Interpretable biomarker programs predict treatment response in lupus nephritis: patient-level validation across four regimens
- 1Phaseo AI
Abstract
A large share of late-stage clinical trial failures reflects not the underlying biology of the target but the composition of the enrolled population: trials recruit patients in whom the drug cannot work. Methods that identify likely responders before treatment therefore address a failure mode that better target selection alone cannot.
We applied interpretable machine learning to gene-expression data from a treatment-response cohort in lupus nephritis (GSE224705; 21,914 genes across 319 samples) covering four regimens: mycophenolate mofetil (MMF), azathioprine (AZA), hydroxychloroquine (HC) and standard of care (SOC). We independently reconstructed the expression matrix and metadata, rebuilt the treatment-specific cohorts, and derived compact multi-gene programs that separate responders from non-responders within each treated population.
Two results follow. First, discriminative performance is strongly graded by regimen. Compact programs of five to ten genes achieved patient-level AUROC of 0.847 (MMF) and 0.866 (AZA), but only 0.718 (HC) and 0.623 (SOC); the SOC programs performed close to chance (MCC 0.119, balanced accuracy 0.555). A regimen in which response is not transcriptionally discriminable is an actionable finding for trial design rather than a null result. Second, the programs proved considerably more stable than the differential-expression lists that generated them: reconstructed counts of significant genes differed markedly from the published analysis (222 vs. 46 for MMF; 4,455 vs. 157 for AZA; 6 vs. 24 for HC; 5 vs. 11 for SOC), yet the dominant biology and the predictive performance were preserved. Programs were also non-redundant: removing a single gene (TUBB2A) from the MMF program reduced AUROC by approximately 0.17. At the pathway level, 13 cross-treatment enrichment relationships remained significant after adjustment, indicating that response landscapes are treatment-specific yet coupled.
Patient-generalisable programs of this kind offer a concrete near-term route to enrichment-style trial design, identifying before enrolment which patients a given therapy suits. Our results also caution that the number of differentially expressed genes is a poor proxy for the strength or stability of a response signal.
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BibTeX
@misc{kumar2026interpretable,
title = {{Interpretable biomarker programs predict treatment response in lupus nephritis: patient-level validation across four regimens}},
author = {P. Kumar and A. Aurange and S. Keshari and S. Nalwad},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.13.26362948},
url = {https://www.medrxiv.org/content/10.64898/2026.09.13.26362948v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Interpretable biomarker programs predict treatment response in lupus nephritis: patient-level validation across four regimens
AU - P. Kumar
AU - A. Aurange
AU - S. Keshari
AU - S. Nalwad
PY - 2026
DA - 2026/09/13
PB - medRxiv
DO - 10.64898/2026.09.13.26362948
UR - https://www.medrxiv.org/content/10.64898/2026.09.13.26362948v1
AB - A large share of late-stage clinical trial failures reflects not the underlying biology of the target but the composition of the enrolled population: trials recruit patients in whom the drug cannot work. Methods that identify likely responders before treatment therefore address a failure mode that better target selection alone cannot. We applied interpretable machine learning to gene-expression data from a treatment-response cohort in lupus nephritis (GSE224705; 21,914 genes across 319 samples) covering four regimens: mycophenolate mofetil (MMF), azathioprine (AZA), hydroxychloroquine (HC) and standard of care (SOC). We independently reconstructed the expression matrix and metadata, rebuilt the treatment-specific cohorts, and derived compact multi-gene programs that separate responders from non-responders within each treated population. Two results follow. First, discriminative performance is strongly graded by regimen. Compact programs of five to ten genes achieved patient-level AUROC of 0.847 (MMF) and 0.866 (AZA), but only 0.718 (HC) and 0.623 (SOC); the SOC programs performed close to chance (MCC 0.119, balanced accuracy 0.555). A regimen in which response is not transcriptionally discriminable is an actionable finding for trial design rather than a null result. Second, the programs proved considerably more stable than the differential-expression lists that generated them: reconstructed counts of significant genes differed markedly from the published analysis (222 vs. 46 for MMF; 4,455 vs. 157 for AZA; 6 vs. 24 for HC; 5 vs. 11 for SOC), yet the dominant biology and the predictive performance were preserved. Programs were also non-redundant: removing a single gene (TUBB2A) from the MMF program reduced AUROC by approximately 0.17. At the pathway level, 13 cross-treatment enrichment relationships remained significant after adjustment, indicating that response landscapes are treatment-specific yet coupled. Patient-generalisable programs of this kind offer a concrete near-term route to enrichment-style trial design, identifying before enrolment which patients a given therapy suits. Our results also caution that the number of differentially expressed genes is a poor proxy for the strength or stability of a response signal.
ER -