Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information

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 patientsgene, protein, and microRNA expression as well as clinical, pathologic and mutation informationto 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. What's new? The use of -omics technologies in the investigation of tumor biomarkers has led to the generation of multiple types of data sets. But which of those sets are most useful for prognostic assessment of disease is unclear. Here, for prognostic accuracy in metastatic melanoma after resection, a combination of clinicopathologic variables and RNA expression data was found to out-perform information derived from -omics platforms alone. Among patients, however, the prognostic accuracy of different data types varied. The findings suggest that more data does not necessarily increase prognostic accuracy and that patient evaluation must rely on both traditional and novel approaches.