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

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New Study Highlights Practical Data Needs to Successfully Apply Pharmacogenomics and Precision Medicine

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.

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Commentary Describes How Digital Twins Could Transform Cancer Care

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.

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Latest Podcast Features NCI Leaders Sharing How Data and Tech Shape the Future of Cancer Research

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.

Figure shows the graphical abstract from the recent article by Hong et al., “Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models,” which was published in Cell Reports Medicine. Using this machine learning model, researchers were able to interpret histological slides to identify four key subtypes of endometrial cancer (CNV-H, CNV-L, MSI-H, and POLE).

NCI-funded Machine Learning Gives New Insight into Endometrial Cancer

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.

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It Takes a Village: Recent Article Shows Strength of Community in Addressing Childhood Cancer

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.

Image of a cloud made up of different container technology icons (i.e. docker, snakemake)

Learn More About the Cloud and Container Technology Driving Cancer Data Science at the Containers and Workflow Interest Group (CWIG) Webinars

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.

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Apply to Attend a Combination Therapies for Cancer Treatment Micro Lab In September

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.

Illustration of a digitally rendered cell.

Apply Now for the "Accelerating Precision Radiation Oncology Through Advanced Computing and Artificial Intelligence" Workshop Series

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.

Graphical image depicting a Vitruvian man-like central figure with arms and legs outstretched, surrounded by scientific elements such as DNA strands, cells, atoms, etc.

Using 'Omics to Understand Cancer

Drs. Daoud Meerzaman of NCI’s Center for Biomedical Informatics and Information Technology (CBIIT) and Barbara K. Dunn of NCI’s Biometry Research Group recently served as Guest Editors for a special virtual issue of the journal, Frontiers in Oncology. This collection of articles shows how ‘omics data are helping scientists to better understand the prevention, detection, and treatment of cancer. The issue explores the full range of ‘omics findings, from identifying biomarkers for diagnosing and treating cancer, to uncovering the mechanisms for disease.

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