Protein Biomarker Signatures Outperform Single Markers in Early Disease Detection

When it comes to early disease detection, the limits of single protein biomarkers have become increasingly clear. Classic examples, such as PSA for prostate cancer or troponin for myocardial infarction, while valuable, fail to capture the complexity of a disease that may involve multiple pathways and subtle pathophysiological changes. 

Thanks to advances in multiplex proteomics, researchers can now profile hundreds or even thousands of proteins simultaneously using minimal sample volumes. Platforms such as Olink Explore (including the high-throughput Olink Explore HT) and bead-based multiplex immunoassays (e.g., Luminex xMAP technology) go beyond the one-marker-one-disease model, delivering comprehensive protein “fingerprints” that illuminate entire networks of disease-related biomarkers and thereby enhance diagnostic accuracy.

At Protavio, we leverage these advanced multiplex technologies to discover and validate multi-protein biomarker panels that enable earlier and more reliable diagnoses. Olink-certified biomarker discovery services and assay development services are integrated on the Luminex xMAP platform. This setup enables complex multiplex readouts. Combined with robust data analysis, these readouts can be translated into clinically actionable tests. The approach supports a wide range of medical conditions.

These examples underscore the transformative potential of multi-protein signature panels in improving early disease detection and effective monitoring by driving the development of next-generation in vitro diagnostics.

Why Biomarker Signatures Go Beyond Single Protein Biomarkers?

Single protein biomarker tests can be extremely useful if that one protein is highly specific for a disease. However, most conditions, particularly cancers, cardiovascular diseases, and neurodegenerative disorders, involve multiple pathways and cellular processes. A single protein may only capture one aspect of that complex pathology, leading to suboptimal accuracy or missing the disease’s earliest signals. By contrast, combining multiple proteins into a biomarker signature offers:

• Higher Sensitivity and Specificity: Different proteins can reflect various facets of the disease. A carefully selected panel integrates these signals for a more robust diagnostic readout.
  
• Earlier Detection: Disease-related proteins often change at different times. Multiplex assays can detect subtle shifts in many targets, increasing the chance of catching the disease early.
  
• Biological Insight: When a set of proteins tracks disease progression, it points toward the biological pathways involved, offering mechanistic understanding alongside diagnostic information.

Compelling Case Studies from Real-World Applications

Cancer: Multi-Protein Panels for Earlier Detection

Cancer is perhaps the clearest demonstration of how multi-analyte biomarker signatures can outperform single biomarkers. Traditional tumor biomarkers, like CA-125 for ovarian cancer or carcinoembryonic antigen (CEA) for colorectal and certain other cancers, often lack the sensitivity or specificity needed for early-stage detection. Researchers can pinpoint a small panel that best discriminates cancer from healthy or benign conditions by measuring hundreds of proteins at once.

For ovarian cancer in particular, a high-accuracy 11-protein panel detecting ovarian cancer stages I–IV was developed using Olink Explore platform for biomarker discovery. This plasma protein signature included well-established markers such as MUCIN-16 (CA-125) and WFDC2 (HE4) alongside novel proteins, together exhibiting strong diagnostic performance in validation (AUC = 0.94, sensitivity 85% and specificity 93%) [1]. Notably, this multi-protein panel outperformed individual biomarkers and, in certain settings, even matched or exceeded the diagnostic accuracy of imaging methods (transvaginal ultrasound). This highlights the potential of multiplex blood-based assays to deliver consistent, operator-independent results for early cancer detection.

Another striking example comes from gastric cancer, where a 19-protein signature identified via Olink PEA technology, achieved 93% sensitivity and 100% specificity (AUC = 0.99) for early-stage disease. Importantly, the combined panel far outperformed any single biomarker in diagnosing early-stage patients, underscoring the advantages of multiplex discovery [2]. Similarly, a recent study utilized the Olink Explore 384 Cardiometabolic panel and identified a 13-protein panel with high performance in distinguishing not only gastric cancer stage I-IV, but also the precancerous lesion high-grade intraepithelial neoplasia (HGIN) from controls [3].

These findings in ovarian and gastric cancer hint at a broader trend: multi-protein signatures demonstrate a superior ability to capture subtle biochemical indicators that single markers often fail to identify. Such an approach could enable detection of malignancies at preclinical stages, well before symptoms arise, providing a powerful diagnostic fingerprint. Similar multi-marker strategies are now being examined in other cancers (colorectal, lung etc.), leveraging high-throughput proteomics to discover signatures that might eventually support routine blood testing for early cancer detection.

Cardiovascular Disease: Moving Beyond Troponin

Cardiovascular diagnostics have historically focused on proteins like troponin (for myocardial infarction) and BNP/NT-proBNP (for heart failure). While these biomarkers are invaluable, recent research using multiplex proteomics has shown that combining cardiac injury markers with inflammatory and renal stress markers can provide a more complete picture. A notable example is the work by Siegbahn et al. (2023), who utilized Olink’s Proximity Extension Assay (PEA) technology to develop and validate the CVD-21 panel, which measures 21 proteins associated with cardiovascular risk.

By analyzing plasma samples from 4,224 chronic coronary syndrome patients, the researchers found that that models incorporating biomarkers such as MMP-12, U-PAR, REN, VEGF-D, FGF-23, TFF3, ADM, and SCF in addition to high-sensitive cardiac troponin T (hs-TnT), provided superior prognostic value for major adverse cardiovascular events, underscoring the panel’s potential in enhancing CVD risk assessment and patient management ​[4].

Neurological Diseases: Capturing Complexity

Neurological and neurodegenerative diseases often involve subtle, multifactorial disruptions across diverse biological pathways, including inflammation, proteostasis, and neuronal integrity. Multiplex proteomics offers a powerful lens to untangle these complex molecular biomarker signatures. In multiple sclerosis (MS), a chronic autoimmune neuroinflammatory condition, neurofilament light (NfL) has emerged as a promising biomarker, yet it does not always reflect the full spectrum of disease activity. Using Olink’s PEA technology, researchers identified a 21-protein biomarker signature. This signature reflects multiple aspects of MS pathology, including immune signaling, neuronal damage, and glial activation.

The discovery led to the development of the MSDA (Multiple Sclerosis Disease Activity) panel. In validation studies, the panel’s composite score showed strong correlations with MRI findings and clinical progression. It consistently outperformed individual biomarkers like NfL in tracking the disease trajectory. [5,6,7]. While not intended to replace current diagnostic standards, the MSDA panel enhances risk stratification and supports more informed treatment decisions by offering a broader, systems-level view of MS pathophysiology.

Building on this approach, a separate study leveraging Olink technology identified a four-protein panel comprising sNfL, uPA, hK8, and DSG3. This panel works as a biomarker signature that significantly improves the ability to distinguish relapses from remission in relapsing-remitting MS (RRMS) patients [8]. This multi-analyte model achieved a higher classification accuracy (AUC = 0.87) compared to sNfL alone (AUC = 0.69), highlighting the added value of protein biomarker signatures in capturing the dynamic and heterogeneous nature of MS disease activity.

Data Analysis: Converting Large Proteomic Datasets into Diagnostic Signatures

Developing a multi-protein signature is as much a data science challenge as a biochemical one. Olink and other multiplex platforms can generate massive datasets, and specialized machine learning or statistical methods are essential to identify which combinations of proteins maximize diagnostic performance. Typical steps include:

1. Feature Selection: Algorithms like elastic net regression or random forest (Boruta) sift through hundreds of proteins to find those most relevant to the disease.
   
2. Model Training and Validation: Researchers build diagnostic or prognostic models, often logistic regression or survival models, that combine selected proteins into a single “risk score” or probability metric.
   
3. Clinical Translation: The final model is validated on independent cohorts. Once confirmed, clinicians can run a simplified panel of proteins, input those values into a model, and receive a clear “disease risk” or “activity score.”
   

Looking Ahead

From cancer to cardiovascular and neurological disorders, multi-protein biomarker signatures are already demonstrating remarkable gains in early detection and risk stratification. With multiplex proteomics and robust data analytics, it is increasingly feasible to measure far more proteins per sample—and do so at scale. As computational analyses continue to advance, expect to see an expansion of custom panels and lab-developed tests that capture disease complexity in a single assay.

Key takeaways:

• In proteomics, multiplex biomarker signatures enable the simultaneous measurement of hundreds/thousands of proteins, revealing disease signatures that single markers miss.
  
• These multi-protein panels consistently outperform individual biomarkers, delivering more accurate diagnoses and, crucially, earlier detection.
  
• Rigorous data analysis (machine learning, feature selection, and validation) turns these large datasets into clinically actionable tests.
  
• As these signatures move from proof-of-concept to clinical practice, they herald a shift away from one-marker-at-a-time diagnostics toward more comprehensive, systems-level insights that can truly transform patient care.
  

As an Olink-certified service provider with extensive expertise in multiplex immunoassays and IVD development, Protavio is here to help you harness this new era of biomarker discovery and clinical diagnostics. Click here to visit our website and learn more.

References 

1. Enroth, S., Berggrund, M., Lycke, M., Broberg, J., Lundberg, M., Assarsson, E., Olovsson, M., Stålberg, K., Sundfeldt, K., & Gyllensten, U. (2019). High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer. Communications Biology, 2, 221. https://doi.org/10.1038/s42003-019-0464-9
2. Shen, Q., Polom, K., Williams, C., de Oliveira, F. M. S., Guergova-Kuras, M., Lisacek, F., Karlsson, N. G., Roviello, F., & Kamali-Moghaddam, M. (2019). A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer. EBioMedicine, 44, 322–333. https://doi.org/10.1016/j.ebiom.2019.05.044
3. Feng, T., Jie, M., Deng, K., Yang, J., & Jiang, H. (2024). Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer. Clinica Chimica Acta, 558, Article 119675. https://doi.org/10.1016/j.cca.2024.119675
4. Siegbahn, A., Eriksson, N., Assarsson, E., Lundberg, M., Ballagi, A., Held, C., Stewart, R. A. H., White, H. D., Åberg, M., & Wallentin, L. (2023). Development and validation of a quantitative proximity extension assay instrument with 21 proteins associated with cardiovascular risk (CVD-21). PLoS ONE, 18(11), e0293465. https://doi.org/10.1371/journal.pone.0293465
5. Qureshi, F., Hu, W., Loh, L., Patel, H., DeGuzman, M., Becich, M., Rubio da Costa, F., Gehman, V., Zhang, F., Foley, J., & Chitnis, T. (2023). Analytical validation of a multi‐protein, serum‐based assay for disease activity assessments in multiple sclerosis. Proteomics Clinical Applications, 17(3), e2200018. https://doi.org/10.1002/prca.202200018
6. Chitnis, T., Foley, J., Ionete, C., El Ayoubi, N. K., Saxena, S., Gaitan-Walsh, P., Lokhande, H., Paul, A., Saleh, F., Weiner, H., Qureshi, F., Becich, M. J., Rubio da Costa, F., Gehman, V. M., Zhang, F., Keshavan, A., Jalaleddini, K., Ghoreyshi, A., & Khoury, S. J. (2023). Clinical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis. Clinical Immunology, 253, 109688. https://doi.org/10.1016/j.clim.2023.109688
7. Jalaleddini, K., Jakimovski, D., Keshavan, A., McCurdy, S., Leyden, K., Qureshi, F., Ghoreyshi, A., Bergsland, N., Dwyer, M. G., Ramanathan, M., Weinstock‐Guttman, B., Benedict, R. H. B., & Zivadinov, R. (2024). Proteomic signatures of physical, cognitive, and imaging outcomes in multiple sclerosis. Annals of Clinical and Translational Neurology, 11(3), 729–743. https://doi.org/10.1002/acn3.51996
8. Gawde, S., Agasing, A., Bhatt, N., Toliver, M., Kumar, G., Massey, K., Nguyen, A., Mao-Draayer, Y., Macwana, S., DeJager, W., Guthridge, J. M., Pardo, G., Dunn, J., & Axtell, R. C. (2022). Biomarker panel increases accuracy for identification of an MS relapse beyond sNfL. Multiple Sclerosis and Related Disorders, 63, Article 103922. https://doi.org/10.1016/j.msard.2022.103922