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With an estimated 1.8 million new cases in 2018 alone, prostate cancer is the fourth most common cancer in the world. Catching the disease early increases the chances of survival, but this cancer remains difficult to detect.
The best diagnostic test currently available measures the blood level of a protein called the prostate-specific antigen (PSA for short). Heightened amounts of PSA may mean that the patient has cancer, but 15% of individuals with prostate cancer have normal levels of the protein, and many healthy people can have high amounts of PSA. This blood test is therefore not widely accepted as a reliable diagnostic tool.
Other methods exist to detect prostate cancer, yet their results are limited. A small piece of the prostate can be taken for analysis, but results from this invasive procedure are often incorrect. Scans can help to spot a tumor, but they are not accurate enough to be conclusive on their own. New tests are therefore urgently needed.
Prostate cancer is often associated with changes in the immune system that can be detected through a blood test. In particular, the appearance of a type of white blood (immune) cells called natural killer cells may be altered. Yet, it was unclear whether measurements based on these cells could help to detect prostate cancer and assess the severity of the disease.
Here, Hood, Cosma et al. collected and examined the natural killer cells of 72 participants with slightly elevated PSA levels and no other symptoms. Amongst these, 31 individuals had prostate cancer and 41 were healthy. These biological data were then used to produce computer models that could detect the presence of the disease, as well as assess its severity. The algorithms were developed using machine learning, where previous patient information is used to make prediction on new data. This work resulted in a new detection tool which was 12.5% more accurate than the PSA test in detecting prostate cancer; and in a detection tool that was 99% accurate in predicting the risk of the disease (in terms of clinical significance) in individuals with prostate cancer.
Although these new approaches first need to be validated in the clinic before being deployed, they could ultimately improve the detection and diagnosis of prostate cancer, saving lives and reducing the need for further tests.