Identification of serum inflammatory markers as classifiers of lung cancer mortality for stage I adenocarcinoma

Abstract

Background: Lung cancer is the leading cause of cancer-related mortality worldwide. Low-dose CT (LDCT) imaging is now recommended to screen high-risk lung cancer individuals in the USA. LDCT has resulted in increased detection of stage I lung cancer for which the current standard of care is surgery alone. However, approximately 30% of these patients develop recurrence and therefore are in need of further treatment upon diagnosis. This study aims to explore blood-based inflammatory biomarkers to identify patients at high-risk of mortality for which additional treatment modalities can be offered at time of diagnosis.

Patients and Methods: Recent work on a small panel of circulating cytokines identified elevated levels of IL-6, a pro-inflammatory cytokine, as an indicator of poor survival for lung cancer patients. To reflect the broader role of inflammation in lung cancer, we examined a large panel of 33 inflammatory proteins in the sera of 129 lung cancer patients selected from the National Cancer Institute-Maryland case-control study. To reduce heterogeneity, we specifically focused our study on stage I lung adenocarcinoma patients.

Results: We replicated the previous observations that IL-6 is associated with prognosis of lung cancer and extended its utility to prognosis in this highly-selected population of stage I lung adenocarcinoma patients. In addition, we developed a multi-marker, combined prognostic classifier that includes the pro-inflammatory Th-17 cell effector cytokine, IL-17. Patients with high levels of IL-6 and IL-17A had a significantly adverse survival compared with patients with low levels (P for trend <0.0001). Patients in the high risk group, with high levels of both proteins had a 5-year survival rate of 46% in comparison to 93% for those with low levels of both markers. Furthermore, we validated the same trends for the IL-6 and IL-17A prognostic signature in an independent data set.

Conclusions: The results identified here justify further investigation of this novel, combined cytokine prognostic classifier for the identification of high-risk stage I lung adenocarcinoma patients. This classifier has the much-needed potential to identify patients at high risk of recurrence and thus prospectively identify the subset of patients requiring more aggressive treatment regimens at the time of diagnosis.