BioBERT-Derived Clinical TextRepresentations and Sparse PrincipalComponents of Multi-Omics Data for BreastCancer Survival Prediction: ALeakage-Controlled Cross-ValidatedBenchmark

Background: Reported performance for multi-modal cancer prognostic models is often obtained from a single train-test split and without comparison against a simple clinical baseline. We evaluated an integrated framework combining language-model representations of clinical records with multi-omics data under a leakage-controlled protocol. Methods: Clinical variables from 589 TCGA-BRCA patients were converted to natural language sentences and encoded with BioBERT; four molecular modalities were reduced by Sparse principal component analysis. Clinical-only, multi-omics-only and integrated feature sets were evaluated with Cox regression, XGBoost and random survival forest (RSF) using repeated stratified cross-validation with pooled out-of-fold predictions, against a marginal Kaplan-Meier null and a conventional age-and-stage Cox model. Results: Among 589 patients with 88 deaths (14.9%; median follow-up 33.7 months), the integrated RSF model performed best (Harrell's C 0.685, 95% CI 0.620-0.754; Uno's C 0.748), exceeding the conventional clinical Cox model (0.637, 0.559-0.707). The ordering integrated > clinical-only > multi-omics-only held in all nine feature-set by algorithm comparisons and at all three component settings tested. The integrated model significantly outperformed every multi-omics-only model (P = 0.008-0.044) but not clinical-only models (dC = 0.037, P = 0.128). Brier scores approached the null reference at 2 and 3 years and improved modestly at 5 years. Conclusion: Integration gave the highest discrimination and exceeded a conventional clinical model, but the gain over clinical features within the same pipeline was not statistically resolvable at this event count. Reporting simple baselines, null-model calibration and cross-validated estimates should be standard in multi-modal prognostic modelling.