Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Abstract

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Keywords: drug resistance; oncoproteomics; phosphoproteomics; proteogenomics; single-cell proteomics.

Figure 1

Chalk talk highlighting the multiomics approach our group has taken to reveal the underlying biology of solid and liquid tumors. New mechanistic insights and therapeutic targets have emerged from our global and phosphoproteomic profiling of ovarian carcinoma (12) and AML (37). Abbreviation: AML, acute myeloid leukemia.

Figure 2

Radar plots comparing the analytical figures of merit for proteomics modes. (a) Targeted approaches include selected reaction monitoring (SRM) and internal standard triggered–parallel reaction monitoring (IS-PRM). (b) Discovery/global approaches include tandem mass tag with serial posttranslational modification enrichment (TMT-PTM) and data-independent acquisition (DIA). (c) Spatial and single-cell approaches. Protein coverage refers to the number of proteins that can be quantified in an experiment. Dynamic range is defined as the concentration range of proteins that can be accurately quantified. Reproducibility is the coefficient of variance of replicate analyses. Ease of implementation indicates how accessible the methodology is to general practitioners. Sample throughput is the number of patient samples that can be analyzed per unit time. Input requirement is defined as the amount of specimen needed for analysis with single-cell methods having the smallest sample requirement.

Figure 3

Summary of scientific workflow tools to enable precision medicine proteomic analyses. (Top) Standardized databases enable storage of data in machine-readable formats. (Middle) Public repositories, continuous integration, and container registries enable tool developers for each of the five steps (from left to right) of analysis to create state-of-the-art tools and also maintain all versions. (Bottom) Scientific workflow languages link tools in sequence as needed by scientists, who provide standardized parameter files to reproduce analysis uniformly across large cohorts. Abbreviations: API, Application Programming Interface; FDR, false discovery rate.

Figure 4

Leveraging proteogenomics in precision medicine. (Top) Sample procurement requires proper processing of clinical samples. (Right) Selection of mass spectrometry technology requires balancing trade-offs from each technology. (Left) Mapping proteogenomic measurements to clinical outcomes requires assembling diverse bioinformatic tools.