PDX bank from urothelial cancer to guide precision medicine.
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ABSTRACT:
PROVIDER: PRJNA754098 | ENA |
REPOSITORIES: ENA
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Project description:We developed preclinical PDX models, recapitulating the molecular heterogeneity of MIBCs and UTUC, which will represent an essential tool in therapy development. Pharmacological characterization of the PDXs suggested that upper urinary tract and bladder cancers (UCC/ SCC) with similar molecular characteristics could benefit from the same treatments, and showed a benefit for combined FGFR/EGFR inhibition in FGFR3-mutant PDXs, compared to FGFR inhibition alone.
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Project description:Treatment paradigms for patients with upper tract urothelial carcinoma (UTUC) are typically extrapolated from studies of bladder cancer despite their distinct clinical and molecular characteristics. A major hurdle to the advancement of UTUC research is the lack of disease-specific models. Here, we report the establishment of patient derived xenograft (PDX) and cell lines models that reflect the heterogeneity of the human disease. Models demonstrated high genomic concordance with the tumors from which they were derived with muscle-invasive tumors more likely to successfully engraft. Treatment of PDX with chemotherapy recapitulated responses observed in the patients. Analysis of a S310F HER2 mutant PDX suggested that an antibody drug conjugate targeting HER2 would have superior efficacy to HER2-selective kinase inhibitors. In sum, the biologic and phenotypic concordance between patient and PDXs suggests that these models could facilitate studies of intrinsic and acquired resistance and the development of personalized medicine strategies for UTUC patients.
2020-01-29 | GSE134292 | GEO
Project description:Developing animal models representating the cancer biology of advanced prostate cancer patients is challenging but essential for delivering individualized medical therapies. In an effort to develop patient derived xenograft (PDX) models, we took the metastatic site tissue from the rib lesion twice (ie, before and after enzalutamide treatment) over a twelve week period and implanted subcutaneously and under the renal capsule in immuno-deficient mice. To characterize and compare the genome and transcriptome landscapes of patient tumor tissues and the corresponding PDX models, we performed whole exome and transcriptome sequencing for metastatic tumor tissue as well as its derived PDXs. We demonstrated the feasibility of developping PDX models from patient who developed castrate-resistant prostate cancer. Our data suggested PDX models preserve the patient’s genomic and transcriptomic alterations in high fidelity, as illustrated by somatic mutation, copy number variation, gene fusion and gene expression. RNA sequencing of prostate cancer tumor tissue and derived xenograft using Illumina HiSeq 2000.
2015-06-30 | E-GEOD-70380 | biostudies-arrayexpress
Project description:Developing animal models representating the cancer biology of advanced prostate cancer patients is challenging but essential for delivering individualized medical therapies. In an effort to develop patient derived xenograft (PDX) models, we took the metastatic site tissue from the rib lesion twice (ie, before and after enzalutamide treatment) over a twelve week period and implanted subcutaneously and under the renal capsule in immuno-deficient mice. To characterize and compare the genome and transcriptome landscapes of patient tumor tissues and the corresponding PDX models, we performed whole exome and transcriptome sequencing for metastatic tumor tissue as well as its derived PDXs. We demonstrated the feasibility of developping PDX models from patient who developed castrate-resistant prostate cancer. Our data suggested PDX models preserve the patient’s genomic and transcriptomic alterations in high fidelity, as illustrated by somatic mutation, copy number variation, gene fusion and gene expression.
2015-06-30 | GSE70380 | GEO
Project description:To probe the tissue source (cancer cell VS stromal cell) of gene expression in the mixed tumor samples, we took advantage of a set of Urothelial Cancer patient-derived xenograft (PDX) models given that the transcriptome in these models is a mixture of human RNA (derived from cancer cells) and mouse RNA (derived from stromal cells).
2018-08-01 | GSE116159 | GEO
Project description:The treatment of urothelial carcinoma (UC) is challenging given its molecular heterogeneity and variable response to current therapies. To address this, many tools, including tumor biomarker assessment and liquid biopsies, have been developed to predict prognosis and treatment response. Approved therapeutic modalities for UC currently include chemotherapy, immune checkpoint inhibitors, receptor tyrosine kinase inhibitors, and antibody drug conjugates. Ongoing investigations to improve the treatment of UC include the search for actionable alterations and the testing of novel therapies. An important objective in recent studies has been to increase efficacy while decreasing toxicity by taking into account unique patient and tumor-related factors-an endeavor called precision medicine. The aim of this review is to highlight advancements in the treatment of UC, describe ongoing clinical trials, and identify areas for future study in the context of precision medicine.
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Project description:Patient-Derived Xenografts of Upper Tract Urothelial Cell Carcinoma: a tool for precision medicine
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Project description:Large-scale screening of drug sensitivity on cancer cell models can mimic in vivo cellular behavior providing wider scope for biological research on cancer. Since the therapeutic effect of a single drug or drug combination depends on the individual patient's genome characteristics and cancer cells integration reaction, the identification of an effective agent in an in vitro model by using large number of cancer cell models is a promising approach for the development of targeted treatments. Precision cancer medicine is to select the most appropriate treatment or treatments for an individual patient. However, it still lacks the tools to bridge the gap between conventional in vitro cancer cell models and clinical patient response to inhibitors. An optimal two-layer decision system model is developed to identify the cancer cells that most closely resemble an individual tumor for optimum therapeutic interventions in precision cancer medicine. Accordingly, an optimal grid parameters selection is designed to seek the highest accordance for treatment selection to the patient's preference for drug response and in vitro cancer cell drug screening. The optimal two-layer decision system model overcomes the challenge of heterology data comparison between the tumor and the cancer cells, as well as between the continual variation of drug responses in vitro and the discrete ones in clinical practice. We simulated the model accuracy using 681 cancer cells' mRNA and associated 481 drug screenings and validated our results on 315 breast cancer patients drug selection across seven drugs (docetaxel, doxorubicin, fluorouracil, paclitaxel, tamoxifen, cyclophosphamide, lapitinib). Comparing with the real response of a drug in clinical patients, the novel model obtained an overall average accordance over 90.8% across the seven drugs. At the same time, the optimal cancer cells and the associated optimal therapeutic efficacy of cancer drugs are recommended. The novel optimal two-layer decision system model was used on 1097 patients with breast cancer in guiding precision medicine for a recommendation of their optimal cancer cells (30 cancer cells) and associated efficacy of certain cancer drugs. Our model can detect the most similar cancer cells for each individual patient. A successful clinical translation model (optimal two-layer decision system model) was developed to bridge in-vitro basic science to clinical practice in a therapeutic intervention application for the first time. The novel tool kills two birds with one stone. It can help basic science to seek optimal cancer cell models for an individual tumor, while prioritizing clinical drugs' recommendations in practice. Tool associated platform website: We extended the breast cancer research to 32 more types of cancers across 45 therapy predictions. The website is set up and can be accessed by the link: https://pcm2019.shinyapps.io/drug_response_prediction/.
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Project description:Analysis of primary PDAC cells established from Pdx-1CreAPCL/+p53L/L and Pdx-1Crep53L/L mice. APC haploinsufficiency combined with P53 loss in the pancreas drives MCN progression in mice. Results provide insight into molecular mechanisms invloved in the MCN formation of Pdx-1Cre APCL/+P53L/L mice. Pdx-1CreAPC+/LP53L/L PDAC cell lines and 2 Pdx-1CreP53L/L ductal cell lines were analyzed.
2014-10-01 | E-GEOD-61894 | biostudies-arrayexpress