Determination of prognosis in metastatic melanoma through integration of clinico‐pathologic, mutation, mRNA, microRNA, and protein information. Issue 4 (24th July 2014)

• Record Type: Journal Article
• Authors: Jayawardana, Kaushala; Schramm, Sarah‐Jane; Haydu, Lauren; Thompson, John F.; Scolyer, Richard A.; Mann, Graham J.; Müller, Samuel; Yang, Jean Yee Hwa
• Is Part Of: International journal of cancer. Volume 136:Issue 4(2015:Feb. 15)
• Page Start: 863
• Page End: 874
• Publication Date: 2014-07-24
• Subjects: Cancer -- Periodicals; Cancer -- Prevention -- Periodicals; 616.994
• DOI: 10.1002/ijc.29047
• Languages: English
• ISSNs: 0020-7136

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>In patients with metastatic melanoma, the identification and validation of accurate prognostic biomarkers will assist rational treatment planning. Studies based on "‐omics" technologies have focussed on a single high‐throughput data type such as gene or microRNA transcripts. Occasionally, these features have been evaluated in conjunction with limited clinico‐pathologic data. With the increased availability of multiple data types, there is a pressing need to tease apart which of these sources contain the most valuable prognostic information. We evaluated and integrated several data types derived from the same tumor specimens in AJCC stage III melanoma patients—gene, protein, and microRNA expression as well as clinical, pathologic and mutation information—to determine their relative impact on prognosis. We used classification frameworks based on pre‐validation and bootstrap multiple imputation to compare the prognostic power of each data source, both individually as well as integratively. We found that the prognostic utility of clinico‐pathologic information was not out‐performed by any of the various "‐omics" platforms. Rather, a combination of clinico‐pathologic variables and mRNA expression data performed best. Furthermore, a patient‐based classification analysis revealed that the prognostic accuracy of various data types was not the same for different patients. This indicates that ongoing development in the individualized evaluation of melanoma patients must take account of the value of both traditional and novel "‐omics" measurements.</p> </abstract>