Background: The purpose of this study was to explore the immunohistochemical and mutational status of the tyrosine kinases KIT and platelet derived growth receptor-alpha (PDGFRA) in Merkel cell carcinoma (MCC). Specifically, we examined the mutated exons in gastrointestinal stromal cell tumors that may confer a treatment response to imatinib mesylate.
Methods: We evaluated KIT and PDGFRA immunostaining in 23 examples of MCC utilizing laser capture microdissection to obtain pure samples of tumor genomic DNA from 18 of 23 examples of MCC. PCR amplification and sequencing of KIT exons 9, 11, 13 and 17, and PDGFRA exons 10, 12, 14 and 18 for mutations was performed.
Results: Fifteen of 23 tumors (65%) demonstrated CD117 expression and 22 of 23 tumors (95%) demonstrated PDGFRA expression. A single heterozygous KIT exon 11 base change resulting in an E583K mutation was discovered in 12 of 18 (66%) examples of MCC. In addition, a single nucleotide polymorphism was detected in eight of 18 tumors (44%) in exon 18 of PDGFRA (codon 824; GTC > GTT).
Conclusions: We discovered a novel somatic KIT exon 11 E583K mutation in 66% of tumors. This mutation has been previously described in a human with piebaldism and appears to represent an inactivating mutation. Therefore, despite expression of CD117 and PDGFRA, the absence of activating mutations in these tyrosine kinases makes KIT and PDGFRA unlikely candidates of MCC oncogenesis.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/cup.12160 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Dermal CIC-Rearranged Sarcoma With Neuroendocrine Differentiation Mimicking Merkel Cell Carcinoma.
J Cutan Pathol
December 2024
SkinPath Solutions, Smyrna, Georgia, USA.
Capicua transcriptional repressor (CIC)-rearranged sarcoma (CRS) is a rare and recently described tumor that most commonly affects patients between 15 and 30 years of age. It is an undifferentiated round cell malignancy, with a disease defining CIC fusion, with double homeobox 4 (DUX4) being the most common partner. Here, we report a 77-year-old woman who presented with a cutaneous thigh mass with a clinical morphology suggesting Merkel cell carcinoma.
View Article and Find Full Text PDFConsensus on the key characteristics of metabolism disruptors.
Nat Rev Endocrinol
November 2024
Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
Metabolism-disrupting agents (MDAs) are chemical, infectious or physical agents that increase the risk of metabolic disorders. Examples include pharmaceuticals, such as antidepressants, and environmental agents, such as bisphenol A. Various types of studies can provide evidence to identify MDAs, yet a systematic method is needed to integrate these data to help to identify such hazards.
View Article and Find Full Text PDFSparse Bayesian correntropy learning for robust muscle activity reconstruction from noisy brain recordings.
Neural Netw
February 2025
Center for Advanced Intelligence Project, RIKEN, Tokyo, 103-0027, Japan; Department of Computational Brain Imaging, Advanced Telecommunication Research Institute International, Kyoto, 619-0237, Japan.
Sparse Bayesian learning has promoted many effective frameworks of brain activity decoding for the brain-computer interface, including the direct reconstruction of muscle activity using brain recordings. However, existing sparse Bayesian learning algorithms mainly use Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to large performance degradation for sparse Bayesian learning algorithms.
View Article and Find Full Text PDFCompetencies related to generalism for Japanese medical undergraduates: Essential skills for comprehensive care.
Med Teach
September 2024
Center for General Medicine Education, School of Medicine, Keio University, Shinjuku-ku, Tokyo, Japan.
Objective: To identify generalism-related competencies that medical students in Japan should acquire in order to provide comprehensive care for patients.
Methods: The team responsible for developing the new 'Generalism' section of the 2022 revision of the Model Core Curriculum for Medical Education in Japan (MCC) consisted of nine members from diverse medical backgrounds across Japan. We adopted pragmatism paradigm and analyzed to identify decision-making processes using a qualitative document analysis.
Comparing Commercial and Open-Source Large Language Models for Labeling Chest Radiograph Reports.
Radiology
October 2024
From the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 149 Thirteenth St, Charlestown, MA 02129 (F.J.D., T.R.B., M.C.C., A.E.K., C.P.B.); Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany (F.J.D., L.D., F.A.M., F.B., L.J.); Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Mass (L.J.); Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany (L.C.A.); Mass General Brigham Data Science Office, Boston, Mass (J.S., T.S., C.P.B.); Microsoft Health and Life Sciences (HLS), Redmond, Wash (J.M.); Klinikum rechts der Isar, Technical University of Munich, Munich, Germany (K.K.B.); Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany (K.K.B.); and Department of Cardiovascular Radiology and Nuclear Medicine, Technical University of Munich, School of Medicine and Health, German Heart Center, TUM University Hospital, Munich, Germany (K.K.B.).
Article Synopsis
- Advances in large language models (LLMs) have led to numerous commercial and open-source models, but there has been no real-world comparison of OpenAI's GPT-4 against these models for extracting information from radiology reports.
- The study aimed to compare GPT-4 with several leading open-source LLMs in extracting relevant findings from chest radiograph reports using datasets from the ImaGenome and Massachusetts General Hospital.
- Results showed that GPT-4 slightly outperformed the best open-source model, Llama 2-70B, in terms of accuracy scores, with both showing strong performance in extracting findings from the reports.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!