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Background/aim: Fibrolamellar hepatocellular carcinoma (FLHCC) is a rare tumor presenting in younger patients without chronic liver disease. Up to 80-100% develop recurrent disease, necessitating additional surgery or systemic treatment. Systemic options and pre-clinical treatment studies are lacking. We previously described patient-derived xenograft (PDX) development, allowing for pre-clinical studies. Herein, we develop FLHCC PDX models and utilize these to define tumor characteristics and determine the efficacy of systemic agents.
Materials And Methods: Primary and lymph node metastatic tumor tissues were obtained at the time of FLHCC resection in two patients. Tumor lysates were screened for protein upregulation. Cell lines were generated from metastatic and primary tumor tissue. The viability of the cell lines was assessed after treatment with temsirolimus, gemcitabine/oxaliplatin, and FOLFIRINOX. Two PDX models were developed from metastatic tissue. For in vivo studies, tumor-bearing mice were treated with temsirolimus, FOLFIRINOX, and Gemcitabine/oxaliplatin.
Results: PDX models were successfully generated from metastatic FLHCC, which closely recapitulated the original tumor. Upregulation of mTOR was seen in metastatic tissue compared to primary tumors. Cell lines from metastatic tissue demonstrated significant sensitivity to temsirolimus. In vivo testing of PDX models demonstrated a significant response to single-agent temsirolimus with minimal toxicity.
Conclusion: Herein, we demonstrate the feasibility of developing PDX models that closely recapitulate FLHCC. Upregulation of mTOR was seen in metastatic tissue compared to primary tissue. The efficacy of mTOR inhibition with temsirolimus treatment suggests that the upregulation of the mTOR pathway may be a significant mechanism for growth in metastatic lesions and a potential target for therapeutics.
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http://dx.doi.org/10.21873/invivo.13290 | 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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