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Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence
Much research in the behavioral sciences aims to characterize the “typical” person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which explicit evidence is rarely presented. Mean effect size varies with both within-participant effect size and population prevalence (proportion of population showing effect). Few studies consider how prevalence affects mean effect size estimates and existing estimators of prevalence are, conversely, confounded by uncertainty about effect size. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size by probabilistically clustering participant-level data based on their likelihood under a null distribution. Our approach, for which we provide a software tool, outperforms existing prevalence estimation methods when effect size is uncertain and is sensitive to differences in prevalence or effect size across groups or conditions.
Therapeutic vulnerabilities and pan-cancer landscape of BRAF class III mutations in epithelial solid tumors
Kinase-impaired class III BRAF mutations have recently received attention as a possible prognostic factor and therapeutic target. Class III BRAF variants differ from class I and class II mutations in terms of mechanism of pathway activation and therapeutic vulnerabilities. Genomic landscape analyses of tumors in large real-world cohorts represent a great opportunity to further characterize tumor-related molecular events and treatment vulnerabilities, however, such data is not yet available for tumors with BRAF class III mutations.
The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers
Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events.
Prognostic, biological, and structural implications of FLT3-JMD point mutations in acute myeloid leukemia: an analysis of Alliance studies
The FLT3 gene frequently undergoes mutations in acute myeloid leukemia (AML), with internal tandem duplications (ITD) and tyrosine kinase domain (TKD) point mutations (PMs) being most common. Recently, PMs and deletions in the FLT3 juxtamembrane domain (JMD) have been identified, but their biological and clinical significance remains poorly understood. We analyzed 1660 patients with de novo AML and found FLT3-JMD mutations, mostly PMs, in 2% of the patients. Patients with FLT3-JMD mutations had a higher relapse rate and shorter disease-free survival than those with FLT3-TKD, whereas their relapse rate, disease-free and overall survival were not significantly different from those of FLT3-ITD-positive patients. In vitro experiments showed that FLT3-JMD PMs transformed hematopoietic cells and responded well to type I and II FLT3 inhibitors. Molecular dynamics simulations were used to explore the conformational changes of JMD PMs relative to wild-type FLT3. These mutations exhibited constrained domain motions with wider gate openings, potentially enhancing drug binding. Altered residue interactions and structural changes shed light on their unique functional mechanisms, with increased allosteric pathways suggesting reduced interactions with other residues. We conclude that patients with FLT3-JMD PMs represent uncommon but important subset with distinct molecular and biological features, and may benefit from FLT3 inhibitors.
The genomic landscape of gene-level structural variations in Japanese and global soybean Glycine max cultivars
Japanese soybeans are traditionally bred to produce soy foods such as tofu, miso and boiled soybeans. Here, to investigate their distinctive genomic features, including genomic structural variations (SVs), we constructed 11 nanopore-based genome references for Japanese and other soybean lines. Our assembly-based comparative method, designated ‘Asm2sv’, identified gene-level SVs comprehensively, enabling pangenome analysis of 462 worldwide cultivars and varieties. Based on these, we identified selective sweeps between Japanese and US soybeans, one of which was the pod-shattering resistance gene PDH1. Genome-wide association studies further identified several quantitative trait loci that accounted for large-seed phenotypes of Japanese soybean lines, some of which were also close to regions of the selective sweeps, including PDH1. Notably, specific combinations of alleles, including SVs, were found to increase the seed size of some Japanese landraces. In addition to the differences in cultivation environments, distinct food processing usages might result in changes in Japanese soybean genomes.
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