High fibrinogen-albumin ratio index predicts poor prognosis for lung adenocarcinoma patients undergoing epidermal growth factor receptor-tyrosine kinase inhibitor treatments.

Epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKIs) have become the preferred therapy as first-line treatment of non-small cell lung cancer patients harboring sensitizing EGFR mutations. However, the prognostic indicators are limited. The present study aimed to assess the prognostic value of immune-inflammation factors, fibrinogen-albumin ratio index (FARI), neutrophil to lymphocyte ratio (NLR), and platelet to lymphocyte ratio (PLR) in EGFR-Mutant lung adenocarcinoma patients receiving first-generation EGFR-TKIs treatment.194 patients were included in this retrospective analysis. FARI was calculated as fibrinogen / albumin. Receiver operating characteristic curve was used to evaluate the optimal cut-off value for FARI, NLR, and PLR to progression free survival (PFS). Univariate and multivariate survival analysis were performed to identify factors correlated with PFS and overall survival (OS).Applying cut-offs of ≥0.08 (FARI), ≥3.28 (NLR), and ≥273.85 (PLR), higher FARI or NLR was associated with worse Eastern Cooperative Oncology Group performance status (ECOG PS) (P = .018, .002, respectively), and there were more males in high NLR group (P = .043). In univariate analysis, ECOG PS status, NLR, PLR, and FARI were significantly associated with PFS (P = .017, .004, <.001, .001, respectively) as well as OS (P < .001, = .001, .002, .023, respectively). In multivariate analysis, PLR (hazard ratios [HR] 1.692; 95% CI 1.054-2.715; P = .029) and FARI (HR 1.496; 95% CI 1.031-2.172; P = .034) were independent prognostic factors for PFS. While only ECOG PS status (HR 2.052; 95% CI 1.272-3.310; P = .003) was independently correlated with OS.FARI is independently associated with PFS in EGFR-Mutant lung adenocarcinoma patients receiving first-line EGFR-TKIs treatment.

Introduction

Lung cancer is the leading cause of cancer death among both in China and worldwide,[ among which non-small cell lung cancer (NSCLC) account for the majority of all cases. The mutation rate of Epidermal Growth Factor Receptor(EGFR) varies across different ethnicities, and Asians have the highest mutation rate among all ethnicities.[ During the last decades, EGFR Tyrosine Kinase Inhibitors (EGFR-TKIs) have changed the strategy of treatment and prolonged the progression-free survival (PFS) among patients harboring EGFR mutations.[ However, drug resistance inevitably emerges[ and becomes the major challenge of clinical work. Therefore, it is urgent to identify prognostic factors can predict target therapy efficacy.

Growing evidence shows inflammation plays a critical role in tumorigenesis and progression of cancer, and inflammation also affects immune surveillance and responses to therapy.[ A few immune-inflammation based indicators have been studied as prognostic indicators in solid cancers, such as neutrophil-to-lymphocyte ratio (NLR)[ and platelet-to-lymphocyte ratio (PLR).[ Besides, immune cells and inflammatory proteins such as albumin (Alb) and fibrinogen (Fib) have different expressions in cancer patients.[ Alb and Fib are routinely tested to evaluate nutritional, inflammatory and coagulation status. However, the prognostic value of fibrinogen-albumin ratio index (FARI) in EGFR-Mutant advanced NSCLC patients treated with EGFR-TKI has been rarely reported.

In the present study, a cohort of 194 advanced lung adenocarcinoma patients carrying EGFR mutations were retrospectively analyzed to evaluate the clinical significance and prognostic value of NLR, PLR, and FARI in NSCLC patients receiving EGFR-TKI treatment.

Patients and methods

Patients

Patients harboring EGFR driven mutations pathological diagnosed as lung adenocarcinoma from January 2016 to December 2018 were collected at the Second Xiangya Hospital in Changsha, China. Patients in our study underwent first generation EGFR-TKI (gefitinib, erlotinib, or icotinib) as the first-line treatment, and blood test was taken within 1 week prior to the treatment. Patients with history of other malignant tumors, chronic inflammatory diseases, recent steroid therapy, acute infection or inflammation were excluded. Ethical approval was obtained from the Second Xiangya Hospital, Central South University.

Data collection

The clinical demographics (including age, gender, etc), smoking history, brain metastasis status, Eastern Cooperative Oncology Group (ECOG) score, EGFR mutation status and blood routine test, and blood biochemistry records were obtained from the electronic medical record system of the Second Xiangya Hospital. The FARI, NLR, and PLR were calculated as follows: FARI = fibrinogen / albumin; NLR = neutrophil counts / lymphocyte counts, PLR = platelet counts / lymphocyte counts. performance status (ECOG PS) was used to evaluate the physical status (on a scale of 0 to 5, with higher scores indicating a deteriorated general condition). The PFS was calculated from the date of diagnosis to the date of disease progression based on response evaluation criteria in solid tumors 1.1, or death. The overall survival (OS) was calculated from the date of diagnosis to the date of death for any reason or to the last date of follow-up. The last follow-up occurred in March 1, 2020.

Statistical Methods

All the statistical analyses were performed using SPSS 20.0. Receiver operating characteristic curves were used to calculate the optimal cut-off value for FARI, NLR, and PLR. Survival analysis was performed using Kaplan–Meier method. Chi-square test and log-rank test were used to compare the baseline clinical characteristics and survival curves, respectively. The predictive factors for survival were evaluated by univariate and multivariate analyses via the Cox hazards regression analysis. All tests were 2-sided and P < .05 was statistically significant.

Results

Patient characteristics

Based on the inclusion criteria, 194 advanced lung adenocarcinoma patients with EGFR mutations were enrolled. The characteristics are shown in Table 1. The mean age of all enrolled patients was 60 with the range of 28 to 88 years, and 88 (45.4%) patients were male. 57 patients (29.4%) had a smoking history. This is consistent with the epidemiological pattern that the EGFR mutation rate is higher in females and non-smokers. The majority (156, 80.4%) had an ECOG score of 0 to 1. There were 24 patients with brain metastasis, and 5 patients received whole brain radiation therapy (WBRT) concurrent with TKI. Among all the patients, 79 (40.7%) patients carried L858R mutation in exon 21, 108 (55.7%) had exon 19 deletion mutation, and 7 (3.6%) had other rare mutations.

Table 1

clinicopathological characteristics of patients.

Characteristics	Number (%)
Age
Median	60
Range	28-88
Gender
Male	88 (45.4%)
Female	106 (54.6%)
Smoking status
Non-smoker	137 (70.6%)
Current or ex-smoker	57 (29.4%)
ECOG PS
0-1	156 (80.4%)
2	38 (19.6%)
Brain metastasis
Yes	24 (12.4%)
No	170 (87.6%)
EGFR Mutation
L858R	79 (40.7%)
19-DEL	108 (55.7%)
Other	7 (3.6%)

Cut-off value of FARI, NLR, PLR, and the association with clinical characteristics

Receiver operating characteristic analysis was used to get the cut-off value of FARI, NLR, PLR. As shown in Figure 1, the area under the curve for PFS were 0.659, 0.517, and 0.522. The optimal cutoff values of FARI, NLR, and PLR were 0.08, 3.28 and 273.85 for the prediction of PFS. The comparison of clinical characteristics between different groups in terms of FARI, NLR, and PLR is shown in Table 2. Patients with high FARI or NLR were more likely to have worse ECOG PS score (P = .018, .002, respectively). There were more males in high NLR patients compared with low NLR group(P = .043). Other factors, including age, smoking status, brain metastasis, and EGFR mutation status are balanced between High/Low FARI, High/Low NLR, and High/Low PLR groups. It means that when testing the prognosis of FARI, NLR and PLR on patients’ prognosis, all these factors are not confounding factors.

Figure 1

Receiver operating characteristic curve analysis for optimal cut-off value of NLR, PLR, and FARI for PFS. FARI = fibrinogen-albumin ratio index, NLR = neutrophil to lymphocyte ratio, PFS = progression free survival, PLR = platelet to lymphocyte ratio.

Table 2

Clinicopathological characteristics according to FARI, NLR and PLR.

Characteristics	FARI high	FARI low	P	NLR high	NLR low	P	PLR high	PLR low	P
Age
<65	62 (32.0%)	79 (40.7%)	.078	65 (33.5%)	76 (39.2%)	.334	28 (14.4%)	113 (50.3%)	.091
≥65	31 (16.0%)	22 (11.3%)		29 (14.9%)	24 (12.2%)		5 (2.6%)	48 (24.7%)
Gender
Male	44 (22.7%)	44 (22.7%)	.666	50 (25.8%)	38 (19.6%)	.043	15 (7.7%)	73 (37.6%)	1.000
Female	49 (25.3%)	57 (29.4%)		44 (22.7%)	62 (32.0%)		18 (9.3%)	88 (45.4%)
Smoking status
No	68 (35.1%)	69 (35.6%)	.529	66 (34.0%)	71 (36.6%)	1.000	25 (12.9%)	112 (57.7%)	.536
Yes	25 (12.9%)	32 (16.5%)		28 (14.4%)	29 (14.9%)		8 (4.1%)	49 (25.3%)
ECOG PS
0-1	68 (35.1%)	88 (45.5%)	.018	67 (34.5%)	89 (45.9%)	.002	23 (11.9%)	133 (68.6%)	.096
2	25 (12.9%)	13 (6.7%)		27 (13.8%)	11 (5.7%)		10 (5.2%)	28 (14.4%)
Brain metastasis
Yes	14 (7.2%)	10 (5.2%)	.286	11 (5.7%)	13 (6.7%)	.830	5 (2.6%)	19 (9.8%)	.568
No	79 (40.7%)	91 (46.9%)		83 (42.8%)	87 (44.8%)		28 (14.4%)	142 (73.2%)
EGFR Mutation
L858R	43 (22.2%)	36 (18.6%)	.091	39 (20.1%)	40 (20.6%)	.183	11 (5.7%)	68 (35.1%)	.243
19-DEL	49 (25.3%)	595 (30.4%)		54 (27.8%)	54 (27.8%)		22 (11.3%)	86 (44.3%)
Other	1 (0.5%)	6 (3.1%)		1 (0.5%)	6 (3.1%)		0 (0.9%)	7 (3.6%)

Univariate and multivariate Cox regression analysis for PFS and OS

Univariable analyses showed that the ECOG PS status, NLR, PLR and FARI were significantly associated with PFS (P = .017, .004, <.001, .001, respectively, Fig. 2) as well as OS (P < .001, = .001, .002, .023, respectively, Fig. 3), while other clinical characteristics including age, gender, smoking status, brain metastasis or mutation types showed no statistically significance in the present analysis (Table 3).

Figure 2

Kaplan–Meier curves of PFS according to NLR(A), PLR(B), and FARI(C). PFS = progression free survival, NLR = neutrophil to Lymphocyte Ratio, PLR = platelet to lymphocyte ratio, FARI = fibrinogen-albumin ratio index.

Figure 3

Kaplan–Meier curves of OS according to NLR(A), PLR(B), and FARI(C). OS = overall survival, NLR = neutrophil to lymphocyte ratio, PLR = platelet to lymphocyte ratio, FARI = fibrinogen-albumin ratio index.

Table 3

Univariate analysis of potential factors associated with PFS and OS.

	PFS			OS
Variables	case	MST (m)	P	case	MST (m)	P
Age
<65	141	14 ± 0.80	.299	141	27 ± 1.97	.596
≥65	53	18 ± 5.59		53	31 ± 5.76
Gender
Male	88	12 ± 1.15	.560	88	24 ± 1.79	.276
Female	106	14 ± 0.69		106	29 ± 2.30
Smoking status
No	137	14 ± 0.72	.274	137	29 ± 2.33	.226
Yes	57	12 ± 1.52		57	24 ± 1.26
ECOG PS
0-1	156	17 ± 0.88	.017	156	29 ± 1.44	<.001
2	38	11 ± 0.94		38	19 ± 1.31
Brain metastasis
Yes	24	11 ± 0.93	.453	24	24 ± 1.84	.82
No	170	14 ± 0.99		170	27 ± 2.11
EGFR Mutation
L858R	79	12 ± 1.18	.233	79	22 ± 2.88	.087
19-DEL	108	15 ± 1.04		108	28 ± 1.87
Other	7	12 ± 1.96		7	NR
NLR
High	94	12 ± 0.61	.004	94	22 ± 1.46	.001
Low	100	16 ± 1.42		100	31 ± 1.87
PLR
High	33	10 ± 1.76	<.001	33	19 ± 1.71	.002
Low	161	15 ± 1.43		161	29 ± 1.97
FARI
High	93	12 ± 0.54	.001	93	24 ± 2.10	.023
Low	101	17 ± 2.40		101	30 ± 2.34

In the multivariate Cox regression analysis, PLR (hazard ratios [HR] 1.692; 95% CI 1.054–2.715; P = .029) and FARI (HR 1.496; 95% CI 1.031–2.172; P = .034) were independent prognostic factors for PFS. While only ECOG PS status (HR 2.052; 95% CI 1.272–3.310; P = .003) was identified as independent predictor of OS in patients receiving EGFR-TKIs as first-line treatment for advanced lung adenocarcinoma (Table 4).

Table 4

Multivariable Cox regression analyses for PFS and OS.

	PFS		OS
Variables	HR (95% CI)	P	HR (95% CI)	P
ECOG PS
High	1.343 (0.869-2.074)	.184	2.052 (1.272-3.310)	.003
Low
NLR
High	1.252 (0.848-1.847)	.258	1.487 (0.961-2.301)	.075
Low
PLR
High	1.692 (1.054-2.715)	.029	1.556 (0.911-2.656)	.105
Low
FARI
High	1.496 (1.031-2.172)	.034	1.264 (0.832-1.922)	.273
Low

Discussion

We evaluated the prognostic value of immune-inflammation factors, FARI, NLR, and PLR which could be obtained from blood test before treatment, in advanced lung adenocarcinoma patients with EGFR mutations. The clinical characteristics showed patients with FARI ≥ 0.08 or NLR ≥ 3.28 were more likely to have worse ECOG PS scores, and there were more males in the NLR high group than controls. Further, the pretreatment FARI and PLR were independent prognostic factors for PFS, while only ECOG PS score could independently suggest prognosis for OS in the present study.

Inflammation is a recognized hallmark of cancer.[ Increasing evidence shows inflammatory responses is involved in different stages of tumor development, including initiation, promotion, malignant conversion, invasion, and metastasis, and inflammation also affects immune surveillance and responses to therapy.[ Tumor cells could secret pro-inflammatory factors, and systemic inflammation could in return promote tumor cell proliferation, angiogenesis, and inhibit host anti-tumor immune response.[ Systemic inflammation is widely evidenced by a number of markers at clinical thresholds across tumor types and geographical locations, and patients with inflammation were more likely to have a worse prognosis.[ Well-established inflammation-based indicators, such as NLR[ and PLR,[ have been reported to be associated with prognosis in various cancers.

Fibrinogen, an important protein in coagulation process, also possess pro-inflammatory activity, and has been reported to play roles in tumor progression.[ The development of cancer induces a host response, and inflammatory mediators are secreted and disturb the hemostatic balance, leading to the increase of the blood pro-thrombotic potential. Plasma fibrinogen contributes to metastasis by promoting adhesion and survival of tumor cells after invasion in lung cancer mouse model.[ Albumin is routinely tested in cancer patients before treatment. Albumin synthesis is suppressed in the later stages of disease, malnutrition and inflammation. Inflammatory cytokines such as tumor necrosis factor (TNF)-α and IL-6 suppressed synthesis of albumin, leading to hypoproteinemia in NSCLC patients.[ A Systematic Review reported higher serum albumin levels was associated with better survival in lung cancer in 9 out of 10 studies.[

FARI is a better indicator since it can comprehensively reflect inflammation and nutrition status compared with plasma fibrinogen or albumin alone. Previous studies have demonstrated prognostic value of FARI and its superiority over NLR and PLR in non-small cell lung cancer[ and colorectal cancer.[ Li SQ, et al[ found that clinical outcome of high albumin-to-fibrinogen ratio AFR (>7.3) in stage II–III NSCLC patients undergoing chemo-radiotherapy was significantly superior to the low AFR cases. Another research in advanced NSCLC patients undergoing first-line platinum-based chemotherapy reported that patients with high AFR (>8.02) has significantly improved PFS and OS compared with low AFR group.[

Nowadays, the treatment of lung cancer is multimodality comprehensive treatment, including surgery, chemotherapy, radiation therapy, target therapy, and immunotherapy. Previous studies about immune and inflammation indicators in NSCLC mainly focused on patients receiving resection, chemotherapy and radiation therapy. Reports on the prognostic role of those indicators in EGFR-mutant NSCLC patients receiving EGFR-TKI as first-line treatment is rare. Aguiar-Bujanda D et al reported NLR is an independent prognostic factor for OS in Western European patients with EGFR-mutant NSCLC treated with EGFR-TKIs.[ Previously our group has demonstrated that systemic immune-inflammation index (SII) is an independent prognostic factor for poor survival in advanced EGFR-Mutant lung adenocarcinoma patients treated with first-generation TKIs.[ In the present study, for the first time the prognostic value of FARI is evaluated in the EGFR-mutant population, we found FARI was and independent prognostic factors for PFS (HR 1.496; 95% CI 1.031–2.172; P = .034).

In our study, FARI, as well as PLR, were independent prognostic factors for PFS, which is partially consistence with previous study in lung cancer. We haven’t got a statistically significant difference of FARI in predicting OS. As multimodality comprehensive treatment for lung cancer is available, the different choices in following lines treatment makes it hard to computation.

Studies about therapeutic strategies focusing on inflammation and mal-nutrition may provide new insights to suppress tumor progression and improve the prognosis of cancer patients. A clinical trial using traditional Chinese medicine reported that the wheat-size moxibustion therapy reduced NLR and improved the immune function and quality of life in the patients of NSCLC.[ Cachexia is a complication of cancer, especially in advanced stage, whose major manifestation is loss of muscle and fat mass, and is correlated with shorter survival and worse quality-of-life.[ A research in rats showed the reversal of cancer cachexia could prolong survival.[ A clinical trial in NSCLC patients with cachexia demonstrated anamorelin could improve body weight and anorexia–cachexia symptoms.[ Further studies in researches and even clinical practice, on effective interventions based on inflammation and mal-nutrition in cancer is needed.

There is obviously limitation of this study. This was a single center retrospective study with a comparably small sample size. The number of patients with brain metastasis and ECOG PS score of >2 was small, thus the imbalance between groups may bring some bias. Further prospective studies with a multiple-central design and large sample size is warranted to validate the role of the FARI in predicting clinical efficacy in EGFR-mutant NSCLC receiving first-line EGFR-TKIs treatment.

In conclusion, FARI, a simple, economical biomarker which reflect the inflammation and nutrition status, is independently associated with PFS in EGFR-Mutant lung adenocarcinoma patients receiving first-line EGFR-TKIs treatment, and it worth further research to confirm the prognostic value in clinical practice.

Author contributions

Conceptualization: Tao Hou.

Data curation: Xiayan Zhao, Na Zhang, Haixia Zhang.

Investigation: Xiayan Zhao, Na Zhang, Haixia Zhang, Ping Liu, Fang Wu.

Methodology: Xiayan Zhao, Tao Hou, Chunhong Hu, Xianling Liu.

Resources: Tao Hou.

Supervision: Chunhong Hu, Xianling Liu.

Writing – original draft: Xiayan Zhao.

Writing – review & editing: Ping Liu, Jinan Ma, Chunhong Hu, Xianling Liu, Tao Hou.