• Researchers at Moffitt Cancer Center have developed a computer model that can help predict which cases of non-small cell lung cancer (NSCLC) will respond to immunotherapy treatment and which will not.
• The model can also predict the risk of death after immunotherapy treatment, which can help determine who should and should not receive the therapy.

Researchers have developed a new tool that can help determine which cases of non-small cell lung cancer will not respond to immunotherapy treatment. Researchers at the Moffitt Cancer Center have created a computer prediction model that uses images from CT scans to identify people who will not benefit from the treatment.

Immunotherapy is used for treating types of cancer, including NSCLC. Immunotherapy medications help boost the immune system to make it more effective against cancer. They inhibit the interaction of three types of molecules:

• CTLA-4, or cytotoxic T-lymphocyte-associated antigen 4
• PD-1, or programmed death 1
• PD-L1, or programmed death ligand 1

These molecules usually act to dampen the immune system. The immunotherapy drugs serve to inhibit the molecules, enabling the immune system to remain active and fight the cancer.

Past research has found immunotherapy to be effective in 20 percent to 50 percent of cases of advanced NSCLC. When it works, the response is typically strong and can improve overall survival. For this new study, researchers developed their model to determine who will and will not respond well to immunotherapy before the medication is given. This can help people avoid the cost and potential side effects of unnecessary treatment that will not help them in the long run.

Doctors have looked at PD-L1 expression patterns in tumor cells as a biomarker to determine if a person would respond well to immunotherapy treatment. However, many studies have noted that this expression doesn’t always correlate with response. For example, tumors with low PD-L1 expression may still respond well to immunotherapy. Collecting and analyzing tissue samples is also time-consuming and expensive. These drawbacks prompted researchers at Moffitt Cancer Center to explore a new way to determine who would and would not respond well to immunotherapy.

The recent study — published in JNCI Cancer Spectrum — found that images from CT scans can be combined with clinical data to determine whether or not a case of NSCLC will respond to immunotherapy. This method allows doctors and researchers to look at the entire lung using standard imaging techniques, rather than employing an invasive biopsy for a small piece of tissue.

“Quantitative image-based features, or radiomics, reflect underlying pathophysiology and tumor heterogeneity and have advantages over tissue-based biomarkers as they can be rapidly extracted using standard-of-care medical images and capture data from the entire tumor rather than a small portion of the tumor that is biopsied and assayed,” stated Dr. Matthew Schabath, an associate member of the Department of Cancer Epidemiology at Moffitt and a researcher on the project.

Researchers looked at images and clinical data from 180 people with NSCLC who were treated with anti-PD-1/PD-L1 therapy — with or without anti-CTLA-4 therapy. Overall, they found 16 clinical features in the data that coordinated with response to immunotherapy. Importantly, the number of metastatic sites (where cancer has spread to other parts of the body) and the level of a protein in the blood called serum albumin were significantly associated with overall survival.

The researchers then divided the model into four groups based on the risk of death after immunotherapy treatment: low risk, moderate risk, high risk, and very high risk. This can help doctors determine who is not an ideal candidate for immunotherapy, reducing the risk of adverse treatment effects.