{
  "abstract": "Cancer patients with clinically similar tumors can exhibit substantial differences in disease progression and treatment response. Patient-specific gene regulatory networks (GRNs) may capture regulatory heterogeneity that is not represented by gene expression alone. However, strategies for clustering patients using these high-dimensional networks have not been systematically evaluated and it remains unclear whether clustering based directly on millions of patient-specific regulatory edge weights is computationally feasible at scale. We benchmarked feature-selection and clustering approaches for patient-specific GRNs inferred with PANDA and LIONESS across 9,778 primary tumor samples from 33 cancer types. We compared three data representations: gene expression, gene indegrees summarizing regulatory input to each gene, and complete networks of transcription factor-gene edge weights. Twenty combinations of four feature-selection methods and five clustering algorithms were evaluated using consensus clustering. Performance was assessed based on mean silhouette score, PAC score, concordance, computational time, and memory usage. Standard deviation-based feature selection combined with k-means clustering achieved the strongest overall performance for both GRN-derived representations, with overall scores of 0.997 for edge weights and 0.985 for indegrees, while remaining computationally efficient. Edge-weight- and indegree-derived clusters showed clinical, biological, and prognostic associations that were complementary to those obtained from gene expression. Together, these findings provide a practical framework for network-based cancer patient stratification and highlight the potential of GRN-derived features to reveal clinically relevant tumor heterogeneity beyond gene expression alone.",
  "abstract_shown": true,
  "affiliations": [
    "University of Helsinki"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "P. Sarantidis"
    },
    {
      "name": "T. Belova"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Kuijjer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9j32tg9ncwtq3.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.20.759648",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.759648v1.full",
  "id": "9j32tg9ncwtq3",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.759648v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Gene Regulatory Network Analysis"
    ]
  ],
  "openalex": "W7223573040",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.759648v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9j32tg9ncwtq3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:37Z",
      "native_id": "10.64898/2026.10.20.759648",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.20.759648v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "A Pan-Cancer Benchmark of Clustering Strategies for Patient-Specific Gene Regulatory Networks",
  "title_text": "A Pan-Cancer Benchmark of Clustering Strategies for Patient-Specific Gene Regulatory Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:37Z",
  "url": "https://research.draconitic.com/paper/9j32tg9ncwtq3",
  "venue": "bioRxiv",
  "versions": []
}