Quantitative AI yields algorithmic RNA biomarkers from a rheumatoid arthritis clinical trial, accurately predicting individual patient responses to anti-TNF treatment, providing a novel approach to companion diagnostic discovery.

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<ns3:p>Background The derivation of novel biomarkers from biomedical data to accurately predict individual patient’s responses, would be an advance. We hypothesized that quantitative AI designed to analyze complex data, based on evolutionary computation, could identify algorithmic biomarkers from baseline data in a clinical trial, predictive of individual therapeutic responses. Methods A previously published randomized placebo controlled clinical trial in which patients with active rheumatoid arthritis (RA) naive to anti-tumor necrosis factor (TNF) therapy, were randomized to receive infliximab or placebo was analyzed. Baseline peripheral blood gene expression data, plus the treatment variable, infliximab or placebo, yielding 52,379 variables were available for 59 patients. The variable for analysis was a decrease in Disease Activity Score-28 (DSA28) score of 1.2. At 14 weeks, 20 of the 30 patients receiving infliximab had responded, and ten of the 29 patients receiving placebo had responded. Findings The AI derived a discovery algorithm, with 4 gene expression variables plus treatment assignment, that predicted responders versus non-responders for all 59 patients, with 100% accuracy. We present the discovery algorithm to enable transparent verification. Excluding the 4 gene expression variables, we then derived similarly accurate predictive algorithms with 4 other gene expression variables. We tested the hypothesis that the software could derive algorithms as predictors of treatment response applying just these 8 discovery gene expression variables to 6 previously published independent datasets. In each validation analysis, the accuracy of the algorithmic predictors surpassed those benchmarks previously reported, using a variety of analytic approaches. Conclusions AI based on evolutionary computation summarized a clinical trial, with transparent biomarker algorithms derived from baseline data, correctly predicting the outcome for all patients. The biomarker variables, validated in 6 independent cohorts, are now in development as a clinical test. This approach may expedite the discovery of companion diagnostics.</ns3:p>