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Breast cancer is a heterogeneous disease with numerous histological subtypes. Invasive lobular cancer (ILC) is the most common special subtype, accounting for 10-15% of all breast cancers. The pathognomonic feature of ILC is the loss of E-cadherin (CDH1), which leads to a unique single-file growth pattern of discohesive cells. Although ILCs show better prognostic factors than the most common No Special Type (NST) of breast cancer, patients with ILC have worse long-term outcomes, which is not well understood. In this study, we aimed to identify and characterize Patient-Derived Xenograft (PDX) models of ILC based upon the presence of truncating mutations and/or low mRNA expression among 128 human breast cancer PDX models. We selected 8 PDX models for validation using Immunohistochemical (IHC) analysis for E-Cadherin, p120, ER, PR, and HER2. We confirmed that seven of these PDX models are indeed ILC while one was identified as mixed NST-ILC PDX. Molecular analysis of the confirmed ILC PDX models showed enrichment of truncating mutations, significantly lower levels of mRNA expression and predominantly luminal subtypes compared to NST PDX models, in line with the molecular characteristics of human ILC disease. The commonly altered genes in the ILC PDX models included (57%), (57%) and (57%) among others. Our study confirms and characterizes new ILC PDX models, offering valuable tools to advance our understanding of human ILC biology and support the development of innovative treatment strategies.
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http://dx.doi.org/10.1101/2025.08.21.669685 | 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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