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Background <p>This study tackles the challenge of developing reliable prognostic models for time-to-event (TTE) outcomes using high-dimensional omics data in head and neck cancers. Resampling methods, particularly nested cross-validation, are considered as standard for model hyperparameter selection and performance evaluation. When handling clustered data, balancing the random partition of the cross-validation folds to minimize optimism bias and instability could be tested. This work compares the performance of three nested cross-validation implementations, including random assignment of the folds, clustering-based resampling, and internal-external validation using an hold out approach.</p> Method <p>We analyzed two head and neck squamous cell carcinoma (HNSCC) cohorts: The Cancer Genome Atlas (TCGA) and SCANDARE (NCT03017573), with clinical data and transcriptomic data normalized as log-transcripts per million. Three model selection methods LASSO, IPF-Lasso, and Priority-LASSO were evaluated within five nested cross-validation frameworks: Standard nested cross-validation, Clustering-based nested-cross validation, nested-cross validation with Combat correction, Nested cross-validation for optimization combined with hold-out for validation, Nested cross-validation for optimization combined with hold-out and ComBat correction for validation. Predictive performance was assessed using 3-year AUC and Integrated Brier Score (IBS).</p> Results <p>We analyzed data from 581 patients (mean age 61.0 years, 33.6% female) across TCGA-HNSC (<i>n</i> = 505) and SCANDARE (<i>n</i> = 76). Clustering analyses, using UMAP and k-means, identified three transcriptomic clusters. Validation strategies demonstrated reduced instability for Lasso (<i>p</i> &lt; 0.001), IPF-Lasso (<i>p</i> &lt; 0.001) and Priority-lasso (<i>p</i> &lt; 0.001) without apparent optimism in discrimination and calibration metrics with stratified nested cross-validation (SNCV), supporting its utility. As an application using IPF-Lasso Cox models with SNCV, we integrated clinical and transcriptomic data, selecting 35 prognosis variables of head and neck carcinomas. This model achieved a 3-year AUC of 0.71 and IBS of 0.08.</p> Conclusion <p>Clustering-based nested cross-validation combined with stratified cross-validation offers a robust compromise for developing high-dimensional survival models and evaluating their predictive performance. This approach leverages clustering-derived stratification to balance heterogeneity in the dataset within cross-validation folds, although the training and test sets remain derived from the pooled dataset rather than fully independent cohorts.</p>

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A clustering-stratified cross-validation framework for validating omics survival models: application to head and neck cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月16日

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  • Antoine Dubray-Vautrin,
  • Olivier Choussy,
  • Constance Lamy,
  • Grégoire Marret,
  • Joey Martin,
  • Jerzy Klijanienko,
  • Sophie Vacher,
  • Ladidi Ahmanache,
  • Ivan Bieche,
  • Célia Dupain,
  • Christophe Le Tourneau,
  • Jimmy Mullaert

摘要

Background

This study tackles the challenge of developing reliable prognostic models for time-to-event (TTE) outcomes using high-dimensional omics data in head and neck cancers. Resampling methods, particularly nested cross-validation, are considered as standard for model hyperparameter selection and performance evaluation. When handling clustered data, balancing the random partition of the cross-validation folds to minimize optimism bias and instability could be tested. This work compares the performance of three nested cross-validation implementations, including random assignment of the folds, clustering-based resampling, and internal-external validation using an hold out approach.

Method

We analyzed two head and neck squamous cell carcinoma (HNSCC) cohorts: The Cancer Genome Atlas (TCGA) and SCANDARE (NCT03017573), with clinical data and transcriptomic data normalized as log-transcripts per million. Three model selection methods LASSO, IPF-Lasso, and Priority-LASSO were evaluated within five nested cross-validation frameworks: Standard nested cross-validation, Clustering-based nested-cross validation, nested-cross validation with Combat correction, Nested cross-validation for optimization combined with hold-out for validation, Nested cross-validation for optimization combined with hold-out and ComBat correction for validation. Predictive performance was assessed using 3-year AUC and Integrated Brier Score (IBS).

Results

We analyzed data from 581 patients (mean age 61.0 years, 33.6% female) across TCGA-HNSC (n = 505) and SCANDARE (n = 76). Clustering analyses, using UMAP and k-means, identified three transcriptomic clusters. Validation strategies demonstrated reduced instability for Lasso (p < 0.001), IPF-Lasso (p < 0.001) and Priority-lasso (p < 0.001) without apparent optimism in discrimination and calibration metrics with stratified nested cross-validation (SNCV), supporting its utility. As an application using IPF-Lasso Cox models with SNCV, we integrated clinical and transcriptomic data, selecting 35 prognosis variables of head and neck carcinomas. This model achieved a 3-year AUC of 0.71 and IBS of 0.08.

Conclusion

Clustering-based nested cross-validation combined with stratified cross-validation offers a robust compromise for developing high-dimensional survival models and evaluating their predictive performance. This approach leverages clustering-derived stratification to balance heterogeneity in the dataset within cross-validation folds, although the training and test sets remain derived from the pooled dataset rather than fully independent cohorts.