Quantitative AI based on evolutionary computation and information theory combined with mathematical functions yields algorithmic RNA biomarkers from a randomized rheumatoid arthritis clinical trial, accurately predicting individual patient responses to anti-TNF treatment, to enable precision medicine
Authors: Horgan Kevin , McDermott Michael F. , Harrington Douglas , Simonyan Vahan , Lilley Patrick 
Published: January 2024

Background

The routine derivation of novel biomarkers from therapeutic clinical trials to accurately predict individual patient’s responses, would be a significant advance. We hypothesized that quantitative AI software designed to analyze complex biomedical data, based on evolutionary computation and information theory combined with a wide range of mathematical functions, could identify algorithmic biomarkers from baseline data in a clinical trial, predictive of individual responses to therapy.

Methods and Findings

A previously published randomized double-blind placebo controlled clinical trial was analyzed. Patients with active rheumatoid arthritis (RA) on a stable dose of methotrexate and naive to anti-tumor necrosis factor (TNF) biologic therapy, were randomized to receive infliximab or placebo. The primary endpoint was synovial disease activity assessed by magnetic resonance imaging. Secondary endpoints included the Disease Activity Score 28 (DAS28). Baseline peripheral blood gene expression variable data were available for 59 patients, plus the treatment variable, infliximab or placebo, yielding a total of 52,379 baseline variables. The binary dependent variable for analysis was DAS28 response, defined by a decrease in DAS28 score of 1.2, at 14 weeks. At 14 weeks, 20 of the 30 patients receiving infliximab had responded, and ten of the 29 patients receiving placebo had responded. The software derived a discovery algorithm, with 4 gene expression variables plus treatment assignment and 12 mathematical operations, that correctly predicted responders versus non-responders for all 59 patients with available gene expression data, giving 100% accuracy, 100% sensitivity and 100% specificity. We present the discovery algorithm to provide transparency and to enable verification. Excluding the 4 gene expression variables, we then derived similarly accurate predictive algorithms with 4 other gene expression variables. We then tested the hypothesis that the software could derive algorithms as predictors of treatment response to anti-TNF biologic therapy using just these 8 discovery gene expression variables using previously published independent datasets from 6 RA studies. In each validation analysis, the accuracy of the algorithmic predictors we derived surpassed those benchmarks previously reported by the original study authors using a variety of approaches, including machine learning.

Conclusions and Relevance

Software based on evolutionary computation and information theory combined with mathematical functions, summarized the outcome of a clinical trial, with transparent biomarker algorithms derived from baseline data, correctly predicting the clinical outcome for all 59 RA patients. The biomarker variables were validated in 6 independent RA cohorts and are now in development as a clinical test. This approach may simplify and expedite the discovery and development of companion diagnostic algorithmic biomarkers accurately predicting individual treatment response, - potentially accelerating the deployment of precision medicine. Original Trial Registration used for analysis: ClinicalTrials.gov registration:NCT01313520