Patient derived tumor xenografts (PDXs) are important models for pre-clinical testing in cancer research and personalized medicine. PDXs often represent patient tumors with high similarity in terms of histology and driver mutations. However, certain limitations exist that warrant a detailed understanding of PDX heterogeneity and evolution. Hynds et al. demonstrate the relevance of primary tumor heterogeneity in PDX model establishment and explore multi-region sampling to determine the extent to which PDXs represent primary tumors.
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http://dx.doi.org/10.1038/s41467-024-47607-8 | DOI Listing |
Adv Exp Med Biol
January 2025
Laboratory of Preclinical Investigation, Translational Research Department, Institut Curie, Paris, France.
Patient-derived xenografts (PDX) of breast cancer, obtained from the engraftment of tumour samples into immunodeficient mice, are the most effective preclinical models for studying the biology of human breast cancer and for the evaluation of new anti-cancer treatments. Notably, breast cancer PDX preserve the phenotypic and molecular characteristics of the donor tumours and reproduce the diversity of breast cancer. This preservation of breast cancer biology involves a number of different aspects, including tumour architecture and morphology, patterns of genomic alterations and gene expression, mutational status, and intra-tumour heterogeneity.
View Article and Find Full Text PDFJ Nanobiotechnology
January 2025
Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning, China.
Gynecologic cancers (GCs), including cervical cancer (CC), ovarian cancer (OC), endometrial cancer (EC), as well as vulvar and vaginal cancers, represent major health threats to women, with increasing incidence rates observed globally. Conventional treatments, such as surgery, radiation therapy, and chemotherapy, are often hindered by challenges such as drug resistance and recurrence, contributing to high mortality rates. Organoid technology has emerged as a transformative tool in cancer research, offering in vitro models that closely replicate the tumor cell architecture and heterogeneity of primary cancers.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Division of Hematology/Oncology, Department of Internal Medicine, University of California Davis School of Medicine, University of California Davis Comprehensive Cancer Center, Sacramento, CA 95817, USA.
Patient-centered precision oncology strives to deliver individualized cancer care. In lung cancer, preclinical models and technological innovations have become critical in advancing this approach. Preclinical models enable deeper insights into tumor biology and enhance the selection of appropriate systemic therapies across chemotherapy, targeted therapies, immunotherapies, antibody-drug conjugates, and emerging investigational treatments.
View Article and Find Full Text PDFJCO Oncol Adv
December 2024
Department of Surgery, Oregon Health & Science University, Portland, OR.
Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths with a 5-year survival rate of 13%. Surgical resection remains the only curative option as systemic therapies offer limited benefit. Poor response to chemotherapy and immunotherapy is due, in part, to the dense stroma and heterogeneous tumor microenvironment (TME).
View Article and Find Full Text PDFbioRxiv
December 2024
Department of Biomedical Engineering and Computational Biology Program, OHSU, Portland, OR, USA.
Multiplexed tissue imaging (MTI) technologies enable high-dimensional spatial analysis of tumor microenvironments but face challenges with technical variability in staining intensities. Existing normalization methods, including z-score, ComBat, and MxNorm, often fail to account for the heterogeneous, right-skewed expression patterns of MTI data, compromising signal alignment and downstream analyses. We present UniFORM, a non-parametric, Python-based pipeline for normalizing both feature- and pixel-level MTI data.
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