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Multicenter Study
. 2010 Oct 12;103(8):1229-36.
doi: 10.1038/sj.bjc.6605849. Epub 2010 Sep 21.

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

A Mitra  1 , C Conway, C Walker, M Cook, B Powell, S Lobo, M Chan, M Kissin, G Layer, J Smallwood, C Ottensmeier, P Stanley, H Peach, H Chong, F Elliott, M M Iles, J Nsengimana, J H Barrett, D T Bishop, J A Newton-Bishop
Affiliations
Multicenter Study

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

A Mitra et al. Br J Cancer. .

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 × 10⁻⁷), 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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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Receiver operating characteristic (ROC) analysis of predictors of SNB positivity for tumour subset group. Green: Breslow, mitotic rate, tumour site, age sex and SPP1 expression, AUC=72.6%. Red: Breslow, mitotic rate, tumour site, age and sex, AUC=68.6%. Black: SPP1 expression alone, AUC=65.7%. Blue: Breslow thickness alone, AUC=60.9%.
Figure 2
Figure 2
Receiver operating characteristic (ROC) analysis of predictors of relapse for the total study group. Blue: clinico-pathological features (Breslow, mitotic count, vessel invasion, ulceration, site, age, sex) and SNB status, AUC=76.0%. Red: clinico-pathological features alone, AUC=71.0%. Black: SNB status alone, AUC=57.0%.
Figure 3
Figure 3
Receiver operating characteristic (ROC) analysis of predictors of relapse for tumour subset group. Purple: clinico-pathological features, SNB status, SPP1 expression, AUC=72.7%. Dark blue: clinico-pathological features, SNB status, AUC=72.7%. Green: clinico-pathological features and SPP1 expression, AUC=67.4%. Red: clinico-pathological features alone, AUC=66.2%. Light blue: SPP1 expression alone, AUC=59.0%. Black: SNB status alone, AUC=53.7%.
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