A Pan-Cancer Benchmark of Clustering Strategies for Patient-Specific Gene Regulatory Networks
- 1University of Helsinki
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.
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BibTeX
@misc{sarantidis2026pancancer,
title = {{A Pan-Cancer Benchmark of Clustering Strategies for Patient-Specific Gene Regulatory Networks}},
author = {P. Sarantidis and T. Belova and M. Kuijjer},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.20.759648},
url = {https://www.biorxiv.org/content/10.64898/2026.10.20.759648v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A Pan-Cancer Benchmark of Clustering Strategies for Patient-Specific Gene Regulatory Networks
AU - P. Sarantidis
AU - T. Belova
AU - M. Kuijjer
PY - 2026
DA - 2026/10/20
PB - bioRxiv
DO - 10.64898/2026.10.20.759648
UR - https://www.biorxiv.org/content/10.64898/2026.10.20.759648v1
AB - 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.
ER -