Metabolomic Profiling of Dried Blood Spots for Breast Cancer Detection: A Multi-Classifier Validation Study in 2,734 Participants

Background. Breast cancer (BC) remains the most diagnosed malignancy and leading cancer-related cause of mortality in women worldwide. Although blood-based untargeted metabolomics has emerged as a promising modality for detecting early-stage BC, the clinical translation of this approach has been bottlenecked by two unresolved issues: (i) the field has almost exclusively relied on serum or plasma, which require venipuncture and cold-chain logistics, and (ii) machine-learning models reported on such data are frequently validated with protocols that are blind to analytical batch structure, producing optimistically biased performance estimates. Methods. We present a breast cancer detection study based on dried blood spots (DBS), an analytical matrix that enables self-collection and ambient-temperature shipping. A cohort of 2,734 participants (114 biopsy-confirmed BC cases; 2,620 non-cancer controls) was profiled by untargeted LC-MS/MS on a Thermo Scientific Orbitrap IQ-X coupled to a Vanquish UHPLC. A 39-metabolite panel meeting MSI Level 1 identification criteria was pre-specified a priori from the published breast-cancer metabolomics literature, frozen prior to LC-MS acquisition, and applied to the present cohort without any feature selection on the data. Six standard supervised-learning architectures (LASSO, Elastic Net, Linear SVM, PLS-DA, OPLS-DA, XGBoost) were evaluated on this pre-specified panel; OPLS-DA is reported only in the sex-matched subgroup analysis where a single-seed 5-fold stratified protocol permits a directly comparable fit. Per-batch control-median normalization is applied upstream; kNN imputation, log transform, and robust scaling are fit within each training fold. The evaluation battery comprises batch-aware StratifiedGroupKFold CV at single-seed (seed=42) with inter-seed SD quantified across 10 independent seeds, batch-aware nested CV, a 100-seed held-out 20%-batch validation with disjoint-batch isotonic probability calibration (30% calibration partition), PPV/NPV reporting at multiple operating points and three deployment prevalences, subgroup analyses by TNM stage and tumor grade, pathway-ablation sensitivity analysis, and a 1,000-iteration permutation test. Results. Under batch-aware evaluation (StratifiedGroupKFold, single-seed=42), AUC ranged from 0.914 to 0.949 across classifiers, with LASSO achieving 0.928 and XGBoost 0.949; inter-seed SD across 10 seeds was 0.002-0.006. At 95% specificity, LASSO reached 75.4% sensitivity and XGBoost 81.6%. Held-out batch validation (100 seeds) yielded mean AUC 0.912 for Elastic Net and 0.935 for XGBoost, confirming robust generalization. All 39 panel features showed high coefficient stability, and permutation testing on representative classifiers (LASSO, Linear SVM, PLS-DA) yielded p <= 0.001. Subgroup analyses showed weaker detection of stage IIA tumors (AUC 0.87, n=40) compared with stage IIB/IIIA (AUC 0.95), consistent with stronger metabolic signatures in more advanced disease. Bootstrap coefficient consistency of the Elastic Net classifier confirmed that all 39 panel features received a non-zero multivariate weight in >=80% of 100 stratified bootstraps. Conclusions. On this cohort of diagnosed, pre-treatment breast-cancer cases, DBS LC-MS metabolomic profiling delivers classification performance (AUC 0.928 for LASSO and 0.949 for XGBoost under batch-aware GroupKFold CV at single-seed=42; held-out AUC 0.912-0.935) that is robust across classifier families and biological pathways. The DBS matrix is non-radiating, self-collectable by finger-prick, and mailable at ambient temperature. Performance is weaker on stage IIA than on more advanced disease, and prospective validation in an independent asymptomatic screening cohort is required before clinical positioning as a decentralized triage modality.