Aims To develop and validate an updated version of KidneyIntelX (kidneyintelX.dkd) to stratify patients for risk of progression of diabetic kidney disease (DKD) stages 1 to 3, to simplify the test for clinical adoption and support an application to the US Food and Drug Administration regulatory pathway.Methods We used plasma biomarkers and clinical data from the Penn Medicine Biobank (PMBB) for training, and independent cohorts (BioMe and CANVAS) for validation. The primary outcome was progressive decline in kidney function (PDKF), defined by a =40% sustained decline in estimated glomerular filtration rate or end-stage kidney disease within 5 years of follow-up.ResultsIn 573 PMBB participants with DKD, 15.4% experienced PDKF over a median of 3.7 years. We trained a random forest model using biomarkers and clinical variables. Among 657 BioMe participants and 1197 CANVAS participants, 11.7% and 7.5%, respectively, experienced PDKF. Based on training cut-offs, 57%, 35% and 8% of BioMe participants, and 56%, 38% and 6% of CANVAS participants were classified as having low-, moderate- and high-risk levels, respectively. The cumulative incidence at these risk levels was 5.9%, 21.2% and 66.9% in BioMe and 6.7%, 13.1% and 59.6% in CANVAS. After clinical risk factor adjustment, the adjusted hazard ratios were 7.7 (95% confidence interval [CI] 3.0-19.6) and 3.7 (95% CI 2.0-6.8) in BioMe, and 5.4 (95% CI 2.5-11.9) and 2.3 (95% CI 1.4-3.9) in CANVAS, for high- versus low-risk and moderate- versus low-risk levels, respectively.ConclusionsUsing two independent cohorts and a clinical trial population, we validated an updated KidneyIntelX test (named kidneyintelX.dkd), which significantly enhanced risk stratification in patients with DKD for PDKF, independently from known risk factors for progression.

BACKGROUND OncoMasTR is a recently developed multigene prognostic test for early-stage breast cancer. The test has been developed in a kit-based format for decentralized deployment in molecular pathology laboratories. The analytical performance characteristics of the OncoMasTR test are described in this study. METHODS Expression levels of 6 genes were measured by 1-step reverse transcription-quantitative PCR on RNA samples prepared from formalin-fixed, paraffin-embedded (FFPE) breast tumor specimens. Assay precision, reproducibility, input range, and interference were determined using FFPE-derived RNA samples representative of low and high prognostic risk scores. A pooled RNA sample derived from 6 FFPE breast tumor specimens was used to establish the linear range, limit of detection, and amplification efficiency of the individual gene expression assays. RESULTS The overall precision of the OncoMasTR test was high with an SD of 0.16, which represents less than 2% of the 10-unit risk score range. Test results were reproducible across 4 testing sites, with correlation coefficients of 0.94 to 0.96 for the continuous risk score and concordance of 86% to 96% in low-/high-risk sample classification. Consistent risk scores were obtained across a > 100-fold RNA input range. Individual gene expression assays were linear up to quantification cycle values of 36.0 to 36.9, with amplification efficiencies of 80% to 102%. Test results were not influenced by agents used during RNA isolation, by low levels of copurified genomic DNA, or by moderate levels of copurified adjacent nontumor tissue. CONCLUSION The OncoMasTR prognostic test displays robust analytical performance that is suitable for deployment by local pathology laboratories for decentralized use.

Background The KidneyIntelX™ test applies a machine learning algorithm that incorporates plasma biomarkers and clinical variables to produce a composite risk score to predict a progressive decline in kidney function in patients with type 2 diabetes (T2D) and early-stage chronic kidney disease (CKD). The following studies describe the analytical validation of the KidneyIntelX assay including impact of observed methodologic variability on the composite risk score. Methods Analytical performance studies of sensitivity, precision, and linearity were performed on three biomarkers assayed in multiplexed format: kidney injury molecule-1 (KIM-1), soluble tumor necrosis factor receptor-1 (sTNFR-1) and soluble tumor necrosis factor receptor-2 (sTNFR-2) based on Clinical Laboratory Standards Institute (CLSI) guidelines. Analytical variability across twenty (20) experiments across multiple days, operators, and reagent lots was assessed to examine the impact on the reproducibility of the composite risk score. Analysis of cross-reactivity and interfering substances was also performed. Results Assays for KIM-1, sTNFR-1 and sTNFR-2 demonstrated acceptable sensitivity. Mean within-laboratory imprecision coefficient of variation (CV) was established as less than 9% across all assays in a multi-lot study. The linear range of the assays was determined as 12–5807 pg/mL, 969–23,806 pg/mL and 4256–68,087 pg/mL for KIM-1, sTNFR-1 and sTNFR-2, respectively. The average risk score CV% was less than 5%, with 98% concordance observed for assignment of risk categories. Cross-reactivity between critical assay components in a multiplexed format did not exceed 1.1%. Conclusions The set of analytical validation studies demonstrated robust analytical performance across all three biomarkers contributing to the KidneyIntelX risk score, meeting or exceeding specifications established during characterization studies. Notably, reproducibility of the composite risk score demonstrated that expected analytical laboratory variation did not impact the assigned risk category, and therefore, the clinical validity of the reported results.

Abstract This abstract was withdrawn by the authors. Citation Format: Loughman T, Wang C-JA, Dynoodt P, Fender B, Lopez-Ruiz C, Barron S, Stapleton S, O'Leary D, Fabre A, Quinn C, Nodin B, Jirström K, Bracken A, Gallagher WM. Withdrawn [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-08-31.

Background: OncoMasTR is a new multigene prognostic test that was discovered via a novel transcriptional network analysis method that identified upstream Master Transcription Regulators (MTRs), which regulate previously identified prognostic biomarkers. The optimised OncoMasTR signature incorporating clinicopathological information has been shown to be significantly prognostic for predicting distant recurrence in two independent cohorts (TransATAC & a subset of TAILORx from participating Irish centres). The analytical performance characteristics of the OncoMasTR test, comprising solely three prognostic MTRs, were determined. Methods: Relative gene expression levels were measured by RT-qPCR. Assay precision and input ranges were determined using a panel of samples representative of low and high recurrence risk tested across a number of runs incorporating different sources of variation. Serial dilutions of a pooled patient RNA sample was used to establish the linear range and efficiency of the individual gene assays. Results: The overall standard deviation of the OncoMasTR risk score was 0.15, which represents less than 2% of the 10-unit risk score range. The majority of the variability in OncoMasTR results was related to within-run variation (78.2%) with other between-run variation sources contributing relatively little (PCR instrument (0.6%), assay operator (5.2%), reagent lots (7.3%) or loading position (8.7%)). Consistent risk scores were measured for individual samples from 40ng down to < 1ng RNA per PCR reaction. Individual gene assays were linear over >500-fold RNA input range corresponding to CT values of 23 – 36, demonstrating the ability of the test to reliably detect low level expression of the OncoMasTR panel. Importantly, PCR efficiencies were similar for the individual MTR and reference gene assays which ranged from 79 – 95%. Conclusions: The OncoMasTR prognostic test displays robust analytical and clinical performance and is being launched as a CE-marked test. The concise nature of the three gene signature and a simplified workflow that can be readily adopted using standard laboratory equipment will enable convenient qualification by local laboratories for decentralised use. Legal entity responsible for the study: OncoMark Limited. Funding: EU Horizon 2020 SME Instrument. Disclosure: T. Loughman, A. Chan-Ju Wang, P. Dynoodt, B. Fender, C. Lopez Ruiz, S. Barron, S. Stapleton: Employee: OncoMark Limited. D. O'Leary, W.M. Gallagher: Director and shareholder: OncoMark Limited. A. Fabre, C. Quinn: Consultant histopatholgist: OncoMark Limited. A. Bracken: Co-inventor and patent holder for OncoMasTR. All other authors have declared no conflicts of interest.