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Background: Malignant phyllodes tumour (MPT) is a rare breast malignancy with epithelial and mesenchymal features. Currently, there are no appropriate research models or effective targeted therapeutic approaches for MPT.
Methods: We collected fresh frozen tissues from nine patients with MPT and performed whole-exome and RNA sequencing. Additionally, we established patient-derived xenograft (PDX) models from patients with MPT and tested the efficacy of targeting dysregulated pathways in MPT using the PDX model from one MPT.
Results: MPT has unique molecular characteristics when compared to breast cancers of epithelial origin and can be classified into two groups. The PDX model derived from one patient with MPT showed that the mouse epithelial component increased during tumour growth. Moreover, targeted inhibition of platelet-derived growth factor receptor (PDGFR) and phosphoinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) by imatinib mesylate and PKI-587 showed in vivo tumour suppression effects.
Conclusions: This study revealed the molecular profiles of MPT that can lead to molecular classification and potential targeted therapy, and suggested that the MPT PDX model can be a useful tool for studying the pathogenesis of fibroepithelial neoplasms and for preclinical drug screening to find new therapeutic strategies for MPT.
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http://dx.doi.org/10.1038/s41416-022-02064-2 | 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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