Monitoring Retinoblastoma by Machine Learning of Aqueous Humor Metabolic Fingerprinting

Abstract

The most common intraocular pediatric malignancy, retinoblastoma (RB), accounts for ≈10% of cancer in children. Efficient monitoring can enhance living quality of patients and 5-year survival ratio of RB up to 95%. However, RB monitoring is still insufficient in regions with limited resources and the mortality may even reach over 70% in such areas. Here, an RB monitoring platform by machine learning of aqueous humor metabolic fingerprinting (AH-MF) is developed, using nanoparticle enhanced laser desorption/ionization mass spectrometry (LDI MS). The direct AH-MF of RB free of sample pre-treatment is recorded, with both high reproducibility (coefficient of variation < 10%) and sensitivity (low to 0.3 pmol) at sample volume down to 40 nL only. Further, early and advanced RB patients with area-under-the-curve over 0.9 and accuracy over 80% are differentiated, through machine learning of AH-MF. Finally, a metabolic biomarker panel of 7 metabolites through accurate MS and tandem MS (MS/MS) with pathway analysis to monitor RB is identified. This work can contribute to advanced metabolic analysis of eye diseases including but not limited to RB and screening of new potential metabolic targets toward therapeutic intervention.