Liver metastases license neutrophils through IL-1 to potentiate cancer progression.

Liver metastases are associated with poor cancer outcomes in many solid malignancies, but the factors influencing the trajectory of patients with liver metastases are poorly defined. It is known that liver metastases suppress systemic antitumor immunity; however, the underlying mechanisms remain incompletely described. We report that liver metastases promote disease progression in patients and preclinical models.

Effectiveness and safety of immune checkpoint inhibitors in Black patients versus White patients in a US national health system: a retrospective cohort study.

Background: Black patients were severely under-represented in the clinical trials that led to the approval of immune checkpoint inhibitors (ICIs) for all cancers. The aim of this study was to characterise the effectiveness and safety of ICIs in Black patients.

Methods: We did a retrospective cohort study of patients in the US Veterans Health Administration (VHA) system's Corporate Data Warehouse containing electronic medical records for all patients who self-identified as non-Hispanic Black or African American (referred to as Black) or non-Hispanic White (referred to as White) and received PD-1, PD-L1, CTLA-4, or LAG-3 inhibitors between Jan 1, 2010, and Dec 31, 2023.

A landmark federal interagency collaboration to promote data science in health care: Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now.

Objectives: In 2016, the Department of Veterans Affairs (VA) and the Department of Energy (DOE) established an Interagency Agreement (IAA), the Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION) research collaboration.

Materials And Methods: Oversight fell under the VA Office of Research Development (VA ORD) and DOE headquarters. An Executive Committee and 2 senior scientific liaisons work with VA and DOE leadership to optimize efforts in the service of shared scientific goals.

Pan-Cancer Survival Impact of Immune Checkpoint Inhibitors in a National Healthcare System.

Background: The cumulative, health system-wide survival benefit of immune checkpoint inhibitors (ICIs) is unclear, particularly among real-world patients with limited life expectancies and among subgroups poorly represented on clinical trials. We sought to determine the health system-wide survival impact of ICIs.

Methods: We identified all patients receiving PD-1/PD-L1 or CTLA-4 inhibitors from 2010 to 2023 in the national Veterans Health Administration (VHA) system (ICI cohort) and all patients who received non-ICI systemic therapy in the years before ICI approval (historical control).

Using electronic health record metadata to predict housing instability amongst veterans.

Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity.

All-Atom Simulations Uncover Structural and Dynamical Properties of STING Proteins in the Membrane System.

Recent studies have shown that the stimulator of interferon gene (STING) protein plays a central role in the immune system by facilitating the production of type I interferons in cells. The STING signaling pathway is also a prominent activator of cancer-killing T cells that initiate a powerful adaptive immune response. Since biomolecular signaling pathways are complicated and not easily identified through traditional experiments, molecular dynamics (MD) has often been used to study structural and dynamical responses of biological pathways.

Assessment of chemical-crosslink-assisted protein structure modeling in CASP13.

With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches.

Evaluation of the scale-consistent UNRES force field in template-free prediction of protein structures in the CASP13 experiment.

The recent NEWCT-9P version of the coarse-grained UNRES force field for proteins, with scale-consistent formulas for the local and correlation terms, has been tested in the CASP13 experiment of the blind-prediction of protein structure, in the ab initio, contact-assisted, and data-assisted modes. Significant improvement of the performance has been observed with respect to the CASP11 and CASP12 experiments (by over 10 GDT_TS units for the ab initio mode predictions and by over 15 GDT_TS units for the contact-assisted prediction, respectively), which is a result of introducing scale-consistent terms and improved handling of contact-distance restraints. As in previous CASP exercises, UNRES ranked higher in the free modeling category than in the general category that included template based modeling targets.

Purely Structural Protein Scoring Functions Using Support Vector Machine and Ensemble Learning.

The function of a protein is determined by its structure, which creates a need for efficient methods of protein structure determination to advance scientific and medical research. Because current experimental structure determination methods carry a high price tag, computational predictions are highly desirable. Given a protein sequence, computational methods produce numerous 3D structures known as decoys.

In situ data analytics and indexing of protein trajectories.

The transition toward exascale computing will be accompanied by a performance dichotomy. Computational peak performance will rapidly increase; I/O performance will either grow slowly or be completely stagnant. Essentially, the rate at which data are generated will grow much faster than the rate at which data can be read from and written to the disk.

WeFold: a coopetition for protein structure prediction.

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs.

Miravirsen (SPC3649) can inhibit the biogenesis of miR-122.

MicroRNAs (miRNAs) are short noncoding RNAs, which bind to messenger RNAs and regulate protein expression. The biosynthesis of miRNAs includes two precursors, a primary miRNA transcript (pri-miRNA) and a shorter pre-miRNA, both of which carry a common stem-loop bearing the mature miRNA. MiR-122 is a liver-specific miRNA with an important role in the life cycle of hepatitis C virus (HCV).

Creating supersecondary structures with BuildBeta.

BuildBeta is a feature of the ProteinShop software designed to thoroughly sample a protein conformational space given the protein's sequence of amino acids and secondary structure predictions. It targets proteins with beta sheets because they are particularly challenging to predict due to the complexity of sampling long-range strand pairings. Here we discuss some of the most difficult targets in the recent 9th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and show how BuildBeta can leverage some of the most successful methods in the category "template-free modeling" by augmenting their sampling capabilities.

BuildBeta--a system for automatically constructing beta sheets.

We describe a method that can thoroughly sample a protein conformational space given the protein primary sequence of amino acids and secondary structure predictions. Specifically, we target proteins with beta-sheets because they are particularly challenging for ab initio protein structure prediction because of the complexity of sampling long-range strand pairings. Using some basic packing principles, inverse kinematics (IK), and beta-pairing scores, this method creates all possible beta-sheet arrangements including those that have the correct packing of beta-strands.

SEA domain proteolysis determines the functional composition of dystroglycan.

Post-translational modifications of the extracellular matrix receptor dystroglycan (DG) determine its functional state, and defects in these modifications are linked to muscular dystrophies and cancers. A prominent feature of DG biosynthesis is a precursor cleavage that segregates the ligand-binding and transmembrane domains into the noncovalently attached alpha- and beta-subunits. We investigate here the structural determinants and functional significance of this cleavage.

ProteinShop: a tool for interactive protein manipulation and steering.

We describe ProteinShop, a new visualization tool that streamlines and simplifies the process of determining optimal protein folds. ProteinShop may be used at different stages of a protein structure prediction process. First, it can create protein configurations containing secondary structures specified by the user.

A physical approach to protein structure prediction.

We describe our global optimization method called Stochastic Perturbation with Soft Constraints (SPSC), which uses information from known proteins to predict secondary structure, but not in the tertiary structure predictions or in generating the terms of the physics-based energy function. Our approach is also characterized by the use of an all atom energy function that includes a novel hydrophobic solvation function derived from experiments that shows promising ability for energy discrimination against misfolded structures. We present the results obtained using our SPSC method and energy function for blind prediction in the 4th Critical Assessment of Techniques for Protein Structure Prediction competition, and show that our approach is more effective on targets for which less information from known proteins is available.