Systemic Inflammation Index and Tumor Glycolytic Heterogeneity Help Risk Stratify Patients with Advanced Epidermal Growth Factor Receptor-Mutated Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitor Therapy

Kun-Han Lue; Chun-Hou Huang; Tsung-Cheng Hsieh; Shu-Hsin Liu; Yi-Feng Wu; Yu-Hung Chen

doi:10.3390/cancers14020309

Systemic Inflammation Index and Tumor Glycolytic Heterogeneity Help Risk Stratify Patients with Advanced Epidermal Growth Factor Receptor-Mutated Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitor Therapy

Cancers (Basel). 2022 Jan 8;14(2):309. doi: 10.3390/cancers14020309.

Authors

Kun-Han Lue¹, Chun-Hou Huang², Tsung-Cheng Hsieh³, Shu-Hsin Liu^{1

4}, Yi-Feng Wu^{5

6}, Yu-Hung Chen^{4

5}

Affiliations

¹ Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien 970302, Taiwan.
² Department of Nursing, Tzu Chi University, Hualien 970374, Taiwan.
³ Institute of Medical Sciences, Tzu Chi University, Hualien 970374, Taiwan.
⁴ Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan.
⁵ School of Medicine, College of Medicine, Tzu Chi University, Hualien 970374, Taiwan.
⁶ Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan.

Abstract

Tyrosine kinase inhibitors (TKIs) are the first-line treatment for patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma. Over half of patients failed to achieve prolonged survival benefits from TKI therapy. Awareness of a reliable prognostic tool may provide a valuable direction for tailoring individual treatments. We explored the prognostic power of the combination of systemic inflammation markers and tumor glycolytic heterogeneity to stratify patients in this clinical setting. One hundred and five patients with advanced EGFR-mutated lung adenocarcinoma treated with TKIs were retrospectively analyzed. Hematological variables as inflammation-induced biomarkers were collected, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation index (SII). First-order entropy, as a marker of heterogeneity within the primary lung tumor, was obtained by analyzing ¹⁸F-fluorodeoxyglucose positron emission tomography images. In a univariate Cox regression analysis, sex, smoking status, NLR, LMR, PLR, SII, and entropy were associated with progression-free survival (PFS) and overall survival (OS). After adjusting for confounders in the multivariate analysis, smoking status, SII, and entropy, remained independent prognostic factors for PFS and OS. Integrating SII and entropy with smoking status represented a valuable prognostic scoring tool for improving the risk stratification of patients. The integrative model achieved a Harrell's C-index of 0.687 and 0.721 in predicting PFS and OS, respectively, outperforming the traditional TNM staging system (0.527 for PFS and 0.539 for OS, both p < 0.001). This risk-scoring model may be clinically helpful in tailoring treatment strategies for patients with advanced EGFR-mutated lung adenocarcinoma.

Keywords: epidermal growth factor receptor (EGFR); lung adenocarcinoma; prognostic biomarker; systemic inflammation index (SII); tumor heterogeneity; tyrosine kinase inhibitor (TKI).