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Krikun, E.

Publications and source records attributed to Krikun, E..

2 recordsLinked to original sources

When Prognostic Compatibility Does Not Guarantee Transfer Utility in Event-Limited Cross-Species Survival Modeling

Cross-species molecular transfer may improve prognostic modeling in rare cancers, but under severe target-event scarcity the same data can easily be used to fit, choose, and evaluate adaptation, increasing the risk of negative transfer and model-selection bias. We evaluated canine-to-human osteosarcoma survival transfer using a chronologically frozen design. DOG2 served as the canine source domain, 50 MSigDB Hallmark modules defined the shared representation, and a known-truth benchmark comprised 180 scenarios and 21,600 replicates spanning 5 - 40 target events and multiple transport regimes. A frozen safety rule compared low-rank and module-selective adaptation; later diagnostics and a 6,000-replicate disjoint-seed mechanism experiment could not alter that decision. Neither selectable architecture met the negative-transfer limit (0.215 and 0.486 versus 0.10), although oracle analyses showed that the threshold was attainable. Prespecified post-HOLD weighting and threshold sensitivities showed that benchmark composition amplified the magnitude of A3 failure but did not create its instability, whereas A2 was comparatively insensitive to weighting. Training-set prognostic compatibility identified generator regimes (AUROC 0.980) but did not reliably order transfer benefit versus harm. Prediction-time gate hardening improved discrimination and reduced negative transfer, although its composite criterion was not met. Outcome-blind DOG2 - TARGET analysis showed heterogeneous Hallmark preservation. In 86 TARGET cases with 29 events, the frozen canine classical model had IBS 0.159 versus 0.166 for a fold-local no-covariate Kaplan - Meier reference, while the human interpretation remained unresolved. Measurable prognostic compatibility therefore did not guarantee safe architecture-level transfer utility.

bioinformatics↗

External Evaluation of Multi-Omics Prognostic Models Across TCGA-BRCA and METABRIC: Transportability, Stability, and Incremental Performance

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.

bioinformatics↗