Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation.

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins.

Predictive Radiation Oncology - A New NCI-DOE Scientific Space and Community.

With a widely attended virtual kickoff event on January 29, 2021, the National Cancer Institute (NCI) and the Department of Energy (DOE) launched a series of 4 interactive, interdisciplinary workshops-and a final concluding "World Café" on March 29, 2021-focused on advancing computational approaches for predictive oncology in the clinical and research domains of radiation oncology. These events reflect 3,870 human hours of virtual engagement with representation from 8 DOE national laboratories and the Frederick National Laboratory for Cancer Research (FNL), 4 research institutes, 5 cancer centers, 17 medical schools and teaching hospitals, 5 companies, 5 federal agencies, 3 research centers, and 27 universities. Here we summarize the workshops by first describing the background for the workshops.

Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery.

Conventional drug discovery is long and costly, and suffers from high attrition rates, often leaving patients with limited or expensive treatment options. Recognizing the overwhelming need to accelerate this process and increase success, the ATOM consortium was formed by government, industry, and academic partners in October 2017. ATOM applies a team science and open-source approach to foster a paradigm shift in drug discovery.

The generality of the GUGA MRCI approach in COLUMBUS for treating complex quantum chemistry.

The core part of the program system COLUMBUS allows highly efficient calculations using variational multireference (MR) methods in the framework of configuration interaction with single and double excitations (MR-CISD) and averaged quadratic coupled-cluster calculations (MR-AQCC), based on uncontracted sets of configurations and the graphical unitary group approach (GUGA). The availability of analytic MR-CISD and MR-AQCC energy gradients and analytic nonadiabatic couplings for MR-CISD enables exciting applications including, e.g.

Predicting tumor cell line response to drug pairs with deep learning.

Background: The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity.

Results: We present a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, our model explains 94% of the response variance.

Topological Structure Determination of RNA Using Small-Angle X-Ray Scattering.

Knowledge of RNA three-dimensional topological structures provides important insight into the relationship between RNA structural components and function. It is often likely that near-complete sets of biochemical and biophysical data containing structural restraints are not available, but one still wants to obtain knowledge about approximate topological folding of RNA. In this regard, general methods for determining such topological structures with minimum readily available restraints are lacking.

Modeling RNA topological structures using small angle X-ray scattering.

Detailed understanding of the structure and function relationship of RNA requires knowledge about RNA three-dimensional (3D) topological folding. However, there are very few unique RNA entries in structure databases. This is due to challenges in determining 3D structures of RNA using conventional methods, such as X-ray crystallography and NMR spectroscopy, despite significant advances in both of these technologies.

Coiled-coil protein composition of 22 proteomes--differences and common themes in subcellular infrastructure and traffic control.

Background: Long alpha-helical coiled-coil proteins are involved in diverse organizational and regulatory processes in eukaryotic cells. They provide cables and networks in the cyto- and nucleoskeleton, molecular scaffolds that organize membrane systems and tissues, motors, levers, rotating arms, and possibly springs. Mutations in long coiled-coil proteins have been implemented in a growing number of human diseases.

Compilation of mRNA polyadenylation signals in Arabidopsis revealed a new signal element and potential secondary structures.

Using a novel program, SignalSleuth, and a database containing authenticated polyadenylation [poly(A)] sites, we analyzed the composition of mRNA poly(A) signals in Arabidopsis (Arabidopsis thaliana), and reevaluated previously described cis-elements within the 3'-untranslated (UTR) regions, including near upstream elements and far upstream elements. As predicted, there are absences of high-consensus signal patterns. The AAUAAA signal topped the near upstream elements patterns and was found within the predicted location to only approximately 10% of 3'-UTRs.

Genome-wide identification of Arabidopsis coiled-coil proteins and establishment of the ARABI-COIL database.

Increasing evidence demonstrates the importance of long coiled-coil proteins for the spatial organization of cellular processes. Although several protein classes with long coiled-coil domains have been studied in animals and yeast, our knowledge about plant long coiled-coil proteins is very limited. The repeat nature of the coiled-coil sequence motif often prevents the simple identification of homologs of animal coiled-coil proteins by generic sequence similarity searches.