Dataset Information

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

ABSTRACT:

Francesca Petralia,Nicole Tignor,Boris Reva, et al., (2020) Cell 183, 1962-1985

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.

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 (https://cbttc.org). The consortia have contributed a Pediatric Brain Tumor Atlas (PBTA) dataset, 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 (kidsfirstdrc.org) 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 (https://commonfund.nih.gov/KidsFirst).

Mass Spectrometry raw data generation along with preliminary analyses were performed at the Thermo Fisher Scientific Center for Multiplexed Proteomics (TCMP), Harvard Medical School (HMS) under the direction of Prof. Steven Gygi. Samples were prepared using the streamline (SL)-TMT protocol (Navarrete-Perea et al., 2018) and MS analysis was performed using the SPS-MS3 strategy (Ting et al., 2011) developed in the Gygi lab. Additional data analyses from the CPTAC Common Data Analysis Pipeline (Rudnick et al., 2016) and from the University of Michigan Proteomics and Integrative Bioinformatics Laboratory (https://github.com/Nesvilab) are also provided. All raw and processed genomic data, as well as pathology reports, radiology reports, MRIs, histology slide images are accessible via the Kids First DRC (Data Resource Center).

INSTRUMENT(S): Orbitrap Fusion Lumos, Orbitrap Fusion

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Steven P Gygi

PROVIDER: MSV000086810 | MassIVE | Thu Feb 04 21:43:00 GMT 2021

REPOSITORIES: MassIVE

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Project description:<a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Colon_Cancer_Proteome" target="_blank">Explore This Study at the NCI Proteomic Data Commons</a><a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13438.html" target="_blank">Zhang, B., et al., Nature (2014) doi:10.1038/nature13438 Published online</a>Proteomes of colon and rectal tumors previously characterized by the Cancer Genome Atlas (TCGA) were analyzed along with integrated proteogenomic analyses. Ninety-five TCGA tumor samples were used in this study from 90 patients, with 5 samples representing different portions from the same tumor. The samples originated from two TCGA cohorts: 64 are from <a href="https://portal.gdc.cancer.gov/projects/TCGA-COAD" target="_blank">Colon Adenocarcinoma (COAD)</a> samples and 31 are from the <a href="https://portal.gdc.cancer.gov/projects/TCGA-READ" target="_blank">Rectum Adenocarcinoma (READ)</a> collection. The primary data from the liquid chromatography-tandem mass spectrometry (LC-MS/MS) global proteomic profiling of each tumor sample is associated with a data set in the table below. Three protein assemblies are provided from different protein database searches.TCGA_VU_N95 This assembly reflects the 95 TCGA samples for 90 tumors, spanning three search engines (MS-GF+, MyriMatch, and Pepitome), employing a standard RefSeq database and NIST spectral library.TCGA_VU_N95_Custom The Custom assembly represents the same samples, the sequence database has been augmented with nonsynonymous sequence variants detected by TCGA. TCGA_VU_N95-Normal_VU_N60 The third assembly contains the searches of the 95 TCGA samples employed in the first assembly alongside the 60 normal colons analyzed by Vanderbilt University as a control, employing the same three search engines, a standard RefSeq database, and a NIST spectral library.TCGA_Colorectal_Cancer_proBAM_PSM_genome_mapping_files Peptide spectrum matches from the TCGA_VU_N95_Custom IDPicker3 protein assembly were converted to protein BAM (proBAM) format for visualization, available in the metadata folder below.COAD tumor sample genomic data can be downloaded from <a href="https://portal.gdc.cancer.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.program.name%22,%22value%22:%5B%22TCGA%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-COAD%22%5D%7D%7D%5D%7D" target="_blank">here</a>. READ tumor sample genomic data can be downloaded from <a href="https://portal.gdc.cancer.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.program.name%22,%22value%22:%5B%22TCGA%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-READ%22%5D%7D%7D%5D%7D" target="_blank">here</a>. Normal colon epithelium sample mass spectrometry data can be downloaded from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S019" target="_blank">here</a>. Mass spectrometry data for comparison and reference (CompRef) sample standards run with this study can be downloaded from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S017" target="_blank">here</a>. Peptide-Spectrum-Matches and Protein Reports from the CPTAC Common Data Analysis Pipeline (CDAP) can be downloaded from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S016" target="_blank">here</a>. Network analysis of this study can be viewed at the NetGestalt CRC Portal <a href="http://www.netgestalt.org/index.html" target="_blank">here</a>. This work was accomplished by the Proteome Characterization Center (PCC) at Vanderbilt University led by Dr. Daniel C. Liebler.<ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S022">https://cptac-data-portal.georgetown.edu/cptac/s/S022</a> on 08/26/19</li><li>Dataset source: <a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Colon_Cancer_Proteome" target="_blank">https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Colon_Cancer_Proteome</a></li></ul>

Project description:<a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Colon_Cancer_Proteome" target="_blank">Explore This Study at the NCI Proteomic Data Commons</a>The goal of the CPTAC, TCGA Cancer Proteome Study of Colorectal Tissue is to analyze the proteomes of TCGA tumor samples that have been comprehensively characterized by molecular methods (<a href="http://www.ncbi.nlm.nih.gov/pubmed/22810696" target="_blank">Cancer Genome Atlas Network, Nature 2012</a>).Ninety-five TCGA tumor samples were used in this study from 90 patients, with 5 samples representing different portions from the same tumor. The samples originated from two TCGA cohorts: 64 are from <a href="https://portal.gdc.cancer.gov/projects/TCGA-COAD" target="_blank">Colon Adenocarcinoma (COAD)</a> samples and 31 are from the <a href="https://portal.gdc.cancer.gov/projects/TCGA-READ" target="_blank">Rectum Adenocarcinoma (READ)</a> collection. The primary data from the liquid chromatography-tandem mass spectrometry (LC-MS/MS) global proteomic profiling of each tumor sample is associated with a data set in the table below. This work was accomplished by the Proteome Characterization Center (PCC) at Vanderbilt University led by Dr. Daniel C. Liebler. Clinical data files contain both the human readable TCGA bar codes and the UUIDs for each sample.Complete Data Analysis from Vanderbilt University as published in Nature (Zhang, B., et al., Nature (2014) doi:10.1038/nature13438 Published online) is available <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S022">here</a>.Data available on this study page (peptide spectrum matches and protein reports) are from the CPTAC Common Data Analysis Pipeline.COAD tumor sample genomic data can be downloaded from <a href="https://portal.gdc.cancer.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.program.name%22,%22value%22:%5B%22TCGA%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-COAD%22%5D%7D%7D%5D%7D" target="_blank">here</a>. READ tumor sample genomic data can be downloaded from <a href="https://portal.gdc.cancer.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.program.name%22,%22value%22:%5B%22TCGA%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-READ%22%5D%7D%7D%5D%7D" target="_blank">here</a>. Colon tissue samples (ascending and descending) were obtained from 30 patients. Each sample was analyzed with label free global proteomic profiling. These colon samples, while derived from colon cancer subjects, did not contain tumor. Samples were obtained from the <a href="http://www.vicc.org/jimayersinstitute" target="_blank">Jim Ayers Institute for Precancer Detection and Diagnosis.</a> Data sets below are labeled with the patient identification number and contain data for both ascending and descending tissue samples. Data from colon tumor samples are available in the <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S016" target="_blank">TCGA Colorectal Cancer study</a>.<ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S016">https://cptac-data-portal.georgetown.edu/cptac/s/S016</a> and <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S019">https://cptac-data-portal.georgetown.edu/cptac/s/S019</a> on 06/23/16</li><li>Dataset source: <a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Colon_Cancer_Proteome">https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Colon_Cancer_Proteome</a></li></ul>

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Project description:This is a supplementary study to the CPTAC CCRCC Discovery Study - Proteome. Unlabeled, digested peptide material from individual tissue samples (ccRCC and NAT) was spiked with index Retention Time (iRT) peptides (Biognosys) and subjected to data-independent acquisition (DIA) analysis. Kidney cancer is among the 10 most common cancers in both men and women and each year there are approximately 60,000 new cases with over 14,000 deaths (<a href="https://seer.cancer.gov/statfacts/html/kidrp.html" target="_blank">NCI, Surveillance, Epidemiology and End Results (SEER) Program</a>). Several histological and molecular subtypes have been identified and clear cell renal cell carcinoma (CCRCC) is the most prevalent (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5936048/" target="_blank">Hsieh el al., 2017 Nat Rev Dis Primers</a>). To advance the proteogenomic understanding of CCRCC, the CPTAC program has investigated 110 tumors (CPTAC discovery cohort) 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.nature.com/articles/s41596-018-0006-9" target="_blank">Mertins et al., Nature Protocols 2018</a>). Proteome and phosphoproteome data from the CCRCC tumors is available below along with peptide spectrum analyses (PSMs) and protein summary reports from the CPTAC common data analysis pipeline (CDAP). Clinical data is provided. Additional attributes along with genotypes will be available as cohort characterization proceeds. Genomic data will be available from the NCI Genomic Data Commons. Note: Sample-wise assessment of genomic profiles in this cohort identified seven tumor samples with molecular aberrations atypical for ccRCC. While these seven non-ccRCC samples (C3L-00359-01, C3N-00313-03, C3N-00435-05, C3N-00492-04, C3N-00832-01, C3N-01175-01, C3N-01180-01) and their corresponding NATs (C3N-00435-06, C3N-00492-05, C3N-01175-05) were excluded from the ccRCC cohort in all downstream analyses, the non-ccRCC samples served as useful controls to highlight ccRCC-specific features. These seven samples were therefore annotated as non-ccRCC samples. <ul><li>Dataset imported into MassIVE from <a href="https://pdc.cancer.gov/pdc/study/PDC000200" target="_blank">https://pdc.cancer.gov/pdc/study/PDC000200</a> on 10/04/23</li></ul>

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Project description:Karsten Krug, Eric J. Jaehnig, Shankha Satpathy, Lili Blumenberg, Alla Karpova, Meenakshi Anurag et al., (2020) <a href="https://www.cell.com/cell/fulltext/S0092-8674(20)31400-8" target="_blank">Cell, 183, 1436-1456</a> DOI: https://doi.org/10.1016/j.cell.2020.10.036 The integration of mass spectrometry-based proteomics with next-generation DNA and RNA sequencing profiles tumors more comprehensively. Herein this "proteogenomic" approach was applied to 122 treatment-naive primary breast cancers purposely accrued to preserve post-translational modifications, including protein phosphorylation and acetylation. Proteogenomics challenged standard breast cancer diagnoses, provided detailed analysis of the ERBB2 amplicon, defined tumor subsets that could benefit from immune checkpoint therapy and allowed more accurate assessment of Rb status for the prediction of CDK4/6 inhibitor responsiveness. Phosphoproteomic profiles uncovered novel associations between tumor suppressor loss and targetable kinases. Acetylproteome analysis highlighted acetylation on key nuclear proteins involved in the DNA damage response and revealed cross-talk between cytoplasmic and mitochondrial acetylation and metabolism. Our results underscore the potential of proteogenomics for the clinical investigation of breast cancer through more accurate annotation of targetable pathways and biological features of this remarkably heterogeneous malignancy.Genomic Data for Breast Cancer tumors is available from the NCI Genomic Data Commons (GDC), <a href="https://portal.gdc.cancer.gov/projects/CPTAC-2" target="_blank">here</a> Gene Expression data in GCT file format is available under the metadata section below Proteomic Data Analysis for Breast Cancer tumors (PSMs and Summary Reports) is available from the CPTAC Common Data Analysis Pipeline (Study S039 Early Data Release), <a href="/study-summary/S039" target="_blank">here</a> <ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/study-summary/S060">https://cptac-data-portal.georgetown.edu/study-summary/S060</a> on 06/05/21</li></ul>

Dataset Information

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

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