Melanoma sentinel node biopsy and prediction models for relapse and overall survival

Mitra, A., Conway, C., Walker, C., Cook, M., Powell, B., Lobo, S., Chan, M., Kissin, M., Layer, G., Smallwood, J., Ottensmeier, C., Stanley, P., Peach, H., Chong, H., Elliott, F., Iles, M. M., Nsengimana, J., Barrett, J. H., Bishop, D. T. and Newton-Bishop, J. A. (2010) Melanoma sentinel node biopsy and prediction models for relapse and overall survival. British Journal of Cancer, 103 (8), 1229-1236. (doi:10.1038/sj.bjc.6605849). (PMID:20859289)

Abstract

BACKGROUND:

To optimise predictive models for sentinal node biopsy (SNB) positivity, relapse and survival, using clinico-pathological characteristics and osteopontin gene expression in primary melanomas.

METHODS:

A comparison of the clinico-pathological characteristics of SNB positive and negative cases was carried out in 561 melanoma patients. In 199 patients, gene expression in formalin-fixed primary tumours was studied using Illumina's DASL assay. A cross validation approach was used to test prognostic predictive models and receiver operating characteristic curves were produced.

RESULTS:

Independent predictors of SNB positivity were Breslow thickness, mitotic count and tumour site. Osteopontin expression best predicted SNB positivity (P=2.4 × 10??), remaining significant in multivariable analysis. Osteopontin expression, combined with thickness, mitotic count and site, gave the best area under the curve (AUC) to predict SNB positivity (72.6%). Independent predictors of relapse-free survival were SNB status, thickness, site, ulceration and vessel invasion, whereas only SNB status and thickness predicted overall survival. Using clinico-pathological features (thickness, mitotic count, ulceration, vessel invasion, site, age and sex) gave a better AUC to predict relapse (71.0%) and survival (70.0%) than SNB status alone (57.0, 55.0%). In patients with gene expression data, the SNB status combined with the clinico-pathological features produced the best prediction of relapse (72.7%) and survival (69.0%), which was not increased further with osteopontin expression (72.7, 68.0%).

CONCLUSION:

Use of these models should be tested in other data sets in order to improve predictive and prognostic data for patients.

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