Journal of Pain Research (Feb 2020)
Applying Serum Cytokine Levels to Predict Pain Severity in Cancer Patients
Abstract
Jennifer Fazzari,1 Jesse Sidhu,1 Shreya Motkur,1 Mark Inman,2 Norman Buckley,3 Mark Clemons,4,5 Lisa Vandermeer,5 Gurmit Singh1,3 1Department of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario, Canada; 2Department of Medicine, McMaster University, Hamilton, Ontario, Canada; 3Michael G. DeGroote Institute for Pain Research and Care, McMaster University, Hamilton, Ontario, Canada; 4Department of Medicine, Division of Medical Oncology, The Ottawa Hospital, Ottawa, Canada; 5Cancer Research Program, Ottawa Hospital Research Institute and University of Ottawa, Ottawa, CanadaCorrespondence: Gurmit SinghDepartment of Pathology & Molecular Medicine, McMaster University, 1280 Main Street West, Hamilton, ON, L8N 3Z5, CanadaTel +1 905 525 9140 Ext 28144Email [email protected] and Aim: Cancers originating in the breast, lung and prostate often metastasize to the bone, frequently resulting in cancer-induced bone pain that can be challenging to manage despite conventional analgesic therapy. This exploratory study’s aim was to identify potential biomarkers associated with cancer-induced pain by examining a sample population of breast cancer patients undergoing bisphosphonate therapy.Methods: A secondary analysis of the primary study was performed to quantify serum cytokine levels for correlation to pain scores. Cytokines with statistically significant correlations were then input into a stepwise regression analysis to generate a predictive equation for a patient’s pain severity. In an effort to find additional potential biomarkers, correlation analysis was performed between these factors and a more comprehensive panel of cytokines and chemokines from breast, lung, and prostate cancer patients.Results: Statistical analysis identified nine cytokines (GM-CSF, IFNγ, IL-1β, IL-2, IL-4, IL-5, IL-12p70, IL-17A, and IL-23) that had significant negative correlations with pain scores and they could best predict pain severity through a predictive equation generated for this specific evaluation. After performing a correlation analysis between these factors and a larger panel of cytokines and chemokines, samples from breast, lung and prostate patients showed distinct correlation profiles, highlighting the clinical challenge of applying pain-associated cytokines related to more defined nociceptive states, such as arthritis, to a cancer pain state.Conclusion: Exploratory analyses such as the ones presented here will be a beneficial tool to expand insights into potential cancer-specific nociceptive mechanisms and to develop novel therapeutics.Keywords: biomarkers, cancer-induced pain, nociception, immune factors
