News

Dr. Peng Jiang of NCI’s Center for Cancer Research Cancer Data Science Lab and his postdocs have developed an open-source computational tool called the tumor-resilient T cell (Tres) model. Tres analyzes gene activity in T cells to assess how those cells are likely to fare in an immunosuppressive environment.

Help the FDA’s National Center for Toxicological Research and the precisionFDA optimize data processing pipelines for identifying indels (i.e., insertions/deletions in a genome) in oncopanel sequencing datasets by participating in a sequencing data challenge. Pre-register for the “Indel Calling from Oncopanel Sequencing Data Challenge” before May 2, 2022! Selected participants will be publicly recognized and invited to contribute to a scientific manuscript and a “Top Performer Webinar” that will be open to the public.

IMPROVE focuses on improving deep learning models to predict the efficacy of cancer treatments. The research community, including data scientists and informaticists, is asked to respond to an RFI for creating protocols to evaluate model performance by April 15, 2022, at 5:00 p.m. ET. Additionally, they are encouraged to respond to an RFP for improving model comparison by May 9, 2022.

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.