Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma

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SUMMARY

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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Comprehensive proteogenomic characterization in 103 treatment-naive clear cell renal cell carcinoma patient samples highlights tumor-specific alterations at the proteomic level that are unrevealed by transcriptomic profiling and proposes a revised subtyping scheme based on integrated omics analysis.

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Most cited references 104

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Yoav Benjamini, Yosef Hochberg (1995)

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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

Andréa Haddad, Thomas Carey, Carol Bradford … (2018)

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.

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Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search

Andrew Keller, Eugene Kolker, Ruedi Aebersold … (2003)

We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.

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Author and article information

Journal

Journal ID (nlm-journal-id): 0413066

Journal ID (pubmed-jr-id): 2830

Journal ID (nlm-ta): Cell

Journal ID (iso-abbrev): Cell

Title: Cell

ISSN (Print): 0092-8674

ISSN (Electronic): 1097-4172

Publication date Nihms-submitted: 1 June 2020

Publication date (Print): 31 October 2019

Publication date PMC-release: 11 August 2020

Volume: 179

Issue: 4

Pages: 964-983.e31

Affiliations

[1 ]Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA

[2 ]Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA

[3 ]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

[4 ]Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

[5 ]Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

[6 ]Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA

[7 ]Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA

[8 ]Washington University School of Medicine, St. Louis, MO 63110, USA

[9 ]Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA

[10 ]Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX 77030, USA

[11 ]Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA

[12 ]Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY 10016, USA

[13 ]Department of Medicine, New York University School of Medicine, New York, NY 10016, USA

[14 ]Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA

[15 ]Departments of Medicine and Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO 63110, USA

[16 ]Department of Biochemistry and Cellular Biology, Georgetown University, Washington, DC 20007, USA

[17 ]Brady Urological Institute and Department of Urology, Johns Hopkins University, Baltimore, MD 21231, USA

[18 ]Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA

[19 ]Department of Surgery, Urology Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA

[20 ]Department of Urology, Poznań University of Medical Sciences, Szwajcarska 3, Poznań 61-285, Poland

[21 ]Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA

[22 ]Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA

[23 ]Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 71-252, Poland

[24 ]International Institute for Molecular Oncology, Poznań 60-203, Poland

[25 ]Poznań University of Medical Sciences, Poznan 60-701, Poland

[26 ]Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA

[27 ]Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA

[28 ]Department of Ophthalmology, Johns Hopkins University, Baltimore, MD 21231, USA

[29 ]Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA

[30 ]Sema4, Stamford, CT 06902, USA

[31 ]Department of Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA

[32 ]These authors contributed equally

[33 ]These authors contributed equally

[34 ]Lead Contact

Author notes

[* ]Correspondence: mcieslik@ 123456med.mich.edu (M.C.), dchan@ 123456jhmi.edu (D.W.C.), nesvi@ 123456med.umich.edu (A.I.N.), pei.wang@ 123456mssm.edu (P.W.), huizhang@ 123456jhu.edu (H.Z.)

AUTHOR CONTRIBUTIONS

Conceptualization, H.R., D.C., A.I.N., P.W., and H.Z.; Methodology, D.J.C. and L.C.; Software, F.P., J.P., X.S., Y.H., F.d.V.L., B.R., T.-S.M.L., H.-Y.C., W.M., C.H., A. Krek, Y.L., D.R., L.S.C., U.O., S.V., S.Y., S. Chowdhury, J.J., A. Kong, S.S., D.M.A., N.E., Z.Z., M.C., A.I.N., and P.W.; Validation, S.M.D., K.L.Q., and K.-C.C.; Formal Analysis, D.J.C., S.M.D., F.P., J.P., X.S., Y.H., B.R., T.-S.M.L., H.-Y.C., W.M., C.H., A. Krek, Y.L., D.R., L.S.C., U.O., S.V., Y.W., S.Y., S. Chowdhury, J.J., A. Kong, S.S., D.M.A., A. Colaprico, S. Cao, S. Kalayci, S.M., W.L., K.R., D.G., E.K., G.C.T., B.W., Y.Z., S. Keegan, K.L., F.C., N.E., A.I.N., P.W., and H.Z.; Investigation, D.J.C., L.C., M.S., K.-C.C., D.W.C., and H.Z.; Resources, Q.K.L., C.P.P., G.B., A.A., J.L., and M.T.; Data Curation, D.J.C., S.M.D., W.M., M.A.W., M.S., M.A., M.C., A.I.N., P.W., and H.Z.; Writing – Original Draft, D.J.C., A.I.N., P.W., and H.Z.; Writing – Review & Editing, all authors; Visualization, D.J.C., S.M.D., F.P., J.P., X.S., Y.H., B.R., T.-S.M.L., C.H., C.J.R., A. Krek, Y.L., D.R., U.O., S.V., M.A., A. Calinawan, Z.H.G., Y.Z., and M.C.; Supervision, D.J.C., S.M.D., C.J.R., P.M.P., X.S.C., C.P.P., A.A.H, G.B., J.H., A.A., T.O., J.L., M.W., W.M.L., J.Q., D.F., B.Z., L.D., E.S., A.M.C., Z.Z., G.S.O., D.W.C., A.I.N., P.W., and H.Z.; Project Administration, D.J.C., C.R.K., M.T., M.M., E.S.B., M.M., T.H., A.I.R., H.R., D.W.C., A.I.N., P.W., and H.Z.; Funding Acquisition, B.Z., L.D., D.F., E.S., A.M.C., Z.Z., D.W.C., A.I.N., P.W., and H.Z.

Article

Manuscript ID: NIHMS1597713

DOI: 10.1016/j.cell.2019.10.007

PMC ID: 7331093

PubMed ID: 31675502

SO-VID: a13025c4-5e78-4e3c-b859-7a17d4b84133

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This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma

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SUMMARY

Graphical Abstract

In Brief

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Evolutionary Cell Biology

Most cited references 104

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search

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