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Cancer proteogenomics: current impact and future prospects

Nature Reviews Cancer volume 22, pages 298–313 (2022)Cite this article

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Abstract

Genomic analyses in cancer have been enormously impactful, leading to the identification of driver mutations and development of targeted therapies. But the functions of the vast majority of somatic mutations and copy number variants in tumours remain unknown, and the causes of resistance to targeted therapies and methods to overcome them are poorly defined. Recent improvements in mass spectrometry-based proteomics now enable direct examination of the consequences of genomic aberrations, providing deep and quantitative characterization of tumour tissues. Integration of proteins and their post-translational modifications with genomic, epigenomic and transcriptomic data constitutes the new field of proteogenomics, and is already leading to new biological and diagnostic knowledge with the potential to improve our understanding of malignant transformation and therapeutic outcomes. In this Review we describe recent developments in proteogenomics and key findings from the proteogenomic analysis of a wide range of cancers. Considerations relevant to the selection and use of samples for proteogenomics and the current technologies used to generate, analyse and integrate proteomic with genomic data are described. Applications of proteogenomics in translational studies and immuno-oncology are rapidly emerging, and the prospect for their full integration into therapeutic trials and clinical care seems bright.

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Fig. 1: Typical proteogenomics data.
Fig. 2: Proteogenomic data analyses for biological insight via cloud computing.
Fig. 3: LC-MS/MS workflow for proteomics in CPTAC studies.

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Acknowledgements

This work was supported by the National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) through grants U24CA160034 (to S.A.C.), U24CA210986 (to S.A.C. and M.A.G.), U01CA214125 (to M.E. and S.A.C.), U24CA210979 (to D.R.M.), U24CA210954 (to B.Z.), U24CA210972, U2CCA233303, U54CA224083, U24CA211006 and R01HG009711 (to L.D.) and P50CA186784 (to M.E). M.E. is a Susan G. Komen Foundation Scholar, a McNair Scholar supported by the McNair Medical Institute at The Robert and Janice McNair Foundation, and a recipient of a CPRIT (Cancer Prevention and Research Institute of Texas) Established Investigator Award (RR140027). B.Z. was supported by CPRIT award RR160027 and funding from the McNair Medical Institute at The Robert and Janice McNair Foundation, and is a CPRIT Scholar in Cancer Research and a McNair scholar. The authors thank J. Abelin at the Broad Institute and M. Anurag at the Baylor College of Medicine for their input to the manuscript.

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Authors and Affiliations

  1. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

    D. R. Mani, Karsten Krug, Shankha Satpathy, Karl R. Clauser, Michael A. Gillette & Steven A. Carr

  2. Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA

    Bing Zhang & Matthew Ellis

  3. Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA

    Li Ding

  4. Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA

    Michael A. Gillette

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Contributions

All authors contributed to drafting the overall structure and flow of the manuscript. Subsequently, authors contributed subsections based on their domain of expertise and involvement in relevant studies. D.R.M., K.K., M.A.G. and S.A.C. integrated subsections, incorporated reviewer comments and performed additional revisions.

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Related links

Black Sheep: https://github.com/ruggleslab/blackSheep

cBioPortal: https://www.cbioportal.org/

CDAP: https://pdc.cancer.gov/data-dictionary/harmonization.html

Clinical Proteomic Tumor Analysis Consortium (CPTAC): https://proteomics.cancer.gov/programs/cptac

COSMO: https://github.com/bzhanglab/COSMO

CPTAC (Python package): https://pypi.org/project/cptac/

customProDB: http://bioconductor.org/packages/release/bioc/html/customProDB.html

DreamAI: https://github.com/WangLab-MSSM/DreamAI

FragPipe: https://fragpipe.nesvilab.org/

Genomic Data Commons (GDC): https://gdc.cancer.gov

Genomic Data Commons (GDC) CPTAC Project: https://gdc.cancer.gov/about-gdc/contributed-genomic-data-cancer-research/clinical-proteomic-tumor-analysis-consortium-cptac

HotPho: https://github.com/ding-lab/HotPho_Analysis

International Cancer Proteogenome Consortium (ICPC): https://icpc.cancer.gov/portal

IonQuant: http://ionquant.nesvilab.org/

iProFun: https://rdrr.io/github/songxiaoyu/iProFun/

LinkedOmics: http://www.linkedomics.org/

MassQC: https://massqc.proteomesoftware.com/

MaxQuant: https://www.maxquant.org/

MSFragger: http://msfragger.nesvilab.org/

MSInspector: https://skyline.ms/skyts/home/software/Skyline/tools/details.view?name=MSInspector

multiOmicsViz: https://www.bioconductor.org/packages/release/bioc/html/multiOmicsViz.html

NeoFlow: https://github.com/bzhanglab/neoflow

NetGestalt: http://www.netgestalt.org/

NetSAM: http://www.bioconductor.org/packages/release/bioc/html/NetSAM.html

OmicsEV: https://bzhanglab.github.io/OmicsEV/

PANOPLY: https://github.com/broadinstitute/PANOPLY

Panoptes (1): https://github.com/rhong3/Panoptes

Panoptes (2): https://pypi.org/project/panoptes-he/

PANORAMA: https://panoramaweb.org/home/project-begin.view

Perseus: https://www.maxquant.org/perseus/

PepQuery: http://pepquery.org

Philosopher: https://philosopher.nesvilab.org/

PHOTON: https://github.com/jdrudolph/photon

ProNetView: http://ccrcc.cptac-network-view.org/

Proteomic Data Commons (PDC): https://pdc.cancer.gov

PTMcosmos: https://ptmcosmos.wustl.edu/

PTMsigDB: https://ptmsigdb.org

PTM-SEA: https://github.com/broadinstitute/ssGSEA2.0

PTM-Shepherd: https://ptmshepherd.nesvilab.org/

QUILTS: https://github.com/ekawaler/pyQUILTS

Skyline: https://skyline.ms/project/home/software/Skyline/begin.view

Spectrum Mill: https://proteomics.broadinstitute.org/

Terra: https://app.terra.bio

The Cancer Imaging Archive: https://www.cancerimagingarchive.net

The Cancer Imaging Archive: https://wiki.cancerimagingarchive.net/display/Public/CPTAC+Imaging+Proteomics

TMT-Integrator: https://github.com/Nesvilab/TMT-Integrator

TSNet: https://github.com/petraf01/TSNet

WebGestalt: http://www.webgestalt.org

WikiPathways: https://www.wikipathways.org/index.php/Portal:CPTAC

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Mani, D.R., Krug, K., Zhang, B. et al. Cancer proteogenomics: current impact and future prospects. Nat Rev Cancer 22, 298–313 (2022). https://doi.org/10.1038/s41568-022-00446-5

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