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New approach for risk assessing cancer patients for novel immunotherapy developed

With a risk model that predicts treatment outcomes, treatment can be better targeted at patients who are more likely to benefit from it.

Patient Receives Chemotherapy. Source - National Cancer Institute/ Rhoda Baer (Photographer). Public Domain — CC0 1.0 Universal
Patient Receives Chemotherapy. Source - National Cancer Institute/ Rhoda Baer (Photographer). Public Domain — CC0 1.0 Universal

A risk model based on routine blood work predicts the treatment response and survival of metastatic cancer patients who have been treated with immune checkpoint inhibitors. This model was developed by researchers to help to identify those cancer patients who could benefit from immune checkpoint inhibitors (ICIs). ICIs function as cancer immunotherapies that boost anti-cancer immune responses by targeting immunologic receptors on the surface of T-lymphocytes.

This means ICIs are antibodies that sensitise the body’s own immune system to detect and destroy tumours. However, ICIs are not effective in all patients, and systemic cancer-related inflammation, for example, may influence the efficacy of ICIs. Therefore, medical personnel need to be careful about which patients are given the treatment.

The study was conducted at the University of Eastern Finland and Kuopio University Hospital. With the research, six inflammation-related laboratory parameters were used to create a risk model which allowed for patients receiving ICIs to be classified into low-risk and high-risk groups.

According to Senior Researcher Aino Rönkä of the University of Eastern Finland: “With a risk model that predicts treatment outcomes, treatment can be better targeted at patients who are more likely to benefit from it.”

The study cohort consisted of patients receiving ICIs for metastatic cancers at Kuopio University Hospital Cancer Centre. Patients were given a risk score of 0–6 based on the following inflammation-related laboratory parameters: elevated values of neutrophils, platelets, C-reactive protein (CRP), lactate dehydrogenase, erythrocyte sedimentation rate and the presence of anaemia.

Based on their risk score, patients were next classified into two groups: those with a risk score of 0–3 to the low-risk group indicative of a good prognosis, and those with a risk score of 4–6 to the high-risk group indicative of a poor prognosis.

In the low-risk group, 53.9 percent of patients responded to ICIs, whereas the in the high-risk group the response rate was 30.3 percent. The median overall survival was 27.3 months in the low-risk group, and 10 months in the high-risk group. The risk model appeared to be functional in all the common cancer types studied, that is lung cancer, melanoma and renal cell carcinoma.

It is expected that the risk scoring developed in the study will work as a practical predictive model that could easily be used in cancer patients’ treatment assessment. Targeting ICIs to patients most likely to benefit from them increases the efficacy, safety and cost-effectiveness of treatment. However, the researchers point out that the model still needs to be validated in a prospective, multi-centre setting.

The research appears in the journal BMC Cancer, titled “A practical prognostic peripheral blood-based risk model for the evaluation of the likelihood of a response and survival of metastatic cancer patients treated with immune checkpoint inhibitors”.

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Written By

Dr. Tim Sandle is Digital Journal's Editor-at-Large for science news. Tim specializes in science, technology, environmental, business, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.

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