Fig. 6: Conversion and validation of machine learning models using the nCounter assay.

a Flow diagram outlining the process of converting the RNA-Seq models to a workable nanostring nCounter-based assay. Spare baseline synovial biopsy samples from STRAP were subjected to nCounter assay using a custom synovial 524-gene panel. nCounter data was rescaled to RNA-Seq scale (“pseudo-RNA-Seq”) using linear models for each gene. Rescaled nCounter data was passed to machine learning models from Fig. 4c, and the performance of each model was assessed. b Confusion matrices showing predicted versus actual response, accuracy and balanced accuracy of nCounter assay applied to baseline synovial biopsies for prediction of response defined as DAS28-ESR <3.2 after 16 weeks of treatment. c Receiver operating characteristic (ROC) curve plots and area under the curve (AUC) measurements for prediction of response to etanercept, tocilizumab and rituximab from nCounter assay applied to baseline synovial biopsies from STRAP. d Proposed algorithm for allocation of a new patient to one of three possible biologic therapy categories (TNF-inhibitor, IL6-inhibitor or B-cell depleting agent) based on whichever model gives the highest predicted probability of response. Individuals with low predicted probability (all p < 0.5) of response to all three classes of biologics are categorised as “biomarker negative” and can be offered an alternative class of therapeutic agent. a, d created in BioRender with modifications (https://BioRender.com/r4uqilh).