Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer
Ontology highlight
ABSTRACT: To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. Information on the complete TCGA Ovarian Serous Cystadenocarcinoma (OV) cohort can be found here. Genomic data for the 174 TCGA samples used in the CPTAC Proteome study can be downloaded from here. Peptide-Spectrum-Matches and Protein Reports from the CPTAC Common Data Analysis Pipeline (CDAP) can be downloaded from here.
The Cancer Genome Atlas Research Network published on the Genomic characterization of the OV cohort in Nature on June 30, 2011
INSTRUMENT(S): LTQ Orbitrap Velos, Q Exactive
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Richard Smith Hui Zhang
PROVIDER: MSV000084246 | MassIVE | Wed Aug 28 15:07:00 BST 2019
SECONDARY ACCESSION(S): PXD015908
REPOSITORIES: MassIVE
Publications
Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.
Zhang Hui H Liu Tao T Zhang Zhen Z Payne Samuel H SH Zhang Bai B McDermott Jason E JE Zhou Jian-Ying JY Petyuk Vladislav A VA Chen Li L Ray Debjit D Sun Shisheng S Yang Feng F Chen Lijun L Wang Jing J Shah Punit P Cha Seong Won SW Aiyetan Paul P Woo Sunghee S Tian Yuan Y Gritsenko Marina A MA Clauss Therese R TR Choi Caitlin C Monroe Matthew E ME Thomas Stefani S Nie Song S Wu Chaochao C Moore Ronald J RJ Yu Kun-Hsing KH Tabb David L DL Fenyö David D Bafna Vineet V Wang Yue Y Rodriguez Henry H Boja Emily S ES Hiltke Tara T Rivers Robert C RC Sokoll Lori L Zhu Heng H Shih Ie-Ming IM Cope Leslie L Pandey Akhilesh A Zhang Bing B Snyder Michael P MP Levine Douglas A DA Smith Richard D RD Chan Daniel W DW Rodland Karin D KD
Cell 20160629 3
To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the prote ...[more]
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2018-10-18 | MSV000083043 | MassIVE
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2018-08-31 | MSV000082875 | MassIVE
Project description:Comparison and Reference (CompRef) Samples, initially characterized in the <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S010" target="_blank">System Suitability Study</a>, were analyzed along with the TCGA Ovarian Cancer tumor samples. Pacific Northwest National Laboratory conducted iTRAQ experiments that included proteome (5 Data sets) and phosphoproteome (4 Data sets) interrogation of both the P5 (basal) and P6 (luminal) human-in-mouse xenograft breast carcinoma pooled samples. Johns Hopkins University performed 6 iTRAQ proteome experiments. The CompRef experiments were intercalated between the TCGA Ovarian Cancer experiments to monitor the consistency of laboratory protocols and mass spectrometry instrument performance.<ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S021">https://cptac-data-portal.georgetown.edu/cptac/s/S021</a> on 08/26/19</li></ul>
2019-08-26 | MSV000084236 | MassIVE
Project description:<p><a href="https://cptac-data-portal.georgetown.edu/study-summary/S056" target="_blank">Proteogenomics of Lung Adenocarcinoma, Gillette et al., 2020 publication is here</a><br><br>Lung cancer is a leading cause of cancer death in the United States and in 2019 it is estimated that there will be over 228,000 new cases (<a href="https://www.cancer.org/cancer/non-small-cell-lung-cancer/about/key-statistics.html" target="_blank">American Cancer Society</a>). There are two main types, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). A majority of patients (85%) have NSCLC, with a significant portion of those individuals having a histological subtype lung adenocarcinoma (LUAD) <a href="https://www.ncbi.nlm.nih.gov/pubmed/29364287" target="_blank">Herbst et al., 2018 Nature</a>. To advance the proteogenomic understanding of LUAD, the CPTAC program has investigated 111 tumors, (with 102 tumors paired with normal adjacent tissue samples) and subjected these samples to global proteome and phosphoproteome analysis. An optimized workflow for mass spectrometry of tissues using isobaric tags (TMT (tandem mass tags)-10) was used (<a href="https://www.ncbi.nlm.nih.gov/pubmed/29988108" target="_blank">Mertins et al., Nature Protocols 2018</a>). Proteome and phosphoproteome data from the LUAD discovery cohort is available below along with peptide spectrum matches (PSMs) and protein summary reports from the CPTAC common data analysis pipeline (CDAP).</p><p><em>Clinical Data</em> for LUAD tumors are provided below. Updates will be available as the LUAD cohort characterization proceeds.<br><em>Genomic Data</em> for LUAD tumors is available from the NCI Genomic Data Commons (GDC), <a href="https://portal.gdc.cancer.gov/projects/CPTAC-3" target="_blank">here</a><br><em>Imaging Data</em> for LUAD tumors is available from NCI, The Cancer Imaging Archive (TCIA), <a href="https://wiki.cancerimagingarchive.net/display/Public/CPTAC-LUAD" target="_blank">here</a><br>This release includes over 4500 files and 914 GB of data.</p>
<ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/study-summary/S046">https://cptac-data-portal.georgetown.edu/study-summary/S046</a> on 01/29/21</li></ul>
2021-02-01 | MSV000086793 | MassIVE
Project description:<p>Francesca Petralia,Nicole Tignor,Boris Reva, et al., (2020) <a href="https://www.cell.com/cell/fulltext/S0092-8674(20)31451-3" target="_blank">Cell 183, 1962-1985</a></p><p>We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.</p><p>The Children's Brain Tumor Network (CBTN) and the Pacific Pediatric Neuro-Oncology Consortia (PNOC) are collaborative research consortia focused on identifying therapies for children with brain tumors (<a href="https://cbttc.org" target="_blank">https://cbttc.org</a>). The consortia have contributed a <a href="https://kidsfirstdrc.org/support/studies-and-access/#SD_BHJXBDQK" target="_blank">Pediatric Brain Tumor Atlas (PBTA) dataset</a>, a cohort of 991 brain tumor subject clinical data, with associated whole genome sequencing and RNAseq hosted by the Gabriella Miller Kids First Data Resource (<a href="https://kidsfirstdrc.org/" target="_blank">kidsfirstdrc.org</a>) as part of the Gabriella Miller Kids First Pediatric Research Program (Kids First). Kids First is a Pan NIH Common Fund program dedicated to the development of large-scale data resources to help researchers uncover new insights into the biology of childhood cancer and structural birth defects (<a href="https://commonfund.nih.gov/KidsFirst" target="_blank">https://commonfund.nih.gov/KidsFirst</a>).</p><p>Mass Spectrometry raw data generation along with preliminary analyses were performed at the <a href="https://tcmp.hms.harvard.edu/" target="_blank">Thermo Fisher Scientific Center for Multiplexed Proteomics (TCMP)</a>, Harvard Medical School (HMS) under the direction of <a href="https://cellbio.med.harvard.edu/people/faculty/gygi" target="_blank">Prof. Steven Gygi</a>. Samples were prepared using the streamline (SL)-TMT protocol (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5994137/" target="_blank">Navarrete-Perea et al., 2018</a>) and MS analysis was performed using the SPS-MS3 strategy (<a href="https://www.nature.com/articles/nmeth.1714" target="_blank">Ting et al., 2011</a>) developed in the <a href="https://gygi.med.harvard.edu/" target="_blank">Gygi lab</a>. Additional data analyses from the CPTAC Common Data Analysis Pipeline (<a href="https://www.ncbi.nlm.nih.gov/pubmed/26860878" target="_blank">Rudnick et al., 2016</a>) and from the University of Michigan Proteomics and Integrative Bioinformatics Laboratory (<a href="https://github.com/Nesvilab" target="_blank">https://github.com/Nesvilab</a>) are also provided. All raw and processed genomic data, as well as pathology reports, radiology reports, MRIs, histology slide images are accessible via the <a href="https://portal.kidsfirstdrc.org/dashboard" target="_blank">Kids First DRC (Data Resource Center)</a>.</p>
2021-02-04 | MSV000086810 | MassIVE