{
  "abstract": "PurposeCyclin-dependent kinase 4/6 inhibitors in combination with endocrine therapy have become widely adopted as a first-line treatment approach for hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC). Through a collaboration between Novartis and the US Food and Drug Administration, we integrated multimodal data from phase III MONALEESA trials to identify novel prognostic factors for HR+/HER2-ABC.\n\nExperimental DesignBaseline clinical and genomics data were collected from patients in the MONALEESA-2, -3, and -7 trials. Circulating tumor DNA (ctDNA) from plasma samples and gene expression in archival tumor biopsies were analyzed. Machine learning models were trained (n = 1,355) on progression-free survival (PFS) using multimodal features via fusion models. In a test set (n = 382), prognostic performances were evaluated using the Kaplan-Meier, Cox, and Weibull models.\n\nResultsThe late fusion model achieved a prognostic performance (C-index: 0.727, 95% CI: 0.641-0.799) slightly outperforming early fusion (C-index: 0.718, 95% CI: 0.631-0.791) and single-modality models. Model-based risk stratification identified distinct low- and high-risk groups (PFS HR, 0.28; 95% CI: 0.20-0.38). Among the top 40 features for progression risk (18 clinical, 20 gene expression, and 2 ctDNA), these were associated with higher risk: liver involvement, higher histological grades, early relapse; lower STC2 and RBBP8 expressions and higher FBX05 expression; and higher tumor mutational burden and ctDNA purity.\n\nConclusionsIntegrating clinical and genomic modalities improved progression risk prediction compared with single-modality models, while highlighting salient features that inform the underlying biology and support hypothesis generation.",
  "abstract_shown": true,
  "affiliations": [
    "Novartis Pharmaceuticals Corporation"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 23,
  "authors": [
    {
      "name": "C. Wang"
    },
    {
      "name": "R. Paul"
    },
    {
      "name": "Y. Li"
    },
    {
      "name": "S. Smith"
    },
    {
      "name": "Q. Cao"
    },
    {
      "name": "S. H. Chang"
    },
    {
      "name": "R. Charlab"
    },
    {
      "name": "T. Coroller"
    },
    {
      "name": "R. Huang"
    },
    {
      "name": "L. Amiri-Kordestani"
    },
    {
      "name": "Z. Li"
    },
    {
      "name": "Q. Liu"
    },
    {
      "name": "C. Osgood"
    },
    {
      "name": "S. Peyrard"
    },
    {
      "name": "B. Sahiner"
    },
    {
      "name": "L. Santana-Quintero"
    },
    {
      "name": "F. Su"
    },
    {
      "name": "J. P. Zarate"
    },
    {
      "name": "H. Zhu"
    },
    {
      "name": "D. Ohlssen"
    },
    {
      "name": "N. Petrick"
    },
    {
      "name": "A. Amatya"
    },
    {
      "affs": [
        0
      ],
      "name": "N. Solovieff"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8rb6k4pphz3e7.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.09.26364892",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26364892v1.full",
  "id": "8rb6k4pphz3e7",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26364892v1",
  "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",
      "oncology"
    ],
    [
      "openalex",
      "1306|Breast Cancer Treatment Studies"
    ]
  ],
  "openalex": "W7221723182",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26364892v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8rb6k4pphz3e7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:49Z",
      "native_id": "10.64898/2026.10.09.26364892",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26364892v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Multimodal Machine Learning-Based Clinical and Genomic Prognostic Factors for Progression-Free Survival in Metastatic Breast Cancer Using Phase III MONALEESA Trials",
  "title_text": "Multimodal Machine Learning-Based Clinical and Genomic Prognostic Factors for Progression-Free Survival in Metastatic Breast Cancer Using Phase III MONALEESA Trials",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:49Z",
  "url": "https://research.draconitic.com/paper/8rb6k4pphz3e7",
  "venue": "medRxiv",
  "versions": []
}