This Article Full Text Full Text (PDF) Supplementary Data Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by John, T. Articles by Cebon, J. S. PubMed PubMed Citation Articles by John, T. Articles by Cebon, J. S.

Predicting Clinical Outcome through Molecular Profiling in Stage III Melanoma

Authors' Affiliations: ¹ Ludwig Institute for Cancer Research, Melbourne Centre for Clinical Sciences, Austin Health, Heidelberg, Australia; ² Department of Biochemistry, University of Otago; ³ Pacific Edge Biotechnology Limited, Dunedin, New Zealand; and ⁴ Department of Statistics, University of Auckland, Auckland, New Zealand

Requests for reprints: Jonathan S. Cebon, Ludwig Institute for Cancer Research, Melbourne Centre for Clinical Sciences, Austin Health, Studley Road, Heidelberg, Victoria 3084, Australia. Phone: 61-3-9496-5462; Fax: 61-3-9457-6698; E-mail: Jonathan.Cebon{at}ludwig.edu.au.

Purpose: Patients with macroscopic stage III melanoma represent a heterogeneous cohort with average 5-year overall survival rates of <30%. With current algorithms, it is not possible to predict which patients will achieve longer-term survival. We hypothesized that molecular profiling could be used to identify prognostic groups within patients with stage III melanoma while also providing a greater understanding of the biological programs underpinning these differences.

Experimental Design: Lymph node sections from 29 patients with stage IIIB and IIIC melanoma, with divergent clinical outcome including 16 "poor-prognosis" and 13 "good-prognosis" patients as defined by time to tumor progression, were subjected to molecular profiling using oligonucleotide arrays as an initial training set. Twenty-one differentially expressed genes were validated using quantitative PCR and the 15 genes with strongest cross-platform correlation were used to develop two predictive scores, which were applied to two independent validation sets of 10 and 14 stage III tumor samples.

Results: Supervised analysis using differentially expressed genes was able to differentiate the prognostic groups in the training set. The developed predictive scores correlated directly with clinical outcome. When the predictive scores were applied to the two independent validation sets, clinical outcome was accurately predicted in 90% and 85% of patients, respectively.

Conclusion: We describe a gene expression profile that is capable of distinguishing clinical outcomes in a previously homogeneous group of stage III melanoma patients.