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We explored whether the five previously reported molecular subtypes in breast cancer show a preference for organ-specific relapse and searched for molecular pathways involved. The ''intrinsic'' gene list describing the subtypes was used to classify 344 primary breast tumors of lymph node-negative patients. Fisher exact tests were used to determine the association between a tumor subtype and a particular site of distant relapse in these patients who only received local treatment. Modulated genes and pathways were identified in the various groups using Significance Analysis of Microarrays and Global Testing. Bone relapse patients were most abundant in the luminal subtypes but were found less than expected in the basal subtype. The reverse was true for lung and brain relapse patients with the remark that absence of lung relapse was luminal A specific. Finally, a pleura relapse, although rare, was found almost exclusively in both luminal subtypes. Many differentially expressed genes were identified, of which several were in common in a subtype and the site to which the subtype preferentially relapsed. WNT signaling was up-regulated in the basal subtype and in brain-specific relapse, and downmodulated in the luminal B subtype and in bone-specific relapse. Focal adhesion was found up-regulated in the luminal A subtype but down-regulated in lung relapse. The five major molecular subtypes in breast cancer are evidently different with regard to their ability to metastasize to distant organ(s), and share biological features and pathways with their preferred distant metastatic site. [Cancer Res 2008;68(9):3108-14]

Purpose: Cutaneous melanoma is a common, aggressive cancer with increasing incidence. The identification of melanoma-specific deregulated genes could provide molecular markers for lymph node staging assays and further insight into melanoma tumorigenesis. Experimental Design:Total RNA isolated from 45 primary melanoma,18 benign skin nevi, and 7 normal skin tissue specimens were analyzed on an Affymetrix Hu133A microarray containing 22,000 probe sets. Results: Hierarchical clustering revealed a distinct separation of the melanoma samples from the benign and normal specimens. Novel genes associated with malignant melanoma were identified. Differential gene expression of two melanoma-specific genes, PLAB and L1CAM, were tested by a one-step quantitative reverse transcription-PCR assay on primary malignant melanoma, benign nevi, and normal skin samples, as well as on malignant melanoma lymph node metastasis and melanoma-free lymph nodes. The performance of the markers was compared with conventional melanoma markers such as tyrosinase, gp100, and MART1. Conclusion: Our study systematically identified novel melanoma-specific genes and showed the feasibility of using a combination of PLAB and L1CAM in a reverse transcription-PCR assay to differentiate clinically relevant samples containing benign or malignant melanocytes.

Non-small-cell lung cancers (NSCLC) compose 80% of all lung carcinomas with squamous cell carcinomas (SCC) and adenocarcinoma representing the majority of these tumors. Although patients with early-stage NSCLC typically have a better outcome, 35% to 50% will relapse within 5 years after surgical treatment. We have profiled primary squamous cell lung carcinomas from 129 patients using Affymetrix U133A gene chips. Unsupervised analysis revealed two clusters of SCC that had no correlation with tumor stage but had significantly different overall patient survival (P = 0.036). The high-risk cluster was most significantly associated with down-regulation of epidermal development genes. Cox proportional hazard models identified an optimal set of 50 prognostic mRNA transcripts using a 5-fold cross-validation procedure. Quantitative reverse transcription-PCR and immunohistochemistry using tissue microarrays were used to validate individual gene candidates. This signature was tested in an independent set of 36 SCC samples and achieved 84% specificity and 41% sensitivity with an overall predictive accuracy of 68%. Kaplan-Meier analysis showed clear stratification of high-risk and low-risk patients [log-rank P = 0.04; hazard ratio (HR), 2.66; 95% confidence interval (95% CI), 1.01-7.05]. Finally, we combined the SCC classifier with our previously identified adenocarcinoma prognostic signature and showed that the combined classifier had a predictive accuracy of 71% in 72 NSCLC samples also showing significant differences in overall survival (log-rank P = 0.0002; HR, 3.54; 95% CI,). This prognostic signature could be used to identify patients with early-stage high-risk NSCLC who might benefit from adjuvant therapy following surgery. (Cancer Res 2006; 66(15): 7466-72)