Abstract
Background
This study investigated the relationship between inflammatory biomarkers (lymphocyte ratio [NLR], monocyte‐to‐lymphocyte ratio [MLR], and platelet‐to‐lymphocyte ratio [PLR]) and the treatment outcomes of patients with non‐small cell lung cancer (NSCLC) treated with afatinib.
Methods
The patients with NSCLC treated with afatinib between June 2014 and February 2018 were retrospectively reviewed. Their inflammatory biomarkers and clinical outcomes (progression‐free survival [PFS] and tumor response) were explored using univariate and multivariate analyses.
Results
Among 325 patients, those with an NLR >2.18, MLR >0.19, and PLR >177.73 had significantly worse PFS than those with lower values. After adjusting for performance status, stage, and liver metastasis, the PFS was still unfavorable for a baseline NLR >2.18, MLR >0.19, or PLR > 177.73. Among 188 patients with paired inflammatory values, those whose NLR decreased by >29.5%, MLR decreased by >57.9%, and PLR increased by <18.8% had significantly better PFS. After adjusting for performance status, stage, and liver metastasis, the PFS was significantly unfavorable for an NLR decrease of <29.5% and MLR decrease of <57.9%. Among the patients with tumor response, NLR, MLR, and PLR significantly decreased after treatment (all p < 0.05).
Conclusions
Our study presented the NLR, MLR, and PLR as prognostic factors for patients with NSCLC treated with afatinib. Further investigation into these markers representing the tumor microenvironment and their association with cancer status is crucial for evaluating prognosis and clinical outcomes in patients with NSCLC.
Keywords: monocyte‐to‐lymphocyte ratio, neutrophil‐to‐lymphocyte ratio, platelet‐to‐lymphocyte ratio, prognostic factors, treatment outcomes
EGFRm NSCLC patients (n = 325). EGFRm NSCLC patients (n = 188). Afatinib for 1–3 months. Unfavorable prognostic factors for PFS. Baseline inflammatory values. NLR >2.18, MLR >0.19. PLR >177.73. Changes of inflammatory values. NLR decrease of <29.5%. MLR decrease of <57.9%.
INTRODUCTION
Lung cancer is the leading cause of cancer‐related mortality in both sexes worldwide, ranking as the leading cause of cancer‐related deaths in men and the second leading cause in women. 1 Non‐small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases. Despite advances in diagnostic and treatment modalities, the prognosis of patients with NSCLC remains discouraging. 2 Epidermal growth factor receptor (EGFR) mutations are found in approximately 20% of Caucasian patients and 50%–60% of Asian patients with NSCLC. 3 Afatinib, a second‐generation and irreversible EGFR tyrosine kinase inhibitor (TKI), is one of the preferred first‐line treatments for Asian patients with NSCLC. 4 , 5 The response rate of EGFR‐mutated NSCLC is approximately 60% with afatinib, 6 , 7 , 8 indicating that responses vary among patients. Therefore, an unmet need exists for effective prognostic factors that enable clinicians to understand the prognosis and implement more effective therapeutic strategies.
The microenvironment plays a pivotal role in tumorigenesis, with angiogenesis and inflammation recognized as major tumorigenesis hallmarks. 9 , 10 Consequently, various inflammatory parameters are used to assess inflammatory status within this context. Neutrophils can directly and indirectly promote cancer growth by altering the microenvironment through cytokine and chemokine secretion, potentially impairing the antitumor immune response. 11 , 12 Monocytes play a significant role in the inflammatory processes in various solid tumors, promoting tumorigenesis and invasion. 13 Platelets also contribute to angiogenesis, increased tumor invasion, lymph node metastasis, 14 and enhanced tumor cell growth and progression by affecting the inflammatory state and facilitating cellular interactions. 15 Lymphocytes are crucial in immunological responses contributing to tumor defense. 16 Therefore, since these cells play different roles in tumor progression, the neutrophil‐to‐lymphocyte ratio (NLR), monocyte‐to‐lymphocyte ratio (MLR), and platelet‐to‐lymphocyte ratio (PLR) may be good indicators for the tumor response and survivals, and have been reported as prognostic factors in several malignancies. 17 , 18 , 19 , 20
Since the immune response plays a crucial role in tumor development and progression within the body, evaluating the extent of inflammation in cancer cases may provide valuable insights as a clinical prognostic indicator. This study aimed to investigate the association between the baseline values and changes in inflammatory biomarkers and clinical outcomes in patients with EGFR‐mutated NSCLC treated with afatinib.
METHODS
Patients and data collection
Patients' data were obtained from the Cancer Registry System using the Chang Gung Research Database. 21 The patients with NSLCL starting on afatinib at Linkou Chang Gung Memorial Hospital (CGMH) between June 2014 and February 2018 with available NLR, MLR, and PLR data before treatment were enrolled. Their clinicopathological features, including age, sex, performance score, smoking history, morphology, EGFR mutation type, clinical stage, organ metastasis, and tumor response, were retrospectively reviewed. The NLR, MLR, and PLR values were collected at baseline and within 12 weeks (at least 4 weeks) after therapy initiation. In the second part of the analysis, we examined whether changes in these biomarkers within 12 weeks after treatment initiation could predict clinical outcomes. The last follow‐up date was May 2021. This study was approved by the Institutional Review Board of CGMH (approval no.: 201901395B0). The requirement for patient consent to participate was waived since this study was retrospective.
Treatment and response evaluation
Physicians may adjust the afatinib dose and schedule based on the clinical status and adverse events observed during treatment. Tumor response was assessed based on the Response Evaluation Criteria in Solid Tumors 1.1 guidelines and categorized as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). 22 The patients had no assessment of tumor response before discontinuation of treatment were recorded “not assessed” (NA). Progression‐free survival (PFS) was defined as the duration from the initiation of afatinib to the earliest evidence of radiologically confirmed disease progression, the latest afatinib dose, mortality, or the most recent follow‐up time point. Patients with radiological progression or who died within one month after discontinuing EGFR‐TKIs without subsequent treatment were counted as events. Patients who did not experience disease progression or death until the last follow‐up time were treated as censored data in the PFS survival analysis.
Statistical analysis
The distributions of baseline characteristics, tumor responses, and adverse events of study patients with different blood‐based biomarker values were compared using the t‐test for continuous variables and Fisher's exact test for categorical variables. A series of univariate Cox proportional hazards models were used to initially screen the possibly associated factors with PFS. Those variables whose significance was <0.15 in the univariate Cox models were further included in a multivariate Cox model. 23 Finally, the pairwise comparisons of different blood‐based biomarker values with tumor response and PFS were stratified by several baseline characteristics. A two‐sided p‐value of <0.05 was considered statistically significant. All statistical analyses were performed using SAS software (version 9.4; SAS Institute, Cary, NC, USA).
RESULTS
Summary of baseline information
The analysis of baseline blood‐based biomarkers included 325 patients. Among them, 54.8% were aged <65 years and 56.6% were female. Most patients with NSCLC had a performance score of 0 or 1 (82.8%) and were nonsmokers (70.2%). The median values were 3.33 (range: 0.55–97.50) for the NLR, 0.29 (range: 0.09–3.50) for the MLR, and 174.93 (range: 41.58–3188.12) for the PLR. Adenocarcinoma was the most common morphology (99.1%). Exon 19 deletion and the L858R mutation accounted for 55.7% and 44.3% of all EGFR mutations, respectively. Most patients were classified as stage IV (92.3%), with only 25 (7.7%) in stage III. Liver metastasis was present in 13.2% of patients, while brain metastasis was present in 31.1%. Regarding the number of metastatic sites, 40.9% of patients had 0 or 1 metastatic organ, 47.7% had two or three metastatic organs, and 12% had >4 metastatic organs. Regarding the response to afatinib, 68.3% of patients showed a CR or PR, 17.2% had SD, and 14.5% had PD. Among the patients who received treatment after 1–3 months, 188 had paired data available for review. Their clinical and demographic characteristics were similar to the entire cohort (Table 1).
TABLE 1.
Baseline characteristics.
| Parameters | Baseline (n = 325) | After 1–3 months (n = 188) | ||
|---|---|---|---|---|
| No. | Median (range) or (%) | No. | Median (range) or (%) | |
| Age (years) | ||||
| ≦65 | 178 | 54.8 | 108 | 57.4 |
| >65 | 147 | 45.2 | 80 | 42.6 |
| Sex | ||||
| Male | 141 | 43.4 | 79 | 42.0 |
| Female | 184 | 56.6 | 109 | 58.0 |
| Performance score | ||||
| 0/1 | 269 | 82.8 | 159 | 84.6 |
| 2/3/4 | 56 | 17.2 | 29 | 15.4 |
| Smoking | ||||
| No | 228 | 70.2 | 134 | 71.3 |
| Yes | 94 | 28.9 | 52 | 27.7 |
| Unknown | 3 | 0.9 | 2 | 1.1 |
| NLR | 3.33 (0.55–97.50) | 2.79 (0.83–95.00) | ||
| MLR | 0.29 (0.09–3.50) | 0.25 (0.01–3.00) | ||
| PLR | 174.93 (41.58–3188.12) | 155.99 (34.88–1246.27) | ||
| Morphology | ||||
| Adenocarcinoma | 322 | 99.1 | 186 | 98.9 |
| Nonadenocarcinoma | 3 | 0.9 | 2 | 1.1 |
| Mutation | ||||
| Exon 19 deletion | 181 | 55.7 | 111 | 59.0 |
| L858R | 144 | 44.3 | 77 | 41.0 |
| Stage | ||||
| III | 25 | 7.7 | 17 | 9.0 |
| IV | 300 | 92.3 | 171 | 91.0 |
| Liver metastasis | ||||
| Yes | 43 | 13.2 | 29 | 15.4 |
| No | 282 | 86.8 | 159 | 84.6 |
| Brain metastasis | ||||
| Yes | 101 | 31.1 | 62 | 33.0 |
| No | 224 | 68.9 | 126 | 67.0 |
| Metastatic organ | ||||
| 0/1 | 133 | 40.9 | 74 | 39.4 |
| 2/3 | 153 | 47.1 | 89 | 47.3 |
| ≧4 | 39 | 12.0 | 25 | 13.3 |
| Response | ||||
| CR/PR | 222 | 68.3 | 125 | 66.5 |
| SD | 56 | 17.2 | 26 | 13.8 |
| PD/NA | 47 | 14.5 | 37 | 19.7 |
Abbreviations: CR, complete response; NLR, neutrophil‐to‐lymphocyte ratio; MLR, monocyte‐to‐lymphocyte ratio; NA, not assessed; PD, progressive disease; PLR, platelet‐to‐lymphocyte ratio; PR, partial response; SD, stable disease.
Associations between pretreatment blood‐based biomarkers and clinical outcomes
The associations between pretreatment blood‐based biomarkers and clinical outcomes were explored (Figure 1). Lower pretreatment NLRs (≤2.18; n = 84), MLRs (≤0.19; n = 63), and PLRs (≤177.73; n = 167) were significantly associated with longer median PFS (Figure 1). Additional information regarding the selection of the cutoffs is provided in Figure S1. Among the low biomarker groups, the median PFS was 19.9 (95% confidence interval [CI]: 11.4–28.5) months in the low NLR group (≤2.18), 19.5 (95% CI: 11.7–27.4) months in the low MLR group (≤0.19), and 17.8 (95% CI: 15.5–20.2) months in the low PLR group (≤177.73). In contrast, among the high biomarker groups, the median PFS was 13.4 (95% CI: 12.4–14.5) months in the high NLR group (>2.18), 14.0 (95% CI: 12.9–15.1) months in the high MLR group (>0.19), and 12.9 (95% CI: 11.8–14.0) months in the high PLR group (>177.73). Median PFS differed significantly between the low and high groups for each biomarker (all p < 0.0001) (Figure 1).
FIGURE 1.
The correlations between baseline biomarker values and progression‐free survival (PFS). Lower pretreatment levels of (a) neutrophil‐to‐lymphocyte ratio (NLR) ≤2.18 (n = 84), (b) monocyte‐to‐lymphocyte ratio (MLR) ≤0.19 (n = 63), and (c) platelet‐to‐lymphocyte ratio (PLR) ≤177.73 (n = 167) were significantly associated with a longer median PFS.
Associations between changes in blood‐based biomarkers and clinical outcomes
The associations between NLR, MLR, and PLR changes after 1–3 months of treatment and PFS were further evaluated. Among the 188 patients with paired inflammatory data, those whose NLR decreased by >29.5% (17.8 [95% CI: 15.0–20.6] vs. 13.1 [95% CI: 11.5–14.7] months, p = 0.0011), MLR decreased by >57.9% (17.3 [95% CI: 14.7–20.0] vs. 13.0 [95% CI: 11.5–14.5] months, p = 0.0043), and PLR increased by <18.8% (17.5 [95% CI: 12.9–22.1] vs. 14.9 [95% CI: 13.4–16.5] months, p = 0.016) had significantly better PFS (Figure 2). Additional information regarding the selection of the cutoffs is provided in Figure S2.
FIGURE 2.
The correlations between biomarker changes and progression‐free survival (PFS). Patients whose (a) neutrophil‐to‐lymphocyte ratio (NLR) decreased by >29.5%, (b) monocyte‐to‐lymphocyte ratio (MLR) decreased by >57.9%, and (c) platelet‐to‐lymphocyte ratio (PLR) increased by <18.8% had significantly better PFS.
Associations between blood‐based biomarker changes and treatment response rate
The associations between responses to afatinib and changes in inflammatory markers were explored. Patients showing a CR or PR to afatinib treatment had significant decreases in NLR, MLR, and PLR (all p < 0.05). Patients with SD showed no significant changes in NLR, MLR, and PLR. While patients with PD or NA showed numerical increases in the NLR (p = 0.086), MLR (p = 0.069), and PLR (p = 0.304), these changes were not statistically significant (Figure 3).
FIGURE 3.
The relationships between treatment response and biomarker changes. (a) The relation between the response rate and the change of neutrophil‐to‐lymphocyte ratio (NLR). (b) The relation between the response rate and the change of monocyte‐to‐lymphocyte ratio (MLR). (c) The relation between the response rate and the change of platelet‐to‐lymphocyte ratio (PLR). Patients showing a complete response (CR) or partial response (PR) to treatment showed significant decreases in NLR, MLR, and PLR (all p < 0.05). Patients with stable disease (SD) showed no significant changes in these inflammatory biomarkers. Patients with progressive disease (PD) or not assessed (NA) showed nonsignificant increase in these inflammatory biomarkers.
Univariate and multivariate analyses
We conducted further analyses to examine the relationships and correlations between inflammatory biomarkers, clinical characteristics, and prognosis. This analysis aimed to determine whether inflammatory biomarkers and clinical characteristics truly influence prognosis or are merely coincidental. The univariate and multivariate analysis results are presented in Tables 2 and 3. Univariate analysis found performance score, stage, and liver metastasis to be significant prognostic factors in both the baseline and post‐treatment groups. In order to account for these factors, HRs were calculated before and after adjusting for these three factors (Table 4).
TABLE 2.
The correlations between inflammatory biomarkers, clinical characteristics, and prognosis at baseline (n = 325).
| Parameters | Total N | N of events (%) | Median (months) | 95% CI | p‐value | Hazard ratio | 95% CI | p‐value |
|---|---|---|---|---|---|---|---|---|
| Age (years) | 0.151 | ‐ | ||||||
| ≦65 | 178 | 150 (84.3) | 14.4 | 13.0–15.8 | ||||
| >65 | 147 | 105 (71.4) | 16.4 | 13.7–19.1 | ||||
| Sex | 0.929 | ‐ | ||||||
| Male | 141 | 116 (82.3) | 14.4 | 12.3–16.4 | ||||
| Female | 184 | 139 (75.5) | 15.1 | 13.7–16.6 | ||||
| Performance score | <0.0001 | |||||||
| 0/1 | 269 | 209 (77.7) | 15.9 | 14.4–17.4 | 1 | |||
| 2/3/4 | 56 | 46 (82.1) | 9.8 | 8.3–11.2 | 1.55 | 1.11–2.15 | 0.009 | |
| Smoking | 0.467 | ‐ | ||||||
| No | 228 | 173 (75.9) | 14.9 | 13.3–16.6 | ||||
| Yes | 94 | 80 (85.1) | 14.8 | 11.8–7.7 | ||||
| Unknown | 3 | 2 (66.7) | 9.9 | NA | ||||
| Baseline NLR | <0.0001 | |||||||
| ≦2.18 | 84 | 61 (72.6) | 19.9 | 11.4–28.5 | 1 | |||
| >2.18 | 241 | 194 (80.5) | 13.4 | 12.4–14.5 | 1.45 | 1.05–2.02 | 0.025 | |
| Baseline MLR | <0.0001 | ‐ | ||||||
| ≦0.19 | 63 | 42 (66.7) | 19.5 | 11.7–27.4 | ||||
| >0.19 | 262 | 213 (81.3) | 14.0 | 12.9–15.1 | ||||
| Baseline PLR | <0.0001 | |||||||
| ≦177.73 | 167 | 127 (76.0) | 17.8 | 15.5–20.2 | 1 | |||
| >177.73 | 158 | 128 (81.0) | 12.9 | 11.8–14.0 | 1.56 | 1.19–2.05 | 0.001 | |
| Morphology | 0.159 | ‐ | ||||||
| Adenocarcinoma | 322 | 253 (78.6) | 14.9 | 13.5–16.3 | ||||
| Nonadenocarcinoma | 3 | 2 (66.7) | 2.3 | NA | ||||
| Mutation | 0.591 | ‐ | ||||||
| Exon 19 deletion | 181 | 150 (82.9) | 14.9 | 12.3–17.6 | ||||
| L858R | 141 | 105 (72.9) | 14.8 | 12.7–16.8 | ||||
| Stage | 0.002 | |||||||
| III | 25 | 17 (68.0) | 22.0 | 13.5–30.6 | 1 | |||
| IV | 300 | 238 (79.3) | 14.7 | 13.4–5.9 | 1.57 | 0.94–2.63 | 0.087 | |
| Liver metastasis | 0.002 | |||||||
| Yes | 43 | 34 (79.1) | 12.1 | 8.5–15.7 | 1.51 | 1.04–2.18 | 0.030 | |
| No | 282 | 221 (78.4) | 15.4 | 14.0–16.9 | 1 | |||
| Brain metastasis | 0.065 | ‐ | ||||||
| Yes | 101 | 78 (77.2) | 13.1 | 11.1–15.2 | ||||
| No | 224 | 177 (79.0) | 15.9 | 14.1–17.7 | ||||
| Pleural metastasis | 0.052 | ‐ | ||||||
| Yes | 138 | 114 (82.6) | 14.5 | 12.5–16.4 | ||||
| No | 187 | 141 (75.4) | 15.2 | 12.7–17.7 | ||||
| Lung metastasis | 0.919 | ‐ | ||||||
| Yes | 111 | 87 (78.4) | 14.9 | 13.1–16.8 | ||||
| No | 214 | 168 (78.5) | 14.9 | 13.1–16.6 | ||||
| Bone metastasis | 0.197 | ‐ | ||||||
| Yes | 159 | 126 (79.2) | 14.9 | 13.2–16.7 | ||||
| No | 166 | 129 (77.7) | 14.7 | 12.4–16.9 |
Abbreviations: CI, confidence interval; MLR, monocyte‐to‐lymphocyte ratio; NLR, neutrophil‐to‐lymphocyte ratio; PLR, platelet‐to‐lymphocyte ratio.
TABLE 3.
The correlations between inflammatory markers, clinical characteristics, and prognosis in post‐treatment.
| Parameters | Total N | N of events (%) | Median (months) | 95% CI | p‐value | Hazard ratio | 95% CI | p‐value |
|---|---|---|---|---|---|---|---|---|
| Age (years) | 0.191 | ‐ | ||||||
| ≦65 | 108 | 88 (81.5) | 14.9 | 12.9–17.0 | ||||
| >65 | 80 | 59 (73.7) | 17.5 | 13.2–21.7 | ||||
| Sex | 0.894 | ‐ | ||||||
| Male | 79 | 64 (81.0) | 15.5 | 11.5–19.5 | ||||
| Female | 109 | 83 (76.1) | 15.4 | 13.0–17.9 | ||||
| Performance score | 0.003 | |||||||
| 0/1 | 159 | 123 (77.4) | 17.2 | 15.1–19.2 | 1 | |||
| 2/3/4 | 29 | 24 (82.8) | 9.8 | 7.9–11.6 | 1.78 | 1.13–2.80 | 0.013 | |
| Smoking | 0.193 | ‐ | ||||||
| No | 134 | 103 (76.9) | 15.4 | 13.1–17.8 | ||||
| Yes | 52 | 42 (80.8) | 17.2 | 13.7–20.6 | ||||
| Unknown | 2 | 2 (100.0) | 9.4 | NA | ||||
| NLR decrease (%) | ||||||||
| ≦29.5 | 127 | 98 (77.2) | 17.8 | 15.0–20.6 | 0.001 | 1 | ||
| >29.5 | 61 | 49 (80.3) | 13.1 | 11.5–14.7 | 1.59 | 1.07–2.35 | 0.021 | |
| MLR decrease (%) | 0.004 | |||||||
| ≦57.9 | 156 | 117 (75.0) | 17.3 | 14.7–20.0 | ||||
| >57.9 | 32 | 30 (93.7) | 13.0 | 11.5–14.5 | ||||
| PLR increase (%) | 0.016 | |||||||
| ≦18.8 | 131 | 109 (83.2) | 14.9 | 13.4–16.5 | 1.20 | 0.79–1.82 | 0.383 | |
| >18.8 | 57 | 38 (66.7) | 17.5 | 12.9–22.1 | 1 | |||
| Morphology | 0.105 | ‐ | ||||||
| Adenocarcinoma | 186 | 145 (88.0) | 15.4 | 13.3–17.6 | ||||
| Nonadenocarcinoma | 2 | 2 (100.0) | 2.3 | NA | ||||
| Mutation | 0.718 | ‐ | ||||||
| Exon 19 deletion | 111 | 92 (82.9) | 14.9 | 11.3–18.6 | ||||
| L858R | 77 | 55 (71.4) | 15.9 | 13.2–18.6 | ||||
| Stage | 0.028 | |||||||
| III | 17 | 12 (70.6) | 21.8 | 11.0–32.6 | 1 | |||
| IV | 171 | 135 (78.9) | 14.9 | 13.4–16.3 | 1.46 | 0.79–2.70 | 0.232 | |
| Liver metastasis | 0.018 | |||||||
| Yes | 29 | 22 (75.9) | 13.1 | 10.4–15.8 | 1.38 | 0.86–2.22 | 0.186 | |
| No | 159 | 125 (78.6) | 16.2 | 13.8–18.5 | 1 | |||
| Brain metastasis | 0.053 | ‐ | ||||||
| Yes | 62 | 46 (74.2) | 13.0 | 9.4–16.7 | ||||
| No | 126 | 101 (80.2) | 17.2 | 14.9–19.5 | ||||
| Pleural metastasis | 0.203 | ‐ | ||||||
| Yes | 75 | 61 (81.3) | 14.7 | 13.1–16.3 | ||||
| No | 113 | 86 (76.1) | 17.3 | 14.3–20.4 | ||||
| Lung metastasis | 0.996 | ‐ | ||||||
| Yes | 64 | 47 (73.4) | 17.2 | 14.1–20.2 | ||||
| No | 124 | 100 (80.6) | 15.1 | 13.3–17.0 | ||||
| Bone metastasis | 0.142 | ‐ | ||||||
| Yes | 99 | 77 (77.8) | 15.1 | 13.2–16.9 | ||||
| No | 89 | 70 (78.7) | 16.2 | 11.8–20.6 |
Abbreviations: CI, confidence interval; MLR, monocyte‐to‐lymphocyte ratio; NLR, neutrophil‐to‐lymphocyte ratio; PLR, platelet‐to‐lymphocyte ratio.
TABLE 4.
The HRs for different inflammatory groups before and after adjusting for cancer performance, stage, and liver metastasis.
| Parameters | Crude HR (95% CI) | p‐value | Adjusted HR (95% CI) a | p‐value | |
|---|---|---|---|---|---|
| Baseline NLR | >2.18/≦2.18 | 1.96 (1.46–2.65) | <0.0001 | 1.70 (1.25–2.32) | 0.001 |
| Baseline MLR | >0.19/≦0.19 | 2.06 (1.47–2.89) | <0.0001 | 1.84 (1.31–2.60) | <0.001 |
| Baseline PLR | >177.73/≦177.73 | 1.89 (1.47–2.43) | <0.0001 | 1.74 (1.35–2.25) | <0.0001 |
| NLR decrease (%) | >29.5/≦29.5 | 1.81 (1.26–2.59) | 0.001 | 1.68 (1.16–2.44) | 0.006 |
| MLR decrease (%) | >57.9/≦57.9 | 1.80 (1.20–2.72) | 0.005 | 1.74 (1.15–2.64) | 0.010 |
| PLR increase (%) | ≦18.8/>18.8 | 1.59 (1.09–2.32) | 0.017 | 1.39 (0.94–2.05) | 0.101 |
| NLR | Decrease/increase | 1.18 (1.00–1.40) | 0.050 | 1.10 (0.93–1.31) | 0.272 |
| MLR | Decrease/increase | 1.07 (0.91–1.26) | 0.427 | 1.02 (0.86–1.22) | 0.794 |
| PLR | Decrease/increase | 1.19 (1.01–1.41) | 0.044 | 1.13 (0.95–1.35) | 0.160 |
Abbreviations: CI, confidence interval; HR, hazard ratio; MLR, monocyte‐to‐lymphocyte ratio; NLR, neutrophil‐to‐lymphocyte ratio; PLR, platelet‐to‐lymphocyte ratio.
Adjusted for performance, stage, and liver metastasis.
Before adjustment, PFS was significantly poor for patients with a baseline NLR of >2.18 (HR: 1.96, 95% CI: 1.46–2.65), MLR of >0.19 (HR: 2.06, 95% CI: 1.47–2.89), and PLR >177.73 (HR: 1.89, 95% CI: 1.47–2.89; all p < 0.05). In addition, PFS was significantly unfavorable in patients with a post‐treatment NLR decrease of <29.5% (HR: 1.81, 95% CI: 1.26–2.59, p = 0.001), MLR decrease of <57.9% (HR: 1.80, 95% CI: 1.20–2.72, p = 0.005), and PLR increase of >18.8% (HR: 1.59, 95% CI: 1.09–2.32, p = 0.017). However, the increased PLR (HR: 1.19, 95% CI: 1.01–1.41, p = 0.044) was the only biomarker change significantly affecting PFS; increased NLR (HR: 1.18, 95% CI: 1.00–1.40, p = 0.050) and increased MLR (HR: 1.07, 95% CI: 0.91–1.26, p = 0.017) were not statistically significant.
After adjusting for performance score, stage, and liver metastasis, PFS was still unfavorable for patients with a baseline NLR of >2.18 (HR: 1.70, 95% CI: 1.25–2.32), MLR of >0.19 (HR: 1.84, 95% CI: 1.31–2.60), and PLR >177.73 (HR: 1.74, 95% CI: 1.35–2.25; all p < 0.05). In addition, PFS was still significantly unfavorable for patients with a post‐treatment NLR decrease of <29.5% (HR: 1.68, 95% CI: 1.16–2.44, p < 0.05) and MLR decrease of <57.9% (HR: 1.74, 95% CI: 1.15–2.64, p < 0.05). However, increased NLR (HR: 1.10, 95% CI: 0.94–2.05, p = 0.272) and increased MLR (HR: 1.10, 95% CI: 0.93–1.31, p = 0.794) still did not significantly influence PFS after adjustment. As for post‐treatment value change in PLR, it was determined that there was no statistically significant impact on PFS in patients who experienced post‐treatment increases in PLR (HR: 1.13, 95% CI: 0.95–1.35, p = 0.160) or in those with PLR increases >18.8% (HR: 1.39, 95% CI: 0.94–2.05, p = 0.101) after adjustment.
DISCUSSION
This study demonstrated a significant correlation between baseline values and subsequent changes in inflammatory biomarkers (NLR, MLR, and PLR) with clinical outcomes in patients with NSCLC treated with afatinib. Patients with lower baseline NLR, MLR, and PLR values had significantly favorable PFS. Furthermore, patients with greater decreases in NLR and MLR and smaller increases in PLR after 3 months of treatment also had significantly favorable PFS. These correlations remained significant even after adjusting for the potential confounding factors of performance status, cancer stage, and liver metastasis. Moreover, NLR, MLR, and PLR significantly decreased after treatment in patients showing a tumor response but not in those with SD and PD. These findings suggest that the NLR, MLR, and PLR can be informative prognostic factors for clinical outcomes.
The interaction between the inflammatory microenvironment and tumor progression has been discussed previously. 24 , 25 , 26 , 27 Neutrophils transform into tumor‐associated neutrophils that specifically contribute to tumor progression by inducing genetic instability, promoting tumor growth, stimulating angiogenesis, and facilitating cancer cells' invasive behavior. 15 In addition, neutrophils modulate the microenvironment by secreting cytokines and chemokines, potentially impairing the antitumor immune response. 28 In particular, neutrophilia can inhibit the antitumor immune response by suppressing the cytotoxic activity of immune cells, particularly activated cluster of differentiation 8 (CD8)+ T cells. 29
As part of the mononuclear phagocyte system, monocytes secrete inflammatory mediators such as tumor necrosis factor (TNF) and interleukin (IL)‐6. 30 , 31 They also have tumor‐promoting properties, including facilitating angiogenesis and invasion. 32 Platelets are associated with unfavorable outcomes. They contribute to angiogenesis by adhering to tumor vessels and releasing granules containing angiogenic factors such as vascular endothelial cell growth factor (VEGF) and platelet‐derived endothelial cell growth factor (PDEGF). 33 This association is associated with increased tumor invasion and lymph node metastasis. 34 Platelets also affect the inflammatory state and facilitate cellular interactions within the tumor microenvironment. One such mechanism involves fibrinogen, which promotes the expression of fibroblast growth factor (FGF)‐2. This process contributes to the augmentation of tumor cell growth and progression. 15
While patients with NSCLC who responded to afatinib showed significantly decreased NLR, MLR, and PLR, the lack of significance in the association between pre‐ and post‐treatment inflammatory factors among patients with PD may be attributed to their limited number, potentially reflecting the effectiveness of afatinib in patients with EGFR‐mutated NSCLC (Figure 3). The observed results align with the trend between increased inflammatory factors and cancer status. Notably, no significant difference in response rate was observed when the parameter values changed within specific ranges: NLR decrease of <29.5%, MLR decrease of <57.9%, and PLR increase of <18.8% (Table 5). This lack of significance may be attributed to the possibility that even when the value change does not surpass the cutoff, it can still have a treatment effect. Therefore, regardless of whether the value change exceeds the cutoffs of 29.5%, 57.9%, and 18.8%, all cases show a treatment response, rendering the differences in value change within the specified range nonsignificant.
TABLE 5.
The relationships between treatment response and NLR, MLR, and PLR changes.
| Baseline vs. 1–3 months (n = 188) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Response | NLR decrease (%) | p‐value | MLR decrease (%) | p‐value | PLR increase (%) | p‐value | |||
| ≦29.5 (n = 127) | >29.5 (n = 61) | ≦57.9 (n = 156) | >57.9 (n = 32) | ≦18.8 (n = 131) | >18.8 (n = 57) | ||||
| CR/PR | 80 (63.0) | 45 (73.8) | 0.323 | 101 (64.7) | 24 (75.0) | 0.523 | 31 (54.4) | 94 (71.8) | 0.063 |
| SD | 27 (21.3) | 10 (16.4) | 32 (20.5) | 5 (15.6) | 16 (28.1) | 21 (16.0) | |||
| PD/NA | 20 (15.7) | 6 (9. 8) | 23 (14.7) | 3 (9.4) | 10 (17.5) | 16 (12.2) | |||
| ORR | 0.143 | 0.263 | 0.020 | ||||||
| CR/PR | 80 (63.0) | 45 (73.8) | 101 (64.7) | 24 (75.0) | 31 (54.4) | 94 (71.8) | |||
| SD/PD/NA | 47 (37.0) | 16 (26.2) | 55 (35.3) | 8 (25.0) | 26 (45.6) | 37 (28.2) | |||
| DCR | 0.272 | 0.578 | 0.331 | ||||||
| CR/PR/SD | 107 (84.3) | 55 (90.2) | 133 (85.3) | 29 (90.6) | 47 (82.5) | 115 (87.8) | |||
| PD/NA | 20 (15.7) | 6 (9.8) | 23 (14.7) | 3 (9.4) | 10 (17.5) | 16 (12.2) | |||
| Baseline vs. 1–3 months (n = 188) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Response | NLR | p‐value | MLR | p‐value | PLR | p‐value | |||
| Increase (n = 94) | Decrease (n = 94) | Increase (n = 81) | Decrease (n = 107) | Increase (n = 87) | Decrease (n = 101) | ||||
| CR/PR | 55 (58.5) | 70 (74.5) | 0.042 | 42 (51.9) | 83 (77.6) | <0.001 | 52 (59.8) | 73 (72.3) | 0.189 |
| SD | 21 (22.4) | 16 (17.0) | 19 (23.4) | 18 (16.8) | 21 (24.1) | 16 (15.8) | |||
| PD/NA | 18 (19.1) | 8 (8.5) | 20 (24.7) | 6 (5.6) | 14 (16.1) | 12 (11.9) | |||
| ORR | 0.020 | <0.001 | 0.070 | ||||||
| CR/PR | 55 (58.5) | 70 (74.5) | 42 (51.9) | 83 (77.6) | 52 (59.8) | 73 (72.3) | |||
| SD/PD/NA | 39 (41.5) | 24 (25.5) | 39 (48.1) | 24 (22.4) | 35 (40.2) | 28 (27.7) | |||
| DCR | 0.035 | <0.001 | 0.404 | ||||||
| CR/PR/SD | 76 (80.9) | 86 (91.5) | 61 (75.3) | 101 (94.4) | 73 (83.9) | 89 (88.1) | |||
| PD/NA | 18 (19.1) | 8 (8.5) | 20 (24.7) | 6 (5.6) | 14 (16.1) | 12 (11.9) | |||
Note: Values in parentheses are percentages. All p‐values were <0.05.
Abbreviations: CR, complete response; DCR, disease control rate; NLR, neutrophil‐to‐lymphocyte ratio; MLR, monocyte‐to‐lymphocyte ratio; NA, not assessed; ORR, overall response rate; PD, progressive disease; PLR, platelet‐to‐lymphocyte ratio; PR, partial response; SD, stable disease.
These findings are consistent with previous studies reporting an association between these inflammatory factors and clinical outcomes in patients with lung cancer. 35 , 36 , 37 , 38 However, this is the first study to incorporate all three variables and focus specifically on patients with NSCLC treated with afatinib. It is also the first to uncover associations between these variables and clinical outcomes after adjusting for important factors such as performance status, stage, and liver metastasis. In our study, all patients had the EGFR mutations of exon 19‐del and 21‐L858R, which are common and account for 85% of all EGFR mutations in NSCLC. 39 In addition, afatinib is considered the common first‐line therapy for Asian patients with NSCLC. 6 , 40 , 41 , 42 , 43 , 44 , 45
Despite this study's clinical relevance, it had several limitations that should be acknowledged. First, being retrospective, it is inherently susceptible to selection bias. While subgroup analyses were conducted to account for potential influencing factors, other biases remain possible. Second, its data was derived from a single medical center in Taiwan, which may not fully represent all patients with lung cancer worldwide. Third, since it was limited to afatinib treatment of NSCLC with common EGFR mutations, the roles of inflammatory markers for other TKIs or uncommon EGFR mutations. 46 , 47 , 48 , 49 were not explored. Lastly, the mechanisms underlying the associations between these inflammatory factors and targeted therapy remain unknown. It is unclear whether the observed biomarker changes were due to alterations in inflammatory status or adverse therapy effects. While the most common adverse effects of afatinib were not associated with immunocompromise, they still potentially impact immune status. In addition, our study did not observe the further interaction between the immune system and cancer status.
In conclusion, this study demonstrated strong associations between baseline and changes in NLR, MLR, and PLR values and clinical outcomes in patients with EGFR‐mutated NSCLC treated with afatinib. These inflammatory factors may be helpful predictive markers for selecting treatment. Further studies into the tumor microenvironment and its association with these biomarkers are crucial for evaluating prognosis and clinical outcomes in patients with NSCLC.
AUTHOR CONTRIBUTIONS
Conceptualization: Zi‐Ting Chang, John Wen‐Cheng Chang, Chen‐Te Wu and Chiao‐En Wu. Methodology: Zi‐Ting Chang, John Wen‐Cheng Chang, Chen‐Te Wu, Chih‐Hsi Scott Kuo and Chiao‐En Wu. Investigation: John Wen‐Cheng Chang, Chen‐Yang Huang, Ching‐Fu Chang and Chiao‐En Wu. Software: John Wen‐Cheng Chang, Chen‐Te Wu, Cheng‐Ta Yang, Ping‐Chih Hsu and Chiao‐En Wu. Data curation: Zi‐Ting Chang, John Wen‐Cheng Chang, Chen‐Te Wu, Cheng‐Ta Yang, Ping‐Chih Hsu, Chih‐Hsi Scott Kuo and Chiao‐En Wu. Visualization: How‐Wen Ko, Chen‐Yang Huang, Ching‐Fu Chang and Chiao‐En Wu. Validation: Zi‐Ting Chang, Cheng‐Ta Yang and Ping‐Chih Hsu. Writing‐original draft: How‐Wen Ko and Chiao‐En Wu. Writing‐reviewing and editing: How‐Wen Ko and Chiao‐En Wu. Supervision: Chiao‐En Wu.
FUNDING INFORMATION
This study was supported by grants from Linkou Chang‐Gung Memorial Hospital (CMRPG3M0971, CMRPG3L0912, CMRPG3M0521 and CMRPG3K2171 to CEW), the National Science and Technology Council (grant no. 109‐2314‐B‐182‐080‐MY3, 111‐2811‐B‐182‐017 to CEW).
CONFLICT OF INTEREST STATEMENT
The authors declare there is no conflict of interest.
Supporting information
FIGURE S1. The p‐value indicating the significance of the difference between the two groups was lowest with the neutrophil‐to‐lymphocyte ratio (NLR), monocyte‐to‐lymphocyte ratio (MLR), and platelet‐to‐lymphocyte ratio (PLR) cutoffs of (a) NLR = 2.18, (b) MLR = 0.19, and (c) PLR = 177.73, respectively.
FIGURE S2. The p‐value indicating the significance of the difference between the two groups was lowest with neutrophil‐to‐lymphocyte ratio (NLR), monocyte‐to‐lymphocyte ratio (MLR), and platelet‐to‐lymphocyte ratio (PLR) change cutoffs of (a) a decrease of >29.5% of NLR, (b) a decrease of >57.9% of MLR, and (c) an increase of <18.8% of PLR, respectively.
Chang Z‐T, Hsu P‐C, Ko H‐W, Chang JW‐C, Wu C‐T, Huang C‐Y, et al. Baseline and changes in inflammatory parameters for patients with EGFR ‐mutated NSCLC treated with afatinib. Thorac Cancer. 2025;16(13):e15338. 10.1111/1759-7714.15338
DATA AVAILABILITY STATEMENT
The data supporting the findings of this study are available from the corresponding author on request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
FIGURE S1. The p‐value indicating the significance of the difference between the two groups was lowest with the neutrophil‐to‐lymphocyte ratio (NLR), monocyte‐to‐lymphocyte ratio (MLR), and platelet‐to‐lymphocyte ratio (PLR) cutoffs of (a) NLR = 2.18, (b) MLR = 0.19, and (c) PLR = 177.73, respectively.
FIGURE S2. The p‐value indicating the significance of the difference between the two groups was lowest with neutrophil‐to‐lymphocyte ratio (NLR), monocyte‐to‐lymphocyte ratio (MLR), and platelet‐to‐lymphocyte ratio (PLR) change cutoffs of (a) a decrease of >29.5% of NLR, (b) a decrease of >57.9% of MLR, and (c) an increase of <18.8% of PLR, respectively.
Data Availability Statement
The data supporting the findings of this study are available from the corresponding author on request.