Model Predicts Non-Small Cell Lung Cancer Patient Outcomes to Immunotherapy
Researchers found that the levels of serum albumin and the number of metastatic sites that patients with lung cancer have were significantly associated with overall survival.
Researchers at Moffitt Cancer Center have developed a model to predict the outcomes of patients with non-small cell lung cancer (NSCLC) following immunotherapy, according to a press release.
Immunotherapy agents that inhibit programmed cell death protein 1 (PD1), programmed death-ligand 1 (PD-L1), or the CTLA-4 protein have become widely used in the treatment of NSCLC. Approximately 20% to 50% of patients with advanced NSCLC have strong and prolonged responses to immunotherapy, but the rest of patients often have poor responses. Because of this, the researchers said there is an urgent need to identify biomarkers that can predict which patients will not respond to therapy.
PD-L1 expression measured in a patient’s tumor is commonly used to determine which patients should be treated with anti-PD1 or anti-PD-L1 therapy. However, earlier research has shown that patients may be responsive to these agents even with low PD-L1 expression levels. Other tissue-based biomarkers may be cost-prohibitive or may require a quality and quantity of tissue that could be difficult to obtain.
In an effort to address these issues, researchers at Moffitt Cancer Center created a prediction model that includes information calculated from computed tomography (CT) images that can identify patients who are not likely to respond to immunotherapy. Rather than analyzing common tissue-based biomarkers, the team assessed the potential of using characteristics from pre-treatment CT scans combined with clinical data to identify markers associated with immunotherapy outcomes.
“Quantitative image-based features, or radiomics, reflect the 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,” said Matthew Schabath, PhD, an associate member of the Department of Cancer Epidemiology at Moffitt, in the press release.
The team analyzed clinical characteristics and radiomics features from 180 patients with NSCLC who were treated with anti-PD1/PD-L1 with or without anti-CTLA-4 therapy.
“Our goal was to create a parsimonious model, known as a simple model with the fewest variables and the greatest predictive power possible,” said Bob Gillies, PhD, senior member and chair of the Department of Cancer Physiology, in the press release.
Among 16 clinical features considered, the researchers found that the levels of serum albumin and the number of metastatic sites a patient had were significantly associated with overall survival. Among 213 radiomic features, gray level cooccurrence matrix (GLCM) inverse difference was associated with overall survival (OS), according to the study.
After performing statistical analysis and data modeling, the team found that these characteristics were appropriate parameters for inclusion within the model. Participants were divided into 4 groups according to risk of death following immunotherapy: low risk, moderate risk, high risk, and very high risk.
The researchers validated their model in 2 additional patient populations and confirmed that the group with very high risk had an extremely poor OS after immunotherapy, with a 3-year OS rate of 0%. The group with low risk had a 3-year OS of approximately 40%.
Furthermore, they discovered that the radiomic feature GLCM inverse difference was associated with expression of the gene CAIX, which is involved in tumor hypoxia and regulation of tumor growth and metastasis. Because hypoxia has important implications for all types of cancer development, these results suggest that GLCM inverse difference may be a potential predictor for patient responses to other anti-cancer drugs.
“These results suggest very high-risk patients should either avoid immunotherapy altogether or utilize upfront combination treatments that may yield an improved response,” Schabath said in the press release. “We hope with further study this model can be used to change clinical practice and allow patients to avoid drugs they may not have a response to.”
Moffitt Researchers Develop Model to Predict Non-Small Cell Lung Cancer Patient Outcomes to Immunotherapy. Moffitt Cancer Center; August 20, 2021. Accessed August 24, 2021. https://moffitt.org/newsroom/press-release-archive/moffitt-researchers-develop-model-to-predict-non-small-cell-lung-cancer-patient-outcomes-to-immunotherapy/