Decoding the Clinical and Molecular Signatures of EGFR Common, Compound, and Uncommon Mutations in Non-Small Cell Lung Cancer.

Introduction: Epidermal growth factor receptor (EGFR) mutations are key oncogenic drivers in lung adenocarcinoma (LUAD), predominantly affecting Asian, non-smoking, and female populations. While common mutations, such as exon 19 deletions and L858R, respond well to tyrosine kinase inhibitors (TKIs), uncommon EGFR mutations and compound variants exhibit variable treatment responses. This study aims to compare clinical characteristics and molecular profiles of patients with common, uncommon, and compound EGFR mutations, assessing their implications for therapy outcomes.

A modular toolbox for the optogenetic deactivation of transcription.

Light-controlled transcriptional activation is a commonly used optogenetic strategy that allows researchers to regulate gene expression with high spatiotemporal precision. The vast majority of existing tools are, however, limited to light-triggered induction of gene expression. Here, we inverted this mode of action and created optogenetic systems capable of efficiently terminating transcriptional activation in response to blue light.

SuperSpot: Coarse Graining Spatial Transcriptomics Data into Metaspots.

Summary: Spatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular niches. With current technologies such as VisiumHD, thousands of genes can be detected across millions of spots (also called cells or bins depending on the technologies). Building upon the metacell concept, we present a workflow, called SuperSpot, to combine adjacent and transcriptomically similar spots into "metaspots".

Development of patient-derived lymphomoids with preserved tumor architecture for lymphoma therapy screening.

The efficacy of anti-cancer therapies depends on the genomic composition of the tumor, its microenvironment, spatial organization, and intra-tumor heterogeneity. B-cell lymphomas are a heterogeneous group of tumors emerging from B-cells at different stages of differentiation and exhibiting tumor-specific interactions with the tumor microenvironment. Thus, the effect of drug treatments can be influenced by the tumor composition and functional interactions among immune cells.

Epitope-focused immunogens targeting the hepatitis C virus glycoproteins induce broadly neutralizing antibodies.

Hepatitis C virus (HCV) infection causes ~290,000 annual human deaths despite the highly effective antiviral treatment available. Several viral immune evasion mechanisms have hampered the development of an effective vaccine against HCV, among them the remarkable conformational flexibility within neutralization epitopes in the HCV antigens. Here, we report the design of epitope-focused immunogens displaying two distinct HCV cross-neutralization epitopes.

Study of Photoselectivity in Linear Conjugated Chromophores Using the XMS-CASPT2 Method.

Photoisomerization, the structural alteration of molecules upon absorption of light, is crucial for the function of biological chromophores such as retinal in opsins, proteins vital for vision and other light-sensitive processes. The intrinsic selectivity of this isomerization process (i.e.

Reciprocal inhibition of NOTCH and SOX2 shapes tumor cell plasticity and therapeutic escape in triple-negative breast cancer.

Cancer cell plasticity contributes significantly to the failure of chemo- and targeted therapies in triple-negative breast cancer (TNBC). Molecular mechanisms of therapy-induced tumor cell plasticity and associated resistance are largely unknown. Using a genome-wide CRISPR-Cas9 screen, we investigated escape mechanisms of NOTCH-driven TNBC treated with a gamma-secretase inhibitor (GSI) and identified SOX2 as a target of resistance to Notch inhibition.

Methodological considerations in calculating the minimal clinically important change score for the core outcome measures index (COMI): insights from a large single-centre spine surgery registry.

Introduction: The Minimal Clinically Important Change (MCIC) is used in conjunction with Patient-Reported Outcome Measures (PROMs) to determine the clinical relevance of changes in health status. MCIC measures a change within the same person or group over time. This study aims to evaluate the variability in computing MCIC for the Core Outcome Measure Index (COMI) using different methods.

Estimating pathogen spread using structured coalescent and birth-death models: A quantitative comparison.

Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies - based on the coalescent and the birth-death model - are frequently employed to estimate viral spread between populations.

Factors Guiding Clinical Decision-Making in Genitourinary Oncology.

Introduction: Clinical decision-making in oncology is a complex process, with the primary goal of identifying the most effective treatment tailored to individual cancer patients. Many factors influence the treatment decision: disease- and patient-specific criteria, the increasingly complex treatment landscape, market authorization and drug availability, financial aspects, and personal treatment expertise. In the domain of genitourinary cancers, particularly prostate cancer, decision-making is challenging.

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.

Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for most non-malignant cell types frequently observed in the microenvironment of human tumors.

Molecular and functional landscape of malignant serous effusions for precision oncology.

Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells from patient biopsies. Here, we comprehensively characterize a pan-cancer cohort of 150 malignant serous effusion (MSE) samples at the cellular, molecular, and functional level. We find that MSE-derived cancer cells retain the genomic and transcriptomic profiles of their corresponding primary tumors, validating their use as a patient-relevant model system for solid tumor biology.

Microbial genetic potential differs among cryospheric habitats of the Damma glacier.

Climate warming has led to glacier retreat worldwide. Studies on the taxonomy and functions of glacier microbiomes help us better predict their response to glacier melting. Here, we used shotgun metagenomic sequencing to study the microbial functional potential in different cryospheric habitats, i.

Functional implications of glycans and their curation: insights from the workshop held at the 16th Annual International Biocuration Conference in Padua, Italy.

Dynamic changes in protein glycosylation impact human health and disease progression. However, current resources that capture disease and phenotype information focus primarily on the macromolecules within the central dogma of molecular biology (DNA, RNA, proteins). To gain a better understanding of organisms, there is a need to capture the functional impact of glycans and glycosylation on biological processes.

G-: Knowledge graph neural network for structure-free protein-ligand bioactivity prediction.

Protein-ligand interactions (PLIs) determine the efficacy and safety profiles of small molecule drugs. Existing methods rely on either structural information or resource-intensive computations to predict PLI, casting doubt on whether it is possible to perform structure-free PLI predictions at low computational cost. Here we show that a light-weight graph neural network (GNN), trained with quantitative PLIs of a small number of proteins and ligands, is able to predict the strength of unseen PLIs.

Data-driven prediction of spinal cord injury recovery: An exploration of current status and future perspectives.

Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is a promising approach to enhance the prediction of recovery trajectories, but its integration into clinical practice requires a thorough understanding of its efficacy and applicability. We systematically reviewed the current literature on data-driven models of SCI recovery prediction.

Protein Network Alterations in G-CSF Treated Severe Congenital Neutropenia Patients and Beneficial Effects of Oral Health Intervention.

Purpose: Severe congenital neutropenia (SCN) is a raredisorder characterized by diminished neutrophil levels. Despite granulocytecolony-stimulating factor (G-CSF) treatment, SCN patients remain still prone tosevere infections, including periodontal disease-a significant oral healthrisk. This study investigates the host proteome and metaproteome in saliva andgingival crevicular fluid (GCF) of G-CSF-treated patients.

Development of radiofluorinated MLN-4760 derivatives for PET imaging of the SARS-CoV-2 entry receptor ACE2.

Purpose: The angiotensin converting enzyme 2 (ACE2) plays a regulatory role in the cardiovascular system and serves SARS-CoV-2 as an entry receptor. The aim of this study was to synthesize and evaluate radiofluorinated derivatives of the ACE2 inhibitor MLN-4760. [F]F-MLN-4760 and [F]F-Aza-MLN-4760 were demonstrated to be suitable for non-invasive imaging of ACE2, potentially enabling a better understanding of its expression dynamics.

Context-aware geometric deep learning for protein sequence design.

Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates and element names that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments.

Non-coding variants impact cis-regulatory coordination in a cell type-specific manner.

Background: Interactions among cis-regulatory elements (CREs) play a crucial role in gene regulation. Various approaches have been developed to map these interactions genome-wide, including those relying on interindividual epigenomic variation to identify groups of covariable regulatory elements, referred to as chromatin modules (CMs). While CM mapping allows to investigate the relationship between chromatin modularity and gene expression, the computational principles used for CM identification vary in their application and outcomes.