Blood test can help identify cancer in patients with non-specific symptoms

Photo Credit: Fernando Zhiminaicela

Scientific Frontline: "At a Glance" Summary

• Main Discovery: Researchers identified a specific plasma protein signature capable of detecting cancer in patients presenting with non-specific symptoms such as fatigue, pain, and weight loss.
• Methodology: The study utilized large-scale affinity proteomics to quantify 1,463 proteins in blood samples from nearly 700 patients, comparing cancer cases against a control group that included individuals with other serious non-malignant conditions.
• Key Data: The analysis isolated a distinct protein combination from the 1,463 candidates that distinguishes cancer from inflammatory, autoimmune, and infectious diseases with high precision.
• Significance: This method resolves a common clinical dilemma by effectively filtering patients with vague symptoms, preventing unnecessary invasive investigations for benign cases while ensuring timely diagnostics for cancer patients.
• Future Application: The blood test is intended to serve as a triage tool to identify which patients require prioritization for advanced imaging (PET-CT), with further validation planned for primary care environments.
• Branch of Science: Clinical Oncology and Proteomics.

Mikael Åberg.
Photo Credit: Niklas Norberg Wirtén

A simple blood test can help detect cancer in patients with non-specific symptoms such as fatigue, pain, or weight loss. This is according to a Swedish study from Karolinska Institutet, Danderyd Hospital and others, published in Nature Communications. 

Using proteomics, a method for large-scale protein analysis, the levels of 1,463 different proteins in plasma were measured. The researchers identified a specific combination of proteins, known as a protein signature, that could be linked to a cancer diagnosis. 

“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. 

Charlotte Thålin.
Photo Credit: Carin Wesström

Reflects the clinical reality 

The researchers then developed a model that can distinguish patients with cancer from those with other conditions, such as inflammatory, autoimmune, or infectious diseases, with high precision. 

“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 the Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet and principal investigator for the study. “This reflects the clinical reality, where patients with non-specific symptoms are often difficult to assess.” 

Fredrika Wannberg.
Photo Credit: Carin Wesström

Does not replace other methods 

The researchers emphasize that the method should not replace imaging diagnostics or biopsies but rather serve as a support for prioritizing which patients should be investigated further. 

“The method could help identify which patients should be prioritized for further diagnostics, for example with PET-CT, while avoiding unnecessary investigations in patients without cancer,” says Fredrika Wannberg, resident at Danderyd Hospital and doctoral student at the Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet. 

Further studies are needed before the method can be used clinically. The next step is to test it in primary care, where the incidence of cancer is lower than in specialist care. 

Funding: The research was funded by the Swedish Research Council, the Knut and Alice Wallenberg Foundation and the Jochnick Foundation, among others. The researchers declare no conflicts of interest. 

Published in journal: Nature Communications

Title: Plasma protein profiling predicts cancer in patients with non-specific symptoms

Authors: Fredrika Wannberg, María Bueno Álvez, Alvida Qvick, Tamas Pongracz, Katherina Aguilera, Emma Adolfsson, Louise Essehorn, Max Gordon, Mathias Uhlén, Gisela Helenius, Viktoria Hjalmar, Mikael Åberg, Axel Rosell, and Charlotte Thålin

Source/Credit: Karolinska Institutet | Felicia Lindberg

Reference Number: ongy012026_01

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