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Mass-spectrometry-based proteomic correlates of grade and stage reveal pathways and kinases associated with aggressive human cancers

Oncogene volume 40, pages 2081–2095 (2021)Cite this article

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Abstract

Proteomic signatures associated with clinical measures of more aggressive cancers could yield molecular clues as to disease drivers. Here, utilizing the Clinical Proteomic Tumor Analysis Consortium (CPTAC) mass-spectrometry-based proteomics datasets, we defined differentially expressed proteins and mRNAs associated with higher grade or higher stage, for each of seven cancer types (breast, colon, lung adenocarcinoma, clear cell renal, ovarian, uterine, and pediatric glioma), representing 794 patients. Widespread differential patterns of total proteins and phosphoproteins involved some common patterns shared between different cancer types. More proteins were associated with higher grade than higher stage. Most proteomic signatures predicted patient survival in independent transcriptomic datasets. The proteomic grade signatures, in particular, involved DNA copy number alterations. Pathways of interest were enriched within the grade-associated proteins across multiple cancer types, including pathways of altered metabolism, Warburg-like effects, and translation factors. Proteomic grade correlations identified protein kinases having functional impact in vitro in uterine endometrial cancer cells, including MAP3K2, MASTL, and TTK. The protein-level grade and stage associations for all proteins profiled—along with corresponding information on phosphorylation, pathways, mRNA expression, and copy alterations—represent a resource for identifying new potential targets. Proteomic analyses are often concordant with corresponding transcriptomic analyses, but with notable exceptions.

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Fig. 1: Proteomic and transcriptomic signatures of high grade or high stage cancers, according to cancer type.
Fig. 2: For specific cancer types, the corresponding proteomic signatures of grade or stage are associated with worse patient survival across independent patient cohorts.
Fig. 3: Proteins shared among the cancer type-specific grade or stage proteomic signatures.
Fig. 4: Copy Number Alterations (CNAs) associated with proteomic signatures of higher versus lower grade cancers.
Fig. 5: Pathways associated with proteomic or transcriptomic signatures of high grade cancers.
Fig. 6: Functional evaluation of kinases in malignant uterine cancer cells.

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Acknowledgements

This research was conducted using data made available by The Clinical Proteomic Tumor Analysis Consortium (CPTAC), The Children’s Brain Tumor Tissue Consortium (CBTTC), and The Cancer Genome Atlas (TCGA) Consortium. This work was supported by National Institutes of Health (NIH) grants P30CA125123 (CJC), P20CA221729 (MMM), R00HD096057 (DM), and a Core Facility Support Award from the Cancer Prevention Research Institute of Texas (RP160805 (MMM)). Diana Monsivais holds a PDEP Award from the Burroughs Wellcome Fund. We thank Jonathan Kurie for critical reading of the manuscript.

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Author notes

  1. These authors contributed equally: Y Vasquez, F Chen, Y Zhang

Authors and Affiliations

  1. Center for Drug Discovery, Baylor College of Medicine, Houston, TX, USA

    Diana Monsivais, Yasmin M. Vasquez, John C. Faver & Martin M. Matzuk

  2. Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, USA

    Diana Monsivais, Yasmin M. Vasquez, John C. Faver, Ramya P. Masand & Martin M. Matzuk

  3. Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, USA

    Fengju Chen, Yiqun Zhang, Michael E. Scheurer & Chad J. Creighton

  4. Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA

    Darshan S. Chandrashekar & Sooryanarayana Varambally

  5. Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA

    Darshan S. Chandrashekar & Sooryanarayana Varambally

  6. Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA

    Michael E. Scheurer

  7. Texas Children’s Cancer Center, Texas Children’s Hospital, Houston, TX, USA

    Michael E. Scheurer

  8. The Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, USA

    Sooryanarayana Varambally

  9. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Chad J. Creighton

  10. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    Chad J. Creighton

  11. Department of Medicine, Baylor College of Medicine, Houston, TX, USA

    Chad J. Creighton

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Contributions

Conceptualization: CJC, DM, YV; Methodology: CJC, FC, YZ, DM, YV; Investigation: CJC, FC, YZ, RPM, MES, DM, YV, JCF, MMM; Formal Analysis: CJC, FC, YZ, DSC, JCF; Data Curation: CJC, SV, DSC; Visualization; CJC; Writing: CJC, DM, YV; Manuscript Review: FC, YZ, DSC, RPM, MES; Supervision: CJC, SV, DM, MMM.

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Correspondence to Diana Monsivais or Chad J. Creighton.

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Monsivais, D., Vasquez, Y.M., Chen, F. et al. Mass-spectrometry-based proteomic correlates of grade and stage reveal pathways and kinases associated with aggressive human cancers. Oncogene 40, 2081–2095 (2021). https://doi.org/10.1038/s41388-021-01681-0

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