispredicted subtype to its centroid, and stopping once accuracy failed to improve. Subtype predictor centroids, unsupervised gene lists, and all gene multicohort centroids are available online (http://cancer.unc.edu/nhayes/publications/scc/).
The R library survival was used for survival statistical analyses. Patients dead within 1 month following surgery were considered to have procedure-related complications and not considered in survival analyses. Five patients met this condition all from the UNC cohort. Relapse-free survival (RFS) time was defined as the time from surgery until first relapse or death.
Cores (1 mm) were taken from available UNC cohort tissue blocks and randomly organized into tissue microarray blocks. Consequal 4-μm array block sections were assembled on array slides and stained with H&E, MAC387 (Dako), p63 (Dako), CK7 (Leica Microsystems), and MCM6 (Santa Cruz Biotechnology).
Unsupervised discovery of lung SCC expression subtypes in five cohorts
Lung SCCs are a heterogeneous group of tumors, and therefore, we did a common set of mRNA expression analyses using five previously published lung SCC data sets to determine how many distinct subtypes/groups of disease might exist. These five discovery cohorts were analyzed for the presence of mRNA expression subtypes using the Consensus Clustering methodology (26) as previously described for lung cancer (10). Consensus Clustering is a semiquantitative method for determining an optimal number of mRNA expression clusters/groups. Results show that all five cohorts contain four clusters (Supplementary Fig. S3). There is no compelling evidence for a higher number of clusters. To test if the four clusters from each cohort have the same expression profiles, a published centroid clustering method was followed (10). The centroid clustering shows a four-group structure, where each cohort is in each group, with only one cohort absent in one group (Supplementary Fig. S4). Therefore, the four clusters (mRNA expression subtypes) found in the five discovery cohorts have consistent expression profiles. To derive the optimal subtype for each patient, a multicohort centroid classification was used to assign each patient to a subtype, similar to published methods (28). A centroid clustering based on these optimal subtypes again shows a four-group structure and complete, unambiguous cross-cohort correspondence (Fig. 1). The cross-cohort clustering is statistically significant [Sigclust (31) P values in Fig. 1]. Interestingly, the subtypes have approximately the same prevalence among the discovery cohorts (Table 1). Using biological characteristics described below, the lung SCC mRNA expression subtypes are named primitive, classical, secretory, and basal.
SCC subtype independent validation
Although the four SCC subtypes were “cross-cohort” validated in that they were repeatedly found in five cohorts, this validation was not independent because discovery co-occurred with validation. For an independent validation, we tested the hypothesis that the SCC subtypes will exist in a new discovery-independent cohort. To test this hypothesis, a subtype predictor was built using the discovery cohorts, which consisted of 208 genes and had 94% leave-one-out cross-validation accuracy. Using this predictor, subtype classifications were made for microarrays from a new cohort of 56 lung SCC tumors collected at UNC. All four subtypes were predicted in the UNC cohort and in approximately the same prevalence as the discovery cohorts (Fig. 2; Table 1), which supports subtype reproducibility. To confirm the validity of the predictions, a comparison of expression characteristics between the discovery and UNC cohorts was completed similar to a recent related study (32). We compiled a large validation gene set of the top 100 genes overexpressed and underexpressed per subtype of the discovery cohorts (Fig. 2A), which yielded 1,117 unique genes. Subtype expression patterns are highly concordant between the discovery and UNC cohorts across the validation gene set (Fig. 2A and B), confirming that the large-scale expression patterns are consistent beyond the predictor gene set. In addition, the subtypes of the UNC cohorts are a statistically significant partition of its mRNA expression [SWISSMADE (33) subtypes versus random classes; P < 0.001]. We conclude that the predefined SCC subtypes exist in the UNC cohort and are, therefore, independently validated.
To preliminarily evaluate if clinically applicable biomarkers can distinguish the subtypes, we selected one overexpressed gene per subtype (basal, S100A8; classical, TP63; secretory, KRT7; primitive, MCM6) for immunohistochemical protein expression comparison using a tissue microarray subset of the UNC cohort (n = 38). All antibodies targeting these genes, except MCM6, had sufficient staining for analysis. Protein expression clustering using basal, classical, and secretory samples revealed three essentially mutually exclusive groups with one marker defining each group (Supplementary Fig. S5). These groups were significantly associated with tumor subtype (P = 0.007, Fisher's exact test). This suggests that SCC subtypes can also be distinguished by immunohistochemistry, and future work may find the optimal panel of immunohistochemical antibodies.
Subtypes exhibit distinct biological processes
To discern biological processes associated with each subtype, subtype mRNA expression was evaluated for enrichment in gene ontology, pathway, transcription factor binding site, and cytoband gene sets by GSEA (30). Because of the inherent redundancy in biology, we have collapsed these processes into functional themes (Fig. 3). Here, subtypes are described in terms of overexpression relative to the other subtypes.
The distinctive functional theme of the primitive subtype is cellular proliferation, which includes genes such as minichromosome maintenance 10 (MCM10), E2F transcription factor 3 (E2F3), thymidylate synthetase (TYMS), and polymerase α1 (POLA1), and a published proliferation signature (34). This proliferation theme is overexpressed in the most rapidly growing breast cancer cell lines (35) and in the most poorly differentiated, poor survival tumors from various organ sites (34). Complementary to the cellular proliferation functional theme, target genes of the E2F transcription factor, a known proliferation modulator (36), are overexpressed in this subtype as well as two members of the E2F family, E2F3 and E2F8. Other primitive subtype functional themes are RNA processing and DNA repair, which could be a consequence of the proliferation theme or an independent process.
The classical subtype exhibits the distinctive functional theme of xenobiotic metabolism, which detoxifies foreign chemicals. One study showed overexpression of this theme in smokers' versus nonsmokers' airway transcriptomes, including genes such as GPX2 and ALDH3A1 (37). Furthermore, this subtype is enriched with a gene signature derived from lung cell lines exposed to cigarette smoke, including genes such as AKR1C3 (38). Interestingly, the classical subtype has the greatest concentration of smokers and the heaviest smokers among the subtypes. This theme, including genes such as GPX2, AKR1C1, TXNRD1, and GSTM3, was noted as overexpressed in one head and neck SCC subtype (group 4 in ref. 39), suggesting a possible relative to the lung SCC classical subtype. The classical subtype overexpresses TP63, a transcription factor essential for stratified squamous epithelium development (40) that is more commonly overexpressed and amplified in lung SCC compared with other histologic types (41). Cytoband gene overexpression, a proxy for underlying genomic DNA amplification, suggests that 3q27-28, which contains TP63, is amplified in the classical subtype. The microarrays of this study do not have enough resolution to measure TP63 isoform-specific expression, but this may be a goal of future investigations.
Immune response is the major distinctive functional theme of the secretory subtype and includes genes such as Rho GDP dissociation inhibitor β (ARHGDIB) and tumor necrosis factor receptor 14 (TNFRSF14). Consistent with this theme, the secretory subtype has a NF-κB regulation theme and NF-κB target gene overexpression. This subtype also overexpresses the lung secretory cell markers mucin (MUC1) and pulmonary surfactant proteins (SFTPC, SFTPB, and SFTPD; refs. 7, 42). Interestingly, thyroid transcription factor 1 (NKX2-1/TTF1), known to be highly expressed in adenocarcinoma (43), is overexpressed in the secretory subtype relative to the other SCC subtypes. This commonality could be a result of the glandular cell structure of adenocarcinoma, which perhaps has secretory properties similar to the SCC secretory subtype. A UNC normal lung centroid shows a very similar expression pattern to the secretory subtype over the independent validation gene list, which was selected without considering normal samples (Fig. 2C). To evaluate any possible difference between the secretory subtype samples and normal samples, an unsupervised clustering was completed using only these microarrays (Supplementary Fig. S6). Secretory and normal microarrays clustered with their group in essentially all cases, suggesting that the secretory subtype and normal lung are distinct mRNA expression groups.
The basal subtype expression profile shows a cell adhesion functional theme, including genes such as the laminins (LAMB3 and LAMC2), collagens (COL11A1 and COL17A1), integrins (ITGB4 and ITGB5), and claudin 1 (CLDN1). Additionally, this subtype has an epidermal development theme, including keratin 5 (KRT5), psoriasin (S100A7), and gap junction protein β5 (GJB5). Several of the genes of the basal subtype, such as COL17A1, LAMC2, and CDH3, are common with a head and neck SCC subtype (group 1 in ref. 39) and a breast cancer subtype (basal-like in ref. 9), suggesting that these different organ site subtypes may share biological properties. The basal subtype overexpresses several S100 family genes: S100A2, S100A3, S100A7, S100A8, S100A9, S100A12, and S100A14. S100A8 and S100A9 are highly expressed in the basal layer in psoriatic epidermal tissue (44). S100A2 is a marker specific for the basal layer of the lung epithelium and SCC (45). KRT5 is a basal layer marker in epithelial tissue (46). The basal subtype is enriched with genes whose products are localized in the basement membrane.
In parallel to differential biological functions are patterns of mRNA expression with implications for pharmacologic intervention (Fig. 3). For example, TYMS, a target of antifolates including pemetrexed, is overexpressed in the primitive subtype. The antifolate metabolism pathway is differentially expressed among SCC subtypes, with the secretory subtype showing underexpression and similarity to adenocarcinoma (Supplementary Fig. S7). Overexpression of TYMS has been shown to be related to pemetrexed resistance in a dose-dependent manner in lung cancer cell culture (47). In addition, PARP1, a target of several drugs in development, is overexpressed in the primitive subtype.
SCC subtype tumor morphologic and patient characteristics
The morphologic and patient characteristics of the subtypes are displayed in Table 1. Grade is significantly associated with subtype (P = 0.024, Fisher's exact test). The primitive subtype has an overrepresentation of poorly differentiated tumors, and the basal subtype has an overrepresentation of well-differentiated tumors. Tumor stage is not appreciably different among subtypes, although we note that the classical and secretory subtypes have increased proportions of stage III tumors. The surgical cohorts oversample early stages, and possibly, greater sampling of late-stage patients may find additional subtype-stage associations. Specimen quality metrics of percent tumor, percent necrosis, and percent lymphocyte infiltration are not appreciably different among the subtypes, arguing against sampling artifacts as the source of the subtypes. Two cases of WHO morphologic SCC subclass were definitively called by pathologist review (one basaloid in primitive and classical subtypes), suggesting that these SCC morphologic subclasses are rare.
Patient sex approaches statistically significant association with subtype (P = 0.058, Fisher's exact test). Females are overrepresented in the primitive subtype and males in the classical subtype. Consistent with the smoking expression profile of the classical subtype, the classical subtype has the greatest mean pack-years (73; P = 0.319, Kruskal-Wallis test) and the lowest proportion of nonsmokers (1%; P = 0.214, Fisher's exact test), although these observations do not meet statistical significance.
SCC subtypes have different patient survival outcomes
Overall survival (OS) and RFS outcomes are significantly different among SCC subtypes (Fig. 4). The primitive subtype has worse OS and RFS compared with the other subtypes in all stages and in stage I (Fig. 4), whereas the basal, secretory, and classical seem to have similar outcomes. Considering the UNC cohort alone, the primitive subtype outcome is also worse compared with the other subtypes over all stages (OS: P = 0.066, log-rank test; RFS: P = 0.004, log-rank test) and stage I (OS: P = 0.057, log-rank test; RFS: P = 0.007, log-rank test). In the UNC cohort, 7 of 18 recurrences were extrapulmonary and the basal subtype had the lowest number and proportion (0/3). To evaluate the independent contribution of SCC subtype to patient risk in light of known prognostic factors, univariate and multivariate Cox proportional hazard models were constructed (Supplementary Table S2). Significant univariate predictors were primitive subtype for OS and RFS and tumor stage for OS. Patient age and tumor grade were not significant predictors of either outcome. In multiple variable models, only subtype retained significance for OS and RFS. The nonsignificant prediction of the tumor stage may be due to the underrepresentation of late-stage patients across the cohorts.
Raponi et al. reported two SCC mRNA expression subtypes with a survival difference and provided a list of differentially expressed genes, where high expression of the “majority of the genes were downregulated in the high-risk group” (13). Comparison of Raponi et al.'s microarrays by their gene list and the subtypes discovered in this study shows two clear subtype groups: underexpression (primitive and secretory) and overexpression (basal and classical; Supplementary Fig. S8). Therefore, the four subtypes discovered in this study map to prior results and this study has divided each of the prior subtypes into two new ones and improves the SCC mRNA expression subtype granularity. Interestingly, the Raponi et al. poor survival subtype totals 43% of their patients, where the poor survival subtype of this study (primitive) is 16% of their patients. It seems that a fraction of Raponi et al.'s high-risk subtype shows poor survival outcome relative to the remainder of SCC.
SCC subtypes are similar to different normal lung cell types and SCC cell lines
To evaluate the hypothesis that SCC subtypes are derived from different cell types present in the normal lung, SCC subtypes were compared by mRNA expression to three published model systems. The first model, “Mouse lung development,” is a time series of mouse lungs extracted from embryonic stages to adult (21). Expression similarity is defined as high positive Pearson correlation between an SCC subtype and time points within the model. The primitive subtype shows expression similarity to early-stage mouse lung, and the secretory subtype shows similarity to late-stage mouse lung (Fig. 5A). The second model, “Human bronchial epithelial cell air liquid interface culture” (HBEC-ALIC), is a time series of cultured normal, healthy, human bronchial epithelial cells, in which the early time points consist of stratified basal cells and later time points include secretory and ciliated cells (22). The basal subtype showed expression similarity to the early time points during which basal cells are predominant (Fig. 5B). The primitive and secretory subtypes show expression similarity to the later time points at which there are secretory and ciliated cells. The third model system, “Human microdissected lung cell compartments” (HMLCC), was laser capture microdissected cells contained in surface epithelium and in submucosal glands of normal healthy lung (20). The secretory subtype overexpresses genes that are overexpressed in submucosal glands (Fig. 5C). The basal subtype overexpresses genes that are overexpressed in surface epithelia. The classical subtype does not show appreciable similarity to any specific lung model, is the only subtype to have this property, and could be most similar to multiple or unobserved cell types. Therefore, by the combination of all three lung models, three of the four SCC subtypes have unique similarities to different, normal lung cell types.
In addition to the cell type models, SCC subtypes may correspond to different SCC cell lines, which could establish additional manipulatable models for future investigations into subtype biology. To ascertain if SCC cell lines correspond to different SCC tumor subtypes by mRNA expression, four published SCC cell line microarrays (23) were given subtype classifications by the nearest-centroid predictor. Interestingly, the four cell lines were predicted to be different subtypes (Fig. 2D). Expression of the subtypes between the cell lines and tumors is consistent over the validation gene set (Fig. 2A and D). For example, genes are consistent and mutually exclusive in the cell lines as predicted (HCC15, primitive and MCM10; HCC95, classical and AKR1C3; HCC2450, secretory and MUC1; H157, basal and MMP13).
The principal novel hypothesis tested in this study is that lung SCC expression subtypes exist, are reproducible, are clinically relevant, and exhibit patterns that correlate with unique cell types in the normal lung. These subtypes (primitive, basal, secretory, and classical) were identified in an unbiased and objective manner and are supported by cross-cohort validation using five training cohorts and by independent validation using a sixth cohort, which together total 438 patients. The expression subtypes were also found in a wide variety of patient populations from the United States, Asia, and Europe, in a wide variety of cohort sizes from 36 to 127. All cohorts showed approximately the same subtype proportions (overall: primitive, 16%; classical, 37%; secretory, 26%; basal, 21%). These subtypes were associated with tumor differentiation and patient sex. Survival outcomes are significantly different among the subtypes, and subtype is an independent predictor of survival. Possible limitations of our analysis include possible sample quality artifacts or patient behavior, such as smoking immediately before surgery; however, all six cohorts showed the same results, so any limitation would have to occur in six large, independently collected cohorts.
The SCC expression subtypes are biologically distinct and show similarities to distinct normal lung cell populations. These biological characteristics serve as the basis for the SCC nomenclature. The basal subtype exhibits many characteristics of lung basal cells, such as cell adhesion and epidermal development functional themes, S100A2 and KRT5 basal cell markers, overexpression of genes whose products are localized in the basement membrane, similarity to basal cells in the HBEC-ALIC model, and similarity to surface epithelia in the HMLCC model. The secretory subtype has many features of lung secretory cells, such as surfactant and mucin overexpression, similarity to secretory cells in the HBEC-ALIC model, and similarity to submucosal glands in the HMLCC model. The primitive subtype has a cellular proliferation functional theme, the worst survival outcome, an overabundance of female patients, the most nonsmokers, and an overabundance of poorly differentiated tumors. This subtype is similar to early embryonic mouse lungs, where primitive, less differentiated cells may be predominant and would be consistent with the poorly differentiated nature of these tumors. The primitive subtype also has similarity to late-stage HBEC-ALIC, which could be explained by lung “transient expression” in which differentiation markers are expressed during early lung formation and again in the developed lung (48). Alternatively, a late-emerging and late-active cell type in HBEC-ALIC may be most similar to the embryonic mouse lung. The classical subtype exhibits features representative of typical lung SCC, including the highest prevalence at 37%, overabundance of males, greatest patient smoking behavior, overexpression of TP63, and putative amplification of the TP63-containing locus 3q27-28.
The distinct SCC subtype to cell population similarities could be explained by the SCC subtypes having different ancestor cells. These different ancestor cells could be cell types of distinct lineages or cellular differentiation stages such as proposed in breast cancer (49). This scenario provides a reason why the SCC subtypes have dramatically different mRNA expression. The subtypes could arise by genetic mutation from different ancestors that have different mRNA expression, and this ancestral mRNA expression could persist in progeny tumor cells. This putative subtype ancestral cell information could be used in developing SCC subtype pharmacologic interventions that exploit differences in the ancestral cell types. A caveat to our interpretation of SCC subtype to cell population similarity is that the similarity could be caused by coincidence and expression similarities could reflect similar biology and not similar origin. The lung has multiple proposed cellular development pathways, and future studies that describe the molecular profiles of the lung cell types or lung cancer stem cells would further clarify the putative ancestral cells of the SCC subtypes (50).
The SCC subtypes may have applications in patient care and in cancer research. For instance, patients with the primitive subtype could be treated more aggressively because of the poor survival expectation of this subtype or could be given a more accurate prognosis than by using traditional prognostic factors alone. Basic cancer research could be conducted using the subtype model system partners described in this study. The SCC subtypes could be useful for therapy benefit studies and possibly serve as a foundation for clinical trial selection.
In conclusion, we identified four robust expression subtypes of lung SCC using a multicohort discovery and validation strategy. The subtypes are clinically and phenotypically different, suggesting different therapies.
Disclosure of Potential Conflicts of Interest
D.N. Hayes, C.M. Perou, and P.S. Bernard hold a provisional patent that is related to work described in this manuscript but there is no current financial interest. All other authors have no conflicts of interest.
Grant Support: NIH/National Cancer Institute grant F32CA142039 (M.D. Wilkerson), Lineberger Comprehensive Cancer Center Translational Small Grants Program (D.N. Hayes), NIH/National Center for Research Resources grant K12-RR023248 (D.N. Hayes), and Thomas G. Labrecque Foundation through Joan's Legacy Foundation (D.N. Hayes).
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).
- Received January 22, 2010.
- Revision received May 19, 2010.
- Accepted June 27, 2010.