. 2020 Dec 23;183(7):1962-1985.e31.

doi: 10.1016/j.cell.2020.10.044. Epub 2020 Nov 25.

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

Francesca Petralia¹, Nicole Tignor¹, Boris Reva¹, Mateusz Koptyra², Shrabanti Chowdhury¹, Dmitry Rykunov¹, Azra Krek¹, Weiping Ma¹, Yuankun Zhu², Jiayi Ji³, Anna Calinawan¹, Jeffrey R Whiteaker⁴, Antonio Colaprico⁵, Vasileios Stathias⁶, Tatiana Omelchenko⁷, Xiaoyu Song³, Pichai Raman⁸, Yiran Guo², Miguel A Brown², Richard G Ivey⁴, John Szpyt⁹, Sanjukta Guha Thakurta⁹, Marina A Gritsenko¹⁰, Karl K Weitz¹⁰, Gonzalo Lopez¹, Selim Kalayci¹, Zeynep H Gümüş¹, Seungyeul Yoo¹, Felipe da Veiga Leprevost¹¹, Hui-Yin Chang¹¹, Karsten Krug¹², Lizabeth Katsnelson¹³, Ying Wang¹³, Jacob J Kennedy⁴, Uliana J Voytovich⁴, Lei Zhao⁴, Krutika S Gaonkar⁸, Brian M Ennis², Bo Zhang², Valerie Baubet², Lamiya Tauhid², Jena V Lilly², Jennifer L Mason², Bailey Farrow², Nathan Young², Sarah Leary¹⁴, Jamie Moon¹⁰, Vladislav A Petyuk¹⁰, Javad Nazarian¹⁵, Nithin D Adappa¹⁶, James N Palmer¹⁶, Robert M Lober¹⁷, Samuel Rivero-Hinojosa¹⁸, Liang-Bo Wang¹⁹, Joshua M Wang¹³, Matilda Broberg¹³, Rosalie K Chu¹⁰, Ronald J Moore¹⁰, Matthew E Monroe¹⁰, Rui Zhao¹⁰, Richard D Smith¹⁰, Jun Zhu¹, Ana I Robles²⁰, Mehdi Mesri²⁰, Emily Boja²⁰, Tara Hiltke²⁰, Henry Rodriguez²⁰, Bing Zhang²¹, Eric E Schadt¹, D R Mani¹², Li Ding²², Antonio Iavarone²³, Maciej Wiznerowicz²⁴, Stephan Schürer⁶, Xi S Chen²⁵, Allison P Heath², Jo Lynne Rokita⁸, Alexey I Nesvizhskii²⁶, David Fenyö¹³, Karin D Rodland²⁷, Tao Liu¹⁰, Steven P Gygi⁹, Amanda G Paulovich⁴, Adam C Resnick²⁸, Phillip B Storm²⁹, Brian R Rood³⁰, Pei Wang³¹; Children’s Brain Tumor Network; Clinical Proteomic Tumor Analysis Consortium

Collaborators, Affiliations

Collaborators

Alicia Francis, Allison M Morgan, Angela J Waanders, Angela N Viaene, Anna Maria Buccoliero, Arul M Chinnaiyan, Carina A Leonard, Cassie N Kline, Chiara Caporalini, Christopher R Kinsinger, Chunde Li, David E Kram, Derek Hanson, Elizabeth Appert, Emily A Kawaler, Eric H Raabe, Eric M Jackson, Jeffrey P Greenfield, Gabrielle S Stone, Gad Getz, Gerald Grant, Guo Ci Teo, Ian F Pollack, Jason E Cain, Jessica B Foster, Joanna J Phillips, July E Palma, Karen A Ketchum, Kelly V Ruggles, Lili Blumenberg, Macintosh Cornwell, Mahdi Sarmady, Marcin J Domagalski, Marcin P Cieślik, Mariarita Santi, Marilyn M Li, Matthew J Ellis, Matthew A Wyczalkowski, Meghan Connors, Mirko Scagnet, Nalin Gupta, Nathan J Edwards, Nicholas A Vitanza, Olena M Vaske, Oren Becher, Peter B McGarvey, Ron Firestein, Sabine Mueller, Samuel G Winebrake, Saravana Mohan Dhanasekaran, Shuang Cai, Sonia Partap, Tatiana Patton, Toan Le, Travis D Lorentzen, Wenke Liu, William E Bocik

Affiliations

¹ Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
² Center for Data-Driven Discovery in Biomedicine, Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
³ Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
⁴ Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
⁵ Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
⁶ Department of Pharmacology, Institute for Data Science and Computing, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33146, USA.
⁷ Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
⁸ Center for Data-Driven Discovery in Biomedicine, Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Bioinformatics and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
⁹ Thermo Fisher Scientific Center for Multiplexed Proteomics, Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
¹⁰ Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
¹¹ Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.
¹² Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02412, USA.
¹³ Institute for Systems Genetics; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA.
¹⁴ Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Cancer and Blood Disorders Center, Seattle Children's Hospital, Seattle, WA 98105, USA; Department of Pediatrics, University of Washington, Seattle, WA 98195, USA.
¹⁵ Children's National Research Institute, George Washington University School of Medicine, Washington, DC 20010, USA; Department of Oncology, Children's Research Center, University Children's Hospital Zürich, Zürich 8032, Switzerland.
¹⁶ Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA.
¹⁷ Department of Neurosurgery, Dayton Children's Hospital, Dayton, OH 45404, USA.
¹⁸ Children's National Research Institute, George Washington University School of Medicine, Washington, DC 20010, USA.
¹⁹ Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA.
²⁰ Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
²¹ Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA.
²² Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA.
²³ Institute for Cancer Genetics, Department of Neurology, Department of Pathology and Cell Biology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA.
²⁴ Poznan University of Medical Sciences, 61-701 Poznań, Poland; International Institute for Molecular Oncology, 61-203 Poznań, Poland.
²⁵ Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
²⁶ Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
²⁷ Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, USA.
²⁸ Center for Data-Driven Discovery in Biomedicine, Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA. Electronic address: resnick@email.chop.edu.
²⁹ Center for Data-Driven Discovery in Biomedicine, Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA. Electronic address: storm@email.chop.edu.
³⁰ Children's National Research Institute, George Washington University School of Medicine, Washington, DC 20010, USA. Electronic address: brood@childrensnational.org.
³¹ Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: pei.wang@mssm.edu.

PMID: 33242424
PMCID: PMC8143193
DOI: 10.1016/j.cell.2020.10.044

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

Francesca Petralia et al. Cell. 2020.

. 2020 Dec 23;183(7):1962-1985.e31.

doi: 10.1016/j.cell.2020.10.044. Epub 2020 Nov 25.

Authors

Collaborators

Alicia Francis, Allison M Morgan, Angela J Waanders, Angela N Viaene, Anna Maria Buccoliero, Arul M Chinnaiyan, Carina A Leonard, Cassie N Kline, Chiara Caporalini, Christopher R Kinsinger, Chunde Li, David E Kram, Derek Hanson, Elizabeth Appert, Emily A Kawaler, Eric H Raabe, Eric M Jackson, Jeffrey P Greenfield, Gabrielle S Stone, Gad Getz, Gerald Grant, Guo Ci Teo, Ian F Pollack, Jason E Cain, Jessica B Foster, Joanna J Phillips, July E Palma, Karen A Ketchum, Kelly V Ruggles, Lili Blumenberg, Macintosh Cornwell, Mahdi Sarmady, Marcin J Domagalski, Marcin P Cieślik, Mariarita Santi, Marilyn M Li, Matthew J Ellis, Matthew A Wyczalkowski, Meghan Connors, Mirko Scagnet, Nalin Gupta, Nathan J Edwards, Nicholas A Vitanza, Olena M Vaske, Oren Becher, Peter B McGarvey, Ron Firestein, Sabine Mueller, Samuel G Winebrake, Saravana Mohan Dhanasekaran, Shuang Cai, Sonia Partap, Tatiana Patton, Toan Le, Travis D Lorentzen, Wenke Liu, William E Bocik

Affiliations

¹ Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
² Center for Data-Driven Discovery in Biomedicine, Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
³ Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
⁴ Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
⁵ Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
⁶ Department of Pharmacology, Institute for Data Science and Computing, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33146, USA.
⁷ Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
⁸ Center for Data-Driven Discovery in Biomedicine, Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Bioinformatics and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
⁹ Thermo Fisher Scientific Center for Multiplexed Proteomics, Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
¹⁰ Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
¹¹ Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.
¹² Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02412, USA.
¹³ Institute for Systems Genetics; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA.
¹⁴ Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Cancer and Blood Disorders Center, Seattle Children's Hospital, Seattle, WA 98105, USA; Department of Pediatrics, University of Washington, Seattle, WA 98195, USA.
¹⁵ Children's National Research Institute, George Washington University School of Medicine, Washington, DC 20010, USA; Department of Oncology, Children's Research Center, University Children's Hospital Zürich, Zürich 8032, Switzerland.
¹⁶ Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA.
¹⁷ Department of Neurosurgery, Dayton Children's Hospital, Dayton, OH 45404, USA.
¹⁸ Children's National Research Institute, George Washington University School of Medicine, Washington, DC 20010, USA.
¹⁹ Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA.
²⁰ Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
²¹ Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA.
²² Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA.
²³ Institute for Cancer Genetics, Department of Neurology, Department of Pathology and Cell Biology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA.
²⁴ Poznan University of Medical Sciences, 61-701 Poznań, Poland; International Institute for Molecular Oncology, 61-203 Poznań, Poland.
²⁵ Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
²⁶ Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
²⁷ Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, USA.
²⁸ Center for Data-Driven Discovery in Biomedicine, Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA. Electronic address: resnick@email.chop.edu.
²⁹ Center for Data-Driven Discovery in Biomedicine, Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA. Electronic address: storm@email.chop.edu.
³⁰ Children's National Research Institute, George Washington University School of Medicine, Washington, DC 20010, USA. Electronic address: brood@childrensnational.org.
³¹ Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: pei.wang@mssm.edu.

PMID: 33242424
PMCID: PMC8143193
DOI: 10.1016/j.cell.2020.10.044

Abstract

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.

Keywords: BRAF alteration; CPTAC; CTNNB1 mutation; kinase activity score; kinase substrate regulation; pediatric brain tumor; post-translational modification; proteomic cluster; recurrent versus primary tumors; tumor microenvironment.

PubMed Disclaimer

Conflict of interest statement

Declaration of Interests E.E.S. serves as chief executive officer for Sema4 and has an equity interest in this company.

Figures

**Figure 1.. Proteomic based clustering of pediatric brain tumors.**
A. Summary of pediatric brain tumor cohort. B. Presence of omics data sets for each of the 218 tumor samples. For each sample, the clinical status at sample collection (i.e., post- mortem, post-treatment or treatment naive) is also reported. C. Kaplan Meier curves for OS of patients stratified by proteomic cluster. D. Proteomic clusters and differentially expressed proteins allocated to 14 gene clusters (top heatmap). Each row represents a proteomic cluster, while each column represents a protein. Red/blue colors denote up/down regulation patterns of different proteins in a cluster. Distributions of diagnoses, clinical outcomes, and mutation status among the 8 clusters (top left pie plots), and gene members of key pathways enriched in each gene group (bottom heatmap) are shown. For each pathway, the averaged ssGSEA score in each protein cluster based on global proteomic (Protein) and RNA-seq (RNA-seq) data are illustrated to the right. E. Heatmap of kinase activity scores for the CP tumors (n=16). Silhouette scores (top) measures the cohesiveness of tumors classified as C4 and C8 based on kinase activity score. Kinases involved in AKT1 or ERK1/2 signaling are highlighted in the heatmap. F. Diagram illustrating differences between C4 and C8 CP tumors in terms of phosphorylation abundance and kinase activity for AKT and ERK1/2 signaling members. G. MRM measurements validated different activities of proteins and phosphoproteins between C4 and C8 CPs. The numbers annotated under each pair of boxplots correspond to AUC (area under the curve) for classifying the two groups of CP using the corresponding protein/phosphosite measurement.

**Figure 2.. Immune infiltration in pediatric brain tumors.**
A. Heatmap illustrating cell type compositions, and activities of selected individual gene/proteins and pathways across 5 immune clusters. The heatmap in the first section illustrates the immune/stromal signatures from xCell. The heatmap in the second section illustrates signatures of microglia, neurons and oligodendrocytes derived from single cell sequencing data from Darmanis et al (2017). RNA and protein abundance of key immune-related markers, and ssGSEA scores based on global proteomic data for biological pathways upregulated in different immune groups are illustrated in the remaining sections. B. Contour plot of two-dimensional density based on Macrophage (y-axis) and Microglia scores (x-axis) for different immune clusters. For each immune cluster, key upregulated pathways significant at 10% FDR are reported based on RNAseq (R), global proteomic (P) and phospho-proteomic data (Ph) in the annotation boxes. For Cold-mixed and Cold-medullo clusters, pathways upregulated in both clusters are reported. C. Distribution of pathway scores of Signaling by WNT and Oxidative Phosphorylation based on global proteomic data and RNA stratified by immune clusters. D. Heatmap showing the comparison between immune clusters (columns) with proteomic clusters and different histologies (rows). Each row sums to one, with different entries showing the proportion of tumors allocated to different immune clusters. E. xCell immune/stromal and antigen presentation signatures in *BRAF*^V600E or *BRAF*^Fusion compared to *BRAF*^WT in LGG. F. Distribution of RNA levels of *HLA-A, HLA-B* and *HLA-C* in LGG tumors with different *BRAF* statuses. G. Distribution of macrophage and microglia polarization (M2-M1) in LGG tumors with different *BRAF* statuses.

**Figure 3.. Impact of genomic alterations on transcriptomic, proteomic and phosphoproteomic abundances.**
A. Distribution of protein abundance of BRAF, CTNNB1, and NF1 across tumor samples stratified by different mutation status and diagnoses. Symbols *, **, and *** correspond to p-values less than 0.1, 0.01 and 0.001, respectively. B. DNA copy number amplification/deletion frequencies along chromosome 1 among EP, HGG and MB samples. Genes with detected CNV-RNA/protein or CNV-RNA/protein/phospho cascade events are labelled as vertical bars in the top track. C. Distribution of DNA copy number (log ratio), RNA and protein abundance of RABGAP1L, RAB3GAP2 and FDPS stratified by their amplification statuses in EP, MB and HGG tumors. For RABGAP1L, Symbols *, ** and *** mean the same as in A. “ns” stands for “not significant” (p-value>0.1). D. Illustration of the impact of *CTNNB1* mutation on RNA and protein abundance in CP samples. x-axis (y-axis) represents signed -log10 FDR for testing the association between protein abundances (RNAs) and *CTNNB1* mutation. Cell-Cell Contact Zone (Coagulation) pathway is enriched in the set of proteins up (down) regulated in *CTNNB1* mutant samples. A few members of the WNT Signaling pathway whose protein or phosphosites are associated with *CTNNB1* mutation are highlighted in red. Phosphosites are annotated with “(P)” in their gene symbols. E. Distribution of protein and phosphosite abundances among *CTNNB1* mutant and *CTNNB1* wild-type CP tumors for known key members of the WNT Signaling pathway interacting with β-Catenin and transcription factors regulated by *CTNNB1*. Symbols *, ** and *** correspond to FDR less than 0.1, 0.01 and 0.001, respectively. “ns” stands for “not significant” (FDR >0.1). F. Illustration of the regulatory role of β-Catenin.

**Figure 4.. Phosphoproteomic analysis of kinase activity**
A. Heatmaps showing the global abundance (right panel) and the kinase activity score (left panel) of selected kinases across different histologies. For each kinase, the Pearson’s correlation between its global abundance and kinase activity within each histology is shown in the middle panel. B. Scatterplot showing the global abundance of a particular kinase (x-axis) versus the phospho-abundance of the targeted substrates (y-axis). First row is based on the data from the discovery cohort; while the second row displays the data based on the validation cohort. C. Heatmap showing global proteomic abundance of CDK1, CDK2 and CAMK2A as well as phosphorylation abundance of MCM2 Ser 139, GJA1 Ser 325, GJA1 Ser 314, SYN1 Ser 568 and SYN1 Ser 605 among HGG in the discovery and validation cohorts. D. Diagram showing kinase-substrate associations involved in CNS development in LGG (top-middle panel). Scatter plots showing the association between the global (or phospho) abundance of each kinase (x-axis) and the phospho-abundance of the corresponding substrate (y-axis).

**Figure 5.. Insights from proteogenomic analysis of LGG**
A. Heatmap illustrating ssGSEA scores of selected pathways differentially expressed between LGG tumors with different *BRAF* statuses based on global proteomic data. Dot-plot on the left-side summarizes ssGSEA pathway scores based on RNA data among samples with different *BRAF* statuses. B. Distributions of RNA, TMT protein abundance (TMT Global), and MRM protein abundance (MRM Global) of AKT1, AKT2, and AKT1S1 in samples with different *BRAF* alteration statuses. FDR levels of two-sample comparisons between *BRAF*^V600E/ BRAF^Fusion and *BRAF*^WT are annotated. C. The network topology representing the LGG phosphosite co-expression network module enriched in sites upregulated in *BRAF*^v600E compared to *BRAF*^WT tumors. Phosphosites mapping to genes in the HNRNP family or contained in the MYC Targets pathway are highlighted in red and blue, respectively. D. Scatterplot displaying the association between each phosphosite’s abundance with the global abundance of AKT2 (y-axis) versus the association with *BRAF*^V600E (x-axis). Phosphosites contained in the network module in C are highlighted in red. Boxplots illustrate the distribution of the activity scores (ssGSEA) of the network module in C based on phosphoproteomic data in samples with different BRAF status. Pie-plot shows the proportion of phosphosites contained in the network module in C whose abundances are associated at 5% FDR with the global abundance of AKT2.

**Figure 6.. Insights from proteogenomic analysis of HGG**
A. Scatterplot showing OS of HGG patients versus the global protein abundance of IDH1 and IDH2 in the tumors. B. Heatmap of global abundance of IDH proteins in the discovery cohort. **C, D.** 95% CI of hazard ratio coefficients from Cox-regression for IDH1/2 scores and other covariates based on the discovery cohort (panel C) and Data Set 2 (D). **E, F.** Kaplan-Meier curves of overall survival for HGG H3^Mut samples (grey), H3^WT samples with low IDH1/2 abundance (red) and H3^WT tumors with high IDH1/2 abundance (blue) for the discovery cohort (panel E) and the validation cohort (F).G. Illustration of drug target analysis result. The bottom-left heatmap illustrates the targeting genes (rows) of each detected drugs (columns). For each gene, the z-score comparing its RNA and proteomic abundances between HGG and LGG is shown in the bottom-right heatmap. Mechanism of actions are annotated on the top of the heatmap together with the resulting score from the cMAP analysis. H. Distribution of kinase activity scores of CDK1, CDK2 and MAPK1 among HGG and LGG tumors, with the latter further stratified by BRAF status.

**Figure 7.. Comparison between Initial and Recurrent Tumors**
**A. (a)** Clinical properties and genomic characterization of 18 pairs of IN vs RP tumors. The bar plot illustrates the number of non-synonymous mutations in IN and RP tumors with the number of shared mutations being represented by the shaded area. The potential driver mutation track shows the allele frequencies of somatic mutations of known oncogenes and tumor suppressor genes. Chromosome arm aberrations of each sample and the change of tumor grade from IN to RP of each patient are also shown. **(b)** Differences in ssGSEA score between RP and IN tumors of key molecular pathways associated with different proteomic clusters. The annotation at the bottom indicates the diagnosis and clinical event IDs of the paired samples for each patient. For example, “Epen.496.3319” refers to a pair of EP tumors with IDs: 7316–496 and 7316–3319. B. Distribution of Spearman’s correlation between the proteomic abundance of any pair of tumors within a particular histology. Correlations between the 18 paired IN-RP samples were further labeled in the violin plots. C. Distribution of kinase activity scores of MAPK1/3 among all LGG samples, LGG samples allocated to C4 and LGG samples allocated to C8. IN and RP samples of patients LGG.350.944 and LGG.173.2154 are highlighted.

See this image and copyright information in PMC

Cited by

Multiomic profiling of medulloblastoma reveals subtype-specific targetable alterations at the proteome and N-glycan level.
Godbole S, Voß H, Gocke A, Schlumbohm S, Schumann Y, Peng B, Mynarek M, Rutkowski S, Dottermusch M, Dorostkar MM, Korshunov A, Mair T, Pfister SM, Kwiatkowski M, Hotze M, Neumann P, Hartmann C, Weis J, Liesche-Starnecker F, Guan Y, Moritz M, Siebels B, Struve N, Schlüter H, Schüller U, Krisp C, Neumann JE. Godbole S, et al. Nat Commun. 2024 Jul 24;15(1):6237. doi: 10.1038/s41467-024-50554-z. Nat Commun. 2024. PMID: 39043693
Proteogenomic insights into the biology and treatment of pan-melanoma.
Xiang H, Luo R, Wang Y, Yang B, Xu S, Huang W, Tang S, Fang R, Chen L, Zhu N, Yu Z, Akesu S, Wei C, Xu C, Zhou Y, Gu J, Zhao J, Hou Y, Ding C. Xiang H, et al. Cell Discov. 2024 Jul 23;10(1):78. doi: 10.1038/s41421-024-00688-7. Cell Discov. 2024. PMID: 39039072 Free PMC article.
MOST wanted: navigating the MAPK-OIS-SASP-tumor microenvironment axis in primary pediatric low-grade glioma and preclinical models.
Sigaud R, Brummer T, Kocher D, Milde T, Selt F. Sigaud R, et al. Childs Nerv Syst. 2024 May 25. doi: 10.1007/s00381-024-06463-z. Online ahead of print. Childs Nerv Syst. 2024. PMID: 38789691
Discovery of immunotherapy targets for pediatric solid and brain tumors by exon-level expression.
Shaw TI, Wagner J, Tian L, Wickman E, Poudel S, Wang J, Paul R, Koo SC, Lu M, Sheppard H, Fan Y, O'Neill FH, Lau CC, Zhou X, Zhang J, Gottschalk S. Shaw TI, et al. Nat Commun. 2024 May 3;15(1):3732. doi: 10.1038/s41467-024-47649-y. Nat Commun. 2024. PMID: 38702309 Free PMC article.
Proteomics appending a complementary dimension to precision oncotherapy.
Zhou Z, Zhang R, Zhou A, Lv J, Chen S, Zou H, Zhang G, Lin T, Wang Z, Zhang Y, Weng S, Han X, Liu Z. Zhou Z, et al. Comput Struct Biotechnol J. 2024 Apr 20;23:1725-1739. doi: 10.1016/j.csbj.2024.04.044. eCollection 2024 Dec. Comput Struct Biotechnol J. 2024. PMID: 38689716 Free PMC article. Review.

See all "Cited by" articles

References

1. Abate M, Laezza C, Pisanti S, Torelli G, Seneca V, Catapano G, Montella F, Ranieri R, Notarnicola M, Gazzerro P, et al. (2017). Deregulated expression and activity of Farnesyl Diphosphate Synthase (FDPS) in Glioblastoma. Scientific reports 7, 14123. - PMC - PubMed
1. Apps JR, Carreno G, Gonzalez-Meljem JM, Haston S, Guiho R, Cooper JE, Manshaei S, Jani N, Holsken A, Pettorini B, et al. (2018). Tumour compartment transcriptomics demonstrates the activation of inflammatory and odontogenic programmes in human adamantinomatous craniopharyngioma and identifies the MAPK/ERK pathway as a novel therapeutic target. Acta neuropathologica 135, 757–777. - PMC - PubMed
1. Aran D, Hu Z, and Butte AJ (2017). xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome biology 18, 220. - PMC - PubMed
1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature genetics 25, 25–29. - PMC - PubMed
1. Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B, et al. (2018). Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 173, 371–385.e318. - PMC - PubMed

Publication types

Actions
Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

Collaborators

Affiliations

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

Authors

Collaborators

Affiliations

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Research Materials

Miscellaneous

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

Publication types

MeSH terms

Substances

Related information

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Research Materials

Miscellaneous