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Background: Constitutional mismatch repair deficiency (CMMRD) is a rare disorder resulting from biallelic germline pathogenic variants in mismatch repair genes. This study described the molecular profile of two metachronous brain tumors and a patient-derived xenograft (PDX) from a Brazilian child with CMMRD.
Methods: After PDX development, methylation array, whole exome sequencing, and NanoString techniques were applied to describe the genetic landscape of CMMRD.
Results: A 6½-year-old girl was diagnosed with Sonic Hedgehog (SHH)-activated medulloblastoma and somatic TP53-mutant. After surgery and radiochemotherapy, she remained free of disease progression. At 10 years and 3 months, she developed a diffuse pediatric-type high-grade glioma (dpHGG). The child had a family history of cancer, and subsequent investigation revealed a biallelic germline variant on MSH6 (c.3556+1G>A) with the absence of protein expression in both normal and tumor tissue. A PDX model of the dpHGG was developed. The methylation profile confirmed the diagnosis of both brain tumors and PDX, refining the classification of dpHGG, Rtk1 subtype, subclass A, with an actionable alteration on Platelet-derived growth factor receptor A (PDGFRA). Exome analysis showed high tumor mutational burden, with 3019, 540, and 1049 pathogenic variants in the medulloblastoma, dpHGG, and PDX, respectively. Only the medulloblastoma exhibited microsatellite instability. The CD24, CD47, and CD276 immune checkpoints had elevated messenger RNA levels, yet no programmed death ligand 1 expression was observed in CMMRD-derived tumors.
Conclusion: We report an extensive molecular profile of a CMMRD patient, and the developed PDX model can be applied to explore new therapeutic approaches for CMMRD-associated brain tumors.
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http://dx.doi.org/10.1002/ame2.70069 | DOI Listing |
J Neurooncol
September 2025
Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Purpose: NOTCH3 is increasingly implicated for its oncogenic role in many malignancies, including meningiomas. While prior work has linked NOTCH3 expression to higher-grade meningiomas and treatment resistance, the metabolic phenotype of NOTCH3 activation remains unexplored in meningioma.
Methods: We performed single-cell RNA sequencing on NOTCH3 + human meningioma cell lines.
J Proteome Res
September 2025
State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China.
Hepatocellular carcinoma (HCC) constitutes approximately 90% of liver cancers, yet its early detection remains challenging due to the low sensitivity of current diagnostic methods and the difficulty in identifying minimal cancer cells within the body. This study employed a patient-derived xenograft (PDX) mouse model to screen for biomarkers, leveraging its advantage of low background interference compared to human serum exosome studies. Using a novel microextraction technique, exosomes were isolated from just one microliter of serum from HCC PDX mice, followed by proteomic profiling.
View Article and Find Full Text PDFAdv Sci (Weinh)
September 2025
State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China.
Perineural invasion (PNI) is a common pathological characteristic of pancreatic ductal adenocarcinoma (PDAC), closely linked to postoperative recurrence, metastasis, and unfavorable prognosis. Nevertheless, the precise mechanisms that govern PNI in PDAC remain poorly elucidated. Here, group-specific component protein (GC) is identified as one of the most significantly upregulated genes related to PNI, primarily derived from malignant ductal cells compared to other cell types.
View Article and Find Full Text PDFNAR Genom Bioinform
September 2025
School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
The translatability of patient-derived xenograft (PDX)-generated clinical data into patient-specific outcomes for therapeutic guidance is limited by the challenges in generalizability of models across patients, treatments, and cancer types. Previously, machine learning (ML) models have been developed for the two most abundant cancer types, i.e.
View Article and Find Full Text PDFAn integrated approach is proposed to rapidly evaluate the effects of anticancer treatments in 3D models, combining a droplet-based microfluidic platform for spheroid formation and single-spheroid chemotherapy application, label-free morphological analysis, and machine learning to assess treatment response. Morphological features of spheroids, such as size and color intensity, are extracted and selected using the multivariate information-based inductive causation algorithm, and used to train a neural network for spheroid classification into viability classes, derived from metabolic assays performed within the same platform as a benchmark. The model is tested on Ewing sarcoma cell lines and patient-derived xenograft (PDX) cells, demonstrating robust performance across datasets.
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