Similar Datasets
Project description:DNA methylation alterations are a universal feature of cancer. In prostate cancer, site specific DNA methylation changes have been suggested as driver in disease initial and progression. Here we provide a comprehensive assessment of DNA methylation changes in prostate cancer patient derived xenograft (PDX) models. We delineate patterns of both site specific and global methylation changes and nominate novel candidates for biomarker development. Genome wide DNA methylation profiling of prostate cancer patient derived xenograft and cell line models using Infinium EPIC arrays
2023-12-31 | GSE227695 | GEO
Project description:DNA methylation alterations are a universal feature of cancer. In prostate cancer, site specific DNA methylation changes have been suggested as driver in disease initial and progression. Here we provide a comprehensive assessment of DNA methylation changes in prostate cancer patient derived xenograft (PDX) models. We delineate patterns of both site specific and global methylation changes and nominate novel candidates for biomarker development. Genome wide DNA methylation profiling of prostate cancer patient derived xenograft and cell line models using Infinium EPIC arrays
2023-12-31 | GSE227814 | GEO
Project description:Genome scale analysis of DNA methylation patterns in prostate cancer patient derived xenograft models
| PRJNA947185 | ENA
Project description:DNA methylation alterations are a universal feature of cancer. In prostate cancer, site specific DNA methylation changes have been suggested as driver in disease initial and progression. Here we provide a comprehensive assessment of DNA methylation changes in prostate cancer patient derived xenograft (PDX) models. We delineate patterns of both site specific and global methylation changes and nominate novel candidates for biomarker development.
2023-12-31 | GSE227086 | GEO
Project description:Genome scale analysis of DNA methylation patterns in prostate cancer patient derived xenograft models [EPIC Set 2]
| PRJNA946668 | ENA
Project description:Genome scale analysis of DNA methylation patterns in prostate cancer patient derived xenograft models [EPIC Set 1]
| PRJNA947129 | ENA
Project description:Effective systemic treatment of cancer relies on the delivery of agents with optimal therapeutic potential. The molecular age of medicine has provided genomic tools that can identify a large number of potential therapeutic targets in individual patients, heralding the promise of personalized treatment. However, determining which potential targets actually drive tumor growth and should be prioritized for therapy is challenging. Indeed, reliable molecular matches of target and therapeutic agent have been stringently validated in the clinic for only a small number of targets. Patient-derived xenografts (PDXs) are tumor models developed in immunocompromised mice using tumor procured directly from the patient. As patient surrogates, PDX models represent a powerful tool for addressing individualized therapy. Challenges include humanizing the immune system of PDX models and ensuring high quality molecular annotation, in order to maximize insights for the clinic. Importantly, PDX can be sampled repeatedly and in parallel, to reveal clonal evolution, which may predict mechanisms of drug resistance and inform therapeutic strategy design.
| S-EPMC4433556 | biostudies-literature
Project description:Metastasis is the leading cause of cancer-related deaths worldwide. The epithelial-mesenchymal plasticity (EMP) status of primary tumours has relevance to metastatic potential and therapy resistance. Circulating tumour cells (CTCs) provide a window into the metastatic process, and molecular characterisation of CTCs in comparison to their primary tumours could lead to a better understanding of the mechanisms involved in the metastatic cascade. In this study, paired blood and tumour samples were collected from four prostate cancer patient-derived xenograft (PDX) models (BM18, LuCaP70, LuCaP96, LuCaP105) and assessed using an EMP-focused, 42 gene human-specific, nested quantitative RT-PCR assay. CTC burden varied amongst the various xenograft models with LuCaP96 having the highest number of CTCs per mouse (mean: 704; median: 31) followed by BM18 (mean: 101; median: 21), LuCaP70 (mean: 73; median: 16) and LuCaP105 (mean: 57; median: 6). A significant relationship was observed between tumour size and CTC number (p = 0.0058). Decreased levels of kallikrein-related peptidase 3 (KLK3) mRNA (which encodes prostate-specific antigen; PSA) were observed in CTC samples from all four models compared to their primary tumours. Both epithelial- and mesenchymal-associated genes were commonly expressed at higher levels in CTCs compared to the bulk primary tumour, although some common EMT-associated genes (CDH1, VIM, EGFR, EPCAM) remained unchanged. Immunofluorescence co-staining for pan-cytokeratin (KRT) and vimentin (VIM) indicated variable proportions of CTCs across the full EMP axis, even in the same model. EMP hybrids predominated in the BM18 and LuCaP96 models, but were not detected in the LuCaP105 model, and variable numbers of KRT+ and human VIM+ cells were observed in each model. SERPINE1, which encodes plasminogen activator inhibitor-1 (PAI-1), was enriched at the RNA level in CTCs compared to primary tumours and was the most commonly expressed mesenchymal gene in the CTCs. Co-staining for SERPINE1 and KRT revealed SERPINE1+ cells in 7/11 samples, six of which had SERPINE+KRT+ CTCs. Cell size variation was observed in CTCs. The majority of samples (8/11) contained larger CTCs ranging from 15.3 to 37.8 µm, whilst smaller cells (10.7 ± 4.1 µm, similar in size to peripheral blood mononuclear cells (PBMCs)) were identified in 6 of 11 samples. CTC clusters were also identified in 9/11 samples, containing 2-100 CTCs per cluster. Where CTC heterogeneity was observed in the clusters, epithelial-like cells (KRT+VIM-) were located on the periphery of the cluster, forming a layer around hybrid (KRT+VIM+) or mesenchymal-like (KRT-VIM+) cells. The CTC heterogeneity observed in these models emphasises the complexity in CTC isolation and classification and supports the increasingly recognised importance of the epithelial-mesenchymal hybrid state in cancer progression and metastasis.
| S-EPMC8198708 | biostudies-literature
Project description:Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62-66. ©2017 AACR.
| S-EPMC5738926 | biostudies-literature
Project description:Glioblastoma (GBM) is increasingly recognized as a disease involving dysfunctional cellular metabolism. GBMs are known to be complex heterogeneous systems containing multiple distinct cell populations and are supported by an aberrant network of blood vessels. A better understanding of GBM metabolism, its variation with respect to the tumor microenvironment, and resulting regional changes in chemical composition is required. This may shed light on the observed heterogeneous drug distribution, which cannot be fully described by limited or uneven disruption of the blood-brain barrier. In this work, we used mass spectrometry imaging (MSI) to map metabolites and lipids in patient-derived xenograft models of GBM. A data analysis workflow revealed that distinctive spectral signatures were detected from different regions of the intracranial tumor model. A series of long-chain acylcarnitines were identified and detected with increased intensity at the tumor edge. A 3D MSI dataset demonstrated that these molecules were observed throughout the entire tumor/normal interface and were not confined to a single plane. mRNA sequencing demonstrated that hallmark genes related to fatty acid metabolism were highly expressed in samples with higher acylcarnitine content. These data suggest that cells in the core and the edge of the tumor undergo different fatty acid metabolism, resulting in different chemical environments within the tumor. This may influence drug distribution through changes in tissue drug affinity or transport and constitute an important consideration for therapeutic strategies in the treatment of GBM. SIGNIFICANCE: GBM tumors exhibit a metabolic gradient that should be taken into consideration when designing therapeutic strategies for treatment.See related commentary by Tan and Weljie, p. 1231.
| S-EPMC7073296 | biostudies-literature