Cancer Data Science Pulse

Having trouble staying up to date on cancer variants? CIViC (or Clinical Interpretation of Variants in Cancer) offers an open-source knowledgebase and web interface to help connect researchers to the latest published findings on a full range of variant interpretations.

We asked 8 of our data scientists across the National Cancer Institute to share their advice and career journeys to help you answer the question—what should I know to start my career in cancer data science?

Meet the people who are breaking new ground in the data science field, whether it’s a new tool, a new model, or a completely new way of using data. Here, we’re featuring Svitlana Volkova, Ph.D., chief scientist at Pacific Northwest National Laboratory. She’ll describe how she’s using “foundation models” to give scientists and analysts a new tool for unleashing the power of artificial intelligence (AI).

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.

Data and Artificial Intelligence (AI) are a match seemingly made in heaven. By joining data and AI, scientists are able to shift a lot of the burden associated with using data from human to machine. See why the data-AI relationship works so well for cancer research in this offbeat blog featuring two fictitious characters—Datum and his pal Aida.

At the start of the COVID-19 outbreak, NCI’s Frederick National Laboratory for Cancer Research, along with the Centers for Disease Control and Prevention and the National Institute of Allergy and Infectious Diseases, modified an existing tool used for managing NCI’s clinical trials to create a COVID-19 Seroprevalence Hub (SeroHub) to track COVID-19 seroprevalence across the United States. This blog looks at how SeroHub has evolved since the pandemic first began and shows how it could serve as a blueprint for monitoring future infectious disease outbreaks that threaten public health.

On April 20, Dr. Clemens Grassberger will present the next Data Science Seminar, “Computational and Mathematical Approaches to Modeling Immunotherapy-Radiotherapy Combinations.” Here, Dr. Grassberger describes how combining these two very different therapies—radiation and immunotherapy—may lead to stronger, more effective ways of treating cancer.

On April 6, Dr. Malachi Griffith will present the next Data Science Seminar, “Bioinformatics Approaches for Neoantigen Identification and Prioritization.” Here, Dr. Griffith tells how his tinkering with computers, bioinformatics, and genomics is helping him understand the complexities of this promising research area. If successful, neoantigen-based cancer therapies could prove to be the pinnacle of personalized medicine.

On March 23, Dr. Ben Raphael will present the next Data Science Seminar, “Quantifying tumor heterogeneity using single-cell and spatial sequencing.” In this blog, Dr. Raphael describes how he’s using this technology to dig deeper into the complexity of cancer.

CBIIT’s NIH Data and Technology Advancement (DATA) Scholar, Dr. Jay G. Ronquillo, offers a bird’s-eye view of cloud computing, including tips for managing costs, access, and training to help advance precision medicine and cancer research.