Predictive Markers for Drug Responses

Over 25 years ago, the term “precision medicine” first emerged in The Oncologist.¹ The term was coined to acknowledge the fact that patients respond to drugs differently and can have varying disease severities. Yue Xuan, Senior Product Marketing Manager at Thermo Fisher Scientific, aptly states that “individuals respond differently to drugs depending on factors that influence this response. These include lifestyle, environment, and/or molecular nature from the genome, transcriptome, proteome, and metabolome.” With each patient expressing unique phenotypes from these variables, there arises a need to precisely monitor and predict responses to drug therapies. Assessing patient responses with detailed resolution will help researchers and clinicians refine existing therapeutics and improve patient clinical outcomes.

Introducing precision medicine

Precision medicine is the study of tailoring treatment regimens to account for a patient’s genotype and phenotype.³ Precision medicine ultimately aims to generate and analyze data that can improve healthcare decisions. Any successful study of precision medicine involves developing a framework through which diseases, along with their severities, can be monitored.⁴ These efforts must also consider the dynamic nature of the human body. A patient’s needs will change over time depending on their status, availability, and other factors.⁵

To develop precision medicine, scientists have sought to identify biomarkers of disease and treatment efficacy. A biomarker is defined as a molecule that, when measured, indicates a normal biological process, a pathogenic state, or a response to an intervention or exposure.⁶ Katarina Hornaeus, Global Product Manager at Olink Proteomics, notes the potential of biomarkers for the clinic, “Biomarkers, combined with an understanding of disease mechanisms, would enable clinicians to provide patients the correct treatments early in their disease and thereby improve their clinical outcomes. Clinicians could also stratify patients before initiating treatment to ensure that patients receive the correct treatment.”

Biomarker identification with mass spectrometry

Many clinical samples, such as human plasma, contain thousands of proteins whose abundances vary by as much as 11 orders of magnitude.⁹ Each of these proteins, regardless of concentration, may serve as important biomarkers of health status, disease severity, and therapeutic responses.¹⁰ In recent decades, scientists have developed mass spectrometry as a powerful technique for identifying these proteins.

As Xuan notes, “Mass spectrometry has undergone massive improvements in resolution to identify new proteins. For one, we can quantify different isoforms of the same protein with their modifications intact. For instance, Thermo Fisher Scientific has developed the Orbitrap Exploris Mass 480 MS. Orbitrap allows for label-free quantification of several thousand proteins in tandem and the profiling of post-translational modifications in proteins. It also enables more accurate estimations relative to other mass spectrometers, reducing false-discovery rates and increasing the accuracy of protein quantifications. Technological advances like these can reveal new insights into the mechanisms of drug resistance and other diseases.”

Biomarker identification with multiplex immunoassays

Mass spectrometry is an indispensable tool for discovering novel biomarkers. These proteins, nevertheless, still require validation and verification. Immunoassays can address this need robustly and quickly, using antibodies that specifically bind to an antigen. The amount of antibody that binds to the antigen—a protein, polysaccharide, or lipid—indicates the antigen’s concentration. While initial immunoassays could only quantify one antigen in a reaction, multiplex immunoassays enable the quantification of multiple target molecules in a single reaction. These assays typically employ color-coded beads. Distinguished by the ratio of red to near-infrared dyes, the beads are conjugated with two antibodies that bind to specific antigens. One antibody helps distinguish the bead in which the analyte is bound, and the other is used for fluorescent quantification of the target analyte. This setup enables hundreds of proteins to be quantified concurrently within a single reaction to increase scalability and reduce costs.

Although multiplex immunoassays allow hundreds of potential biomarkers to be quantified, antibody-based assays may suffer from cross-reactivity. In response to this issue, modifications to multiplex immunoassays can increase specificity for biomarker targets of interest. One such modification is manifested in the proximity extension assay (PEA). As Hornaeus explains, “Our PEAs achieve higher specificity using two antibodies that must bind to the correct target for the DNA signal to be amplified, thus quantifying the biomarker of interest. This is because the two antibodies must have complimentary oligonucleotide sequences attached to them and be in proximity with each other to generate the signal.” The ability to use qPCR or next-generation sequencing to measure biomarker concentrations also enables the PEA platform to measure up to 3000 biomarkers simultaneously with data quality similar to single-plex assays. Thus, PEA provides a strong alternative for biomarker development through high-throughput measurements of possible biomarkers.

Conclusion

Precision medicine is fast becoming a life-changing approach to the future of medical research. Patients respond differently to the same medications and have diverse phenotypes for the same disease. In turn, scientists have attempted to develop biomarkers for indicating disease states and responses to therapy. Beginning with ELISAs, scientists have taken the next step by producing assays that screen hundreds of putative molecular biomarkers at the same time. Whereas mass spectrometry helps identify novel proteins for further study, multiplex immunoassays facilitate biomarker validation and verification with high scalability. Further advances to immunoassays with PEA have accompanied high-throughput screening with high sensitivity and specificity to biomarker targets.

References

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3. Kosorok MR, Laber EB. Precision Medicine. Annual Review of Statistics and Its Application. 2019;6(1):263-286. 

4. National Research Council (US) Committee on A Framework for Developing a NewTaxonomy of Disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. National Academies Press (US); 2011. Accessed February 16, 2023. 

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8. Hidalgo SJT, Lawson MT, Luckett DJ, et al. A Master Pipeline for Discovery and Validation of Biomarkers. In: Holzinger A, ed. Machine Learning for Health Informatics: State-of-the-Art and Future Challenges. Lecture Notes in Computer Science. Springer International Publishing; 2016:259-288. 

9. Anderson NL, Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics. 2002;1(11):845-867. 

10. Altelaar AM, Munoz J, Heck AJ. Next-generation proteomics: towards an integrative view of proteome dynamics. Nature Reviews Genetics. 2013;14(1):35-48.

11. Chait BT. Mass Spectrometry: Bottom-Up or Top-Down? Science. 2006;314(5796):65-66. 

12. Alhajj M, Farhana A. Enzyme Linked Immunosorbent Assay. In: StatPearls. StatPearls Publishing; 2022. Accessed November 10, 2022.