Keep up with the latest news from the NCI Center for Biomedical Informatics and Information Technology (CBIIT) and the data science communities.

In RAS-related diseases, such as cancer, mutations in the RAS genes or their regulators render RAS proteins persistently active. Investigating RAS activation events is challenging when using conventional techniques. An unprecedented multiscale platform is using machine learning to change that.

NCI DATA Scholar Dr. Jay G. Ronquillo recently published a study using NIH “All of Us” data and NCI’s Cancer Research Data Commons to better understand pharmacogenomic prescribing and testing patterns across the United States.

Drs. Emily Greenspan and Eric Stahlberg of NCI’s CBIIT and Frederick National Laboratory for Cancer Research, respectively, recently contributed to an article, “Digital twins for predictive oncology will be a paradigm shift for precision cancer care,” published in Nature Medicine. The commentary examines the vision that members of NCI’s Envisioning Computational Innovations for Cancer Challenges (ECCIC) community have for developing cancer patient digital twins. Such a platform could revolutionize how clinicians and policymakers approach cancer care and further advance precision medicine.

In a recent podcast, NCI leaders from CBIIT and the Small Business Innovation Research Development Center shared how technological developments have enhanced cancer research and have helped usher in new diagnostics, treatments, and patient care.

A new publication using NCI funding and resources shows that a machine learning model, called Panoptes, allowed cancer researchers to reliably predict subtypes of endometrial cancer. Such “computational pathology” offers a useful framework for supporting human pathologists, trimming the labor needed to interpret histological findings to under 4 minutes per slide, and eliminating the time and cost of genetic sequencing.

CBIIT Director, Dr. Tony Kerlavage, along with NCI staff and a host of experts in childhood cancer research, recently published an article, “Cancer Informatics for Cancer Centers (CI4CC): Scientific Drivers for Informatics, Data Science, and Care in Pediatric, Adolescent, and Young Adult (AYA) Cancer,” in JCO Cancer Clinical Informatics. The article summarizes the Fall 2020 CI4CC Symposium and showcases the scope of initiatives underway to address childhood cancer, with a particular emphasis on how data science and informatics are helping to support these initiatives.

The relaunched monthly CWIG webinar series will invite researchers from across the globe to discuss the latest advancements in cloud computing technologies, workflow, tools, and packages.

Cancer clinicians, cancer biologists, and computational experts should apply to attend the four Combination Therapies for Cancer Treatment Micro Labs. Participants will work in teams and share ideas, expertise, data, code, results, and more, as well as explore opportunities for new data science and research collaborations. Each team will form, pitch, and refine plans for interdisciplinary strategies, priorities, projects, campaigns, and innovative approaches that advance scientific questions about cancer combinations therapies. One team may receive funding for their project.

NIH and the National Science Foundation are participating in an interagency funding opportunity that seeks to address technological and data science challenges, which require fundamental research and development of new tools, workflows, and methods. Proposals are due Tuesday, February 16, 2021, at 5:00 p.m. (submitter’s local time).

Applications are now being accepted for the NCI-DOE Collaboration Workshop Series on radiation oncology. Join multidisciplinary experts from basic science, clinical practice, and computational science to explore emerging and futuristic opportunities to advance radiation therapy.