Introduction: The Glasgow prognostic score (GPS) is an inflammation-related score based on C-reactive protein and albumin concentrations. Few studies have assessed the correlation between the GPS and the efficacy of chemotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC). Therefore, this study aimed to evaluate the utility of the GPS in predicting the survival outcomes of patients with ES-SCLC. Methods: This retrospective study evaluated patients with ES-SCLC who had undergone chemotherapy between February 2008 and November 2021. GPS values were evaluated before the initiation of first-line chemotherapy. The Kaplan-Meier method and Cox proportional hazards models were used to assess progression-free survival (PFS) and overall survival (OS). Results: The GPS values of the 113 patients were zero (54 patients, 48%), 1 (37 patients, 33%), and 2 (22 patients, 19%). The median follow-up duration was 10.7 months. Median PFS was 6.2, 5.6, and 3.8 months in the GPS 0, 1, and 2 groups, respectively, suggesting that the GPS zero group had a significantly more favorable PFS than the GPS 2 group (p < 0.001). Median OS was 17.1, 9.4, and 5.6 months in the GPS 0, 1, and 2 groups, respectively, suggesting that the GPS zero group had a significantly more favorable OS than the GPS 2 group (p = 0.001). Multivariate analysis confirmed that a GPS of 2 independently predicted unfavorable PFS (hazard ratio [HR], 2.89; 95% confidence interval [CI]: 1.68–4.88; p < 0.001) and OS (HR, 3.49 [95% CI: 1.83–6.63], p < 0.001). Conclusion: The study’s findings suggest that the GPS can predict the survival outcomes of patients with ES-SCLC who have undergone chemotherapy. The GPS is an easy-to-calculate biomarker and would be ideal for routine use in clinical settings.

The incidence of small cell lung cancer (SCLC) has been declining in Japan; however, it still accounts for 10–15% of lung cancer cases [1]. SCLC is the most aggressive and deadly form of lung cancer, characterized by rapid growth, early metastasis, and high rates of acquired therapeutic resistance [2, 3]. Owing to the nature of the disease, most patients would have developed metastatic disease upon diagnosis, leading to poor overall survival (OS) [4]. Survival in patients with extensive-stage SCLC (ES-SCLC) has not improved to a satisfactory level despite progress in the development of various chemotherapeutic agents. Moreover, the introduction of immunotherapy using immune checkpoint inhibitors (ICIs), such as atezolizumab or durvalumab, has recently demonstrated promising survival benefits in clinical trials of this patient population [5‒7].

However, not all patients with SCLC benefit from treatment, and for some patients, the disease course at the time of diagnosis is rapid and aggressive. Thus, clinicians require valuable guiding tools to predict patient prognosis. Therefore, to improve the treatment outcomes of patients with SCLC, predictive and prognostic markers that can identify patients at high risk of unfavorable survival are indispensable.

The Glasgow prognostic score (GPS) is a systemic inflammatory response-based evaluation system that relies on serum C-reactive protein (CRP) and albumin levels. In this context, CRP is a nonspecific inflammatory marker that is also potentially useful in identifying poor nutritional status and the risk of poor OS [8, 9], while albumin is a nutritional marker that is inversely correlated with CRP [10]. The GPS was initially developed by Forrest et al. [11] as a prognostic factor for patients with advanced non-SCLC (NSCLC), and many studies have found the GPS to be an independent predictor of survival in patients with various cancers, including NSCLC [12‒17]. However, reports assessing the correlation between the GPS and efficacy of chemotherapy in patients with ES-SCLC are limited. Hence, we performed this study to assess the utility of the GPS in predicting the survival outcomes of patients with ES-SCLC who had undergone chemotherapy.

Study Design and Patient Selection

This retrospective, observational cohort study was conducted at Kitasato University Hospital between February 2008 and November 2021. The participants were patients with advanced SCLC who had undergone chemotherapy, including platinum-based chemotherapy and platinum-based chemotherapy plus. The inclusion criteria were as follows: (1) histologically or cytologically confirmed SCLC; (2) extensive-stage SCLC; (3) at least one measurable lesion for evaluating disease control or progression; and (4) the ability to receive oral treatment. Data on patient characteristics, including age at diagnosis, sex, Eastern Cooperative Oncology Group (ECOG) performance status (PS) at treatment initiation, smoking status, brain metastasis status, chemotherapy regimen, and laboratory data (e.g., CRP, albumin, lactate dehydrogenase [LDH], and Na levels at treatment initiation), were collected from medical charts. This study was approved by the ethical review board of Kitasato University and its affiliated hospitals (approval number B21-095). The use of the opt-out method in lieu of written informed consent was permitted.

GPS Evaluation

Serum CRP and albumin levels were measured either on the day of initiating first-line chemotherapy or 1 day before. The GPS was categorized into the following three groups: (1) GPS = zero (denoted by a CRP level <1.0 mg/dL and an albumin level ≥3.5 mg/dL), (2) GPS = 1 (denoted either by CRP elevation or albumin decrease alone), and (3) GPS = 2 (indicated by a CRP level ≥1.0 mg/dL and an albumin level <3.5 mg/dL).

Statistical Analysis

We used the χ2 test to evaluate the baseline characteristics, response rate, and rates of second-line chemotherapy initiation according to patient GPS. Progression-free survival (PFS) was defined as the interval between chemotherapy initiation and disease progression or death. OS was defined as the interval between chemotherapy initiation and death. Live patients were censored on the date of the final follow-up. The Kaplan-Meier method was used to estimate survival, and differences were analyzed using the log-rank test. Cox proportional hazards models with stepwise regression were applied to identify factors predictive of PFS and OS, and the results are presented as hazard ratios (HRs) and 95% confidence intervals (CIs). The variables included age, sex, PS, smoking status, status of brain metastasis, Na level, LDH level, GPS, and chemotherapy regimen (chemotherapy vs. chemotherapy + ICI). Primary refractory disease was defined as recurrence during the first-line chemotherapy regimen or <90 days after completing the initial chemotherapy regimen, and sensitive disease was defined as recurrence ≥90 days after completing first-line chemotherapy. Statistical significance was set at p < 0.05. All statistical analyses were performed using SPSS software (version 28.0, Windows; IBM Corp., Armonk, NY, USA).

Patient Characteristics

A total of 113 patients with ES-SCLC were included in the final analysis. The basic patient characteristics are summarized in Table 1. The patient cohort comprised the following: 88% male patients, a median age of 68 years (range, 47–82 years), 74% of patients with PS scores of zero or 1, and 18% with brain metastasis. Platinum-doublet chemotherapy and platinum-doublet chemotherapy plus ICI were administered to 74 (66%) and 28 (24%) patients as first-line chemotherapy, respectively. The median CRP and albumin levels were 0.84 mg/dL and 3.7 g/dL, respectively. The GPS values were zero (54 patients, 48%), 1 (37 patients, 33%), and 2 (22 patients, 19%). As shown in Table 2, sex, PS scores, and LDH levels were significantly correlated with the GPS.

Table 1.

Basic characteristics of the patients (n = 113)

Gender, n (%) 
 Male/female 99/14 (88/12) 
Age, median (range), years 68 (47–82) 
ECOG performance status, n (%) 
 0–1/2–3 84/29 (74/26) 
Smoking status, n (%) 
 Smoker/never smoker 109/4 (96/4) 
Laboratory data, median (range) 
 CRP, mg/dL 0.84 (0.0–19.4) 
 Albumin, g/dL 3.7 (2.2–4.8) 
GPS, n (%) 
 0/1/2 54/37/22 (48/33/19) 
Brain metastasis, n (%) 
 Negative/positive 93/20 (82/18) 
Regimen, n (%) 
 Chemotherapy/chemotherapy plus ICI 85/28 (75/25) 
Chemotherapy regimen, n (%) 
 CBDCA + ETP 56 (50) 
 CDDP + CPT11 18 (16) 
 CDDP + ETP 11 (10) 
 CBDCA + ETP + ICI 23 (20) 
 CDDP + ETP + ICI 5 (4) 
Gender, n (%) 
 Male/female 99/14 (88/12) 
Age, median (range), years 68 (47–82) 
ECOG performance status, n (%) 
 0–1/2–3 84/29 (74/26) 
Smoking status, n (%) 
 Smoker/never smoker 109/4 (96/4) 
Laboratory data, median (range) 
 CRP, mg/dL 0.84 (0.0–19.4) 
 Albumin, g/dL 3.7 (2.2–4.8) 
GPS, n (%) 
 0/1/2 54/37/22 (48/33/19) 
Brain metastasis, n (%) 
 Negative/positive 93/20 (82/18) 
Regimen, n (%) 
 Chemotherapy/chemotherapy plus ICI 85/28 (75/25) 
Chemotherapy regimen, n (%) 
 CBDCA + ETP 56 (50) 
 CDDP + CPT11 18 (16) 
 CDDP + ETP 11 (10) 
 CBDCA + ETP + ICI 23 (20) 
 CDDP + ETP + ICI 5 (4) 

CBDCA + ETP, carboplatin + etoposide; CDDP + CPT11, carboplatin + irinotecan; CDDP + ETP, cisplatine + etoposide.

Table 2.

Patient characteristics and GPS

GPS zero (n = 54)GPS 1 (n = 37)GPS 2 (n = 22)p valuea
Age, years 
 <75/≥75 41/13 30/7 15/7 0.53 
Gender 
 Male/female 43/11 35/2 21/1 0.048 
ECOG performance status score 
 0–1/2–3 52/2 21/16 11/11 <0.001 
Smoking status 
 Smoker/never smoker 52/2 35/2 22/0 0.55 
Brain metastasis 
 Negative/positive 46/8 27/10 20/2 0.16 
LDH value 
 Increasing/normal range 29/25 32/5 22/0 <0.001 
Na value 
 Normal range/decreasing 45/9 31/6 17/5 0.79 
Treatment regimen 
 Chemotherapy/chemotherapy plus ICI 47/7 31/6 17/5 0.25 
GPS zero (n = 54)GPS 1 (n = 37)GPS 2 (n = 22)p valuea
Age, years 
 <75/≥75 41/13 30/7 15/7 0.53 
Gender 
 Male/female 43/11 35/2 21/1 0.048 
ECOG performance status score 
 0–1/2–3 52/2 21/16 11/11 <0.001 
Smoking status 
 Smoker/never smoker 52/2 35/2 22/0 0.55 
Brain metastasis 
 Negative/positive 46/8 27/10 20/2 0.16 
LDH value 
 Increasing/normal range 29/25 32/5 22/0 <0.001 
Na value 
 Normal range/decreasing 45/9 31/6 17/5 0.79 
Treatment regimen 
 Chemotherapy/chemotherapy plus ICI 47/7 31/6 17/5 0.25 

aχ2 test.

Survival Analysis

The cutoff date for survival analysis was July 2022, and the median follow-up period for all patients was 10.7 months (range, 8.8–12.6 months). The Kaplan-Meier curves of PFS according to the GPS are shown in Figure 1. The median PFS was 6.2 (95% CI: 5.8–6.6), 5.6 (95% CI: 4.2–7.0), and 3.8 (95% CI: 3.0–4.6) months in the GPS 0, 1, and 2 groups, respectively, suggesting that the GPS zero group had a significantly more favorable PFS than the GPS 2 group (p < 0.001).

Fig. 1.

Kaplan-Meier plots showing progression-free survival according to the GPS. CI, confidence interval.

Fig. 1.

Kaplan-Meier plots showing progression-free survival according to the GPS. CI, confidence interval.

Close modal

The Kaplan-Meier curves of OS according to the GPS are shown in Figure 2. The median OS was 17.1 (95% CI: 13.0–21.2), 9.4 (95% CI: 3.8–15.0), and 5.6 (95% CI: 3.6–7.6) months in the GPS 0, 1, and 2 groups, respectively, suggesting that the GPS zero group had a significantly more favorable OS than the GPS 2 group (p = 0.001).

Fig. 2.

Kaplan-Meier plots showing OS according to the GPS.

Fig. 2.

Kaplan-Meier plots showing OS according to the GPS.

Close modal

The objective tumor response rates are presented in Table 3. The objective response rate was 69.0% (95% CI: 54.3–83.7). The response rate of patients with a GPS of zero (79.6%) was significantly higher than that of patients with a GPS of 2 (54.5% [95% CI: 38.7–70.4], p = 0.027).

Table 3.

Tumor response according to GPS

All patients (n = 113)GPS zero (n = 54)GPS 1 (n = 37)GPS 2 (n = 22)
Partial response 78 43 23 12 
Stable disease 16 
Progressive disease 19 
Response rate, % 69.0 79.6 62.1 54.5 
95% confidential interval, % 54.3–83.7 66.8–92.4 46.7–77.6 38.7–70.4 
  Reference p = 0.067 p = 0.027a 
All patients (n = 113)GPS zero (n = 54)GPS 1 (n = 37)GPS 2 (n = 22)
Partial response 78 43 23 12 
Stable disease 16 
Progressive disease 19 
Response rate, % 69.0 79.6 62.1 54.5 
95% confidential interval, % 54.3–83.7 66.8–92.4 46.7–77.6 38.7–70.4 
  Reference p = 0.067 p = 0.027a 

aχ2 test.

As shown in Table 4, univariate analysis revealed a significant association of PS (HR 1.56 [95% CI: 1.003–2.43], p = 0.049) and GPS 2 (HR 1.70 [95% CI: 1.30–2.23], p < 0.001) with PFS. In multivariate analysis, GPS 2 (HR 2.89 [95% CI: 1.68–4.88], p < 0.001) was independently associated with PFS. Univariate analysis revealed that PS (HR 2.34 [95% CI: 1.41–3.89], p = 0.001), LDH (HR 2.07 [95% CI: 1.19–3.58], p = 0.01), GPS 2 (HR 3.03 [95% CI: 1.62–5.69], p = 0.001), and treatment regimen (HR 2.93 [95% CI: 1.45–5.95], p = 0.003) were significantly associated with OS. Multivariate analysis further demonstrated a significant association of LDH (HR 1.79 [95% CI: 1.02–3.16], p = 0.044), GPS 2 (HR 3.49 [95% CI: 1.83–6.63], p < 0.001), and treatment regimen (HR, 2.94 [95% CI, 1.43–6.25]; p = 0.003) with OS (Table 5).

Table 4.

Analysis of factors potentially associated with progression-free survival

VariableUnivariate analysisMultivariate analysis
HR (95% CI)p valueHR (95% CI)p value
Gender 
 Female 1 (Ref.)    
 Male 1.09 (0.71–1.68) 0.70   
Age 
 <75 years 1 (Ref.)    
 ≥75 years 1.01 (0.63–1.64) 0.96   
Performance status 
 0–1 1 (Ref.)    
 2–3 1.56 (1.003–2.43) 0.049 Not significant  
Smoking status 
 Never smoker 1 (Ref.)    
 Current or former smoker 2.25 (0.71–7.11) 0.17 Not significant  
Brain metastasis 
 Negative 1 (Ref.)  1 (Ref.)  
 Positive 1.56 (0.95–2.55) 0.078 1.60 (0.96–2.65) 0.070 
LDH 
 Normal range 1 (Ref.)    
 Increasing 1.36 (0.90–2.07) 0.15 Not significant  
Na 
 Normal range 1 (Ref.)    
 Decreasing 1.06 (0.38–1.75) 0.82   
GPS 
 0 1 (Ref.)  1 (Ref.)  
 1 1.42 (0.92–2.19) 0.12 1.30 (0.84–2.03) 0.24 
 2 1.70 (1.30–2.23) <0.001 2.89 (1.68–4.88) <0.001 
Regimen 
 Chemotherapy + ICI 1 (Ref.)    
 Chemotherapy 1.17 (0.75–1.84) 0.49   
VariableUnivariate analysisMultivariate analysis
HR (95% CI)p valueHR (95% CI)p value
Gender 
 Female 1 (Ref.)    
 Male 1.09 (0.71–1.68) 0.70   
Age 
 <75 years 1 (Ref.)    
 ≥75 years 1.01 (0.63–1.64) 0.96   
Performance status 
 0–1 1 (Ref.)    
 2–3 1.56 (1.003–2.43) 0.049 Not significant  
Smoking status 
 Never smoker 1 (Ref.)    
 Current or former smoker 2.25 (0.71–7.11) 0.17 Not significant  
Brain metastasis 
 Negative 1 (Ref.)  1 (Ref.)  
 Positive 1.56 (0.95–2.55) 0.078 1.60 (0.96–2.65) 0.070 
LDH 
 Normal range 1 (Ref.)    
 Increasing 1.36 (0.90–2.07) 0.15 Not significant  
Na 
 Normal range 1 (Ref.)    
 Decreasing 1.06 (0.38–1.75) 0.82   
GPS 
 0 1 (Ref.)  1 (Ref.)  
 1 1.42 (0.92–2.19) 0.12 1.30 (0.84–2.03) 0.24 
 2 1.70 (1.30–2.23) <0.001 2.89 (1.68–4.88) <0.001 
Regimen 
 Chemotherapy + ICI 1 (Ref.)    
 Chemotherapy 1.17 (0.75–1.84) 0.49   
Table 5.

Analysis of factors potentially associated with OS

VariableUnivariate analysisMultivariate analysis
HR (95% CI)p valueHR (95% CI)p value
Gender 
 Female 1 (Ref.)    
 Male 1.38 (0.80–2.38) 0.25   
Age 
 <75 years 1 (Ref.)    
 ≥75 years 1.05 (0.59–1.89) 0.86   
Performance status 
 0–1 1 (Ref.)    
 2–3 2.34 (1.41–3.89) 0.001 Not significant  
Smoking status 
 Never smoker 1 (Ref.)    
 Current or former smoker 1.19 (0.37–3.84) 0.76   
Brain metastasis 
 Negative 1 (Ref.)    
 Positive 1.04 (0.56–1.92) 0.97   
LDH 
 Normal range 1 (Ref.)  1 (Ref.)  
 Increasing 2.07 (1.19–3.58) 0.01 1.79 (1.02–3.16) 0.044 
Na 
 Normal range 1 (Ref.)    
 Decreasing 1.02 (0.56–1.86) 0.96   
GPS 
 0 1 (Ref.)  1 (Ref.)  
 1 1.64 (0.96–2.80) 0.072 1.30 (0.75–2.24) 0.35 
 2 3.03 (1.62–5.69) 0.001 3.49 (1.83–6.63) <0.001 
Treatment regimen 
 Chemotherapy + ICI 1 (Ref.)  1 (Ref.)  
 Chemotherapy 2.93 (1.45–5.95) 0.003 2.94 (1.43–6.25) 0.003 
VariableUnivariate analysisMultivariate analysis
HR (95% CI)p valueHR (95% CI)p value
Gender 
 Female 1 (Ref.)    
 Male 1.38 (0.80–2.38) 0.25   
Age 
 <75 years 1 (Ref.)    
 ≥75 years 1.05 (0.59–1.89) 0.86   
Performance status 
 0–1 1 (Ref.)    
 2–3 2.34 (1.41–3.89) 0.001 Not significant  
Smoking status 
 Never smoker 1 (Ref.)    
 Current or former smoker 1.19 (0.37–3.84) 0.76   
Brain metastasis 
 Negative 1 (Ref.)    
 Positive 1.04 (0.56–1.92) 0.97   
LDH 
 Normal range 1 (Ref.)  1 (Ref.)  
 Increasing 2.07 (1.19–3.58) 0.01 1.79 (1.02–3.16) 0.044 
Na 
 Normal range 1 (Ref.)    
 Decreasing 1.02 (0.56–1.86) 0.96   
GPS 
 0 1 (Ref.)  1 (Ref.)  
 1 1.64 (0.96–2.80) 0.072 1.30 (0.75–2.24) 0.35 
 2 3.03 (1.62–5.69) 0.001 3.49 (1.83–6.63) <0.001 
Treatment regimen 
 Chemotherapy + ICI 1 (Ref.)  1 (Ref.)  
 Chemotherapy 2.93 (1.45–5.95) 0.003 2.94 (1.43–6.25) 0.003 

Disease progression after the initiation of first-line chemotherapy was observed in 50, 35, and 20 patients in the GPS 0, 1, and 2 groups, respectively. The number of patients who received second-line chemotherapy in the GPS 0, 1, and 2 groups was 36 (72.0%), 22 (62.9%), and 10 (50.0%), respectively, with no significant differences. Among the patients who relapsed to first-line chemotherapy, the number of patients with sensitive relapses in the GPS 0, 1, and 2 groups was 22 (44.0%), 14 (40.0%), and 2 (10.0%), respectively, exhibiting significant differences between the GPS zero and 2 groups (p = 0.007) and GPS 1 and 2 groups (p = 0.016). The survival times after the failure of first-line chemotherapy (post-progression survival) were 7.6 (4.2–11.0) and 1.9 (0.0–4.1) months in the GPS 0–1 and GPS 2 groups, respectively, showing a significant difference between the GPS 0–1 and GPS 2 groups (Fig. 3, p = 0.02).

Fig. 3.

Kaplan-Meier plots showing the survival times after the failure of first-line chemotherapy (post-progression survival) according to the GPS.

Fig. 3.

Kaplan-Meier plots showing the survival times after the failure of first-line chemotherapy (post-progression survival) according to the GPS.

Close modal

In the current study, we found that a high GPS (i.e., CRP ≥1.0 mg/dL and albumin <3.5 g/dL) before the initiation of first-line chemotherapy was significantly associated with early disease progression and mortality in patients with ES-SCLC. While few studies have assessed the correlation between the prognosis of patients with SCLC and the GPS, several have revealed that the modified GPS (mGPS), which is based on serum albumin, CRP, and the GPS, is a prognostic indicator of OS in patients with SCLC [17‒20]. Our study’s findings are consistent with those of the abovementioned studies [18‒21]; however, to the best of our knowledge, ours is the first study to assess the relationship between the GPS and the outcomes of chemotherapy in patients with SCLC.

Currently, combination therapy with platinum-based regimens plus ICI is the standard treatment for patients with ES-SCLC [6, 7]. However, only a few reports have indicated an association between the mGPS and OS in patients with ES-SCLC who had undergone combination therapy with platinum-based regimens plus ICI. We would like to emphasize that our study’s findings are meaningful based on the suggested significant association between the GPS and the efficacy of chemotherapy, including platinum-based regimens plus ICI. Meanwhile, similar to the findings of previous randomized clinical trials [6, 7], we demonstrated that the platinum-based regimen plus ICI is a favorable prognostic factor in patients with ES-SCLC, indicating a significant value of this combination regimen for first-line treatment, not only in clinical trials but also in real-world settings. Notwithstanding, the number of patients who received the platinum-based regimen plus ICI in the current study was small. Accordingly, whether the platinum-based regimen plus ICI is an independent prognostic factor, regardless of the GPS, warrants validation via further analyses of larger numbers of patients.

Previously, Mauricio et al. [22] assessed the relationship between the GPS and nutritional status and established a strong correlation between a relatively poor nutritional status and evidence of a systemic inflammatory response measured by the GPS in patients with cancer. In this study, all patients with severe malnutrition had a GPS of 2, whereas most well-nourished patients had a GPS of 0, exhibiting a statistically significant association between their categories of worsening nutritional status and GPS elevation. A systemic inflammatory response plays an important role as a key driver of energy imbalance and muscle-wasting cancer cachexia [23]. The production of proinflammatory cytokines triggers systemic inflammation and causes an acute-phase response with increased CRP and decreased albumin levels [24]. In addition, a significant study reported that the GPS effectively stratified the response to cancer cachexia treatment [25]. Recently, the clinical usefulness of anamorelin, an anti-cachexia drug, in global phase III and domestic phase II studies in Japan has been reported in patients with NSCLC [26, 27]. The present study indicates that systemic inflammation and/or nutritional status should be considered when offering first-line chemotherapy for patients with ES-SCLC. Accordingly, as with patients with NSCLC, evaluating the effectiveness of early interventions, such as cachexia control and nutritional support, may be necessary for patients with SCLC in the future.

The ECOG PS has previously been identified as a potent prognostic factor in patients with lung cancer [28, 29], and it is a subjective index scoring system that is useful in evaluating patients’ general well-being. In contrast, the GPS is an objective index scoring system that can be used to classify patients based on their combined albumin and CRP levels. The GPS can be obtained during routine inpatient blood sampling and is thus easily available; accordingly, it can be readily integrated into clinical practice at most institutions, along with the ECOG PS.

Several previous prospective clinical trials have not indicated chemotherapy outcomes for patients with ES-SCLC exhibiting a systemic inflammatory response, as these studies excluded patients with impaired organ function and active inflammation. Thus, numerous published studies have not sufficiently revealed the treatment outcomes of patients with SCLC and cancer cachexia. In this regard, our findings potentially provide a rationale for adopting cancer cachexia status as a stratification factor in randomized controlled trials.

A previous study indicated that the median survival from the time of relapse following first-line chemotherapy in patients with SCLC receiving only first-line chemotherapy and that of patients receiving second-line chemotherapy were 1.5 and 5.2 months, respectively (p < 0.0001) [30]. Therefore, it is reasonable to conclude that consecutive chemotherapies from first-line to second-line or further lines potentially contribute to survival prolongation in patients with ES-SCLC. The type of relapse after first-line chemotherapy significantly affects the efficacy of second-line therapy in patients with SCLC. Treatment options after first-line treatment are limited in patients with refractory relapses compared with those in patients with sensitive relapses. Notably, the current study demonstrated that GPS evaluation may be a predictor of the type of relapse to first-line chemotherapy, availability of second-line chemotherapy, and survival time after the failure of first-line therapy.

This study has several limitations. First, it was a single-center, retrospective study, and the sample size was insufficient, thus potentially resulting in selection bias. Second, given that the enrolled population was relatively small, the subgroup analysis findings should be interpreted cautiously. Third, 22 patients (19.5%) had a GPS of 2 in this study, which may not be representative of the whole population. This may imply that some patients with a GPS of 2 were actually ineligible for chemotherapy (e.g., very poor PS) and were therefore excluded from the present study. However, using multivariate analysis, we identified GPS 2 as a predictive and prognostic factor in patients with SCLC who received first-line chemotherapy. Notably, the GPS could predict PFS and OS in patients with SCLC independent of PS.

In conclusion, our findings suggest that the GPS can predict the survival outcomes of patients with ES-SCLC who have undergone chemotherapy. While a well-planned, large-sized prospective study is warranted to validate this study’s findings, the GPS is an easy-to-calculate biomarker and would be ideal for routine use in clinical settings.

We are grateful to the staff members of the Department of Respiratory Medicine, Kitasato University School of Medicine, for their suggestions and assistance.

This study was approved by the ethical review board of Kitasato University and its affiliated hospitals (approval number B21-095). The use of the opt-out method in lieu of written informed consent was permitted.

The authors have no conflicts of interest to declare.

The authors declare that no funds, grants, or support were received during the preparation of this manuscript.

Hiroki Yamamoto and Satoshi Igawa conceived the study, participated in its design and coordination, and performed statistical analyses and interpretations. Hiroya Manaka, Kaori Yamada, Yuki Akazawa, and Hiroki Yamamoto performed the data curation. Yoshiro Nakahara, Takashi Sato, Hisashi Mitsufuji, Jiichiro Sasaki, and Katsuhiko Naoki supervised the study. Hiroki Yamamoto, Satoshi Igawa, and Katsuhiko Naoki drafted the manuscript. Satoshi Igawa conceived the study and participated in its concept, design, and coordination. Hiroki Yamamoto, Hiroya Manaka, Yuki Akazawa, Kaori Yamada, and Yuri Yagami performed data acquisition and analysis. Yoshiro Nakahara, Takashi Sato, Hisashi Mitshufuji, and Jiichiro Sasaki edited and reviewed the manuscript. All authors have read and approved the final version of the manuscript.

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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