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<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>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

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  • 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,
  • Charlotte Thålin

摘要

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.