New Test Detects Early-Stage Solid Tumors Using Only Blood Sample

However, these markers tend to become more detectable only when the tumor has significantly developed, making early detection difficult. This delay complicates treatment, as advanced-stage tumors are harder to manage effectively. Although immune system changes during the early stages of cancer have been known for over a century, they have not been utilized for diagnosis until now. Now, a new test targets these early immune disruptions in blood proteins to detect tumors at their earliest stages and even offers insight into a patient’s potential response to treatment.

This innovative test, developed by researchers at the Spanish National Cancer Research Centre (CNIO, Madrid, Spain), with support from the University of Cambridge (Cambridge, UK) and other institutions, aims to detect early-stage tumors with a blood sample. Instead of focusing on tumor-produced biomarkers, the researchers examined the body's immune response to cancer, specifically alterations in blood proteins caused by immune system disruption. Given the complexity of human plasma, which contains more than 5,000 proteins, the team narrowed their study to five amino acids: lysine, tryptophan, tyrosine, cysteine, and non-disulphide bonded cysteine. Fluorogenic reactions were applied to reveal the concentration of these amino acids, and machine learning algorithms were used to detect diagnostic patterns in the data. The test is simple to administer, requiring only a small blood sample and hospital-available reagents. The team is also developing a platform that will automate data analysis to facilitate diagnosis. Importantly, the test can differentiate cancer from other conditions such as COVID-19 and distinguish between different cancer types and stages, offering valuable information for clinical decision-making.

The test was evaluated using blood samples from 170 individuals, correctly identifying 78% of cancers with a 0% false-positive rate. The results, published in Nature Communications, showed that the test also predicted with 100% accuracy which patients would not respond to anti-metastatic treatment, and 87% accuracy in predicting responders, supporting its utility in precision medicine. Researchers highlight that the test's application extends beyond diagnosis to predicting treatment response, aiding personalized care. Although the current sample size has provided promising outcomes, larger datasets are needed to commercialize the platform. To address this, clinical trials are already underway in the UK, while other studies are ongoing in the US and China.

"Our approach has proven particularly effective in detecting tumors at an early stage, which is crucial because, if we detect them early, we can treat many types of cancer," said Gonçalo Bernardes, lead investigator of the study. ‘It is very important to note that by analyzing samples from patients with other diseases, we have found that the signals are different. For example, the immune signals of a person with SARS-CoV are different from the signals of a person with cancer, as are the signals of different types of cancer and even cancer in its different stages. We can identify all of that with our test.”

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