External Evaluation of Multi-Omics Prognostic Models Across TCGA-BRCA and METABRIC: Transportability, Stability, and Incremental Performance
- 1Lakehead University
Abstract
Cross-cohort evaluation of multi-omics prognostic models can fail because molecular features are not assayable, fitted models do not transport, feature selection is unstable, or molecular data add little beyond clinical predictors. We used a dual-track evaluation design with TCGA-BRCA as the source cohort and METABRIC as the independent target cohort. Track A tested outcome-blind transport of fixed historical RNA and copy-number models without target-cohort refitting, whereas Track B reconstructed the dependency-aware selection procedure within METABRIC using leakage-controlled repeated cross-validation. Literal transport provided little or negative incremental overall-survival discrimination: RNA reduced Harrell's C-index relative to clinical prediction (Delta C = -0.0136), while copy number was essentially neutral. Track B showed no reliable overall-survival gain for RNA, copy number, or mutation; methylation and the reconstructed multimodal model performed worse than matched clinical comparators. In a protocol-locked post-hoc extension, the same fixed features were retained but model parameters were re-estimated within METABRIC. Incremental C-index increased by 0.0162 for RNA, 0.0091 for copy number, and 0.0201 for the combined panel. Random-panel benchmarks showed that the RNA gain was common among alternative panels, whereas the historical copy-number panel ranked above all 200 panels sampled from its recovered assayable candidate space. The recurrence-free-survival sensitivity analysis showed a positive RNA contrast (Delta C = 0.0146). These results separate failure of fixed-model transport from loss of information in the underlying feature set and show that successful local redevelopment does not by itself establish feature-set specificity.
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BibTeX
@misc{krikun2026external,
title = {{External Evaluation of Multi-Omics Prognostic Models Across TCGA-BRCA and METABRIC: Transportability, Stability, and Incremental Performance}},
author = {E. Krikun and A. Alkhateeb},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.18.752709},
url = {https://www.biorxiv.org/content/10.64898/2026.09.18.752709v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - External Evaluation of Multi-Omics Prognostic Models Across TCGA-BRCA and METABRIC: Transportability, Stability, and Incremental Performance
AU - E. Krikun
AU - A. Alkhateeb
PY - 2026
DA - 2026/09/18
PB - bioRxiv
DO - 10.64898/2026.09.18.752709
UR - https://www.biorxiv.org/content/10.64898/2026.09.18.752709v1
AB - Cross-cohort evaluation of multi-omics prognostic models can fail because molecular features are not assayable, fitted models do not transport, feature selection is unstable, or molecular data add little beyond clinical predictors. We used a dual-track evaluation design with TCGA-BRCA as the source cohort and METABRIC as the independent target cohort. Track A tested outcome-blind transport of fixed historical RNA and copy-number models without target-cohort refitting, whereas Track B reconstructed the dependency-aware selection procedure within METABRIC using leakage-controlled repeated cross-validation. Literal transport provided little or negative incremental overall-survival discrimination: RNA reduced Harrell's C-index relative to clinical prediction (Delta C = -0.0136), while copy number was essentially neutral. Track B showed no reliable overall-survival gain for RNA, copy number, or mutation; methylation and the reconstructed multimodal model performed worse than matched clinical comparators. In a protocol-locked post-hoc extension, the same fixed features were retained but model parameters were re-estimated within METABRIC. Incremental C-index increased by 0.0162 for RNA, 0.0091 for copy number, and 0.0201 for the combined panel. Random-panel benchmarks showed that the RNA gain was common among alternative panels, whereas the historical copy-number panel ranked above all 200 panels sampled from its recovered assayable candidate space. The recurrence-free-survival sensitivity analysis showed a positive RNA contrast (Delta C = 0.0146). These results separate failure of fixed-model transport from loss of information in the underlying feature set and show that successful local redevelopment does not by itself establish feature-set specificity.
ER -