Whether you are in the data science field, interested in developing computational solutions for clinical oncology, or a clinical researcher, we’ve curated a list of data sets, tools, and learning resources to showcase how these disciplines can and are working together to empower cancer research. Cancer data science can drive clinical oncology forward. For example, computer models can be developed to serve as a cancer patient’s digital twin to capture real-time dynamics to create predictive models and, one day, guide treatment decisions. Also, national sequencing efforts could equip clinicians with the knowledge of how a patient’s genome impacts their response to medications. 

Explore Clinical and Biological Data Online

With so much data available, we’ve pulled together a list of links to data sets for common cancer types across some of our NCI Cancer Research Data Commons (CRDC) resources. Collectively, these resources offer access to more than one million files of experimental and clinical data from landmark NCI studies and other grantee projects. Clinical attributes are as follows:

• Demographics
• Diagnoses
• Treatments
• Environmental Exposure

You can explore high-level trends in these data sets directly from the online portals or analyze the data with the library of robust computational tools provided through NCI’s Cloud Resources.

Having trouble understanding what a particular term means? Our semantics resources and services provide definitions on common data elements and standard data ontologies like CDISC.

Find Bioinformatics Tools for Predictive Oncology

In addition to what the CRDC offers, there are many analytical tools and pipelines to help you mine and extract meaningful insights from clinical data. We’ve added a few selections from our partners focused on supporting predictive and precision oncology analysis, but you can find more tools through the “Resources for Researchers” search engine.

• Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium: This public-private consortium has developed the ATOM Modeling PipeLine (AMPL), an open source, free-to-use software for building and sharing models that advance in silico drug discovery.
• NCI-Department of Energy (DOE) Collaboration: One of the pilot studies from this cross-agency collaboration between NCI and DOE developed predictive artificial intelligence (AI) and machine learning models of drug responses in pre-clinical models of cancer to improve and expedite the selection and development of new targeted therapies. Its tools are available online through their capabilities catalog.
• Informatics Technology for Cancer Research (ITCR): This trans-NCI program supports investigator-initiated and research-driven informatics tool development. The ITCR portfolio shares 15 resources for clinical research, including tools for integrating and analyzing electronic medical records, databases cataloging clinically actionable information for personalized cancer therapy, and education forums. Links to the tools and available tutorials are available online through their tools catalog. For more information about the opportunities through their new training program, read our recent blog post.

Learn About Cancer Data Science in Precision Oncology

If you’re new to the world of cancer data science and its application to clinical research, check out these introductory blog posts covering some of the basics and examples of innovative work made possible through the intersection of these disciplines. For regular updates on NCI’s cancer data science efforts, training events, and blog, subscribe to our weekly RSS.

Are we missing resources you would want to see? We may already have it. Leave a comment and we’ll follow-up with additional information.