Some of the major advances in the field of cancer over the last few years include a shift towards more data intensive and computational workflows through a variety of tools. This shift is highly relevant for cancer biomarkers. The increase of data and computation coupled with emerging capabilities in bioinformatics, machine learning (ML), and artificial intelligence (AI) is unlocking new opportunities to apply data-driven methods to scientific discovery and validation. Rapid advances in areas such as foundation models and generative AI are providing exciting new capabilities in areas such as image segmentation and identification. As research consortia—such as the Early Detection Research Network (EDRN)—continue to capture more data, there are numerous opportunities to leverage and apply shared tools, algorithms, and capabilities within the community. To leverage these capabilities, there is a need to ensure that diverse datasets are captured, structured, and made readily accessible for analysis. Similarly, there is a need to provide packaged tools and services to support the analysis.

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