Validation of melanoma immune profile (MIP) to predict RFS in stage II-III melanoma on adjuvant interferon trial (E1697).

Abstract

e21596 Background: Development of prognostic biomarkers are urgently needed for patients with stage II-III melanoma to stratify for clinical trials. We performed a blinded retrospective prospective validation study of MIP, a previously defined immunogenomic signature using specimens from the E1697 study of adjuvant interferon conducted between 2000 and 2010. Methods: RNA was extracted from 20-micron sections using PureLink FFPE kit and quantified using the nCounter Platform (NanoString) from 169 patients. 7 specimens were excluded because they were stage I tumors, 3 for desmoplastic pathology, and 2 due to lack of clinical follow up. RNA was obtained successfully from all specimens and MIP score was calculated according to published methods. To test signature performance, Kaplan–Meier (KM) curves were plotted with log-rank test, and univariable and multivariable cox proportional hazards models adjusted with significant clinical predictors of lymph node status and ulceration were fitted. Results: Among 157 patients from the E1697 study, 40.8% were female, median age was 52.9 years and 21.7% stage III. with median RFS (mRFS) of 49.2 months. 139 were classifies as low risk and 19 as high risk. KM analysis showed that unfavorable MIP correlates with shortened RFS (p = 0.002, with mRFS 1.85 years vs undefined). Univariable cox analysis also correlated with RFS (p = 0.002, HR = 2.69, 95% Cl: 1.4-5.1). In this cohort lymph node status (p < 0.0001) and ulceration (p = 0.0166) correlated with RFS whereas Breslow depth did not (p = 0.236). MIP remained associated with a prolonged mRFS when lymph node status and ulceration were taken into account in a multivariable model. (p = 0.012, HR = 2.35, 95% Cl: 1.2-4.6). Conclusions: MIP is the first melanoma biomarker to be validated on national trial data in a blinded fashion. It should be further validated in prospective studies for use as a stratification metric in clinical trials.