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Actionable mutations of RET kinase have been identified as oncogenic drivers of solid tumors, including thyroid cancer, metastatic colorectal cancer, and nonsmall cell lung cancer. Although multikinase inhibitors and RET selective inhibitors are used to treat patients with RET alterations, there is insufficient research addressing certain issues: which actionable mutations arise from these therapies, how to improve the clinical response rate to RET inhibitors, and how to design new inhibitors to overcome drug resistance. Therefore, the development of sophisticated tool compounds is required to investigate the molecular mechanisms of actionable mutations and to develop breakthrough therapeutics for different RET alterations. Herein, we present our investigation into the side chains of imidazopyridazine hinge binders that are capable of inducing protein-ligand interaction patterns from the gatekeeper to the waterfront regions. Extending the substituents at the second and sixth positions enhanced the IC up to < 0.5 nM for diverse RET alterations.
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http://dx.doi.org/10.1021/acsmedchemlett.4c00287 | DOI Listing |
Neuroendocrinology
September 2025
Introduction Neuroendocrine tumors (NETs) are a rare and heterogeneous group of neoplasms with both clinical and genetic diversity. The clinical applicability of molecular profiling using liquid biopsy for identifying actionable drug targets and prognostic indicators in patients with advanced NETs remains unclear. Methods In this study, we utilized a custom-made 37 genes panel of circulating tumor DNA (ctDNA) based on next-generation sequencing (NGS) in 47 patients with advanced NETs.
View Article and Find Full Text PDFPediatr Dev Pathol
September 2025
The Hospital for Sick Children, Division of Pathology, Toronto, Canada.
Background: Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood. For stratification purposes, rhabdomyosarcoma is classified into fusion-positive RMS (alveolar rhabdomyosarcoma) and fusion-negative RMS (embryonal or spindle cell/sclerosing, FN-RMS) subtypes according to its fusion status. This study aims to highlight the pathologic and molecular characteristics of a cohort of FN-RMS using a targeted NGS RNA-Seq assay.
View Article and Find Full Text PDFBrief Bioinform
August 2025
Department of Respiratory Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an 710004, China.
Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets.
View Article and Find Full Text PDFJ Liq Biopsy
September 2025
Department of Clinical Oncology, Centre of Cancer Medicine, Li Ka Shing Faculty of Medicine, Hong Kong Special Administrative Region of China.
Background: Comprehensive genomic profiling is crucial for guiding treatment in advanced non-small cell lung cancer (NSCLC). However, tumor tissue-based targeted panel next-generation sequencing (TP-NGS) faces challenges, such as inadequate tissue sampling. Circulating tumor DNA (ctDNA) from peripheral blood has emerged as an alternative.
View Article and Find Full Text PDFNAR Cancer
September 2025
Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
Personalized treatment selection is crucial for cancer patients due to the high variability in drug response. While actionable mutations can increasingly inform treatment decisions, most therapies still rely on population-based approaches. Here, we introduce neural interaction explainable AI (NeurixAI), an explainable and highly scalable deep learning framework that models drug-gene interactions and identifies transcriptomic patterns linked with drug response.
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