Patient overlap, cohort-relative scaling, and biological context in cross-cohort evaluation of a mitochondrial proteostasis-associated score in triple-negative breast cancer

Background Public transcriptomic cohort evaluation can be distorted by patient reuse, dependence among study sources, and cohort-relative normalization. Objective: To evaluate the association between a fixed 15-gene cohort-relative mitochondrial proteostasis score and source-study-defined pathological complete response (pCR) across four accession-defined cohorts from three principal study sources. Methods The fixed score comprised 15 genes with equal positive weights, a specified expected direction, and a platform-coverage rule. After patient-level deduplication and duplicate-aliquot collapse, 285 patients with strict triple-negative breast cancer, including 104 with pCR, were analyzed. Cohort-specific odds ratios (ORs) per one-standard-deviation score increase were pooled by restricted maximum-likelihood random-effects meta-analysis with modified Hartung-Knapp inference. Sensitivity analyses summarized three study sources, used Paule-Mandel heterogeneity estimation, and applied one fixed single-sample RankScore implementation. Results The primary four-cohort estimate was OR 0.958 (95% CI, 0.639–1.437; P = 0.760; tau^2 = 0). The three-source REML estimate was OR 0.971 (95% CI, 0.492–1.914; P = 0.868; tau^2 = 0.0201). The RankScore sensitivity was imprecise (OR 0.613 per 0.10 RankScore; 95% CI, 0.148–2.542; P = 0.354). The overlap assessment identified 57 strict-TNBC patients duplicated between GSE20194 and GSE25055; treating them as an additional accession effect increased nominal n from 285 to 342 without adding independent participants. Contextual NeoTRIP, single-cell, and I-SPY2 analyses were included to characterize biological and treatment context. Conclusions Across four accession-defined cohorts from three principal study sources, the evaluated score implementations did not show a reproducible association with pCR. Source-level estimates remained imprecise. Patient overlap, reference dependence, and limited independent study sources are key considerations in cross-cohort biomarker evaluation.