Pan-cancer analysis of oncogenic MET fusions reveals distinct pathogenomic subsets with differential sensitivity to MET-targeted therapy.

MET fusions (MET-Fs) are oncogenic drivers that remain poorly characterized. Analysis of 56 MET-F-positive tumors from an institutional cohort of 91,119 patients (79,864 DNA-seq plus 11,255 RNA-seq) uncovered two forms of MET-F pathobiology. The first group featured 5' partners with homodimerization domains fused in-frame with MET-tyrosine kinase domain (TKD), primarily originated from translocations, frequently excluded MET exon 14, mediated oncogenesis through cytoplasmic aggregation and constitutive activation, and were markedly sensitive to MET tyrosine kinase inhibitors (TKI) in pre-clinical models and patients with lung cancer.

HER2 Antibody-Drug Conjugates Are Active against Desmoplastic Small Round Cell Tumor.

Purpose: Desmoplastic small round cell tumor (DSRCT) is a rare but highly aggressive soft tissue sarcoma that arises in the abdominopelvic cavity of young males. Since the discovery of EWSR1::WT1 fusion as the driver of DSRCT, no actionable genomic alterations have been identified, limiting disease management to a combination of surgery, chemotherapy, and radiation, with very poor outcomes. Herein, we evaluated ERBB2/HER2 expression in DSRCT as a therapeutic target.

Machine-learning-based structural analysis of interactions between antibodies and antigens.

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem.

Prevalence and Therapeutic Targeting of High-Level ERBB2 Amplification in NSCLC.

Introduction: ERBB2 amplification in lung cancer remains poorly characterized. HER2 (encoded by ERBB2) is a transmembrane tyrosine kinase capable of ligand-independent dimerization and signaling when overexpressed, and a common cause of HER2 overexpression is ERBB2 amplification. Here, we evaluated the clinicopathologic and genomic characteristics of ERBB2-amplified NSCLC and explored a HER2 antibody-drug conjugate (ADC) therapeutic strategy.

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens.

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem.

Mechanical vibration patterns elicit behavioral transitions and habituation in crawling larvae.

How animals respond to repeatedly applied stimuli, and how animals respond to mechanical stimuli in particular, are important questions in behavioral neuroscience. We study adaptation to repeated mechanical agitation using the larva. Vertical vibration stimuli elicit a discrete set of responses in crawling larvae: continuation, pause, turn, and reversal.

Vepafestinib is a pharmacologically advanced RET-selective inhibitor with high CNS penetration and inhibitory activity against RET solvent front mutations.

RET receptor tyrosine kinase is activated in various cancers (lung, thyroid, colon and pancreatic, among others) through oncogenic fusions or gain-of-function single-nucleotide variants. Small-molecule RET kinase inhibitors became standard-of-care therapy for advanced malignancies driven by RET. The therapeutic benefit of RET inhibitors is limited, however, by acquired mutations in the drug target as well as brain metastasis, presumably due to inadequate brain penetration.

Estimating random close packing in polydisperse and bidisperse hard spheres via an equilibrium model of crowding.

We show that an analogy between crowding in fluid and jammed phases of hard spheres captures the density dependence of the kissing number for a family of numerically generated jammed states. We extend this analogy to jams of mixtures of hard spheres in d = 3 dimensions and, thus, obtain an estimate of the random close packing volume fraction, ϕ, as a function of size polydispersity. We first consider mixtures of particle sizes with discrete distributions.

Shadow enhancers modulate distinct transcriptional parameters that differentially effect downstream patterning events.

Transcription in the early Drosophila blastoderm is coordinated by the collective action of hundreds of enhancers. Many genes are controlled by so-called 'shadow enhancers', which provide resilience to environment or genetic insult, allowing the embryo to robustly generate a precise transcriptional pattern. Emerging evidence suggests that many shadow enhancer pairs do not drive identical expression patterns, but the biological significance of this remains unclear.

Robust identification of temporal biomarkers in longitudinal omics studies.

Motivation: Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals.

Tracking Changes in Students' Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining.

Success in online and blended courses requires engaging in self-regulated learning (SRL), especially for challenging STEM disciplines, such as physics. This involves students planning how they will navigate course assignments and activities, setting goals for completion, monitoring their progress and content understanding, and reflecting on how they completed each assignment. Based on Winne & Hadwin's COPES model, SRL is a series of events that temporally unfold during learning, impacted by changing internal and external factors, such as goal orientation and content difficulty.