Clinical Crenolanib Resistance in AML
Ontology highlight
ABSTRACT: FLT3 mutations are commonly detected in Acute Myeloid Leukemia (AML) patients and are associated with poor prognosis. Crenolanib, a potent type I pan-FLT3 (GeneID:2322) inhibitor, is effective against both internal tandem duplications (ITD) and resistance-conferring tyrosine kinase domain (TKD) mutations. While crenolanib monotherapy has demonstrated significant clinical benefit in heavily pretreated relapsed/refractory AML patients, responses are transient and relapse eventually occurs. To investigate the mechanisms of crenolanib resistance, we performed whole exome sequencing of AML patient samples before and after crenolanib treatment (122 samples from 59 patients). Unlike other FLT3 inhibitors, crenolanib did not induce FLT3 activation loop mutations, and mutations of the FLT3 "gatekeeper" residue were infrequent. Instead, mutations of NRAS (GeneID:4893) and IDH2 (GeneID:3418) arose, mostly as FLT3-independent subclones, while TET2 (GeneID:54790) and IDH1 (GeneID:3417) predominantly co-occurred with the FLT3-mutant clone and were enriched in crenolanib poor-responders. The remaining patients exhibited post-crenolanib expansion of mutations associated with epigenetic regulators, transcription factors, and cohesion factors, suggesting diverse non-FLT3 genetic/epigenetic mechanisms of crenolanib resistance. Drug combinations in experimental models restored crenolanib sensitivity.
REPOSITORIES: dbGaP
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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
Project description:<p>The objective of the Time-Dependent Proteome Study, was to evaluate changes in the proteome and phosphoproteome when there is a delay in freezing tissues following sample (tumor) excision. The biospecimens used are from patient-derived ovarian cancer tumors.<br><a href="http://www.ncbi.nlm.nih.gov/pubmed/24719451" target="_blank">Ref: Mertins P et al., (2014) Mol Cell Proteomics, Apr 9. E-Publication</a><br><a href="http://www.ncbi.nlm.nih.gov/pubmed/25670172" target="_blank">Ref: Gajadhar, A.S., et al., (2015) Cancer Res. Apr 1;75(7):1495-503. Epub 2015 Feb 10.</a></p><ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S013">https://cptac-data-portal.georgetown.edu/cptac/s/S013</a> on 11/05/18</li></ul>
2018-11-05 | MSV000083107 | 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:<a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Ovarian_JHU_Glycoproteome|TCGA_Ovarian_JHU_Proteome|TCGA_Ovarian_PNNL_Proteome|TCGA_Ovarian_PNNL_Phosphoproteome_Velos_Qexatvive" target="_blank">Explore This Study at the NCI Proteomic Data Commons</a><p><a href="http://www.ncbi.nlm.nih.gov/pubmed/27372738" target="_blank">Zhang H, Liu T, Zhang Z, Payne SH, Zhang B, McDermott JE et al., Cell (2016), DOI: 10.1016/j.cell.2016.05.069, Published online June 29, 2016.</a></p><p>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.</p><p>Information on the complete TCGA Ovarian Serous Cystadenocarcinoma (OV) cohort can be found <a href="https://portal.gdc.cancer.gov/projects/TCGA-OV" target="_blank">here</a>.<br/>The Cancer Genome Atlas Research Network published on the Genomic characterization of the OV cohort in <a href="http://www.ncbi.nlm.nih.gov/pubmed/21720365" target="_blank">Nature</a> on June 30, 2011</p><p>Genomic data for the 174 TCGA samples used in the CPTAC Proteome study 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-OV%22%5D%7D%7D%5D%7D" target="_blank">here</a>.</p><p>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/S020" target="_blank">here</a>.</p><ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S026">https://cptac-data-portal.georgetown.edu/cptac/s/S026</a> on 08/28/19</li><li>Dataset source: <a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Ovarian_JHU_Glycoproteome|TCGA_Ovarian_JHU_Proteome|TCGA_Ovarian_PNNL_Proteome|TCGA_Ovarian_PNNL_Phosphoproteome_Velos_Qexatvive" target="_blank">https://pdc.esacinc.com/pdc/browse/filters/study_name:...</a></li></ul>
2019-08-28 | MSV000084246 | MassIVE
Project description:<p>Primary torsin dystonias (PTD) are a group of movement disorders characterized by twisting muscle contractures, where dystonia is the only clinical sign (except for tremor) and there is no evidence of neuronal degeneration or an acquired cause. Eleven genes have been mapped for primary dystonia including DYT1 (<a href="https://www.ncbi.nlm.nih.gov/gene/?term=1861" target="_blank">TOR1A</a>), <a href="https://www.ncbi.nlm.nih.gov/gene/1862" target="_blank">DYT2</a>, DYT4 (<a href="https://www.ncbi.nlm.nih.gov/gene/?term=10382" target="_blank">TUBB4A</a>), DYT6 (<a href="https://www.ncbi.nlm.nih.gov/gene/55145" target="_blank">THAP1</a>), <a href="https://www.ncbi.nlm.nih.gov/gene/?term=1866" target="_blank">DYT7</a>, <a href="https://www.ncbi.nlm.nih.gov/gene/?term=93983" target="_blank">DYT13</a>,, <a href="https://www.ncbi.nlm.nih.gov/gene/?term=100216344" target="_blank">DYT17</a>, <a href="https://www.ncbi.nlm.nih.gov/gene/?term=100885773" target="_blank">DYT21</a>, <a href="https://www.ncbi.nlm.nih.gov/gene/?term=25792" target="_blank">CIZ1</a>, DYT24 (<a href="https://www.ncbi.nlm.nih.gov/gene/?term=63982" target="_blank">ANO3</a>), and DYT25 (<a href="https://www.ncbi.nlm.nih.gov/gene/?term=2774" target="_blank">GNAL</a>); however, only 3 genes (<a href="https://www.ncbi.nlm.nih.gov/gene/?term=1861" target="_blank">TOR1A</a>, <a href="https://www.ncbi.nlm.nih.gov/gene/55145" target="_blank">THAP1</a>, and <a href="https://www.ncbi.nlm.nih.gov/gene/?term=2774" target="_blank">GNAL</a>) have mutations in early onset dystonia patients. Each is inherited as an autosomal dominant trait but with reduced penetrance, meaning that individuals can carry the mutation but do not show clinical symptoms.</p> <p>The most common form of PTD is adult onset focal accounting for about 90% of all cases of dystonia with a prevalence estimated at 30/100,000 in the general population. A small percentage of focal cases are due to mutations in <a href="https://www.ncbi.nlm.nih.gov/gene/?term=25792" target="_blank">CIZ1</a>, <a href="https://www.ncbi.nlm.nih.gov/gene/?term=63982" target="_blank">ANO3</a>, <a href="https://www.ncbi.nlm.nih.gov/gene/55145" target="_blank">THAP1</a>, <a href="https://www.ncbi.nlm.nih.gov/gene/?term=10382" target="_blank">TUBB4A</a>, and <a href="https://www.ncbi.nlm.nih.gov/gene/?term=2774" target="_blank">GNAL</a>, but the vast majority is unaccounted for and is most likely multigenic or multifactorial. In this grant, focusing on early onset PTD, we will uncover genes that influence the penetrance of DYT1 (<a href="https://www.ncbi.nlm.nih.gov/gene/?term=1861" target="_blank">TOR1A</a>) dystonia through GWAS and exome sequencing studies. We will identify other genes for early onset PTD using exome sequencing and determine whether variants within the identified early onset PTD genes or the pathways associated with these genes contribute to susceptibility in the most prevalent form of PTD, focal dystonia, using a case:control association study. The proposed research will identify novel PTD risk factors and genes, which in turn should reveal shared and intersecting pathways leading to a better understanding of the molecular basis of PTD and provide the underpinnings for developing new treatments. Additionally, insight into the factors that modulate disease penetrance would be a first step in determine whether these could be modified leading to a reduced incidence of the disease. </p>
| phs001733 | dbGaP
Project description:The objective of the Time-Dependent Proteome Study was to evaluate changes in the proteome and phosphoproteome when there is a delay in freezing tissues following sample (tumor) excision. The biospecimens used are from human-in-mouse breast cancer tumor samples.<br/><a href="http://www.ncbi.nlm.nih.gov/pubmed/24719451" target="_blank">Ref: Mertins P et al., (2014) Mol Cell Proteomics, Apr 9. E-Publication</a><ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S011">https://cptac-data-portal.georgetown.edu/cptac/s/S011</a> and <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S012">https://cptac-data-portal.georgetown.edu/cptac/s/S012</a> on 08/29/19</li></ul>
2019-08-30 | MSV000084260 | MassIVE
Project description:<p>Shankha Satpathy, Eric J. Jaehnig, et al., <a href="https://www.nature.com/articles/s41467-020-14381-2" target="_blank">Nature Communications volume 11, Article number: 532 (2020)</a></p><p>Cancer proteogenomics promises new insights into cancer biology and treatment efficacy by integrating genomics, transcriptomics and protein profiling including modifications by mass spectrometry (MS). A critical limitation is sample input requirements that exceed many sources of clinically important material. Here we report a proteogenomics approach for core biopsies using tissue-sparing specimen processing and microscaled proteomics. As a demonstration, we analyze core needle biopsies from ERBB2 positive breast cancers before and 48-72 h after initiating neoadjuvant trastuzumab-based chemotherapy. We show greater suppression of ERBB2 protein and both ERBB2 and mTOR target phosphosite levels in cases associated with pathological complete response, and identify potential causes of treatment resistance including the absence of ERBB2 amplification, insufficient ERBB2 activity for therapeutic sensitivity despite ERBB2 amplification, and candidate resistance mechanisms including androgen receptor signaling, mucin overexpression and an inactive immune microenvironment. The clinical utility and discovery potential of proteogenomics at biopsy-scale warrants further investigation.<br><br><em>Genomic Data</em> for samples in the Microscaled Proteogenomic Methods Publication are available from the NIH Database of Genotypes and Phenotypes (dbGaP), Study Accession: phs001907.v1.p1, <a href="https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001907.v1.p1" target="_blank">here</a></p>
<ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/study-summary/S051">https://cptac-data-portal.georgetown.edu/study-summary/S051</a> on 06/02/21</li></ul>
2021-06-02 | MSV000087566 | MassIVE
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><p>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>).</p><p>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.</p><p>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>.</p><p>Data available on this study page (peptide spectrum matches and protein reports) are from the CPTAC Common Data Analysis Pipeline.</p><p>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>.<br/>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>.<br/><br/>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>.</p><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>
2016-06-23 | MSV000079852 | MassIVE
Project description:<p>The objective of the Time-Dependent Proteome Study was to evaluate changes in the proteome and phosphoproteome when there is a delay in freezing tissues following sample (tumor) excision. The biospecimens used are from snap-frozen colon adenocarcinoma core biopsy punches.<br/><a href="http://www.ncbi.nlm.nih.gov/pubmed/25670172" target="_blank">Ref: Gajadhar, A.S., et al., (2015) Cancer Res. Apr 1;75(7):1495-503. Epub 2015 Feb 10.</a></p><ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S023">https://cptac-data-portal.georgetown.edu/cptac/s/S023</a> on 08/28/19</li></ul>
2019-08-28 | MSV000084243 | MassIVE
Project description:<p>Lipid lowering therapy using HMG-CoA reductase inhibitors (statins) is associated with an approximately 9-12% increase in the risk of new-onset type 2 diabetes (T2DM). The risk of diabetes could be increased by statins directly; however, genetic approaches have also implicated low LDL cholesterol (LDL-C) concentrations as a risk factor for T2DM. Mendelian randomization studies using functional variants in both HMGCR (<a href="https://www.ncbi.nlm.nih.gov/gene/?term=HMGCR" target="_blank">GeneID:3156</a>) and PCSK9 (<a href="https://www.ncbi.nlm.nih.gov/gene/?term=PCSK9" target="_blank">GeneID:255738</a>) genes found a higher risk of T2DM in individuals with variants associated with lower LDL-C concentrations. Since PCSK9 and HMGCR are involved in lipid metabolism through distinct molecular pathways, the altered glycemic effect associated with variants in both genes is likely to be the result of their common effect on LDL-C concentrations.</p> <p>Despite the findings from statin clinical trials and genetic studies, there is little direct evidence implicating low LDL-C concentrations with increased risk of T2DM. Individuals who have very low LDL-C concentrations not due to lipid lowering therapy can provide insights into the relationship between low LDL-C concentrations and T2DM. Here, we used de-identified electronic health records (EHRs) to test the hypothesis that low LDL-C concentrations are associated with T2DM. </p>
| phs001588 | dbGaP