Abstract Patient-specific tumor heterogeneity is a significant challenge to understanding and treating cancer. The availability of multi-omic data in large public repositories has powered breakthroughs in precision medicine by enabling diversified analysis of datasets that encompass a wide range of patients, subtypes, demographics, and outlier groups1–3. Effective biobanking can play an important role in bridging the gap between big data and translationally actionable research by collecting, characterizing, and distributing diverse patient samples4–6. In light of this, the SCANDARE biobank study was designed with a wide-ranging scientific scope as a platform to drive translational research in oncology. The ongoing prospective, longitudinal study collects patient samples and comprehensive patient data throughout disease progression. Samples, along with clinical and molecular data, are available from SCANDARE cohorts and are currently in use in over 30 distinct research projects7–10. Here, we highlight the capacity for SCANDARE-derived datasets to identify actionable targets in several cancers through analysis of genetic alterations and differential gene expression in longitudinal cohorts and clinical subgroups. We present SCANDARE as a robust, flexible, and successful platform for oncology biobanking that supports advances in precision medicine and translational research. Clinicaltrials.gov registration: NCT03017573.
Tourneau et al. (Mon,) studied this question.
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