Identification of biallelic intronic EPM2A mutations in a Lafora disease kindred
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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.
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 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.
Cancer-independent somatic mutation of the wild-type NF1 allele in normal tissues in neurofibromatosis type 1
Cancer predisposition syndromes mediated by recessive cancer genes generate tumors via somatic variants (second hits) in the unaffected allele. Second hits may or may not be sufficient for neoplastic transformation. Here we performed whole-genome and whole-exome sequencing on 479 tissue biopsies from a child with neurofibromatosis type 1, a multisystem cancer-predisposing syndrome mediated by constitutive monoallelic NF1 inactivation. We identified multiple independent NF1 driver variants in histologically normal tissues, but not in 610 biopsies from two nonpredisposed children. We corroborated this finding using targeted duplex sequencing, including a further nine adults with the same syndrome. Overall, truncating NF1 mutations were under positive selection in normal tissues from individuals with neurofibromatosis type 1. We demonstrate that normal tissues in neurofibromatosis type 1 commonly harbor second hits in NF1, the extent and pattern of which may underpin the syndrome’s cancer phenotype.
Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2
Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). However, Omicron and its sub-lineages, as well as other heavily mutated variants, have rendered many neutralizing antibodies ineffective. Here we show that antibodies with enhanced resistance to the evolution of SARS-CoV-2 can be identified via deep mutational learning. We constructed a library of full-length RBDs of Omicron BA.1 with high mutational distance and screened it for binding to the angiotensin-converting-enzyme-2 receptor and to neutralizing antibodies. After deep-sequencing the library, we used the data to train ensemble deep-learning models for the prediction of the binding and escape of a panel of eight therapeutic antibody candidates targeting a diverse range of RBD epitopes. By using in silico evolution to assess antibody breadth via the prediction of the binding and escape of the antibodies to millions of Omicron sequences, we found combinations of two antibodies with enhanced and complementary resistance to viral evolution. Deep learning may enable the development of therapeutic antibodies that remain effective against future SARS-CoV-2 variants.
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