Dataset Information

SBV - Gene Expression Profiles of Lung Cancer Tumors - Adenocarcinomas and Squamous Cell Carcinomas

ABSTRACT: This dataset encompassing the profiles of 150 lung cancer tumors was developed to serve as test dataset in the SBV IMPROVER Diagnostic Signature Challenge (sbvimprover.com). The aim of this subchallenge was to verify that it is possible to extract a robust diagnostic signature from gene expression data that can identify stages of different types of lung cancer. Participants were asked to develop and submit a classifier that can stratify lung cancer patients in one of four groups – Stage 1 of Adenocarcinoma (AC Stage 1), Stage 2 of Adenocarcinoma (AC Stage 2), Stage 1 of Squamous cell carcinoma (SCC Stage 1) or Stage 2 of Squamous cell carcinoma (SCC Stage 2). The classifier could be built by using any publicly available gene expression data with related histopathological information and was tested on the independent dataset described here. 150 non-small cell lung cancer tumors (adenocarcinoma, AC and squamous cell carcinoma, SCC) of stages I and II were collected by surgical resection from patients who have provided consent. Adenosquamous and large cell tumor samples were excluded. The number of smokers and non-smokers was balanced: there were 41 AC1 (adenocarcinoma stage I), 36 AC2, 34 SCC1, and 39 SCC2 samples. Study pathologists at each of the seven sites (Lebanon, Republic of Moldova, Romania, Russian Federation, Ukraine, Vietnam and United States of America) reviewed both the tumor permanent sections and the frozen sections of the samples. Clinical information was also collected about tumor staging, history of prior cancers, lymph node involvement by lymph node sampling/dissection, smoking history, age, gender.

ORGANISM(S): Homo sapiens

SUBMITTER: Tarca AL

PROVIDER: S-ECPF-GEOD-43580 | biostudies-other | 2013 Nov

REPOSITORIES: biostudies-other

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Project description:BACKGROUND:Targeted therapy for non-small cell lung cancer is histology dependent. However, histological classification by routine pathological assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. Additionally, the effectiveness of immunomarkers is limited by technical inconsistencies of immunostaining and lack of standardization for staining interpretation. RESULTS:Using gene expression profiles of pathologically-determined lung adenocarcinomas and squamous cell carcinomas, denoted as pADC and pSCC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to distinguish ADC from SCC. The signature consists of two genes, KRT5 and AGR2, which has the stable REO pattern of KRT5 > AGR2 in pSCC and KRT5 < AGR2 in pADC. In the two test datasets with relative unambiguous NSCLC types, the apparent accuracy of the signature were 94.44 and 98.41%, respectively. In the other integrated dataset for frozen tissues, the signature reclassified 4.22% of the 805 pADC patients as SCC and 12% of the 125 pSCC patients as ADC. Similar results were observed in the clinical challenging cases, including FFPE specimens, mixed tumors, small biopsy specimens and poorly differentiated specimens. The survival analyses showed that the pADC patients reclassified as SCC had significantly shorter overall survival than the signature-confirmed pADC patients (log-rank p = 0.0123, HR = 1.89), consisting with the knowledge that SCC patients suffer poor prognoses than ADC patients. The proliferative activity, subtype-specific marker genes and consensus clustering analyses also supported the correctness of our signature. CONCLUSIONS:The non-subjective qualitative REOs signature could effectively distinguish ADC from SCC, which would be an auxiliary test for the pathological assessment of the ambiguous cases.

Project description:BACKGROUND:Lung cancer is the worldwide leading cause of death from cancer. Tobacco usage is the major pathogenic factor, but all lung cancers are not attributable to smoking. Specifically, lung cancer in never-smokers has been suggested to represent a distinct disease entity compared to lung cancer arising in smokers due to differences in etiology, natural history and response to specific treatment regimes. However, the genetic aberrations that differ between smokers and never-smokers' lung carcinomas remain to a large extent unclear. METHODS:Unsupervised gene expression analysis of 39 primary lung adenocarcinomas was performed using Illumina HT-12 microarrays. Results from unsupervised analysis were validated in six external adenocarcinoma data sets (n=687), and six data sets comprising normal airway epithelial or normal lung tissue specimens (n=467). Supervised gene expression analysis between smokers and never-smokers were performed in seven adenocarcinoma data sets, and results validated in the six normal data sets. RESULTS:Initial unsupervised analysis of 39 adenocarcinomas identified two subgroups of which one harbored all never-smokers. A generated gene expression signature could subsequently identify never-smokers with 79-100% sensitivity in external adenocarcinoma data sets and with 76-88% sensitivity in the normal materials. A notable fraction of current/former smokers were grouped with never-smokers. Intriguingly, supervised analysis of never-smokers versus smokers in seven adenocarcinoma data sets generated similar results. Overlap in classification between the two approaches was high, indicating that both approaches identify a common set of samples from current/former smokers as potential never-smokers. The gene signature from unsupervised analysis included several genes implicated in lung tumorigenesis, immune-response associated pathways, genes previously associated with smoking, as well as marker genes for alveolar type II pneumocytes, while the best classifier from supervised analysis comprised genes strongly associated with proliferation, but also genes previously associated with smoking. CONCLUSIONS:Based on gene expression profiling, we demonstrate that never-smokers can be identified with high sensitivity in both tumor material and normal airway epithelial specimens. Our results indicate that tumors arising in never-smokers, together with a subset of tumors from smokers, represent a distinct entity of lung adenocarcinomas. Taken together, these analyses provide further insight into the transcriptional patterns occurring in lung adenocarcinoma stratified by smoking history.

Dataset Information

SBV - Gene Expression Profiles of Lung Cancer Tumors - Adenocarcinomas and Squamous Cell Carcinomas

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