Immune checkpoint inhibitors (ICIs) can induce durable responses in a subset of patients with advanced-stage cancers. However, most patients incur treatment costs without experiencing durable clinical benefit.
We developed and evaluated SCORPIO, a machine learning system that utilizes routine blood tests (complete blood count and comprehensive metabolic profile) alongside clinical characteristics from 9,745 ICI-treated patients across 21 cancer types.
In two internal test sets comprising 2,511 patients across 19 cancer types, SCORPIO outperformed tumor mutational burden (TMB) for predicting overall survival at 6, 12, 18, 24 and 30 months and showed superior predictive performance for predicting clinical benefit (tumor response or prolonged stability) compared to TMB.
In external validation, using 10 global phase 3 trials (4,447 patients across 6 cancer types) and a real-world cohort from the Mount Sinai Health System (1,159 patients across 18 cancer types), SCORPIO maintained robust performance in predicting ICI outcomes, surpassing PD-L1 immunostaining.
In conclusion, we developed and tested SCORPIO, a machine learning model that relies on only routine blood tests and basic clinical data to predict clinical outcomes after ICI administration more effectively than existing FDA-approved biomarkers like TMB and PD-L1 immunohistochemistry.
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