Unknown,Transcriptomics,Genomics,Proteomics

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Patient Derived Xenograft for Delivery of Precision Medicine in Castrate Resistant Prostate Cancer

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

SUBMITTER: Hugues Sicotte

PROVIDER: E-GEOD-70380 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Achieved biospecimens annotated with patient clinical characteristics are unique resources for translational research. However, RNA extracted from the achieved tissues is often degraded. RNA degradation can have a significant impact on the measure of transcript abundance that can lead to an increase rate of erroneous differentially expressed genes. Here, we are presenting the transcript integrity number (TIN) algorithm to measure the RNA degradation at transcript level. When applied to RNA-seq datasets generated from human brain Glioblastome cell line, human peripheral blood mononuclear cells, and metastatic castration resistant prostate cancer (mCRPC) clinical tissues, TIN provided a more reliable and more sensitive measure of RNA degradation than RIN, as demonstrated by much higher concordance with the RNA fragment size estimated from read pairs. More importantly, when comparing 10 mCRPC samples with lower RNA quality to another 10 samples with higher RNA quality, we demonstrated that calibrating gene quantification with TIN scores could mitigate RNA degradation effects and greatly improve gene expression analysis. The detected differentially expressed genes before TIN correction were predominantly ribosomal genes. However, when we adjusted gene quantifications with the corresponding TIN scores, we found differentially expressed genes were highly enriched in prostate cancer specific pathways. When further evaluating the performance of TIN correction using synthetic spike-in transcripts with predetermined abundance in RNA-seq data generated from Sequencing Control Consortium (SEQC), we found TIN adjustment had a better control of false positives and false negatives (sensitivity = 0.89, specificity = 0.91), as compared to gene expression analysis results without TIN correction (sensitivity =0.98, specificity = 0.50). RNA sequencing of 20 bone-metastatic castration resistant prostate cancer (mCRPC) using Illumina HiSeq 2500. Out of 20 mCRPC samples, 10 samples have relative low RNA integrity and another 10 samples have relative higher RNA integrity as measured by Agilent RIN score.

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Patient Derived Xenograft for Delivery of Precision Medicine in Castrate Resistant Prostate Cancer

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