Blood-Based Proteomics for Effective Triage of Non-Specific Symptoms in Cancer Diagnosis

Blood-based proteomics shows promise for triaging hard‑to‑explain symptoms

A Swedish research team has reported that a simple blood draw, analyzed with large‑scale proteomics, can help flag cancer among patients who present with non‑specific symptoms such as fatigue, pain, or weight loss. The study, conducted across Diagnostic Centres at Danderyd Hospital and Örebro University Hospital with collaborators at Karolinska Institutet, KTH Royal Institute of Technology, and SciLifeLab Uppsala, identified a protein signature linked to an eventual cancer diagnosis and built a model that distinguished cancer from inflammatory, autoimmune, and infectious conditions. The work was published in Nature Communications in 2025.

“The study shows the potential of large-scale proteomics for extracting clinically relevant information from small amounts of blood,” says Mikael Åberg, associate professor at Uppsala University and head of SciLifeLab Affinity Proteomics Uppsala, where the analyses were performed.

“A particular strength of the study is that the control group consisted largely of patients with other serious conditions that can cause symptoms similar to cancer,” says Charlotte Thålin, senior physician at Danderyd Hospital, adjunct professor at Karolinska Institutet and principal investigator for the study. “This reflects the clinical reality, where patients with non-specific symptoms are often difficult to assess.”

Study at a glance: setting, methods, and intended use

At the heart of the work is a question facing many health systems: can routinely collected blood samples be leveraged to make earlier, safer decisions about which patients with worrying but vague symptoms should move fastest through cancer pathways?

Feature	Details
Patient cohort	Blood samples from nearly 700 adults referred for non‑specific symptoms such as fatigue, pain, weight loss, or general deterioration.
Care setting	Diagnostic Centres at Danderyd Hospital and Örebro University Hospital (Sweden), which receive referrals from primary care and other specialties.
Sample timing	Baseline blood draw at first visit, before diagnostic investigations such as imaging or biopsy began.
Proteomic scope	Quantification of 1,463 plasma proteins using a high‑plex affinity‑based platform.
Key output	Protein signature associated with an eventual cancer diagnosis; a multivariable model that differentiated cancer from other serious conditions.
Intended clinical role	Decision‑support for triage and prioritization in structured diagnostic pathways; explicitly not a replacement for imaging, histopathology, or multidisciplinary review.
Next step	Prospective testing in primary care, where cancer prevalence is lower than in specialty settings and false positives carry greater system‑wide consequences.

Triage, not diagnosis: integrating with imaging and pathology

The investigators emphasize that this approach is designed to assist triage in symptom‑based pathways rather than to supplant confirmatory diagnostics. In practice, a proteomic readout could inform which patients warrant rapid escalation to imaging or specialist review while sparing others from unnecessary procedures and radiation exposure.

“The method could help identify which patients should be prioritised for further diagnostics, for example with PET-CT, while avoiding unnecessary investigations in patients without cancer.”

Fredrika Wannberg, resident at Danderyd Hospital and doctoral student at Karolinska Institutet

For hospital leaders, the proposition is that a standardized blood‑based signal-interpreted alongside clinical assessment and existing risk tools-could support more consistent decisions about who enters fast‑track cancer work‑ups and who can be safely observed or investigated stepwise.

Why health systems are interested in non‑specific symptom pathways

Symptom‑based cancer pathways-sometimes organized as “diagnostic centres” that accept referrals for vague but concerning presentations-aim to shorten time to diagnosis while managing finite imaging and specialist capacity. Sweden, Denmark, and the United Kingdom have built variants of this model; the NHS has deployed Rapid Diagnostic Centres for patients with non‑specific symptoms to reduce multiple sequential referrals and delays.

Diagnostic uncertainty often drives repeated tests, fragmented referrals, and delayed rule‑in/rule‑out decisions.
Non‑specific symptoms are common in primary care; only a minority reflect cancer, which highlights the need for high‑specificity triage tools that avoid overloading specialist services.
Downstream capacity limits-particularly PET‑CT slots and pathology workforce-make prioritization tools attractive if they can maintain safety and public trust.

For context, readers can find NHS information on non‑specific symptom services through its publicly available materials on Rapid Diagnostic Centres.

Potential impact on capacity, equity, and safety

If validated prospectively, a proteomic triage tool could have consequences not just for individual patients but for how cancer pathways are governed and financed.

Capacity management
- More efficient allocation of PET‑CT and urgent imaging for higher‑risk patients, informing local prioritization protocols.
- Reduced use of invasive diagnostics in lower‑risk groups when supported by clinical judgment and safety‑net follow‑up.
Equity considerations
- Standardized algorithms may help reduce variability across clinics, but deployment must account for demographics, comorbidities, and social determinants to avoid embedding bias.
- Access to phlebotomy, cold‑chain logistics, and rapid laboratory turn‑around in rural or under‑resourced settings remains a practical constraint for health authorities.
Patient‑safety guardrails
- Use as adjunctive triage only; abnormal results should prompt guideline‑consistent evaluation, and normal results must not delay care when clinical suspicion is high.
- Transparent cut‑offs, reproducibility, and independent external validation are essential before routine use or reimbursement decisions.

Implementation prerequisites across health systems

Moving from proof‑of‑concept to routine practice will require alignment between clinicians, regulators, and payers. Health‑system leaders considering proteomic triage tests will be looking for evidence and safeguards in several domains:

Domain	Requirements for responsible deployment
Analytical validity	Robust assay performance across laboratories; quality control for high‑plex protein panels; lot‑to‑lot reproducibility; standard operating procedures for sample handling.
Clinical validation	Prospective, multi‑site studies in primary care and diverse populations; reporting of sensitivity, specificity, and predictive values with confidence intervals and pre‑specified endpoints.
Clinical utility	Evidence that triage with proteomics shortens time to diagnosis, optimizes imaging use, and maintains or improves stage‑at‑diagnosis and survival outcomes.
Workflow integration	Clear referral thresholds embedded in national or regional cancer pathways, electronic decision‑support, and feedback loops with imaging, pathology, and primary care services.
Data governance	Privacy protections for biomarker data; transparent model development, audit trails, and monitoring for drift or performance degradation over time.
Cost and payment	Budget impact analyses for laboratories and imaging services; payer coverage policies aligned to demonstrated clinical utility and agreed value‑based criteria.

Regulatory pathways and oversight touchpoints

Because proteomic signatures are likely to be deployed as regulated in vitro diagnostics rather than informal research tools, the governance landscape matters as much as the underlying biology.

European Union
- In vitro diagnostic devices, including proteomic assays, fall under the In Vitro Diagnostic Medical Devices Regulation (IVDR), which raises evidentiary requirements for clinical performance and post‑market surveillance.
- Hospitals pursuing in‑house assays must meet defined conditions on risk management, quality systems, and demonstration that equivalent commercial devices cannot meet local needs.
United States
- Clinical laboratories typically validate tests under CLIA requirements; broader market access often involves US Food and Drug Administration review for safety and effectiveness.
- Coverage and reimbursement can depend on demonstration of clinical utility, with evaluation by public and private payers that are increasingly focused on early‑diagnosis initiatives and value‑based cancer care.

Primary care testing will be the critical proof point

The research team plans to assess performance in primary care, where the pretest probability of cancer is substantially lower than in specialist clinics. That setting will stress‑test how well a proteomic signature preserves specificity while remaining sensitive to early disease-and whether it is acceptable to frontline clinicians and patients.

Key evaluation metrics
- Diagnostic yield among low‑prevalence populations.
- Impact on time to first imaging and time to diagnosis, compared with existing non‑specific symptom pathways.
- Rates of false reassurance, missed cancers, and unnecessary escalation.
System outcomes
- Changes in PET‑CT utilization and wait times.
- Downstream biopsy rates and stage distribution at diagnosis.
- Effects on patient‑reported experience measures in primary care and cancer services.

Funding and institutional backing

The study drew on a mix of public and philanthropic support, signaling interest from major Scandinavian research funders in proteomics‑enabled diagnostics and data‑driven pathway design.

Public and philanthropic support included:
- Swedish Research Council
- Knut and Alice Wallenberg Foundation
- Jochnick Foundation

Bottom line for health leaders

As of January 2026, high‑plex plasma proteomics is emerging as a practical tool to help clinicians triage non‑specific symptoms within structured diagnostic pathways. The Swedish study demonstrates feasibility and face validity in real‑world referral populations, with authors underscoring that imaging and biopsy remain the cornerstones of cancer diagnosis.

For health‑system executives, regulators, and payers, the technology will be judged less on its novelty and more on whether it can be integrated into existing cancer strategies without widening inequities or undermining safety. The next phase-prospective evaluation in primary care with rigorous reporting, external validation, and clear health‑system metrics-will determine whether this approach can be adopted safely, equitably, and at scale.