{
  "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.\n\nWe 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.\n\nTwo 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.\n\nPatient-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.",
  "abstract_shown": true,
  "affiliations": [
    "Phaseo AI"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "P. Kumar"
    },
    {
      "name": "A. Aurange"
    },
    {
      "name": "S. Keshari"
    },
    {
      "name": "S. Nalwad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3rjj3rhvr25ev.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.13.26362948",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.13.26362948v1.full",
  "id": "3rjj3rhvr25ev",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.13.26362948v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "nephrology"
    ],
    [
      "openalex",
      "2745|Systemic Lupus Erythematosus Research"
    ]
  ],
  "openalex": "W7213285009",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.13.26362948v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3rjj3rhvr25ev.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:37Z",
      "native_id": "10.64898/2026.09.13.26362948",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.13.26362948v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:nephrology",
      "subfield": "general"
    }
  ],
  "title": "Interpretable biomarker programs predict treatment response in lupus nephritis: patient-level validation across four regimens",
  "title_text": "Interpretable biomarker programs predict treatment response in lupus nephritis: patient-level validation across four regimens",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:37Z",
  "url": "https://research.draconitic.com/paper/3rjj3rhvr25ev",
  "venue": "medRxiv",
  "versions": []
}