Oncotarget

Research Perspectives:

Using cancer proteomics data to identify gene candidates for therapeutic targeting

Diana Monsivais, Sydney E. Parks, Darshan S. Chandrashekar, Sooryanarayana Varambally and Chad J. Creighton _

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Oncotarget. 2023; 14:399-412. https://doi.org/10.18632/oncotarget.28420

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Abstract

Diana Monsivais1,2,3, Sydney E. Parks1,2,4, Darshan S. Chandrashekar5,6,7, Sooryanarayana Varambally5,6,8 and Chad J. Creighton3,9,10

1 Center for Drug Discovery, Baylor College of Medicine, Houston, TX 77030, USA

2 Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA

3 Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA

4 Cancer and Cell Biology Program, Baylor College of Medicine, Houston, TX 77030, USA

5 O’Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35233, USA

6 Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA

7 Genomic Diagnostics and Bioinformatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA

8 The Informatics Institute, University of Alabama at Birmingham, Birmingham, AL 35233, USA

9 Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA

10 Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA

Correspondence to:

Chad J. Creighton, email: [email protected]

Keywords: proteomics; proteogenomics; multi-omics; cancer; TTK protein kinase

Received: March 08, 2023     Accepted: April 24, 2023     Published: May 04, 2023

Copyright: © 2023 Monsivais et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ABSTRACT

Gene-level associations obtained from mass-spectrometry-based cancer proteomics datasets represent a resource for identifying gene candidates for functional studies. When recently surveying proteomic correlates of tumor grade across multiple cancer types, we identified specific protein kinases having a functional impact on uterine endometrial cancer cells. This previously published study provides just one template for utilizing public molecular datasets to discover potential novel therapeutic targets and approaches for cancer patients. Proteomic profiling data combined with corresponding multi-omics data on human tumors and cell lines can be analyzed in various ways to prioritize genes of interest for interrogating biology. Across hundreds of cancer cell lines, CRISPR loss of function and drug sensitivity scoring can be readily integrated with protein data to predict any gene’s functional impact before bench experiments are carried out. Public data portals make cancer proteomics data more accessible to the research community. Drug discovery platforms can screen hundreds of millions of small molecule inhibitors for those that target a gene or pathway of interest. Here, we discuss some of the available public genomic and proteomic resources while considering approaches to how these could be leveraged for molecular biology insights or drug discovery. We also demonstrate the inhibitory effect of BAY1217389, a TTK inhibitor recently tested in a Phase I clinical trial for the treatment of solid tumors, on uterine cancer cell line viability.


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