| Autor: |
Ho TH; Division of Hematology and Oncology, Mayo Clinic, Scottsdale, Arizona, United States of America., Nateras RN; Department of Urology, Mayo Clinic Hospital, Phoenix, Arizona, United States of America., Yan H; Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, United States of America., Park JG; Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America., Jensen S; Department of Chemistry & Biochemistry, The Biodesign Institute-Center for Personalized Diagnostics, Arizona State University, Tempe, Arizona, United States of America., Borges C; Department of Chemistry & Biochemistry, The Biodesign Institute-Center for Personalized Diagnostics, Arizona State University, Tempe, Arizona, United States of America., Lee JH; Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, United States of America., Champion MD; Division of Biomedical Statistics and Informatics, Mayo Clinic, Scottsdale, Arizona, United States of America., Tibes R; Division of Hematology and Oncology, Mayo Clinic, Scottsdale, Arizona, United States of America., Bryce AH; Division of Hematology and Oncology, Mayo Clinic, Scottsdale, Arizona, United States of America., Carballido EM; Division of Hematology and Oncology, Mayo Clinic, Scottsdale, Arizona, United States of America., Todd MA; Division of Anatomic Pathology, Mayo Clinic, Scottsdale, Arizona, United States of America., Joseph RW; Division of Hematology and Oncology, Mayo Clinic, Jacksonville, Florida, United States of America., Wong WW; Department of Radiation Oncology, Mayo Clinic, Scottsdale, Arizona, United States of America., Parker AS; Departments of Health Sciences Research, Mayo Clinic, Jacksonville, Florida, United States of America., Stanton ML; Department of Laboratory Medicine/Pathology, Mayo Clinic, Scottsdale, Arizona, United States of America., Castle EP; Department of Urology, Mayo Clinic Hospital, Phoenix, Arizona, United States of America. |
| Abstrakt: |
To address the need to study frozen clinical specimens using next-generation RNA, DNA, chromatin immunoprecipitation (ChIP) sequencing and protein analyses, we developed a biobank work flow to prospectively collect biospecimens from patients with renal cell carcinoma (RCC). We describe our standard operating procedures and work flow to annotate pathologic results and clinical outcomes. We report quality control outcomes and nucleic acid yields of our RCC submissions (N=16) to The Cancer Genome Atlas (TCGA) project, as well as newer discovery platforms, by describing mass spectrometry analysis of albumin oxidation in plasma and 6 ChIP sequencing libraries generated from nephrectomy specimens after histone H3 lysine 36 trimethylation (H3K36me3) immunoprecipitation. From June 1, 2010, through January 1, 2013, we enrolled 328 patients with RCC. Our mean (SD) TCGA RNA integrity numbers (RINs) were 8.1 (0.8) for papillary RCC, with a 12.5% overall rate of sample disqualification for RIN <7. Banked plasma had significantly less albumin oxidation (by mass spectrometry analysis) than plasma kept at 25 °C (P<.001). For ChIP sequencing, the FastQC score for average read quality was at least 30 for 91% to 95% of paired-end reads. In parallel, we analyzed frozen tissue by RNA sequencing; after genome alignment, only 0.2% to 0.4% of total reads failed the default quality check steps of Bowtie2, which was comparable to the disqualification ratio (0.1%) of the 786-O RCC cell line that was prepared under optimal RNA isolation conditions. The overall correlation coefficients for gene expression between Mayo Clinic vs TCGA tissues ranged from 0.75 to 0.82. These data support the generation of high-quality nucleic acids for genomic analyses from banked RCC. Importantly, the protocol does not interfere with routine clinical care. Collections over defined time points during disease treatment further enhance collaborative efforts to integrate genomic information with outcomes. |
