Novel Risk Scoring System for Immune Checkpoint Inhibitors Treatment in Non-Small Cell Lung Cancer

Chuling Li; Meiqi Shi; Xinqing Lin; Yongchang Zhang; Shaorong Yu; Chengzhi Zhou; Nong Yang; Jianya Zhang; Fang Zhang; Tangfeng Lv; Hongbing Liu; Yong Song


Transl Lung Cancer Res. 2021;10(2):776-789. 

In This Article


ICIs therapy is considered a milestone in the history of NSCLC treatment. However, only some patients are benefitted due to the lack of comprehensive biomarkers.[10,26] Hence, there is an urgent need to develop a risk scoring system to stratify NSCLC patients. Thus, our study retrospectively investigated factors associated with ICIs treatment response to establish and verify a novel risk scoring system in four centers. Based on the results of univariate and multivariate analyses, the LEM score included ALC (L), ECOG PS (E), and lung/pleural metastasis status (M). A higher LEM score was associated with limited response and inferior PFS, as well as EGFR mutation. Therefore, the LEM score could act as a pre-treatment guide for optimization and candidate selection for ICIs therapy in NSCLC.

Metastatic sites are known to influence the efficacy of cancer treatment by formatting specific tumor microenvironment (TME).[27] Previous studies have shown the association between liver metastasis and a lower ICIs treatment response rate.[11,28] Other studies have also shown that NSCLC patients with pleural metastasis experience more serious adverse events (SAEs), exhibit a limited response to ICIs,[29] and have poor prognosis.[30,31] In this study, we observed that lung/pleura metastases but not liver or brain metastasis influenced ICIs treatment outcomes. Lung/pleura was the most common metastatic site of advanced NSCLC.[32] Additionally, patients with EGFR mutations and anaplastic lymphoma kinase (ALK) rearrangements were more likely to have metastasis to pleura and lung, respectively.[33–35] The results of clinical trials, as well as our study, confirmed that EGFR mutations had a negative impact on ICIs treatment response.[36] This might partially explain why lung/pleura metastasis was a negative factor in the current risk scoring system.

Systemic inflammatory and immune status are known to impact the efficacy of cancer treatments. Several blood parameters are used as ICIs biomarkers. Here, we found a correlation between ALC and ICIs treatment response. PD-1 inhibitors are known to enhance the anti-tumor immunity of T lymphocytes by blocking PD-1 protein, which is expressed on the cell surface. The ICIs treatment focuses on advanced melanoma due to the presence of abundant lymphocytes.[37] Thus, reduced levels of circulating lymphocytes might lead to a decrease in tumor-infiltrating T lymphocytes (TIL) as well as an imbalance in Th-1 and Th-2 phenotypes.[38,39] Additionally, potential inflammatory biomarkers, including NLR, albumin, LDH, C-reactive protein (CRP), neutrophils, and platelets, have been shown to be associated with ICIs treatment response.[40,41] However, in our test cohort, we observed that NLR, albumin and A/G ratio rather than LDH, neutrophils, and platelets were associated with treatment outcomes. This difference might be attributed to the potential bias caused by the confounding factors in real-world data analysis.

Previous studies have shown that PD-L1 and TMB re correlated to clinical benefits of ICIs.[2,8,42] Hu-Lieskovan et al.[43] explored the association between PD-L1/TMB and benefit from pembrolizumab and discovered that PD-L1 was related to ORR [OR, 0.96 (0.93–0.99), P=0.007] and PFS [HR, 0.98 (0.96–0.99), P=0.002], while no such association was found for TMB. However, our novel scoring system showed great predictive value for both ORR [good risk: OR, 0.023 (0.005–0.099); P<0.001] and PFS [good risk: HR, 0.130 (0.084–0.203); P<0.001] even in the absence of PD-L1 and TMB.

Single parameters are known to have limited performance as a prognostic predictor. Previous studies have explored risk stratification score for patients treated with ICIs. Mezquita et al.[21] developed a prognostic index, LIPI, based on a multicenter retrospective study with a total of 466 ICIs-treated NSCLC. This index based on dNLR greater than 3 and LDH greater than upper limit of normal (ULN) was correlated with worse outcomes for ICIs (good, 0 factor; intermediate, 1 factor; poor, 2 factors). Furthermore, it was further verified by Kazandjian[44] and Sorich.[45] Mazzaschi et al.[46] also generated an Immune effector score (IeffS) featuring high soluble PD-L1 (sPD-L1) and low CD8+PD-1+ and NK cells levels, which outperformed LIPI in the prognostic power. It even showed remarkable impact of IeffS and LIPI integration on survival outcome. Kasahara et al.[47] employed Glasgow prognostic score (GPS), which contained CRP and albumin, to predict the efficacy of treatment with PD-1 inhibitors. These retrospective studies had certain limitations regarding the lack of comprehensive clinical and pathological data. Martini et al.[22] also developed a risk scoring criteria for patients with mRCC who were treated with ICIs. These criteria included MLR, sites of metastasis and nutritional index-BMI. Patients were also categorized into 4 groups (good, intermediate, poor, very poor). It turned out to be an effective way to predict survival in mRCC patients receiving ICIs. Also, another study[48] indicated that the ECOG PS, which reflected overall performance status, was better than BMI for risk stratification of survival in patients with metastatic cancer.

Our study had several limitations. First, it was a retrospective study based on real-world data; thus, there was scope for potential bias due to loss to follow-up or missing data. For example, in our study, PD-L1 IHC status and TMB were not routinely tested in our study, especially for those who underwent posterior–line ICIs treatment. Second, we chose a one-year OS rate rather than OS as the observe objective due to insufficient follow-up data. In future studies, we would investigate the association between LEM score and OS. Finally, only a few patients (41/258) had tumor sequencing data. Further efforts are needed to develop a more comprehensive index combining genomic and clinical variables to predict response to ICIs treatment.