Project description:<p>Relapse and treatment resistance are common in children with high-risk neuroblastoma, and novel therapies are needed. Conventional drug discovery is slow, expensive, often fails in practice, and consequently falls short in addressing pediatric and rare conditions. In such instances, drug repurposing is a promising strategy. Here, we used two independent in silico prediction tools including machine learning to identify approved drugs for repurposing against neuroblastoma. The combination of statins and phenothiazines showed strong synergistic effects in human neuroblastoma organoids, decreased tumor growth and prolonged survival in MYCN-amplified neuroblastoma patient-derived xenografts. The drug combination altered cholesterol metabolism through two different mechanisms and induced a phenotypic change towards an adrenergic state in vitro, which was associated with enhanced chemosensitivity. Integration of the drug combination into standard-of-care chemotherapy regressed tumors and prolonged survival in chemoresistant patient-derived xenografts. Thus, a combination of safe and approved medications added to standard-of-care chemotherapy outperforms chemotherapy alone in chemoresistant neuroblastoma.</p>
Project description:The present study describes a novel xenograft-based biomarker discovery platform and proves its usefulness in the discovery of novel serum markers for prostate cancer (PCa). By immunizing immuno-competent mice with serum from nude mice bearing PCa xenografts, an antibody response against xenograft-derived antigens was elicited. By probing protein microarrays with serum from immunized mice, several PCa-derived antigens were identified, of which a subset was successfully retrieved in serum from mice bearing PCa xenografts and validated in human serum samples of PCa patients. In conclusion, this novel method allows for the identification of low abundant cancer-derived serum proteins, circumventing dynamic range and host-response issues in standard patient cohort proteomics comparisons.
2011-12-16 | GSE16220 | GEO
Project description:Evaluating Radiotherapy Effects Using Prostate Cancer Patient-Derived Xenografts
Project description:A set of 17 prostate cancer patient-derived xenografts (PDX, Lin et al 2014, Cancer research) was analyzed by mass spectrometry-based proteomics to characterize the effects of castration in vivo, and the proteome differences between NEPC and prostate adenocarcinomas.
Project description:We profiled the epigenomes of neuroendocrine prostate cancer and prostate adenocarcinoma patient-derived xenografts using ChIP-seq for transcription factors and histone modifications.
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.
