1 Introduction

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and is the leading cause of cancer-related deaths globally [1]. The development of immune checkpoint inhibitors (ICIs), particularly targeting PD-1/PD-L1, has significantly advanced NSCLC treatment. Recent phase III clinical trials, including Checkmate-017 [2], Checkmate-057 [3], KeyNote-010 [4], and the OAK study [5], have demonstrated the efficacy of ICIs such as nivolumab and pembrolizumab in improving overall survival (OS) and progression-free survival (PFS) compared to traditional treatments like docetaxel. However, these studies also reveal that a substantial proportion of NSCLC patients do not respond to ICIs, underscoring the need for reliable biomarkers to identify individuals most likely to benefit from ICI therapy.

Extensive research has been conducted to identify biomarkers linked with the treatment effectiveness of ICIs in patients with advanced NSCLC. Initial findings from a Phase I study of nivolumab revealed a correlation between PD-L1 expression in tumor samples and clinical response [6]. Currently, the expression of PD-L1 in tumors is the only biomarker approved by the U.S. Food and Drug Administration (FDA) for first-line monotherapy with pembrolizumab in patients with advanced NSCLC [4, 7]. However, the prognosis in the Checkmate-017 study [2], where patients greatly benefited from nivolumab, did not correlate with PD-L1 expression. Moreover, ICIs can still benefit NSCLC patients whose tumors are PD-L1-negative. Five-year follow-up data from the CA209-003 study showed 70% of the survivors with advanced NSCLC who received nivolumab had 1% or more PD-L1 expression before treatment, indicating that 30% of the patients with PD-L1 negative status could potentially benefit from ICIs treatment [8]. Collectively, the prognostic impact of PD‐L1 expression needs further investigation.

As the need to improve the efficacy of immunotherapy grows, the emphasis of precision medicine therapies has also increased significantly. Finding specific markers for predicting response to immunotherapy in lung cancer has become an important research direction. Regarding the selection of biomarkers, this article will focus on introducing the Lung Immune Prognostic Index (LIPI). Mezquita and colleagues were the first to explore the relationship between LIPI and the effectiveness of PD-1 inhibitors for lung cancer immunotherapy [9]. LIPI is based on the pre-treatment levels of LDH above the upper normal limit and a dNLR greater than 3. LDH plays a key role in tumor metabolic changes as a rate-limiting enzyme in glycolysis [10], while neutrophils and lymphocytes play a crucial role in the systemic immune response [11, 12]. An elevation in both parameters corresponds to a poor LIPI group, elevation in one parameter to an intermediate LIPI group, and no elevation in either parameter to a good LIPI group. Mezquita’s team found a strong association between the LIPI score and the effectiveness of ICIs therapy in NSCLC patients. After that, other research teams found that the LIPI score is associated with poor survival in patients with advanced hepatocellular carcinoma [13], stomach cancer [14], and osteosarcoma [15] treated with ICIs.

There is no doubt that their findings provide further theoretical support for our study. In addition, we considered that biomarkers derived from blood samples can be quickly and inexpensively obtained before therapy and are more readily accepted by patients. As a result, we wanted to explore further the correlation of LIPI score with outcomes in Chinese patients with advanced NSCLC receiving ICIs.

2 Material and methods

2.1 Study design and data collection

This study was a retrospective analysis of patients with advanced NSCLC receiving treatment with a PD-1 inhibitor (nivolumab, pembrolizumab, and other PD-1 inhibitors) or a PD-L1 inhibitor (atezolizumab, durvalumab, and other PD-L1 inhibitors) at People's Liberation Army General Hospital from March 2015 to March 2019. Enrollment criteria: (1) Patients were aged older than 18 years at admission; (2) Patients diagnosed with NSCLC by histopathological examination, including lung adenocarcinoma and lung squamous carcinoma; (3) Patients diagnosed with advanced, inoperable NSCLC (stage III and IV); (4) Patients had received at least two cycles of ICIs and one efficacy assessment. Exclusion criteria: (1) The patients with a secondary primary tumor. (2) Patients who had severe infections prior to immunotherapy (such as severe respiratory infections, sepsis, severe soft tissue infections, etc.), or those with other hematologic diseases that significantly impact peripheral blood neutrophils and lymphocytes (such as leukemia, lymphoma, myelodysplastic syndromes, etc.); (3) Patients who had used anti-inflammatory drugs, granulocyte colony-stimulating factor, hormonal-based therapy, and other drugs that can affect the results of peripheral blood tests within one month. Ultimately, we included a total of 305 NSCLC patients who underwent ICIs therapy for further analysis.

We reviewed the medical records of all included individuals to record age, sex, smoking status, cancer histology, tumor stage, site(s) of metastases (bone, brain, liver, and lung), Eastern Cooperative Oncology Group (ECOG) score, gene mutations, treatment (mono- or combination therapy), number of treatment lines (1, 2, 3 or more), and response rate. The 8th edition of the TNM staging system was used for staging [16]. LDH and complete blood count at baseline and within 15 days before the first ICIs treatment were recorded from the electronic clinical records; an LDH level above the ULN was considered elevated. The dNLR was calculated and defined as (neutrophil count)/ (white blood cell count − neutrophil count); a level above three was considered elevated. LIPI was calculated based on the elevation of neither parameter (0, good LIPI group), two parameter (1, intermediate LIPI group), or both parameters (2, poor LIPI group) as previously described [9].

Objective tumor responses were evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 [17]. The definition of overall survival (OS) was the duration between the first immunotherapy administration (after admission) and death. The progression-free survival (PFS) duration was calculated from the first immunotherapy administration (after admission) until disease progression or death. The disease control rate (DCR) was the proportion of patients with a complete response, partial response, or stable disease. The definition of the overall response rate (ORR) is the proportion of patients who achieved PR or CR.

2.2 Statistical analysis

The effect of baseline LIPI on OS, PFS, DCR, and ORR was to be determined. Categorical data were compared using the chi-square test or Fisher’s exact test. For non-normally distributed continuous data, the Kruskal–Wallis rank sum test was used. The Kaplan–Meier method was used for survival analysis, and the log-rank test was used to compare survival curves. To identify independent predictors of OS and PFS, univariate and multivariable Cox proportional hazards regression were performed. Univariate and multivariable analyses were also performed for the analysis of DCR and ORR. P-values less than 0.05 were considered significant. Variables that had P-values below 0.1 in the univariable analysis were entered into the multivariable analysis. The date of the last follow-up was 30 April 2021. Statistical analysis was performed using SPSS version 26 (SPSS, Inc.).

3 Results

3.1 Patient characteristics

We examined the clinicopathological characteristics of 305 Chinese patients with advanced NSCLC (Table 1). The median age was 61 years (range: 33–91), 233 patients (76%) were male, and 190 patients (62%) had histories of smoking. Histopathological analysis indicated there were 172 (56%) adenocarcinomas, 113 (37%) squamous cell carcinomas, and 20 (7%) cancers with other histology. There were 66 (22%) stage-III tumors and 239 (78%) stage-IV tumors. There was bone metastasis in 99 patients (32%), brain metastasis in 52 patients (17%), liver metastasis in 32 patients (10%), and lung metastasis in 102 patients (33%). ECOG performance status was below 2 for 278 patients (91%) and was two or more for 27 patients (9%). Data on somatic mutations were available for 200 patients and indicated that 51 patients (17%) had one or more mutations and 149 (49%) did not. Among the 305 patients, 144(47%) received ICI as a single treatment, and 161 (53%) received ICIs in combination with other therapies, including chemotherapy (74, 46%), radiotherapy (12, 7.5%), antiangiogenic therapy (24, 15%), chemotherapy plus antiangiogenic therapy (39, 24%), and chemotherapy plus radiotherapy (12, 7.5%). The group receiving ICIs combined with chemotherapy (ICIs CC) included 44 patients on platinum-based chemotherapy as well as 30 patients on platinum-free chemotherapy.

Table 1 Baseline clinicopathologic characteristics of patients in the three LIPI groups
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At the time of the analysis, 100 patients (33%) received first-line ICIs, 104 patients (34%) received a second-line ICIs, and 101 patients (33%) received third-line or later ICIs. Based on the LIPI score, the 305 patients were categorized as follows: 178 patients in the good LIPI group (0 points), 108 patients in the intermediate LIPI group (1 point), and 19 patients in the poor LIPI group (2 points). These three LIPI groups were well-balanced at baseline.

3.2 Overall survival

The median OS was 6.6 months (95% confidence interval [CI]: 3.6–9.6) in the 2 points LIPI group, 12.8 months (95% CI: 10.4–15.2) in the 1-point LIPI group, and 26.0 months (95% CI: 18.3–33.7) in the 0-point LIPI group (P < 0.001; Fig. 1A, eTable 1). We analyzed sex, age, histology, TNM stage, bone metastasis, brain metastasis, liver metastasis, pulmonary metastasis, ECOG-PS, smoking status, lines of ICIs treatment, treatment (mono- or combination therapy), gene status, and LIPI score in the univariate analysis of OS. The univariate analysis revealed a significant association between OS and factors such as bone metastases, brain metastases, ECOG score, lines of treatment, type of treatment, and LIPI score (Table 2). Multivariate analysis indicated that bone metastases (P = 0.023), ECOG score (P = 0.031), lines of treatment (P < 0.001), type of treatment (P = 0.001), and LIPI score (P < 0.001) were independently related to OS. We presented the results related to OS using a forest plot based on multivariate COX regression analysis (Fig. 2).

Fig. 1
figure 1

Overall survival (A) and progression-free survival (B) in patients with good, intermediate, and poor LIPI scores

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Table 2 Univariate and multivariate Cox hazard analysis of factors associated with overall survival
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Fig. 2
figure 2

A forest plot based on multivariate COX regression analysis results related to Overall Survival

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3.3 Progression-free survival

The median PFS was shorter in the poor LIPI group (3.3 months, 95% CI: 2.8–3.8) compared to the intermediate LIPI group (6.2 months, 95% CI: 4.5–7.9) and the good LIPI group (10.5 months, 95% CI: 8.9–12.1) (both P < 0.001; Fig. 1B, eTable 1). The same factors were analyzed for PFS as were assessed in the univariate and multivariate analyses of OS. Univariate analysis indicated that sex, age, TNM stage at diagnosis, brain metastases, liver metastases, ECOG score, lines of treatment, treatment type, gene status, and LIPI score were significantly associated with PFS (Table 3). Age (P = 0.030), ECOG score (P = 0.004), treatment lines (P = 0.001), treatment type (P = 0.029), and LIPI score (P = 0.001) were independently associated with progression-free survival (PFS) according to multivariate regression analysis.

Table 3 Univariate and multivariate Cox hazard analysis of factors associated with progression-free survival
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3.4 Overall response rate and disease control rate

The ORR was not significantly different in the poor LIPI group (5%), intermediate LIPI group (19%), and good LIPI group (22%) (P = 0.222; Table 4). However, the DCR was significantly different in the poor LIPI group (47%), intermediate LIPI group (64%), and good LIPI group (79%) (P = 0.002; Table 4). In addition, univariate and multivariate analyses of variables related to DCR were conducted (eTable 2, eTable 3). Univariate analysis indicated that age, ECOG score, brain metastases, gene status, treatment type, lines of treatment, and LIPI score were significantly associated with DCR (eTable 2). The multivariate analysis indicated that age (P = 0.008), ECOG score (P = 0.014), treatment type (P = 0.006), lines of treatment (P = 0.001), and LIPI score (P = 0.045) were independently associated with DCR (eTable 3). Notably, relative to the good LIPI group, the poor LIPI group had a significantly lower DCR (odds ratio [OR] = 3.07, 95% CI: 1.05–8.93, P = 0.040), although the difference between the good and intermediate LIPI groups was not significant (OR = 1.73, 95% CI: 0.97–3.09; P = 0.063).

Table 4 Response rate, objective response rate, and disease control rate in the three LIPI groups
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3.5 Subgroup analysis of survival according to the TNM stage

We performed subgroup analysis according to the TNM stage (Fig. 3). Analysis of patients with stage III NSCLC indicated that the three LIPI groups had no significant differences in OS (P = 0.589) or PFS (P = 0.098). For patients with stage IV NSCLC, compared to those with a good LIPI score, the ones with a poor LIPI score demonstrated a worse OS (HR = 4.40, 95% CI: 2.50–7.72, P < 0.001) and worse PFS (HR = 2.63, 95% CI: 1.47–4.72, P = 0.001). Further evaluation of stage IV patients revealed that, when compared to those with a good LIPI score, patients with an intermediate LIPI score experienced a shorter OS (HR = 1.81, 95% CI: 1.29–2.54, P = 0.001) and a shorter PFS (HR = 1.57, 95% CI: 1.17–2.10, P = 0.003).

Fig. 3
figure 3

Effect of LIPI score on overall survival (A, P = 0.5885) and progression-free survival (B, P = 0.0981) in patients with stage III NSCLC; and effect of LIPI score on overall survival (C, P < 0.001) and progression-free survival (D, P = 0.003) in patients with stage IV NSCLC

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3.6 Subgroup analysis of survival according to the LIPI score

We also performed a subgroup analysis of the three LIPI cohorts to determine the impact of PD-1/PDL-1 inhibitor monotherapy and combination therapy (Fig. 4). Analysis of the good LIPI cohort indicated combination therapy provided longer OS than monotherapy (HR = 0.55, 95% CI: 0.36–0.84, P = 0.005), but these two groups had similar PFS (HR = 0.82, 95% CI: 0.58–1.15, P = 0.248). Analysis of the intermediate LIPI cohort indicated that combination therapy provided longer OS (HR = 0.50, 95% CI: 0.32–0.80, P = 0.003) and longer PFS (HR = 0.65, 95% CI: 0.43–0.98, P = 0.036). Analysis of the poor LIPI group indicated the monotherapy and combination therapy groups had no difference in OS (HR = 1.26, 95% CI: 0.47–3.37, P = 0.642) or PFS (HR = 0.85, 95% CI: 0.30–2.42, P = 0.759).

Fig. 4
figure 4

Effect of combination therapy on overall survival (A, P = 0.0046) and progression-free survival (B, P = 0.2483) in patients with good LIPI scores; effect of combination therapy on overall survival (C, P = 0.0029) and progression-free survival (B, P = 0.0360) in patients with intermediate LIPI scores; and effect of combination therapy on overall survival (E, P = 0.6423) and progression-free survival (F, P = 0.7589) in patients with poor LIPI scores

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3.7 Comparative analysis of ICI monotherapy vs ICIs combined with chemotherapy

In the ICI monotherapy group (n = 144), the LIPI 0-point group comprised 82 individuals, the LIPI 1-point group comprised 51 individuals, and LIPI 2-point group comprised 11 individuals. Patients had a median age of 62 years. These three LIPI groups were well-balanced at baseline (eTable 4). The median OS was 12.3 months, with a 95% CI of 10.7 to 13.9, and the median PFS was 6.3 months, with a 95% CI of 4.2 to 8.4. The median OS was 6.7 months (95% confidence interval [CI]: 1.3–12.1), and the median PFS was 3.2 months (95% confidence interval [CI]: 1.8–4.6) in the poor LIPI group; the median OS was 10.5 months (95% CI: 7.9–13.1), and the median PFS was 4.6 months (95% CI: 2.7–6.5) in the intermediate LIPI group; the median OS was 16.4 months (95% CI: 11.4–21.4), and the median PFS was 8.6 months (95% CI: 5.5–11.7) in the good LIPI group. OS and PFS in the good LIPI group were better than those in the other two groups, and the difference was statistically significant (P = 0.0032 and P = 0.0035, respectively; Fig. 5A, B).

Fig. 5
figure 5

Effect of LIPI score on overall survival (A, P = 0.0032) and progression-free survival (B, P = 0.0035) in patients with monotherapy; and effect of LIPI score on overall survival (C, P = 0.0011) and progression-free survival (D, P = 0.0236) in patients with ICIs combined with chemotherapy

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A total of 74 patients were included in the ICIs CC group, 44 in the LIPI 0-point group, 24 in the LIPI 1-point group, and 6 in the LIPI 2-point group. Patients had a median age of 61 years. The baseline balance of patients in the three LIPI groups is shown in eTable 5. The median OS was 28.7 months, with a 95% CI of 17.4 to 40.0, and the median PFS was 10.6 months, with a 95% CI of 9.2 to 12.0. The median OS was 5.7 months (95% confidence interval [CI]: 2.1–9.3), and the median PFS was 5.7 months (95% confidence interval [CI]: 2.6–8.8) in the poor LIPI group; the median OS was 18.2 months (95% CI: 7.9–28.5), and the median PFS was 8.4 months (95% CI: 4.1–12.7) in the intermediate LIPI group, the median OS was 33.5 months and the median PFS was 11.6 months (95% CI: 8.3–14.9) in the good LIPI group. OS and PFS in the good LIPI group were better than those in the other two groups, and the difference was statistically significant (P = 0.0011 and P = 0.0236, respectively; Fig. 5C, D).

4 Discussion

The recent introduction of ICIs, especially drugs that block PD-1 or PD-L1, has been a breakthrough in treating patients with NSCLC. These new treatments significantly prolong the survival time of many patients with NSCLC. As malignant tumor treatments transition into the era of immunotherapy, biomarkers for ICIs have emerged as a focal point in contemporary research. While detecting PD-L1 by immunohistochemistry in tumor tissue has been recommended as a biomarker of ICIs therapy in clinical trials, inadequacies and limitations soon became apparent. Several recent studies suggested that LDH level [18,19,20] and the dNLR [21,22,23,24,25] obtained from peripheral blood tests influenced clinical outcomes in various cancer types. However, single biomarkers may have limitations and inadequacy to affect the accuracy of screening for immunotherapy benefit populations. Therefore, it is necessary to construct stable and effective immunotherapy predictions by combining multiple biomarkers to promote precise anticancer immunotherapy. In 2018, Mezquita and colleagues [9] constructed a lung immune prognostic index (LIPI) and confirmed the value of pretreatment LIPI in predicting response to ICIs in patients with NSCLC in Europe. Therefore, this study aims to examine the association between the LIPI score and the response to ICIs in Chinese patients with advanced NSCLC.

In designing this study, we set specific inclusion and exclusion criteria, aiming to ensure the homogeneity of the study subjects and reduce potential confounding factors. However, these criteria might lead to the exclusion of certain patient groups, potentially limiting the general applicability of our findings. In this study, we primarily focused on Chinese patients with advanced NSCLC undergoing PD-1/L1 treatments. Nevertheless, based on several considerations, we excluded patients who had severe infections or had used anti-inflammatory drugs within a month before immunotherapy. Previous studies have indicated that infections can result in changes in peripheral blood neutrophil counts [26], shifts in the CD8 + T cell subpopulation [27], and even lead to T cell exhaustion [28], putting the body in an immunosuppressed state. Changes in the counts of peripheral lymphocytes and neutrophils can directly impact the dLNR values. Moreover, gut microbiota has been shown to correlate with the efficacy of immunotherapy [29]. The use of antibiotics, especially broad-spectrum antibiotics, may adversely affect the diversity of gut microbiota. Consequently, several studies have further identified an association between antibiotic usage and decreased efficacy of immunotherapy [30, 31].

The current study showed that individuals with good LIPI scores had significantly better OS, PFS, and DCR than those with intermediate or poor LIPI scores. Moreover, our multivariate analysis indicated that the LIPI score could be used as an independent prognostic indicator for OS, PFS, and DCR. Thus, our results are congruent with Mezquita et al. [9] in general. Their research also identified a significant correlation between LIPI scores and OS (P < 0.001), PFS (P = 0.001), and DCR (P = 0.004). We believe that the observation of no significant difference in ORR across the different LIPI score groups is particularly intriguing. This observation suggests that while the LIPI score may be intricately linked to prognosis in certain aspects, patients across all score groups seemed to benefit uniformly in terms of overall response rate. It hints that the LIPI score might not be the decisive factor when assessing treatment response. However, a stark contrast was manifested upon examining DCR: the LIPI 0-point cohort demonstrably outperformed the LIPI 2-point category. This potentially indicates that a greater proportion of patients in the LIPI 2-point group experienced disease stabilization rather than reduction, resulting in a diminished DCR. Furthermore, the compromised OS and PFS of the LIPI 2-point group, relative to the 1-point and 0-point groups, accentuate the therapeutic response and durability deficiencies inherent in this category. These insights offer valuable perspectives, shedding light on the intricate relationship between the LIPI score and treatment outcomes. Concurrently, these findings underscore the imperative for future research to delve into the multifaceted relationship between other potential prognostic indicators and the responses or outcomes of ICIs therapy.

The Food and Drug Administration (FDA) and the Chinese National Medical Products Administration (NMPA) approved the combination of pembrolizumab with pemetrexed/paclitaxel and platinum-based therapies as a first-line treatment for NSCLC, based on the findings from the KEYNOTE-189 [32, 33] / KEYNOTE407 [34, 35] trials. In our study, patients treated with immunotherapy in the first line had better OS and PFS than those treated in the second line and beyond. In reviewing literature on NSCLC immunotherapy, we observed that some findings diverged from our results. For example, a real-world study about the correlation between PD-1 inhibition efficacy and NSCLC patient characteristics found a better prognosis (OS and PFS) for patients with first-line PD-1 inhibitors than for second-line and further-line use in univariate analysis. But no correlation was found in the multivariate analysis [36]. Furthermore, Shiroyama's study did not find a correlation between immunotherapy efficacy and treatment lines [37]. Immunotherapy has marked a pivotal turning point in NSCLC treatment, raising patients' hopes significantly. Consequently, choosing the timing of immunotherapy application is also a significant area of research. We anticipate that large-sample prospective studies will provide clearer insights. To the best of our knowledge, the goal of combination therapy is to slow down the pace of drug resistance and improve survival rates. In the treatment of NSCLC, many prospective studies (KEYNOTE-021 study [38], KEYNOTE-189 study [32], KEYNOTE 407 study [35]) compared pembrolizumab in combination with platinum-containing chemotherapy to chemotherapy alone. The results demonstrated that the combined group was more effective than the chemotherapy-only group. After thoroughly analyzing the treatment modalities in our trial, we found that the combination therapy group outperformed the monotherapy group in both OS and PFS, irrespective of whether the combination therapy involved solely chemotherapy or other combinations.

In contrast to Mezquita’s studies, we conducted more subgroup analyses and observed that the LIPI score had significant effects on the OS and PFS for patients with stage IV NSCLC but not for patients with stage III NSCLC. It is an interesting finding, even though we could not find a definite explanation for this phenomenon. However, the number of patients with stage III was relatively small compared to stage IV, which may have affected the statistical analysis. Further investigation in future experiments is necessary. Upon further analyzing the subgroups, we found that the efficacy of monotherapy vs. combination therapy on OS correlated with LIPI 0-point and LIPI 1-point groups. However, in LIPI 2 points group, combination therapy did not provide more survival benefits compared to monotherapy. This led us to question whether ICI monotherapy might be a better choice than complex combination therapy patterns for patients with high-scoring LIPI NSCLC undergoing ICI-alone therapy.

In 2019, Durand’s team presented their research results at the ESMO conference through a poster session [39]. To assess the correlation between LIPI score and immunotherapy efficacy, they included 930 first-line treatments of advanced NSCLC patients and divided patients into four groups (ICI monotherapy, CTLA4-inhibitor + ICI, ICIs CC, platinum-based chemotherapy) according to the type of therapy. We mainly focused on the statistical results of the two groups: the ICI monotherapy group(n = 561) and the ICIs CC group (n = 186). They discovered that, in these two groups, patients with a lower LIPI score had longer OS, and the p-value was less than 0.0001. However, the correlation between PFS and LIPI score was statistically significant only for patients in the ICI monotherapy group (P = 0.008). Subsequently, in 2020, Wang and colleagues showed that LIPI could be prognostic markers of OS (P < 0.001) and PFS (P = 0.023) for ICI monotherapy but not for ICIs combination chemotherapy [40]. Thus, current studies on the prediction of LIPI for immunotherapy have inconsistent results. Their study results intrigued us. So, we also analyzed the correlation between LIPI score and prognosis in NSCLC patients who were further divided into ICI monotherapy and ICIs CC. However, our findings indicated that LIPI could serve as prognostic markers of treatment response not only to ICI monotherapy but also to ICIs CC. We considered the disparity between our study's results and those of other studies could be mainly attributable to the retrospective nature of these studies, which may be more susceptible to bias than prospective cohort studies. Moreover, all the patients included in Durand's study are receiving first-line therapy. Furthermore, while all patients in the combination group from both Durand’s and Wang's studies received platinum-based chemotherapy, our analysis also encompassed a subset of patients undergoing other chemotherapy types. Meanwhile, based on the conference poster, we are only privy to a portion of the inclusion criteria. Unfortunately, due to the relatively small number of participants in other combined therapeutic groups in our research, an in-depth analysis of the performance of LIPI score among these different subgroups was impossible. Therefore, additional research is essential to solidify the significance of the LIPI score in lung cancer treatments. If confirmed, using the LIPI index will better help clinicians identify NSCLC patients who benefit most from ICIs therapy. Additionally, the potential significance of the LIPI score across varied combination therapy patterns for lung cancer warrants more in-depth exploration.

As we know from the literature, other score systems are also associated with the efficacy of immunotherapy. Tumor immune dysfunction and exclude (TIDE) is a scoring system for predicting the response of ICIs based on two mechanisms of tumor immune escape (the dysfunctional state of cytotoxic T cells and the prevention of T cell infiltration) [41]. TIDE score has been verified to be negatively correlated to the responding rate to ICIs treatment. IMmuno-PREdictive Score (IMPRES) is the prediction model developed for metastatic melanoma by Professor Noam Auslander [42]. This scoring system includes 15 pairwise transcriptomics relations between immune checkpoint genes. IMPRES is significantly and positively associated with high-immune response in ICIs treatment of melanoma patients. However, these studies have generally relied on limited sample sizes. Some patients may require an additional procedure to obtain further pathological tissue. Such conditions intensify the stress for patients and their families, impacting their physical and emotional well-being as well as their financial circumstances. We considered the LIPI an economical, easily performed, and independent prognostic indicator for patients. The LIPI score offers excellent clinical utility. Nevertheless, identifying more valuable markers for tumor immunotherapy continues to be a challenging journey.

Although our study is not the first to evaluate the correlation between LIPI scores and immunotherapy efficacy in NSCLC patients, to our knowledge, we have analyzed more subgroups than previous studies and revalidated the contentious statistical analyses in other studies. Throughout this process, we identified specific concerns that merit further exploration.

While our study yielded encouraging results, it also had several limitations. First, this study was retrospective. Some of our subgroups had smaller sample sizes, potentially affecting the statistical power. Second, LDH and dNLR are only partial indicators of the immune response. As such, the LIPI score doesn't fully encapsulate the intricate nature of the immune milieu. Therefore, there is a need to incorporate additional biomarkers to enhance the model's predictive accuracy. Third, we did not assess the relationship between PD-L1 expression and the LIPI score as the PD-L1 status was not determined for patients on second-line or subsequent ICIs. Fourth, the results of our study could have been influenced using different cut-off points to define LIPI status, a subject we did not investigate.

5 Conclusions

In summary, we confirmed a correlation between the LIPI score and the outcome of ICIs therapy in Chinese patients with advanced NSCLC. The LIPI score, being straightforward to calculate, effectively identifies patients who stand to benefit the most from ICIs therapy. It is necessary to perform prospective studies to confirm the validity of the LIPI score in predicting the prognosis of patients with advanced NSCLC who receive ICIs therapy.