Johns Hopkins Launches Open Source Cancer Study Database

New database structure allows researchers to more easily study multiple types of cancer data in one setting.

Published on Mar. 5, 2026

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Researchers from the Johns Hopkins Kimmel Cancer Center and The Johns Hopkins University have created a novel database structure called AstroID that allows investigators anywhere to more easily study multiple types of cancer data — including laboratory results, genetic sequencing and imaging data — in one setting. The structure, built in a commercial web-based application called REDCap, can be subsequently scaled to accommodate thousands of patients and the spatial characterization of billions of cancer cells.

Why it matters

The new AstroID database structure aims to address the inefficiencies and duplication of efforts that have historically plagued cancer research, where each study has required manually compiling data from various sources. By organizing clinical, laboratory, and specimen data in a standardized way, AstroID enables researchers to more easily access and analyze comprehensive cancer data across multiple studies and tumor types.

The details

AstroID organizes clinical and correlating blood and tissue specimen information in six tiers, including details about the patient, diagnosis, clinical events, specimens, and how those specimens are processed in the lab. This structure allows researchers to ask questions across all of this data that has already been gathered, and across tumor types, combining it in the context of the longitudinal patient experience. The AstroID platform has already been deployed at Johns Hopkins for 16 different patient groups with multiple tumor types, with over 1 billion cancer cells spatially mapped and tagged with clinical information.

  • AstroID was described in a publication in the Journal for Immunotherapy of Cancer on December 25, 2025.
  • The AstroID platform has now been deployed in Johns Hopkins laboratories for 16 different patient groups.

The players

Johns Hopkins Kimmel Cancer Center

A cancer research and treatment center at Johns Hopkins University in Baltimore, Maryland.

The Johns Hopkins University

A private research university in Baltimore, Maryland.

Janis M. Taube, M.D.

Director of the Division of Dermatopathology and co-director of the Tumor Microenvironment Laboratory at the Bloomberg~Kimmel Institute for Cancer Immunotherapy.

Alexander Szalay, Ph.D.

Bloomberg Distinguished Professor and professor in the Department of Computer Science at The Johns Hopkins University, and director of the Institute for Data Intensive Science at Johns Hopkins.

Elizabeth Will

A postdoctoral student at Johns Hopkins who helped develop the AstroID platform.

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What they’re saying

“What this structure does is allow me to ask questions across all of this data that's already been gathered, and across tumor types, and combine it all together in the context of the longitudinal patient experience.”

— Janis M. Taube, M.D., Director of the Division of Dermatopathology and co-director of the Tumor Microenvironment Laboratory at the Bloomberg~Kimmel Institute for Cancer Immunotherapy (Journal for Immunotherapy of Cancer)

“What we are trying to do is to scale out so we can handle patients on the order of hundreds or thousands of patients in a study.”

— Alexander Szalay, Ph.D., Bloomberg Distinguished Professor and professor in the Department of Computer Science at The Johns Hopkins University, and director of the Institute for Data Intensive Science at Johns Hopkins (Journal for Immunotherapy of Cancer)

What’s next

The AstroID platform is now publicly available for any researcher to use, with the code hosted on GitHub. The Johns Hopkins team plans to continue expanding the use of AstroID for cancer studies and potentially adapt the structure to characterize longitudinal biospecimens from other disease processes as well.

The takeaway

The AstroID database structure developed at Johns Hopkins represents a significant advancement in cancer research, enabling more comprehensive and efficient analysis of patient data across multiple studies and tumor types. By making this open-source platform available to researchers worldwide, it has the potential to accelerate progress in understanding and treating cancer.