From Custodial Models to Reproducibility Frameworks: Shifting Bases of Interpretive Authority in Cancer Prevention Research

Cancer prevention research has historically derived interpretive authority from custodianship of rare pre-malignant human tissues collected at singular clinical moments. Access to these specimens has conferred both scientific and institutional credibility, as such samples cannot be regenerated or independently re-collected. However, advances in computational reproducibility and the expansion of public multi-omic repositories have introduced an alternative evidentiary framework. Public datasets now encompass petabyte-scale genomic, transcriptomic, and proteomic resources spanning multiple cancer types, while single-cell atlases enable cell-of-origin inference across cohorts without direct tissue access. This paper argues that the field is undergoing a structural transition in the basis of interpretive authority. We propose a three-phase framework: (I) custodial primacy, where credibility derives from tissue access; (II) dual authority, where custodial and computational claims coexist in tension; and (III) distributed reproducibility, where interpretive legitimacy increasingly depends on cross-cohort validation and externally testable analysis. While material access to private biobanks remains structurally protected, interpretive claims derived from those samples are increasingly exposed to challenge through public data reanalysis. Drawing on examples from breast cancer, renal cell carcinoma, pancreatic cancer, and serous ovarian cancer, the analysis demonstrates how computational approaches have revised established cell-of-origin models without requiring direct access to the original tissues. The paper situates this shift within broader reproducibility concerns in cancer biology and examines implications for funding mechanisms, institutional strategy, data-sharing practices, and investigator training. Rather than displacing custodial research, the emerging framework repositions tissue access within a plural evidentiary landscape where scientific credibility is increasingly grounded in reproducibility, transparency, and cross-cohort synthesis.