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

## TL;DR

A blood test using protein profiling can detect cancer in patients with vague symptoms, helping prioritize those needing further diagnostic tests.

## Contribution

A novel blood-based proteomics model is developed to identify cancer in patients with non-specific symptoms.

## Key findings

- 29 plasma proteins were found to be associated with new cancer diagnoses.
- A model with an area under the curve of 0.80 was developed to distinguish cancer from non-cancer cases.
- The model effectively differentiates cancer from autoimmune, inflammatory, and infectious diseases.

## 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 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.

Cancer detection is challenging, especially in patients with diffuse symptoms that overlap with non-malignant conditions. Here, the authors develop a proteomics- and minimally invasive blood sampling-based classification model as a triage tool to identify and prioritize individuals at high cancer risk that would benefit the most from rapid and highly sensitive diagnostic modalities.