Researchers from the University of Eastern Finland and Kuopio University Hospital have developed a risk model to identify whether patients living with metastatic cancer could benefit from immune checkpoint inhibitors (ICIs).
The risk model, which relies on routine blood work, is a practical predictive model that could improve cancer patients’ treatment assessments.
Estimated to be responsible for around 90% of cancer deaths , metastatic cancer occurs when cancer spreads from its site of origin to another part of the body.
Used to treat a variety of different cancers, including non-small lung cancer, melanoma and glioblastoma, ICIs are antibodies which sensitise the body’s immune system to detect and destroy tumours.
However, in some instances, this treatment can influence the efficacy of ICIs in patients.
Patients receiving ICIs for metastatic cancer at the Kuopio University Hospital Cancer Centre were given a risk score of zero to six based on six inflammation-related laboratory parameters: elevated values of neutrophils, platelets, C-reactive protein, lactate dehydrogenase, erythrocyte sedimentation rate and the presence of anaemia.
Using the risk score, researchers determined two classified groups: patients with a risk score of zero to three, indicative of a good prognosis (low-risk group) and those with a risk score of four to six, indicative of a poor prognosis (high-risk group).
Approximately 53.9% of patients from the low-risk group responded to ICIs with a median overall survival of 27.3 months in comparison to the high-risk group, which had a response rate of 30.3% and a median overall survival of ten months.
Additionally, the study showed that the risk model was functional for all the common cancer types, including lung cancer, melanoma and renal cell carcinoma.
“With a risk model that predicts treatment outcomes, treatment can be better targeted at patients who are more likely to benefit from it,” said senior researcher, Aino Rönkä, University of Eastern Finland.
Not only will targeting ICIs to patients most likely to benefit from them improve cancer treatment, but it will also increase the efficacy, safety and cost-effectiveness of the treatment.
