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Blood
January 2025
Fred Hutchinson Cancer Center, Seattle, Washington, United States.
Sclerosis is a highly morbid manifestation of chronic GVHD (cGVHD), associated with distressing symptoms and significant long-term disability. A patient-reported outcome measure (PRO) for cGVHD-associated sclerosis is essential to advance therapeutic trials. We aimed to develop a PRO for adults with cGVHD-associated sclerosis and evaluate and refine its content validity.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Molecular & Cellular Biosciences, University of Cincinnati, Cincinnati, OH 45267.
TGFβ family ligands are synthesized as precursors consisting of an N-terminal prodomain and C-terminal growth factor (GF) signaling domain. After proteolytic processing, the prodomain typically remains noncovalently associated with the GF, sometimes forming a high-affinity latent procomplex that requires activation. For the TGFβ family ligand anti-Müllerian hormone (AMH), the prodomain maintains a high-affinity interaction with its GF that does not render it latent.
View Article and Find Full Text PDFNat Cancer
January 2025
Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
Human tumors are diverse in their natural history and response to treatment, which in part results from genetic and transcriptomic heterogeneity. In clinical practice, single-site needle biopsies are used to sample this diversity, but cancer biomarkers may be confounded by spatiogenomic heterogeneity within individual tumors. Here we investigate clonally expressed genes as a solution to the sampling bias problem by analyzing multiregion whole-exome and RNA sequencing data for 450 tumor regions from 184 patients with lung adenocarcinoma in the TRACERx study.
View Article and Find Full Text PDFCancer Cell
December 2024
Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA. Electronic address:
Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic.
View Article and Find Full Text PDFAm J Psychiatry
January 2025
Directorate of Behavioral Health, Walter Reed National Military Medical Center, Bethesda, MD (Wolfgang); Departments of Psychiatry (Wolfgang) and Medical and Clinical Psychology (Gray), Uniformed Services University, Bethesda, MD; Departments of Psychiatry (Wolfgang, Krystal), Neuroscience (Krystal), and Psychology (Krystal), Yale University School of Medicine, New Haven, CT; Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin Dell Medical School (Fonzo, Nemeroff); Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, UCLA (Grzenda); Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis (Widge); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University and Veterans Affairs Palo Alto Health Care System, Palo Alto, CA (Rodriguez).
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