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

Disclosures

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

In This Article

Results

Baseline Clinical Features of the Cohort

Table 1 summarizes the baseline characteristics of 258 NSCLC patients. In the pooled cohort, the median age of patients was 61 years (range, 38–81); 200 (77.5%) patients were male; 150 (58.1%) patients had a history of smoking. Of the 258 NSCLC patients, 88 (34.1%) had the squamous subtype, and 170 (65.9%) had the non-squamous subtype. Most patients (81.4%) were in good physical health with ECOG PS <2. Approximately 121 (47.6%) patients had contralateral lung or pleura metastasis, while 27 (10.6%) had liver metastasis. Additionally, 150 (58.1%) patients received PD-1 inhibitors as the first-line or second-line of treatment. Of the 258 patients enrolled in this study, 54 were treated with PD-1 inhibitor monotherapy, while others received a combination treatment of PD-1 inhibitor and chemotherapy.

Univariate and Multivariate Analyses of Baseline Characteristics

The univariate analyses of routine laboratory parameters revealed that there was a significant difference in the levels of ALC [(1.191±0.068) ×109/L vs. (1.787±0.122) ×109/L; P<0.001] (Figure S2A), NLR (5.410±0.679 vs. 3.717±0.373; P<0.05) (Figure S2B), albumin level (35.48±0.637 vs. 37.40±0.768 g/L; P<0.05) (Figure S2C), and A/G ratio (1.185±0.047 vs. 1.309±0.036; P<0.05) (Figure S2D) in the DCB and NDB groups. However, we found no significant difference for other parameters among the groups (Figure S3).

After univariate analyses, marginally significant (P<0.1) factors and demographic characteristics were included for multivariate analyses (Table 2). The ORR was 36.8% implying that of the 87 patients, 32 responded. Response to ICIs treatment was associated with ECOG PS [OR, 5.242; 95% confidence interval (CI), 1.056–26.016; P=0.043], ALC (OR, 0.197; 95% CI, 0.071–0.545; P=0.002), and lung/pleura metastasis (OR, 3.638; 95% CI, 1.090–12.143; P=0.036). Cox regression model revealed that inferior PFS was associated with ECOG PS ≥2 (HR, 2.312; 95% CI, 1.183–4.516; P=0.014) and lung/pleura metastasis (HR, 2.019; 95% CI, 1.063–3.836; P=0.032). However, patients with higher ALC had significantly longer PFS (HR, 0.561; 95% CI, 0.341–0.922; P=0.023).

Analyses of the LEM Risk Scoring System

The final selected variables were ALC, ECOG PS and lung/pleura metastasis. Relative weights were based on odds ratio and hazard ratio of multivariate analyses (high HR/OR: weighted value =3; intermediate HR/OR: weighted value =2; low HR/OR: weighted value =1). Weighted values were assigned to each parameter, and LEM score was the sum of weighted values of each variable (Table 3).

In the test set, based on the LEM score, patients were divided into three risk groups [good (37.9%), intermediate (32.2%), and poor (29.9%)]. We found that a good risk (HR, 0.216; 95% CI, 0.117–0.398; median PFS, 9.9 months; P<0.001) and an intermediate risk (HR, 0.322; 95% CI, 0.176–0.591; median PFS, 7.0 months; P<0.001) was associated with longer PFS compared with a poor risk (median PFS, 2.1 months). The validation set further verified these results [good (45.6%), intermediate (45.6%), and poor (8.8%)]. Patients with good risk (HR, 0.076; 95% CI, 0.038–0.150; median PFS, 12.5 months; P<0.001) or intermediate risk (HR, 0.240; 95% CI, 0.130–0.443; median PFS, 3.9 months; P<0.001) trended toward longer PFS than those with poor risk (median PFS, 2.1 months) (Figure 1 and Table 4). Further subgroup analyses based on smoking status, age, and histology revealed similar association between LEM score and PFS (log-rank P<0.001) (Figure 2). Figure S4 also shows the one-year OS rate based on the LEM score.

Figure 1.

Progression-free survival (PFS) based on the LEM score. (A) The pooled cohort (P<0.001); (B) The test set (P<0.001); (C) The validation set (P<0.001). LEM score: Good: 0–1; Intermediate: 2–3; Poor: 4–6.

Figure 2.

Subgroup analyses of progression-free survival (PFS) based on the LEM score in the pooled cohort based on the (A,B) smoking status (smoker vs. non-smoker, P<0.001); (C,D) age (<65 vs. 65, P<0.001) and (E,F) histology (squamous vs. non-squamous, P<0.001). LEM score: Good: 0–1; Intermediate: 2–3; Poor: 4–6.

We found that LEM score was associated with ORR (P<0.001), and ORR ranged from 7.7% (for poor risk) to 54.5% (for good risk) in the test set. The pooled cohort also showed significant difference in ORR: 7.3% (for poor risk) vs. 55.9% (for good risk) (OR, 0.023; 95% CI, 0.005–0.099; P<0.001) and 33.0% (for intermediate risk) (OR, 0.078; 95% CI, 0.005–0.099; P<0.001) (Table S1).

Association Between Gene Mutation and LEM Score

Tumor tissue from 41 patients (NDB: n=29; DCB: n=12) were processed for DNA sequencing. We limited our research to 34 genes which were either highly deleterious or had mutations in at least three patients. These variants were used for further analysis. Figure 3 shows the gene profiles (frameshift insertion or deletion, splice-site, or missense mutation) for the two groups, and Table S2 shows the corresponding LEM scores.

Figure 3.

Gene alterations landscape of patients in the NDB and DCB groups. Thirty-four genes are shown, which were highly deleterious or had variants in at least three patients. Each column represents one patient. TMB value and clinical features are shown at the top. NDB (N): no durable benefit; DCB (D): durable clinical benefit.

The most frequent mutations detected were TP53 (n=27, 65.9%), EGFR (n=12, 29.3%), KRAS (n=11, 26.8%), and PIK3CA (n=8, 19.5%). The distribution of genetic alterations in KRAS (NDB: 6/29 vs. DCB: 5/12), PIK3CA (NDB: 6/29 vs. DCB: 2/12) and TP53 (NDB: 20/29 vs. DCB: 7/12) was similar for the NDB/DCB group. Nevertheless, mutations in EGFR (ex19del: 6/12; ex20ins: 5/12; ex21L858R: 1/12) were enriched in samples of the NDB group vs. the DCB group with negligible significance (11/29 vs. 1/12; Fisher's exact P=0.073; OR, 6.722; 95% CI, 0.760–59.479). Here, mutations in ARID2, CCNE1, CDKN2A, MET, PKHD1, SETD and RAF1 were all observed in the NDB group. Additionally, mutations in LRP1B, NTRK3 and TERT in the DCB group were more than that in the NDB group.

Next, we cross-validated our data with the published datasets to verify our findings. Datasets from Rizvi[25] (n=240) and Helmann[19] (n=75) contained both genomic data and clinical response (NDB vs. DCB) to ICIs treatment in NSCLC. It showed that EGFR mutations were related to ICIs treatment response in both our cohort (P=0.004) as well as datasets of Rizvi and Helmann (P=0.036). Additionally, in Rizvi and Helmann's dataset, mutations in AR (P=0.036), FAT1 (P=0.036) and KMT2C (P=0.036) trended toward DCB, while in our cohort, TP53 mutations were associated with NDB (P=0.029) (Figure 4A). Due to the small sample size of our study, we further combined our data with these two datasets. Mutations in FAT1 (OR, 0.502; P=0.087), FBXW7 (OR, 0.342; P=0.087), KMT2C (OR, 0.5; P=0.075), and STK11 (OR, 1.766; P=0.082) were associated with treatment response with negligible significance; however, EGFR mutation (OR, 3.149; P=0.001) showed statistically significance (Figure 4B).

Figure 4.

Gene alteration distribution associated with ICIs treatment response. (A) The comparison of gene alterations that were differentially expressed in the NDB/DCB group between our data and Rizvi and Hellmann datasets based on -log10 (P value). Red lines indicate P<0.05. (B) OR values of gene alterations that were differentially expressed in the NDB/DCB group with negligible significance (FAT1, FBXW7, KMT2C, STK11 P<0.1; EGFR P<0.05) in combined datasets. Red and blue colors color indicate negative and positive factors for ICIs; (C) LEM scores differed between EGFR-mutated/wildtype groups, *, P<0.05. (D) LEM scores showed no significant difference among the EGFR mutation sites. Mut, mutated; WT, wildtype; ex19del: exon 19 deletions; ex20ins: exon 20 insertions; ex21L858R: exon 21 L858R.

We further explored the association between individual EGFR mutations and LEM scores. Data derived from our pooled set showed that LEM scores of patients with EGFR mutations had a higher LEM score than those with wildtype EGFR [(3.000±0.359) ×109/L vs. (2.182±0.142) ×109/L; P=0.025] (Figure 4C), while there was no difference among EGFR mutation sites (Ex19del, Ex20ins and Ex21L858R) (Figure 4D).

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