Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.
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ABSTRACT:
SUBMITTER: Petralia F
PROVIDER: S-EPMC8143193 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.
Petralia Francesca F Tignor Nicole N Reva Boris B Koptyra Mateusz M Chowdhury Shrabanti S Rykunov Dmitry D Krek Azra A Ma Weiping W Zhu Yuankun Y Ji Jiayi J Calinawan Anna A Whiteaker Jeffrey R JR Colaprico Antonio A Stathias Vasileios V Omelchenko Tatiana T Song Xiaoyu X Raman Pichai P Guo Yiran Y Brown Miguel A MA Ivey Richard G RG Szpyt John J Guha Thakurta Sanjukta S Gritsenko Marina A MA Weitz Karl K KK Lopez Gonzalo G Kalayci Selim S Gümüş Zeynep H ZH Yoo Seungyeul S da Veiga Leprevost Felipe F Chang Hui-Yin HY Krug Karsten K Katsnelson Lizabeth L Wang Ying Y Kennedy Jacob J JJ Voytovich Uliana J UJ Zhao Lei L Gaonkar Krutika S KS Ennis Brian M BM Zhang Bo B Baubet Valerie V Tauhid Lamiya L Lilly Jena V JV Mason Jennifer L JL Farrow Bailey B Young Nathan N Leary Sarah S Moon Jamie J Petyuk Vladislav A VA Nazarian Javad J Adappa Nithin D ND Palmer James N JN Lober Robert M RM Rivero-Hinojosa Samuel S Wang Liang-Bo LB Wang Joshua M JM Broberg Matilda M Chu Rosalie K RK Moore Ronald J RJ Monroe Matthew E ME Zhao Rui R Smith Richard D RD Zhu Jun J Robles Ana I AI Mesri Mehdi M Boja Emily E Hiltke Tara T Rodriguez Henry H Zhang Bing B Schadt Eric E EE Mani D R DR Ding Li L Iavarone Antonio A Wiznerowicz Maciej M Schürer Stephan S Chen Xi S XS Heath Allison P AP Rokita Jo Lynne JL Nesvizhskii Alexey I AI Fenyö David D Rodland Karin D KD Liu Tao T Gygi Steven P SP Paulovich Amanda G AG Resnick Adam C AC Storm Phillip B PB Rood Brian R BR Wang Pei P
Cell 20201125 7
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 tre ...[more]
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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:Pituitary neuroendocrine tumor (PitNET) is one of the most common intracranial tumors. Due to its extensive tumor heterogeneity and the lack of high-quality tissues for biomarker discovery, the causative molecular mechanisms are far from being fully defined. Therefore, more studies are needed to improve the current clinicopathological classification system, and advanced treatment strategies such as targeted therapy and immunotherapy are yet to be explored. Here, we performed the largest integrative genomics, transcriptomics, proteomics, and phosphoproteomics analysis reported to date for a cohort of 200 PitNET patients. Genomics data indicate that GNAS copy number gain can serve as a reliable diagnostic marker for hyperproliferation of the PIT1 lineage. Proteomics-based classification of PitNETs identified 7 clusters, among which, tumors overexpressing epithelial-mesenchymal transition (EMT) markers clustered into a more invasive subgroup. Further analysis identified potential therapeutic targets, including CDK6, TWIST1, EGFR, and VEGFR2, for different clusters. Immune subtyping to explore the potential for application of immunotherapy in PitNET identified an association between alterations in the JAK1-STAT1-PDL1 axis and immune exhaustion, and between changes in the JAK3-STAT6-FOS/JUN axis and immune infiltration. These identified molecular markers and alternations in various clusters/subtypes were further confirmed in an independent cohort of 750 PitNET patients. This proteogenomic analysis across traditional histological boundaries improves our current understanding of PitNET pathophysiology and suggests novel therapeutic targets and strategies.
2022-10-17 | PXD031467 |
Project description: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. VIDEO ABSTRACT.
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Project description:The evolutionary trajectory of glioblastoma (GBM) is a multifaceted biological process that extends beyond genetic alterations alone. Here, we perform an integrative proteogenomic analysis of 123 longitudinal glioblastoma pairs and identify a highly proliferative cellular state at diagnosis and replacement by activation of neuronal transition and synaptogenic pathways in recurrent tumors. Proteomic and phosphoproteomic analyses reveal that the molecular transition to neuronal state at recurrence is marked by post-translational activation of the wingless-related integration site (WNT)/ planar cell polarity (PCP) signaling pathway and BRAF protein kinase. Consistently, multi-omic analysis of patient-derived xenograft (PDX) models mirror similar patterns of evolutionary trajectory. Inhibition of B-raf proto-oncogene (BRAF) kinase impairs both neuronal transition and migration capability of recurrent tumor cells, phenotypic hallmarks of post-therapy progression. Combinatorial treatment of temozolomide (TMZ) with BRAF inhibitor, vemurafenib, significantly extends the survival of PDX models. This study provides comprehensive insights into the biological mechanisms of glioblastoma evolution and treatment resistance, highlighting promising therapeutic strategies for clinical intervention.
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Project description:Medullary thyroid carcinoma (MTC) is a rare neuroendocrine malignancy derived from parafollicular cells (C cells) of the thyroid. Here we presented a comprehensive multi-omics landscape of 102 MTCs through whole-exome sequencing, RNA sequencing, DNA methylation array, proteomic and phosphoproteomic profiling. Integrated analyses identified BRAF and NF1 as novel driver genes in addition to the well-characterized RET and RAS proto-oncogenes. Proteome-based stratification of MTCs revealed three molecularly heterogeneous subtypes named as: (1) Metabolic, (2) Basal and (3) Mesenchymal, which are distinct in genetic drivers, epigenetic modification profiles, clinicopathologic factors and clinical outcomes. Furthermore, we explored putative therapeutic targets of each proteomic subtype, and found that two tenascin family members TNC/TNXB might serve as potential prognostic biomarkers for MTC. Collectively, our study expands the knowledge of MTC biology and therapeutic vulnerabilities, which may serve as an important resource for future investigation on this malignancy.
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Project description:Despite increasing evidence that antitumor immune control exists in the pediatric brain, these findings have yet to be exploited successfully in the clinic. A barrier to development of immunotherapeutic strategies in pediatric brain tumors is that the immunophenotype of these tumors' microenvironment has not been defined. To address this, the current study used multicolor FACS of disaggregated tumor to systematically characterize the frequency and phenotype of infiltrating immune cells in the most common pediatric brain tumor types. The initial study cohort consisted of 7 pilocytic astrocytoma (PA), 19 ependymoma (EPN), 5 glioblastoma (GBM), 6 medulloblastoma (MED), and 5 nontumor brain (NT) control samples obtained from epilepsy surgery. Immune cell types analyzed included both myeloid and T cell lineages and respective markers of activated or suppressed functional phenotypes. Immune parameters that distinguished each of the tumor types were identified. PA and EPN demonstrated significantly higher infiltrating myeloid and lymphoid cells compared with GBM, MED, or NT. Additionally, PA and EPN conveyed a comparatively activated/classically activated myeloid cell-skewed functional phenotype denoted in particular by HLA-DR and CD64 expression. In contrast, GBM and MED contained progressively fewer infiltrating leukocytes and more muted functional phenotypes similar to that of NT. These findings were recapitulated using whole tumor expression of corresponding immune marker genes in a large gene expression microarray cohort of pediatric brain tumors. The results of this cross-tumor comparative analysis demonstrate that different pediatric brain tumor types exhibit distinct immunophenotypes, implying that specific immunotherapeutic approaches may be most effective for each tumor type.
| S-EPMC3827919 | biostudies-literature
Project description:Although papillary thyroid cancer (PTC) has a good prognosis, its recurrence rate is high and remains a core concern in the clinic. Molecular factors contributing to different recurrence risks (RRs) remain poorly defined. Here, we perform an integrative proteogenomic and metabolomic characterization of 102 Chinese PTC patients with different RRs. Genomic profiling reveals that mutations in MUC16 and TERT promoter as well as multiple gene fusions like NCOA4-RET are enriched by the high RR. Integrative multi-omics analyses further describe the multi-dimensional characteristics of PTC, especially in metabolism pathways, and delineate dominated molecular patterns of different RRs. Moreover, the PTC patients are clustered into four subtypes (CS1: low RR and BRAF-like; CS2: high RR and metabolism type, worst prognosis; CS3: high RR and immune type, better prognosis; CS4: high RR and BRAF-like) based on the omics data. Notably, the subtypes display significant differences considering BRAF and TERT promoter mutations, metabolism and immune pathway profiles, epithelial cell compositions, and various clinical factors (especially RRs and prognosis) as well as druggable targets. This study can provide insights into the complex molecular characteristics of PTC recurrences and help promote early diagnosis and precision treatment of recurrent PTC.
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Project description:BackgroundUrothelial carcinoma (UC) is the most common pathological type of bladder cancer, a malignant tumor. However, an integrated multi-omics analysis of the Chinese UC patient cohort is lacking.MethodsWe performed an integrated multi-omics analysis, including whole-exome sequencing, RNA-seq, proteomic, and phosphoproteomic analysis of 116 Chinese UC patients, comprising 45 non-muscle-invasive bladder cancer patients (NMIBCs) and 71 muscle-invasive bladder cancer patients (MIBCs).ResultProteogenomic integration analysis indicated that SND1 and CDK5 amplifications on chromosome 7q were associated with the activation of STAT3, which was relevant to tumor proliferation. Chromosome 5p gain in NMIBC patients was a high-risk factor, through modulating actin cytoskeleton implicating in tumor cells invasion. Phosphoproteomic analysis of tumors and morphologically normal human urothelium produced UC-associated activated kinases, including CDK1 and PRKDC. Proteomic analysis identified three groups, U-I, U-II, and U-III, reflecting distinct clinical prognosis and molecular signatures. Immune subtypes of UC tumors revealed a complex immune landscape and suggested the amplification of TRAF2 related to the increased expression of PD-L1. Additionally, increased GARS, related to subtype U-II, was validated to promote pentose phosphate pathway by inhibiting activities of PGK1 and PKM2.ConclusionsThis study provides a valuable resource for researchers and clinicians to further identify molecular pathogenesis and therapeutic opportunities in urothelial carcinoma of the bladder.
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Project description:To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.
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Project description:Chronic inflammation of the colon causes genomic and/or transcriptomic events, which can lead to expression of non-canonical protein sequences contributing to oncogenesis. To better understand these mechanisms, Rag2-/-Il10-/- mice were infected with Helicobacter hepaticus to induce chronic inflammation of the cecum and the colon. Transcriptomic data from harvested proximal colon samples were used to generate a customized FASTA database containing non-canonical protein sequences. Using a proteogenomic approach, mass spectrometry data for proximal colon proteins were searched against this custom FASTA database using the Galaxy for Proteomics (Galaxy-P) platform. In addition to the increased abundance in inflammatory response proteins, we also discovered several non-canonical peptide sequences derived from unique proteoforms. We confirmed the veracity of these novel sequences using an automated bioinformatics verification workflow with targeted MS-based assays for peptide validation. Our bioinformatics discovery workflow identified 235 putative non-canonical peptide sequences, of which 58 were verified with high confidence and 39 were validated in targeted proteomics assays. This study provides insights into challenges faced when identifying non-canonical peptides using a proteogenomics approach and demonstrates an integrated workflow addressing these challenges. Our bioinformatic discovery and verification workflow is publicly available and accessible via the Galaxy platform and should be valuable in non-canonical peptide identification using proteogenomics.
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