Abstract

Background: Precision-based personalised biomarkers able to identify both low-risk and high-risk patient subpopulations with localised cutaneous melanoma are urgently needed to guide clinical follow up and treatment stratification.
We recently validated the combined immunohistochemical expression of AMBRA1 and Loricrin (AMBLor) in the epidermis overlying non-ulcerated AJCC stage I/II melanomas as prognostic biomarker able to accurately identify genuinely low-risk patient subpopulations (NPV >96%, clinical sensitivity >95%, Ewen et al Brit J Dermatol. 2024). To further identify distinct subsets of patients with non-ulcerated AJCC stage I/II melanomas ar high risk of metastasis, the present study aimed to develop a machine learning (ML) risk-prediction model combining AMBLor ‘at -risk’ status with specific patient clinical and tumour pathological features.
Methods: Using commonly and widely used ML models, ML algorithms were trained and tested using three internationally distinct retrospective-prospective cohorts of AMBLor at-risk non-ulcerated AJCC stage I/II melanomas (n=552).
Results: Based on a training: test data split of 50:50, 20% of patients were defined as high-risk, with a 5-year recurrence-free survival (RFS) probability of 56% (Log-rank [Mantel-Cox) P < 0.0001, HR 6.88, 95% CI 3[PL1].03-15.63, clinical specificity 87.2%, PPV 44.4%).
Further validation of the ML algorithms in a 4th independent retrospective-prospective cohort of 120 AMBLor at-risk non-ulcerated localised melanomas derived from the UK identified 24% patients as high-risk, with a 5-year RFS of 56.3% (Log-rank [Mantel-Cox) P < 0.0001, HR 7.59, 95% CI 2.94-19.6, clinical specificity 82.1%, PPV 50%).
Conclusions: Through the proven negative predictive power of AMBLor with the cumulative power of prognostic clinical and pathological features these novel translationally relevant data provide an improved risk- prediction model to stratify patients with non-ulcerated localised melanomas at low or high risk of tumour recurrence thereby aiding optimal personalised patient management and treatment stratification.