Applicability of selected reaction monitoring for precise screening tests

ABSTRACT

Introduction

The proactive identification of diseases through screening tests has long been endorsed as a means to preempt symptomatic onset. However, such screening endeavors are fraught with complications, such as diagnostic inaccuracies, procedural risks, and patient unease during examinations. These challenges are amplified when screenings for multiple diseases are administered concurrently. Selected Reaction Monitoring (SRM) offers a unique advantage, allowing for the high-throughput quantification of hundreds of analytes with minimal interferences.

Areas covered

Our research posits that SRM-based assays, traditionally tailored for single-disease biomarker profiling, can be repurposed for multi-disease screening. This innovative approach has the potential to substantially alleviate time, labor, and cost demands on healthcare systems and patients alike. Nonetheless, there are formidable methodological hurdles to overcome. These include difficulties in detecting low-abundance proteins and the risk of model overfitting due to the multiple functionalities of single proteins across different disease spectrums – issues especially pertinent in blood-based assays where detection sensitivity is constrained. As we move forward, technological strides in sample preparation, online extraction, throughput, and automation are expected to ameliorate these limitations.

Expert Opinion

The maturation of mass spectrometry’s integration into clinical laboratories appears imminent, positioning it as an invaluable asset for delivering highly sensitive, reproducible, and precise diagnostic results.

KEYWORDS:

• Screening test
• selected reaction monitoring
• FDA/LDT-target
• multi-marker panel
• multi-disease surveillance

Article highlights

• Established the practicability of simultaneous disease screening utilizing Selected Reaction Monitoring (SRM) assays.

• Introduces protein biomarkers that have received approval or clearance from the U.S. Food and Drug Administration (FDA), while also suggesting computational tools designed to enhance the robustness of SRM assays.

• Explores critical obstacles in the implementation of SRM-based diagnostic techniques within a clinical setting.

Acknowledgments

A.R.S.and H.S.K. supervised the project. A.R.S., W.J.K., W.S.L., and J.H.P. wrote the manuscript. Throughout the writing process, A.R.S. and H.S.K. provided critical revisions and feedback.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

One peer reviewer on this manuscript received an honorarium from Expert Review of Proteomics for their review work but have no other relevant financial relationships to disclose. The remaining reviewers have no other relevant financial relationships or otherwise to disclose.

Author contributions

A. Son and H. Kim contributed to the study concept and design of the study and the drafting of the manuscript. W. Kim, W. Lee, and J. Park contributed to the drafting of the manuscript.

Key definitions

Analytical Sensitivity: This refers to the ability of a diagnostic test to correctly identify the presence of a particular substance or marker (e.g. DNA, RNA, protein) at low concentrations. It’s a measure of how well the test can detect small amounts of the target analyte, with higher sensitivity indicating that the test can detect lower concentrations of the analyte.

Analytical Specificity: This is the ability of a test to correctly identify only the intended analyte without being influenced by the presence of other substances. A test with high analytical specificity would not give positive results for substances other than the target analyte, thus minimizing false positive results.

Diagnostic Accuracy: This measures the overall ability of a test to correctly identify or classify the presence or absence of a disease condition. It is typically represented as a percentage and is calculated from the test’s sensitivity (true positive rate) and specificity (true negative rate) in identifying the disease.

Analytical Validity: This encompasses both the analytical sensitivity and specificity of a test but goes further to include the entire range of the test’s ability to accurately and reliably measure the analyte of interest in the clinical samples being tested. Analytical validity is a broader term that assesses whether the test accurately and reliably identifies or measures the specific analyte or marker of interest in various conditions and sample types.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14789450.2024.2350975

Additional information

Funding

This work was supported by research fund of Chungnam National University. The authors declare that there are no conflicts of interest related to this research.