Quantitative Phosphoproteomics in Cancer Research: Pathways to Targeted Therapies

By Reginald DaveyReviewed by Lily Ramsey, LLM

Aberrant phosphorylation is associated with multiple pathologies, including cancer. Understanding the role this plays in the formation and progression of cancer is therefore essential for treating this major killer.

In the past two decades, phosphoproteomics has emerged as an important scientific field in the analysis of phosphorylation sites in both normal and pathological conditions. This helps clinical scientists better understand pathologies such as cancer. This article will analyze this subject.

​​​​​​​Image Credit: PeopleImages.com - Yuri A/Shutterstock.com

Phosphoproteomics: Foundations and Techniques

Phosphorylation occurs on the side chains of amino acids (typically serine, tyrosine, and threonine) in mammalian cells. Although these are the three most common amino acids it affects, it can also occur on other residues.

The addition of a phosphate group to these amino acid residues can have multiple effects, such as altering a protein’s stability, conformation, activity, or a myriad of altered protein-protein interactions.¹

Therefore, phosphorylation plays a key role in regulating signaling networks, both inter- and intracellular. It is also critical for signal transduction pathways and is reversible. Phosphorylation-mediated signaling cascades regulate many major cellular processes, such as apoptosis, migration, and proliferation.¹

Since the discovery of phosphorylation, scientists have been interested in its intimate role in a myriad of biological functions. This has led to the formation of the field of phosphoproteomics, which was pioneered in 2002 by Ficarro et al. This scientific field concerns the large-scale analysis of phosphorylation sites in biological cells.

The field has seen significant development over the intervening decades. While it uses various instruments and approaches, mass spectrometry-based techniques are typically employed. Other techniques used in phosphoproteomics include different forms of western blotting, reverse-phase protein microarrays, immunohistochemistry, and immunofluorescence.¹

Applications in Cancer Research

A phosphoproteomics approach can provide unique information on signaling pathway activity that genetic or transcriptomic techniques cannot. This is useful for several clinical fields, including cancer research.

Cancer is caused by mutated genes and, if untreated, is usually fatal. Mutations associated with cancer include transcriptional over- or under-expression and copy number alterations. Phosphorylation cooperates with enzymes and other regulatory mechanisms, regulating intracellular signal fluxes and other biological processes.²

Studying the phosphoproteome is critical for the understanding of oncogenic biology. As phosphorylation sites are the subject of kinase activity, understanding the phosphoproteome provides key information on pathway activities in a tumor which are driven by kinases.

Thus, oncogenic pathway activation can be understood irrespective of whether it occurs via genetic, epigenetic, or micro-environmental means.²

Quantitative phosphoproteomics, which uses methods such as label-free SILAC (stable isotope labeling with amino acids in cell culture) and TMT (tandem mass tags) enables the discovery and identification of several new anti-cancer targets for drug therapies.

The field has many applications in cancer research, such as signaling dynamics, exploring drug modes of action, personalized medicine, cancer phenotyping, understanding disease mechanisms, and multi-omic integration.

Kinase-substrate enrichment analysis (KSEA) is one phosphoproteomics approach that provides a reliable method of characterizing kinase activity.

This quantitative approach is better than analyzing individual phosphorylation sites, and many studies have followed in the wake of its development as a technique for unveiling phosphorylation sites involved in cancer development and progression.

For instance, the inferred kinase activity (INKA) algorithm was developed by the Jimenez laboratory, and has been successfully used to treat acute myeloid leukaemia cell lines by Cordeo et al.²

Data from phosphoproteomics studies has also been used to reconstruct kinase networks involved in tumor formation. Phonemes, developed by the Saez-Rodriguez laboratory, uses Gaussian mixture models of phosphorylation sites and has identified CDK regulation by MTOR.

Furthermore, a global analysis using a KSEA-based approach has provided significant data on kinase-kinase activity in cancer biology.²

Challenges in Phosphoproteomic Analysis

Despite significant progress in the field since its inception, several key challenges persist with phosphoproteomics research. For one, phosphorylation is fast, dynamic, and prone to errors.

Additionally, stoichiometric information can often be missing during analysis, begging the question as to how many phosphorylation sites are relevant, and which are just “noise.”³

Other issues include poor behavior during chromatography, a lack of label-free quantification, and general limitations of peptide-centric proteomics.

While these challenges persist, special attention to study design and the quality of obtained data can help overcome them. Moreover, improvements in technologies such as mass spectrometry are starting to solve many historical issues within the field.

Integrating Phosphoproteomics with Other Omics Approaches

Integrating phosphoproteomics with other omics approaches provides a highly promising avenue of cancer research that can overcome some of the historical issues in the field.

A growing number of studies are focusing on this approach, although many still do not include phosphoproteomics data.⁴

Integrating data from phosphoproteomics studies can help uncover more robust biomarkers and therapeutic targets than single omics approaches by providing clues to signaling events that other approaches, such as metabolomics and genomics, cannot.

Thus, this approach can provide a more holistic view of how cancer forms and progresses, vastly improving clinical outcomes for patients.

Future Directions in Phosphoproteomics for Targeted Cancer Therapy

Like all scientific fields, quantitative phosphoproteomics can benefit from the implementation of Industry 4.0 technologies such as AI, machine learning, and neural networks.

These can help aid the discovery of new phosphorylation sites and potential therapeutic targets by vastly improving the rapid acquisition of relevant data, training computers to look for new targets in a more efficient manner.

Further integration of phosphoproteomics with other omics-based approaches will also serve to improve the discovery of phosphorylation sites, and real-time phosphoproteomic monitoring is becoming a reality.

Furthermore, CLIA-approved methods, improved computational methods based on systems-level quantitative analysis and continued improvement in sensitivity are helping to push the field forward.¹

Final Thoughts

Cancer has an extraordinarily complex pathology that is still poorly understood, although massive strides in therapy have been made over the past few decades thanks to fields such as quantitative phosphoproteomics.

Scientists now better understand phosphorylation's key role in pathologies such as cancer.

Whilst key challenges still exist, improvements in technology and the identification of multiple cancer-relevant phosphorylation sites are providing new drug targets that could help save countless lives worldwide every year.

References and Further Reading

1. Gerristen, J.S & White, F.M. (2021) Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells Expert Rev Proteomics 18(8) pp. 661-674 [online] PubMed Central. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8628306/ (Accessed on 14 May 2024)
2. Higgins, L et al. (2023) Principles of phosphoproteomics and applications in cancer research Portland Press Opt2Play 480(6) pp. 403-420 [online] PubMed Central. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212522/ (Accessed on 14 May 2024)
3. Solari, F.A et al. (2015) Why phosphoproteomics is still a challenge Molecular Biosystems 11 pp. 1487-1493 [online] pubs.rcs.org. Available at: https://pubs.rsc.org/en/content/articlelanding/2015/mb/c5mb00024f (Accessed on 14 May 2024)
4. Mantini, G et al. (2020) Computational Analysis of Phosphoproteomics in Multi-Omics Cancer Studies Proteomics and Systems Biology 21(3-4) 1900312 [online] Wiley Analytical Science. Available at: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.201900312 (Accessed on 14 May 2024)