Transforming clinical trial data into future discoveries
Many Alliance clinical trials generate valuable scientific translational research data that extends well beyond its clinical findings. A-STOR (Alliance Standardized Translational ‘Omics Resource) ensures these translational research data continue to advance cancer research long after a clinical trial has reached its clinical endpoints.
A-STOR is the Alliance’s secure, HIPAA-compliant repository for clinical and translational research data. Developed through a collaboration between the Alliance Translational Research Program (TRP), the Statistics and Data Management Center (SDMC), The Ohio State University, and the University of North Carolina, A-STOR transforms complex clinical trial data into a standardized, reusable resource that accelerates cancer research.
By integrating clinical, genomic, pathology, and other translational research data from both Alliance and legacy cooperative group trials, A-STOR creates one of the most comprehensive research resources available for collaborative cancer research.
After a clinical trial reaches its primary endpoints, the Alliance works with investigators to curate, standardize, and securely store clinical and translational research data. Rather than remaining with a single completed study, these data become a lasting research resource that can be used by approved investigators to answer new scientific questions, validate discoveries, and advance cancer care.
When a clinical trial is designed, it usually focuses on a specific primary goal (such as whether Drug A extends survival compared to Drug B). However, as science advances, new biological questions arise. Researchers can use A-STOR data to look back at completed trials and explore new scientific theories that were not part of the original study design without needing to fund, recruit, and conduct a new multi-year study.
A major bottleneck in cancer care is predicting which patients will respond to different treatment. Because A-STOR stores deep multi-omic data (such as DNA and RNA sequencing) alongside patient outcomes, researchers can look for genetic patterns, mutations, or cellular signatures. If a specific genetic mutation consistently correlates with a positive treatment response or resistance, researchers may identify a potential biomarker to guide future patient care.
Because A-STOR harmonizes data across Alliance and legacy cooperative group studies, researchers can combine datasets to study rare cancers, rare molecular subtypes, and other populations that may not be adequately represented in a single trial.
By pairing baseline genetic data with information collected as a patient’s disease changes over time, researchers can better understand why some therapies stop working. For example, analyzing circulating tumor DNA (ctDNA) or post-treatment sequencing may reveal genetic changes associated with treatment resistance. Understanding these mechanisms can help researchers develop new combination therapies, identify potential treatment targets, and guide the design of future cancer treatments
Modern computational biology increasingly relies on artificial intelligence and machine learning to analyze complex datasets, identify patterns, and predict clinical outcomes. These approaches depend on large amounts of high-quality, well-curated, and standardized data. A-STOR provides researchers with harmonized clinical and molecular datasets that can support the development and validation of computational models to better understand tumor biology, treatment response, toxicity, and patient outcomes.
A-STOR supports a broad range of clinical and translational research data, including:
- Clinical and outcomes datasets
- DNA and RNA sequencing data
- SNP and genome-wide association (GWAS) data
- Digital pathology images
- Circulating tumor DNA (ctDNA) data
- NanoString and other molecular profiling platforms
- Additional biomarker and translational research datasets
Through controlled access for approved investigators, A-STOR helps researchers around the world explore new scientific questions, validate discoveries, and accelerate progress in cancer research.
Approved investigators may request access to eligible Alliance data through the Alliance Data Sharing Request Process. You can also explore studies and available biospecimens through the NCI NCTN Navigator and NCTN Biospecimen Catalog.
Alliance for Clinical Trials in Oncology (Alliance)
- W. Fraser Symmans, MB, ChB — Translational Research Program Director and Principal Investigator
- Yujia Wen, MD, PhD — Director of Translational Research Operations and Data Sharing Sharing Committee Co-Chair
- Selina Chow, MD — Data Sharing Committee Co-Chair
- Ann Oberg, PhD — Director of Computational Genomics and Bioinformatics
- Ashton Banks – Program Coordinator
- Vanshika Mullick – Data Sharing Coordinator
The Ohio State University (OSU)
- Daniel Stover, MD — A-STOR Co-Chair
- James Blachly, MD — A-STOR Co-Chair
- Shawn Striker, MS – Senior Web Development Analyst
- Deloris Veney, MACPR, CCRP – A-STOR Research Administration Management Analyst
University of North Carolina (UNC)
- Benjamin Vincent, MD — Translational Bioinformatics Chair