Baseline Biomarkers of Immune Checkpoint Inhibitor Clearance and Efficacy in Cancer Cachexia
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Date
2024-08
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The Ohio State University
Abstract
Background: Cancer cachexia is a complex metabolic syndrome that is characterized by the loss of body weight, specifically skeletal muscle and adipose tissue. It leads to a lower quality of life and accounts for an estimated ~20% of cancer-related mortalities. Despite cancer cachexia’s impact on patient survival, it remains underrecognized and underdiagnosed. Immune checkpoint inhibitors (ICIs) are monoclonal antibodies that target the body’s immune checkpoint proteins to stimulate the immune system to fight cancer. ICI therapy has vastly improved the treatment of certain cancers but only a limited number of patients respond. The reasons for this variable response are largely unknown but the rate of ICI clearance has been shown to be a predictor of response to ICI therapy. Also, clearance has been shown to coincide with some markers of cancer cachexia. This study aimed to determine baseline relationships among biomarkers of cachexia, ICI clearance, and ICI efficacy using clinical data from cancer patients. Identifying biomarkers that link cancer cachexia and ICI clearance to outcomes may help to increase our understanding of the relationships between cancer cachexia, elevated ICI clearance, and durable responses to ICIs.
Methods: The study population is part of the ongoing non-interventional clinical trial, OSU20001. Data from 48 patients with either non-small cell lung cancer (n=37) or renal cell carcinoma (n=9), receiving pembrolizumab (n=28) or nivolumab (n=17) were analyzed which included computed tomography images, baseline clinical lab results and cytokine signatures taken at predetermined timepoints.
Results: Baseline biomarkers of interest were analyzed by linear regression against lean mass index (LMI) and ICI baseline clearance (ICI CL). In ICI CL vs. biomarkers, IL-6 (p=0.024), albumin (p=0.038), ferritin (p=0.0084), and absolute monocyte count (p=0.0016) all gained significance. In LMI vs. biomarkers, adiponectin (p=0.022), absolute neutrophil count (p=0.022), and platelet (p=0.038) gained significance. In addition to linear regression, a survival with competing risk analysis was completed based on patient’s either receiving a nivolumab or pembrolizumab treatment. In patients with nivolumab, CC5a (p=0.032), CCL5 (p<0.001), SerpinE1 (p=0.001), CRP (p=0.01), Beta2m (p=0.002), pentraxin2 (p<0.001), Alpha2m (p=0.024), and absolute eosinophil count (p=0.007) were all significant with CCL5, SerpinE1, CRP, Alpha2m, and absolute eosinophil count would be thought to increase risk of death. In patients with pembrolizumab, the biomarkers that obtained a significant p-value were ICAM1 (p=0.019), Ferritin (p=0.049), TIMP1 (p=0.038), AST (p=0.02) and LDH (p=0.045) with ICAM, ferritin, and TIMP1 thought to increase risk of death.
Conclusions: The IL-6 and albumin results agreed with previous literature findings as known markers of cachexia and clearance. There were several interesting results such as the significant relationships between BDNF, adiponectin, and platelet, and ICI CL or cachexia. Ferritin was the one biomarker that was associated with cachexia, ICI CL, and survival, suggesting that baseline ferritin could be a helpful biomarker in determining a patient’s response to ICI therapy. This new information could be helpful in determining the relationship between cancer patients, cachexia, and the variable response to ICI therapy.
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Keywords
cancer, immune checkpoint inhibitors, clinical trial, biomarkers, pharmaceutical sciences