Current paradigms hold that lung carcinomas arise from pleuripotent stem cells capable of differentiation into one or several histological types. These paradigms suggest lung tumor cell ontogeny is determined by consequences of gene expression that recapitulate events important in embryonic lung development. Using oligonucleotide microarrays, we acquired gene profiles from 32 microdissected non-small-cell lung tumors. We determined the 100 top-ranked marker genes for adenocarcinoma, squamous cell, large cell, and carcinoid using nearest neighbor analysis. Results were validated by immunostaining for 11 selected proteins using a tissue microarray representing 80 tumors. Gene expression data of lung development were accessed from a publicly available dataset generated with the murine Mu11k genome microarray. Self-organized mapping identified two temporally distinct clusters of murine orthologues. Supervised clustering of lung development data showed large-cell carcinoma gene orthologues were in a cluster expressed in pseudoglandular and canalicular stages whereas adenocarcinoma homologues were predominantly in a cluster expressed later in the terminal sac and alveolar stages of murine lung development. Representative large-cell genes (E2F3, MYBL2, HDAC2, CDK4, PCNA) are expressed in the nucleus and are associated with cell cycle and proliferation. In contrast, adenocarcinoma genes are associated with lung-specific transcription pathways (SFTPB, TTF-1), cell adhesion, and signal transduction. In sum, non-small-cell lung tumors histology gene profiles suggest mechanisms relevant to ontogeny and clinical course. Adenocarcinoma genes are associated with differentiation and glandular formation whereas large-cell genes are associated with proliferation and differentiation arrest. The identification of developmentally regulated pathways active in tumorigenesis provides insights into lung carcinogenesis and suggests early steps may differ according to the eventual tumor morphology.

Gene expression profiles of resected tumors may predict treatment response and outcome. We hypothesized that profiles derived from lung tumor biopsies would discriminate tumor-specific gene signatures and provide predictive information about outcome. Lung carcinoma specimens were obtained from 23 patients undergoing computed tomography-guided transthoracic biopsy or endobronchial brushing for undiagnosed nodules. Excess tissue was processed for gene profiling. We built class prediction models for lung cancer histology and for cancer outcome. The histology model used an F test to identify 99 genes that were differentially expressed among lung cancer subtypes. The histology validation set class prediction accuracy rate was 86%. The outcome model used the maximum difference subset algorithm to identify 42 genes associated with high risk for cancer death. The outcome training set class prediction accuracy rate was 87%. In conclusion, gene expression profiles of biopsy specimens of lung cancers identify unique tumoral signatures that provide information about tissue morphology and prognosis. The use of specimens acquired from lung biopsy procedures to identify biomarkers of clinical outcome may have application in the management of patients with lung cancer. The procedures are safe and feasible; the efficacy and utility of this strategy will ultimately be determined by prospective clinical trials.

BACKGROUND Recently, the authors identified molecular signatures and pathways associated with nonsmall cell lung carcinoma histology and lung development. They hypothesized that genetic classifiers of histology would provide insight into lung tumorigenesis and would be associated with clinical outcome when evaluated in a broader set of specimens. METHODS Associations between patient survival and immunostaining for 11 representative histologic classifiers (epidermal growth factor receptor [EGFR], CDK4, syndecan‐1, singed‐like, TTF‐1, keratin 5, HDAC2, docking protein 1, integrin α3, P63, and cyclin D1) were examined using a tissue microarray constructed from nonsmall cell lung carcinoma specimens. RESULTS Sixty‐three tumors were examined, including 43 adenocarcinomas, 11 large cell carcinomas, and 9 squamous cell carcinomas. Sixty‐three percent of tumors were clinical Stage I lesions, and 37% were Stage II–III lesions. In a multivariate analysis that controlled for age, gender, and race, syndecan‐1 expression was found to be associated with a significant reduction in the risk of death (hazard ratio, 0.31 [95% confidence interval, 0.18–0.87]; P < 0.05). Multivariate analysis also indicated that EGFR expression was associated with a significant reduced risk of death. CONCLUSIONS The authors demonstrated that expression of either of the nonsmall cell lung carcinoma subtype classifiers syndecan‐1 and EGFR was associated with a 30% reduction in the risk of death, with this reduction being independent of histology and other confounders. The results of the current study suggest that loss of expression of these histologic classifiers is associated with biologic aggressiveness in lung tumors and with poor outcome for patients with such tumors. If their significance can be validated prospectively, these biomarkers may be used to guide therapeutic planning for patients with nonsmall cell lung carcinoma. Cancer 2004. © 2004 American Cancer Society.

Monkeypox is a rare infection caused by a virus that circulates in some animals in forested areas of Central and West Africa, but cases recently have been reported in people in multiple countries.

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