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

Wannberg, Fredrika ; Álvez, María Bueno ; Qvick, Alvida ; Pongracz, Tamas ; Aguilera, Katherina ; Adolfsson, Emma ; Essehorn, Louise ; Gordon, Max ; Uhlén, Mathias and Helenius, Gisela LU , et al. (2025) In Nature Communications 17(1).
Abstract

Cancer detection is challenging, especially in patients with diffuse symptoms that overlap with non-malignant conditions. Here we show that plasma protein profiling can identify cancer among patients with non-specific symptoms. Using proximity extension assay-based proteomics of 1463 plasma proteins from 456 patients presenting with non-specific symptoms sampled prior to cancer diagnostic work-up and diagnosis, we identify 29 proteins associated with new cancer diagnoses. We develop a model able to stratify 160 cancer cases and 296 non-cancer cases with an area under the curve of 0.80, maintaining performance (0.82) in an independent replication cohort of 238 patients. The model also distinguishes cancer from autoimmune, inflammatory... (More)

Cancer detection is challenging, especially in patients with diffuse symptoms that overlap with non-malignant conditions. Here we show that plasma protein profiling can identify cancer among patients with non-specific symptoms. Using proximity extension assay-based proteomics of 1463 plasma proteins from 456 patients presenting with non-specific symptoms sampled prior to cancer diagnostic work-up and diagnosis, we identify 29 proteins associated with new cancer diagnoses. We develop a model able to stratify 160 cancer cases and 296 non-cancer cases with an area under the curve of 0.80, maintaining performance (0.82) in an independent replication cohort of 238 patients. The model also distinguishes cancer from autoimmune, inflammatory and infectious diseases. Designed as a triage tool, our model based on a blood test could help prioritize patients at higher cancer risk for rapid and highly sensitive diagnostic modalities such as positron emission tomography–computed tomography. These findings emphasize the potential of blood proteome profiling to support timely diagnosis and transform clinical medicine.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature Communications
volume
17
issue
1
article number
151
publisher
Nature Publishing Group
external identifiers
  • scopus:105026913946
  • pmid:41457066
ISSN
2041-1723
DOI
10.1038/s41467-025-67688-3
language
English
LU publication?
yes
additional info
Publisher Copyright: © The Author(s) 2025.
id
a416764e-78d8-4449-b533-756238d8d1fd
date added to LUP
2026-03-09 16:35:55
date last changed
2026-04-06 22:48:20
@article{a416764e-78d8-4449-b533-756238d8d1fd,
  abstract     = {{<p>Cancer detection is challenging, especially in patients with diffuse symptoms that overlap with non-malignant conditions. Here we show that plasma protein profiling can identify cancer among patients with non-specific symptoms. Using proximity extension assay-based proteomics of 1463 plasma proteins from 456 patients presenting with non-specific symptoms sampled prior to cancer diagnostic work-up and diagnosis, we identify 29 proteins associated with new cancer diagnoses. We develop a model able to stratify 160 cancer cases and 296 non-cancer cases with an area under the curve of 0.80, maintaining performance (0.82) in an independent replication cohort of 238 patients. The model also distinguishes cancer from autoimmune, inflammatory and infectious diseases. Designed as a triage tool, our model based on a blood test could help prioritize patients at higher cancer risk for rapid and highly sensitive diagnostic modalities such as positron emission tomography–computed tomography. These findings emphasize the potential of blood proteome profiling to support timely diagnosis and transform clinical medicine.</p>}},
  author       = {{Wannberg, Fredrika and Álvez, María Bueno and Qvick, Alvida and Pongracz, Tamas and Aguilera, Katherina and Adolfsson, Emma and Essehorn, Louise and Gordon, Max and Uhlén, Mathias and Helenius, Gisela and Hjalmar, Viktoria and Åberg, Mikael and Rosell, Axel and Thålin, Charlotte}},
  issn         = {{2041-1723}},
  language     = {{eng}},
  month        = {{12}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{Plasma protein profiling predicts cancer in patients with non-specific symptoms}},
  url          = {{http://dx.doi.org/10.1038/s41467-025-67688-3}},
  doi          = {{10.1038/s41467-025-67688-3}},
  volume       = {{17}},
  year         = {{2025}},
}