Abstract
Background
Inflammatory biomarkers in the peripheral blood have been established as predictors for immunotherapeutic efficacy in advanced non-small cell lung cancer (NSCLC). Whether they can also predict major pathological response (MPR) in neoadjuvant setting remains unclear.
Methods
In this multi-center retrospective study, 122 and 92 stage I-IIIB NSCLC patients from six hospitals who received neoadjuvant chemoimmunotherapy followed by surgery were included in the discovery and external validation cohort, respectively. Baseline and on-treatment neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR) and systemic immune-inflammation index (SII) were calculated and associated with MPR. Furthermore, resected tumor samples from 37 patients were collected for RNA-sequencing to investigate the immune-related tumor microenvironment.
Results
In both the discovery and validation cohorts, the on-treatment NLR, dNLR, PLR, and SII levels were significantly lower in the patients with MPR versus non-MPR. On-treatment SII remained an independent predictor of MPR in multivariate logistic regression analysis. The area under the curve (AUC) of on-treatment SII for predicting MPR was 0.75 (95%CI, 0.67–0.84) in the discovery cohort. Moreover, the predictive value was further improved by combining the on-treatment SII and radiological tumor regression data, demonstrating an AUC of 0.82 (95%CI, 0.74–0.90). The predictive accuracy was validated in the external cohort. Compared with the SII-high group, patients with SII-Low were associated with the activated B cell receptor signaling pathway and a higher intratumoral immune cell infiltration level.
Conclusions
On-treatment SII was independently associated with MPR in NSCLC patients receiving neoadjuvant chemoimmunotherapy. Further prospective studies are warranted.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00262-022-03262-w.
Keywords: Biomarkers, Neoadjuvant immunotherapy, Major pathological response, NLR, SII, Non-small cell lung cancer
Introduction
Immune checkpoint inhibitors (ICIs) that target programmed cell death protein 1 (PD-1) and its ligand PD-L1 have changed the standard of care for patients with advanced non-small cell lung cancer (NSCLC) [1]. More recently, several clinical trials have demonstrated that ICIs alone or combined with chemotherapy show promising clinical efficacy in operable NSCLC [2–4]. And more recently, nivolumab with platinum-doublet chemotherapy has been approved by the Food and Drug Administration (FDA) for patients with resectable NSCLC in the neoadjuvant setting. Our previous report also showed that neoadjuvant immunotherapy or chemoimmunotherapy for resectable NSCLC was feasible and resulted in an improved major pathological response (MPR, defined as ≤ 10% viable tumor in resected tumor specimens) [5, 6]. Previous research has revealed that MPR following neoadjuvant chemotherapy is strongly associated with improved survival [7]. Thus, in multiple clinical trials, MPR was used as a surrogate primary endpoint of overall survival (OS). Preliminary results of the NADIM study also supported that patients receiving neoadjuvant chemoimmunotherapy who achieved MPR had a significantly higher progression free survival (PFS) at 24 months than those with an incomplete pathological response (88.4 versus 57.1%, P = 0.01) [4]. Consequently, identifying biomarkers to predict which patients will benefit most from neoadjuvant chemoimmunotherapy is of great importance. However, PDL1 protein expression and tumor mutation burden (TMB), widely used to predict immunotherapeutic efficacy in advanced solid tumors, show limited predictive value in the context of neoadjuvant immunotherapy for resectable NSCLC [2, 4]. Additionally, the association between radiological and pathological response after neoadjuvant immunotherapy is controversial [8]. In certain cases with MPR, the tumor diameter may even enlarge due to immune cell infiltration [2]. Thus, MPR may not be predicted precisely by the radiological response. Therefore, other biomarkers capable of predicting MPR are urgently required.
Recent studies have demonstrated that the cancer-related inflammation response is closely involved in tumorigenesis, local immune responses, disease progression, and patient prognosis [9]. Furthermore, in multiple advanced solid tumors treated with ICIs, inflammatory biomarkers in the peripheral blood, such as the neutrophil-to-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII), have been found to predict response rate and survival [10–12]. Low NLR, SII, either at baseline or on-treatment, are associated with a better clinical outcome [13–15]. Compared with other established biomarker, inflammatory biomarkers in the peripheral blood are advantageous, because they are inexpensive to assess, easily accessible, and obtained in a minimally invasive manner. However, it is unknown, whether these inflammatory biomarkers could also be used to predict immunotherapeutic efficacy in neoadjuvant immunotherapy settings. Therefore, we aimed to analyze the association between inflammatory biomarkers and MPR in NSCLC patients and the underlying biological basis in the present study.
Materials and Methods
Study design and participants
Stage I-IIIB NSCLC patients who underwent curative surgery after neoadjuvant chemoimmunotherapy at 6 hospitals in China from June 2018 to March 2021 were retrospectively reviewed. The exclusion criteria included patients with N3 disease, or harboring epidermal growth factor receptor (EGFR)/anaplastic lymphoma kinase (ALK) driver mutations or with incomplete data. Patients treated with other neoadjuvant therapies or those who participated in any clinical trials were not enrolled in the study.
The clinical and pathological data of the patients from Shanghai Pulmonary Hospital (n = 122) were obtained as a discovery cohort to explore the association between inflammatory biomarkers in peripheral blood and MPR. Patients from Shanghai Chest Hospital (n = 59), Changhai Hospital (n = 9), Affiliated Hospital of Xuzhou Medical University (n = 8), Ningbo No. 2 Hospital (n = 9), and The First Hospital of Lanzhou University (n = 7) were combined as an external validation cohort (n = 92).
Additionally, resected tumor samples from 37 patients in the discovery cohort were available for RNA sequencing to further investigate the association of inflammatory biomarkers and immune cell infiltration and immune-related pathways. The study design is illustrated in Figure S1.
Data collection
All patients underwent routine baseline tumor diagnosis and staging. The preoperative assessments included a contrast-enhanced computed tomography (CT) scan or a positron emission tomography (PET)/CT scan, and brain imaging with CT or magnetic resonance imaging. Invasive mediastinal nodal staging with endobronchial ultrasound (EBUS) was recommended for patients with a suspicion of N2 disease. Patients were staged according to the AJCC Lung Cancer Staging, 8th edition [16]. A multidisciplinary team discussed the treatment regimens for each patient as we previously reported [5, 6] and all patients signed an informed consent form before treatment. Chemoimmunotherapy was administrated every 3 weeks for 2–4 cycles before surgical resection. After two treatment cycles, a chest CT scan was repeated; tumor response was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1.
The pathological response was evaluated as previously described [17]. Briefly, the presence of 10% or fewer viable tumor cells in the primary tumor was defined as MPR, and no viable tumor cells in both primary tumor and dissected lymph nodes was defined as complete pathologic response (CPR).
Clinicopathologic variables, including age, sex, smoking status, histology, pre-treatment tumor PDL1 expression, clinical and pathological stage, and regimen and cycle of neoadjuvant therapy, were collected from the patients’ electronic medical records. The patients’ complete blood count (CBC) with differential at baseline (within 7 days prior to neoadjuvant chemoimmunotherapy) and on-treatment (3 ± 1 weeks from the initiation of neoadjuvant chemoimmunotherapy) were also collected. The definition of inflammatory biomarkers in the peripheral blood was as follows: NLR, absolute neutrophil count/absolute lymphocyte count; derived NLR (dNLR), absolute neutrophil count/ (white blood cell count—absolute neutrophil count); MLR, absolute monocyte count/absolute lymphocyte count; PLR, absolute platelet count/absolute lymphocyte count; and systemic immune-inflammation index (SII), absolute platelet count × NLR.
RNA sequencing
Resected primary tumor samples from 37 patients in the discovery cohort were collected into RNAlater and stored at − 80 °C until RNA extraction. Total RNA was isolated and purified using Trizol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer's procedure. The RNA amount and purity of each sample was quantified using NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA). The RNA integrity was assessed by Agilent 2100 with RNA integrity number (RIN) > 7.0. The TruSeq RNA Access Library Prep Kit (Illumina) was used to construct RNA-seq library according to the manufacturer’s protocol. After library quality assessment, we performed the paired-end sequencing on an Illumina Novaseq™ 6000 following the vendor's recommended protocol.
Identification of differentially expressed genes (DEGs)
The normalized read count data for all the expressed coding transcripts were processed by the R package “limma” to identify DEGs between the on-treatment SII-Low and SII-High groups [18]. A cut-off of a gene-expression log2-transformed fold change of > 1 or < -1 and p < 0.05 was applied to select the most DEGs.
Pathway enrichment analyses
A network-based pathway enrichment analysis was performed using the DEGs across the on-treatment SII-Low and SII-High groups in the bulk-tissue RNA-seq data. The upregulated and downregulated differentially expressed genes were selected as input for the network-based pathway enrichment analysis using the ReactomeFiViz51 application in Cytoscape. Pathway enrichment was calculated using the GO databases with a hypergeometric test FDR < 0.01.
Immune cell infiltration analyses
We used a single-sample gene set enrichment analysis (ssGSEA) to estimate immune cell infiltration, which calculates separate enrichment scores for each pairing of a sample and gene set [19]. The ssGSEA analysis was performed in the R package GSVA to calculate the signal enrichment score of each immune cell type as previously reported [20]. The relative immune cell infiltration levels were compared between the on-treatment SII-Low and SII-High patients.
Statistical analysis
Fisher’s exact test and non-parametric Mann–Whitney U test were performed to compare the categorical data and continuous data between the MPR group and non-MPR group, respectively. Spearman’s correlation was used to assess the relationship between the variables. The receiver operating characteristic (ROC) curve analysis was utilized to investigate the association of the inflammatory biomarkers in the peripheral blood with MPR and to determine the optimal cut-off values. The Delong method was used to compare the area under the curve (AUC) of the different ROCs. Univariate and multivariate logistic regression models were also performed to identify possible predictors for MPR, and factors with a univariate significance of P < 0.1 were entered into the multivariate analysis (Forward stepwise). The graphs were created using GraphPad Prism (version 8.0, LaJolla, CA) and R software (version 4.1.0). The statistical analyses were done using SPSS (version 26, IBM Corp., Armonk, NY). P values less than 0.05 (two-sided) was considered statistically significant.
Results
Patient characteristics
The discovery and validation cohorts consisted of 122 and 92 patients, respectively, who received neoadjuvant chemoimmunotherapy followed by surgery (Table 1). In terms of the ICI regimens, all the patients received anti-PD1 inhibitors, and no anti-PDL1 inhibitor nor anti-CTLA blockade were identified.
Table 1.
Baseline characteristics of patients according to pathological response in the discovery and validation cohorts
| Variables | Discovery cohort | Validation cohort | ||||||
|---|---|---|---|---|---|---|---|---|
| Total (n = 122) | MPR (n = 62) | Non-MPR (n = 60) | P value | Total (n = 92) | MPR (n = 51) |
Non-MPR (n = 41) |
P value | |
| Age (years) | 62.5 (56–67) | 62 (56–66) | 63 (55–67) | 0.77 | 63 (57–66) | 63 (58–66) | 61 (55–66.5) | 0.24 |
| Gender | 0.39 | 0.47 | ||||||
| Male | 109 (89.3) | 57 (91.9) | 52 (86.7) | 79 (85.9) | 45 (88.2) | 34 (82.9) | ||
| Female | 13 (10.7) | 5 (8.1) | 8 (11.3) | 13 (14.1) | 6 (11.8) | 7 (17.1) | ||
| Smoking status | 0.94 | 0.66 | ||||||
| Never | 39 (32) | 20 (32.3) | 19 (31.7) | 27 (29.3) | 14 (27.5) | 13 (31.7) | ||
| Ever | 83 (68) | 42 (67.7) | 41 (68.3) | 65 (70.7) | 37 (72.5) | 28 (68.3) | ||
| Clinical stage | 0.46 | 0.45 | ||||||
| II | 34 (27.9) | 15 (24.3) | 19 (31.7) | 19 (20.7) | 10 (19.6) | 9 (22.0) | ||
| IIIA | 56 (45.9) | 28 (45.2) | 28 (46.7) | 49 (53.3) | 30 (58.8) | 19 (46.3) | ||
| IIIBa | 32 (26.2) | 19 (30.6) | 13 (21.7) | 24 (26.0) | 11 (21.6) | 13 (31.7) | ||
| Induction cycle | 0.3 | 0.52 | ||||||
| 2 | 74 (60.7) | 39 (62.9) | 35 (58.3) | 61 (66.3) | 35 (68.6) | 26 (63.4) | ||
| 3 | 34 (27.9) | 14 (22.6) | 20 (33.3) | 27 (29.3) | 13 (25.5) | 14 (34.1) | ||
| 4 | 14 (11.5) | 9 (14.5) | 5 (8.3) | 4 (4.4) | 3 (5.9) | 1 (2.4) | ||
| Histology | 0.31 | 0.07 | ||||||
| LUAD | 37 (30.3) | 16 (25.8) | 21 (35.0) | 27 (29.3) | 10 (19.6) | 17 (41.5) | ||
| SQQ | 75 (61.5) | 39 (62.9) | 36 (50.0) | 56 (60.9) | 35 (68.6) | 21 (51.2) | ||
| Others | 10 (8.2) | 7b(11.3) | 3c(5.0) | 9 (9.8) | 6d(11.8) | 3e(7.3) | ||
| PDL1 expression | 0.46 | 0.57 | ||||||
| < 1% | 45 (36.9) | 21 (33.9) | 24 (40.0) | 29 (31.5) | 18 (35.3) | 11 (26.8) | ||
| 1%–49% | 40 (32.8) | 21 (33.9) | 19 (31.7) | 19 (20.7) | 11 (21.6) | 8 (19.5) | ||
| ≥ 50% | 20 (16.4) | 13 (21.0) | 7 (11.7) | 29 (31.5) | 16 (31.4) | 13 (31.7) | ||
| Unknown | 17 (13.9) | 7 (11.2) | 10 (16.7) | 15 (16.3) | 6 (11.8) | 9 (22.0) | ||
| Pathological stage | < 0.01 | < 0.01 | ||||||
| 0f | 32 (26.2) | 32 (51.6) | 0 | 24 (26.1) | 24 (47.1) | 0 | ||
| I | 35 (28.7) | 16 (25.8) | 19 (31.7) | 20 (21.7) | 10 (19.6) | 10 (24.4) | ||
| II | 22 (18.0) | 4 (6.5) | 18 (30.0) | 25 (27.2) | 10 (19.6) | 15 (36.6) | ||
| IIIA | 29 (23.8) | 10 (16.1) | 19 (31.7) | 16 (17.4) | 6 (11.8) | 10 (24.4) | ||
| IIIB | 4 (3.3) | 0 | 4 (6.7) | 7 (7.6) | 1 (2.0) | 6 (14.5) | ||
| Radiological response | 0.08 | |||||||
| SD | 41 (33.6) | 16 (25.8) | 25 (41.7) | 28 (30.4) | 12 (23.5) | 16 (39.0) | 0.11 | |
| PR/CR | 81 (66.3) | 46 (74.2) | 35 (58.3) | 64 (69.6) | 39 (76.5) | 25 (61.0) | ||
Continuous data are summarized as median and interquartile range (IQR). Categorical data are summarized as numbers and percentages. Abbreviations: MPR, major pathological response; LUAD, lung adenocarcinoma; SQQ, squamous carcinoma. SD, stable disease; PR, partial response; CR, complete response. aall stage IIIB patients were T3-4N2M0 bOne patient was diagnosed with large cell neuroendocrine carcinoma and six patients who were diagnosed with NSCLC (NOS) preoperatively achieved complete pathological response (CPR) and could not be further distinguished. cadenosquamous carcinoma (n = 1) and large cell carcinoma (n = 2). dall were NSCLC (NOS) eLarge cell neuroendocrine carcinoma (n = 2) and pleomorphic carcinoma (n = 1) fPatients with CPR were staged as 0
Most patients were clinical stage III (72.1 of the discovery cohort and 79.3% of the validation cohort) and received two cycles of neoadjuvant therapy (60.7 of the discovery cohort and 66.3% of the validation cohort). Radiologically, the neoadjuvant chemoimmunotherapy’s objective response rate (ORR) was 66.3 and 69.6% in the discovery and validation cohorts, respectively. No progression of disease occurred during neoadjuvant therapy. Pathologically, 62 patients (60.8%) achieved MPR, of whom 32 (26.2%) achieved CPR in the discovery cohort. In the validation cohort, 51 patients (55.4%) achieved MPR, of whom 24 (26.1%) achieved CPR. Patients with a partial response (PR) and complete response (CR) were more likely to reach MPR than those with a stable disease (SD). However, this difference was not statistically significant (P = 0.08 for the discovery cohort and P = 0.11 for the validation cohort, Table 1).
No difference was observed between the MPR and non-MPR groups regarding age, gender, smoking status, clinical stage, induction cycle, histology, and PDL1 expression (Table 1). Also, the regimens of chemotherapy were similar between the two groups (Table S1).
Association of baseline and on-treatment inflammatory biomarkers in the peripheral blood with MPR
The level of baseline inflammatory biomarkers, including NLR, dNLR, MLR, PLR, and SII, were similar between the MPR and non-MPR groups in both the discovery and validation cohorts (Fig. 1, Table S2). However, patients with MPR had a significantly lower level of on-treatment NLR (median 2.11 vs. 2.93, P < 0.001), dNLR (median 1.44 versus 1.97, P < 0.001), PLR (median 131.9 versus. 159.0, P < 0.01), and SII (median 442.8 versus 740.4, P < 0.001) in the discovery cohort (Fig. 1A). Similarly, in the validation cohort, the level of on-treatment NLR, dNLR, MLR, PLR, and SII was also lower in the MPR group than non-MPR group (Fig. 1B, Table S2).
Fig. 1.
Comparison of baseline and on-treatment NLR, dNLR, MLR, PLR, and SII according to pathological response in the discovery (A) and validation cohorts (B). Legend: Baseline (PRE, green dots), On-treatment (POST, red dots). Nonparametric Mann–Whitney test was used for comparisons (ns, not significant; *P < .05; **P < .01; ***P < .001)
Moreover, the inflammatory biomarkers changes were also predictors of MPR. In the discovery cohort, 62.7% of patients with a decrease in NLR (fold change < 1) achieved MPR, while only 31.9% of patients with an increase in NLR achieved MPR (P < 0.01, Fig. 2A). Likewise, patients with a decrease in dNLR (62.0 versus. 38.0%, P < 0.01), PLR (59.2 versus. 49.8%, P = 0.02), and SII (61.4 versus. 38.6%, P < 0.01) from baseline were also more likely to achieve MPR (Fig. 2A). The results were similar in the validation cohort (Fig. 2B).
Fig. 2.
Fold change (POST/PRE) of NLR, dNLR, MLR, PLR, and SII of MPR and Non-MPR patients in the discovery (A) and validation cohorts (B). Legend: fold change < 1 (green squares) indicating a decrease after treatment, fold change > 1 (red squares) indicating an increase after treatment
Furthermore, ROC curves were used to investigate the predictive value of the baseline and on-treatment inflammatory biomarkers for MPR. In both the discovery and validation cohorts, the baseline inflammatory biomarkers showed limited predictive value for MPR (AUC values < 0.60, Figure S2). On the other hand, ROC curves to predict MPR revealed AUC values of 0.75 (95%CI, 0.67–0.84) for on-treatment SII, 0.70 (95%CI, 0.61–0.79) for on-treatment NLR, 0.72 (95%CI, 0.62–0.80) for on-treatment dNLR, and 0.66 (95%CI, 0.56–0.79) for on-treatment PLR in the discovery cohort (Fig. 3A). In the validation cohort, on-treatment SII, NLR, dNLR, and PLR were also predictive of MPR with AUC values of 0.74 (95%CI, 0.64–0.83), 0.68 (95%CI, 0.57–0.78), 0.73 (95%CI, 0.62–0.83), and 0.68 (95%CI, 0.56–0.78), respectively (Fig. 3B). The optimal cut-off values of the on-treatment inflammatory biomarkers were determined by the Youden index in the discovery cohort (Table S3). The cut-off value established in the discovery cohort also hold value to discriminate MPR from non-MPR in the validation cohort (Table S4). In particular, the best cut-off value of on-treatment SII was 493.9 and this value had a sensitivity of 0.66, a specificity of 0.77, a positive predictive value of 0.75, and a negative predictive value of 0.69 in the discovery cohort. Similarly, in the validation cohort, the same SII cut-off value achieved a sensitivity of 0.71, a specificity of 0.73, a positive predictive value of 0.77, and a negative predictive value of 0.67 (Table S4).
Fig. 3.
ROC curves of on-treatment inflammatory biomarkers and radiological tumor regression (RTR) for MPR in the discovery (A) and validation (B) cohorts
Meanwhile, patients were dichotomized based on the optimal cut-off values determined by the ROC curves in the discovery cohort (Table S3). Univariate logistic regression analysis revealed that the on-treatment NLR, dNLR, MLR, PLR, and SII and the change of NLR, dNLR, PLR, and SII were predictors of MPR in both the discovery and validation groups (Table 2). Furthermore, the multivariate logistic regression analysis showed that on-treatment NLR and SII remained independent predictors of MPR in the discovery cohort. While in the validation cohort, only on-treatment SII remained an independent predictor of MPR (Table S5).
Table 2.
Univariate analysis for variables associated with MPR in the discovery and validation cohorts
| Variables | Discovery cohort | Validation cohort | ||
|---|---|---|---|---|
| OR (95%CI) | P value | OR (95%CI) | P value | |
| Age (< 62.5 vs. > 62.5) | 1.13 (0.56–2.32) | 0.73 | 1.50 (0.66–3.43) | 0.34 |
| Gender (male vs. female) | 1.75 (0.54–5.70) | 0.35 | 1.54 (0.48–5.01) | 0.47 |
| Smoking status (never vs. ever) | 0.97 (0.46–2.98) | 0.94 | 0.82 (0.33–2.00) | 0.66 |
| Clinical stage (II vs. III) | 0.69 (0.31–1.53) | 0.36 | 0.87 (0.32–2.39) | 0.78 |
| Induction cycle (2 vs. 3/4) | 1.21 (0.58–2.51) | 0.61 | 1.26 (0.53–3.01) | 0.60 |
| Histology (SQQ vs. Others) | 1.31 (1.64–2.68) | 0.46 | 1.73 (0.74–4.03) | 0.21 |
| PDL1 (≥ 1% vs. Others) | 1.59 (0.78–3.25) | 0.21 | 1.07 (0.47–2.44) | 0.87 |
| NLR at baseline (Low vs. High) | 1.89 (0.78–4.55) | 0.16 | 1.58 (0.68–3.70) | 0.29 |
| dNLR at baseline (Low vs. High) | 2.21 (0.93–5.26) | 0.07 | 2.43 (0.85–6.98) | 0.10 |
| MLR at baseline (Low vs. High) | 3.47 (0.89–13.51) | 0.07 | 3.00 (0.99–9.13) | 0.05 |
| PLR at baseline (Low vs. High) | 2.45 (0.96–6.27) | 0.06 | 2.02 (0.88–4.65) | 0.10 |
| SII at baseline (Low vs. High) | 1.91 (0.89–4.13) | 0.09 | 1.89 (0.71–4.99) | 0.2 |
| On-treatment NLR (Low vs. High) | 5.78 (2.64–12.62) | < 0.001 | 2.87 (1.22–6.71) | 0.02 |
| On-treatment dNLR (Low vs. High) | 4.57 (2.12–9.84) | < 0.001 | 3.09 (1.31–7.28) | 0.01 |
| On-treatment MLR (Low vs. High) | 3.07 (1.43–6.58) | < 0.01 | 1.87 (0.76–4.64) | 0.17 |
| On-treatment PLR (Low vs. High) | 3.83 (1.77–8.29) | < 0.01 | 4.90 (1.90–12.61) | < 0.01 |
| On-treatment SII (Low vs. High) | 7.59 (3.37–17.12) | < 0.001 | 6.54 (2.62–16.37) | < 0.001 |
| NLR change (Down vs. Up) | 3.58 (1.66–7.75) | < 0.01 | 1.89 (0.81–4.43) | 0.14 |
| dNLR change (Down vs. Up) | 3.77 (1.70–8.34) | < 0.01 | 1.92 (0.83–4.46) | 0.13 |
| MLR change (Down vs. Up) | 0.86 (0.52–2.17) | 0.86 | 1.92 (0.81–4.51) | 0.14 |
| PLR change (Down vs. Up) | 2.48 (1.17–5.26) | 0.02 | 3.18 (1.35–7.48) | < 0.01 |
| SII change (Down vs. Up) | 4.06 (1.78–9.26) | < 0.01 | 3.54 (1.41–8.92) | < 0.01 |
The cutoff values of baseline and on-treatment inflammatory biomarkers were determined by ROC curves. Abbreviations: MPR, major pathological response; OR, odds ratio; CI, Confidence interval; SQQ, squamous carcinoma
Combining inflammatory biomarkers in the peripheral blood with radiological regression to predict MPR
Although the RECIST criteria response categories did not predict MPR, radiological tumor regression (RTR) was closely correlated with pathological regression (R = 0.4294, P < 0.0001, Figure S3), and the AUC values for the ROC curves used to predict MPR were 0.68 (95%CI, 0.59–0.77) and 0.67 (95%CI, 0.56–0.77) in the discovery and validation cohorts, respectively (Fig. 3). Therefore, we next investigated whether combining the inflammatory biomarkers in the peripheral blood with radiological regression could further improve the predictive value for MPR. Given that on-treatment SII predicted MPR more precisely than the other biomarkers, we only focused on on-treatment SII at this point. Based on the logistic regression, a risk score for each patient was calculated using a formula derived from the on-treatment SII and RTR status weighted by their regression coefficient [21]. Thus, combining the radiological tumor regression and SII data, the AUC increased to 0.82 (95%CI, 0.74–0.90, P = 0.02) in the discovery cohort and 0.80 (95%CI, 0.69–0.88, P = 0.03) in the validation cohort. Therefore, radiological tumor regression could be combined with on-treatment SII to predict MPR better.
Biological basis of inflammatory biomarkers in the peripheral blood predicting MPR
To further investigate the biological underpinning of the inflammatory biomarkers in peripheral blood for predicting MPR, resected tumor samples from 37 patients in the discovery cohort were collected for RNA sequencing. Based on the cut-off value of on-treatment SII determined by the ROC curve, 20 patients were SII-Low while 17 patients were SII-high. One hundred seventy-one transcripts were upregulated in the SII-Low group, while 156 transcripts were downregulated (Figure S4). The upregulated genes were significantly enriched for immune-related pathways, such as the humoral immune response, B cell receptor signaling pathway, complement activation classical pathway, and antigen binding pathway. In contrast, downregulated genes were mainly enriched for chromosome segregation and cell cycle checkpoint pathways (Fig. 4B). Furthermore, the immune cell infiltration analysis conducted by the ssGSEA method showed that compared with the SII-high group, patients with SII-Low had a significantly higher intratumoral infiltration of activated CD8+ T cells, activated B cells, eosinophils, type 1 T-helper cells, follicular helper T cells, macrophages, activated dendritic cells, and monocytes (Fig. 4A).
Fig. 4.
Transcriptional analysis of resected tumor samples from 37 patients in the discovery cohort (A) Intratumoral infiltration of immune cells in SII-Low and SII-High patients. Nonparametric Mann–Whitney test was used for comparisons (*P < .05; **P < .01) (B) Gene Ontology analysis of genes upregulated (left) and downregulated (right) in SII-Low patients. BP: Biological process; CC, Cellular component; MF, Molecular function
Discussion
Compared with conventional chemotherapy, neoadjuvant chemoimmunotherapy significantly improves the rate of MPR in resectable NSCLC, ranging from 57 to 83% in different clinical trials [3, 4, 22]. Recently, the phase 3 CheckMate -816 trial reported that, neoadjuvant chemoimmunotherapy resulted in significant improvements in both MPR and event-free survival compared to chemotherapy alone. Thus, biomarkers to predict MPR after neoadjuvant chemoimmunotherapy may be a priority in the future scenario of resectable NSCLC. In this multi-center retrospective study, we found and validated that on-treatment inflammatory biomarkers in the peripheral blood, especially SII, were closely associated with MPR in patients who received neoadjuvant chemoimmunotherapy, which indicated that these biomarkers could not only be used in advanced NSCLC but also be used in the neoadjuvant setting to predict immunotherapeutic efficacy.
To our knowledge, this is the first study to investigate the predictive value of inflammatory biomarkers in the peripheral blood for MPR in NSCLC. Previously, the Checkmate 159 [23] and NADIM clinical trial [24, 25] revealed that, T-cell receptor (TCR) clonality or evenness and specific immune cells, such as CD4+PD-1+ cells, in the peripheral blood were also associated with MPR or CPR. Although clonality and immune cell subpopulations in peripheral blood mononuclear cells (PBMCs) are more directly related to anti-tumor immunity and achieve better predictive accuracy than inflammatory biomarkers derived from CBC, they are more costly and time-consuming to obtain. In addition, the utilization of inflammatory biomarkers in peripheral blood derived from CBC with differential to predict MPR in NSCLC patients receiving neoadjuvant chemoimmunotherapy could become a routine procedure in clinical practice.
Regarding the association of inflammatory biomarkers in the peripheral blood and treatment efficacy in advanced NSCLC receiving immunotherapy, some studies only focus on baseline inflammatory biomarkers and demonstrate that baseline NLR, PLR, and SII are independent predictors for ORR, PFS, and OS [11, 26, 27]. In contrast, others investigate baseline and on-treatment inflammatory biomarkers, but the results are inconsistent. Several studies show that baseline and on-treatment inflammatory biomarkers in the peripheral blood are predictors of immunotherapy efficacy [28, 29]. Still, other studies [30, 31] indicate that only on-treatment and not baseline biomarkers are associated with immunotherapy response, which is similar to our work. Moreover, in accordance with previous reports [32, 33], we also found that patients with a decrease from baseline inflammatory biomarkers were more likely to achieve a better treatment response. Thus, more studies are needed to further investigate the predictive value of baseline inflammatory biomarkers in both advanced and locally advanced NSCLC.
NLR and PLR are the most widely used inflammatory biomarkers in peripheral blood, while SII is usually neglected. SII, defined as absolute platelet count × NLR, could be considered a combination of NLR and PLR and thus might be a better predictive biomarker. Bauckneht and colleagues found that in advanced NSCLC treated with Nivolumab, NLR, dNLR, and SII were all predictive factors for OS by the univariate analysis. However, only SII was independently associated with OS by the multivariate analysis [34]. A machine learning model was recently utilized to predict immunotherapy response across multiple cancer types. Both NLR and platelets were included in the model [35], reinforcing the superiority of these two biomarkers combined. Similarly, in the present study, we found that the predictive value of on-treatment SII was superior to other inflammatory biomarkers. Therefore, SII should also be included in further studies.
The association between radiological tumor regression and MPR in previous clinical trials is inconsistent. Gao et al. found no significant correlation between the decrease of lesion diameter and pathologic response [8]. On the contrary, radiological tumor regression was correlated with pathological regression (R = 0.399, P = 0.004) in the SAKK 16/14 clinical trial [22], which is similar to our results. However, the predictive value of radiological tumor regression for MPR has not been mentioned before. The present study showed that some inflammatory biomarkers in peripheral blood outperformed radiological tumor regression in identifying patients who would achieve MPR. In addition, the predictive value was further improved by combining radiological tumor regression with on-treatment SII, indicating that radiological response and on-treatment inflammatory biomarkers might be complementary.
It is still unclear why inflammatory biomarkers in the peripheral blood can predict immunotherapy efficacy. Lymphocyte have long been thought to play a significant role in the anti-tumor response. Tumor-infiltrating lymphocytes (TILs) are reported to be closely related to immunotherapy efficacy [36]. Also, neutrophils release vascular endothelial growth factor (VEGF), matrix metalloproteinase 9 (MMP-9), and other cytokines to change the tumor microenvironment [37]. Previous reports demonstrate that platelets interact with tumor cells and protect them from immune surveillance, explaining why patients with low PLR had better immunotherapy efficacies [38]. Recently, Alessi et al. demonstrate that advanced NSCLC patients with low dNLR have significantly higher numbers of tumor-associated CD8 + , FOXP3 + , PD-1 + immune cells, and PD-1 + CD8 + T cells than those with high dNLR, thus have higher ORR, longer PFS and OS [39]. In the present study, we also found that patients with lower level of on-treatment SII had significantly higher intratumoral infiltration of immune cells. And the better immunotherapy response in SII-Low patients may be attributed to the B cell-mediated immune response activation. Interestingly, recent studies have also indicated that, B cells are deeply involved in anti-tumor immunity during immunotherapy [40]. Still, the underlying mechanism of how these inflammatory biomarkers in the peripheral blood influence immunotherapy warrants further investigation.
There are several limitations to our study. First, this is a retrospective study, and the sample size is relatively small. Although patients from six centers were included, the results could not be considered definitive. Second, inflammatory biomarkers in the peripheral blood can be influenced by infections, steroid treatments, or other medications, which were not assessed in the present study. Third, the cut-off values of the inflammatory biomarkers were not universal in the different studies. In our study, the cut-off values for on-treatment SII were not identical; although, they were similar in the discovery and validation cohorts (493.9 and 508.6, respectively). Thus, determining the optimal cut-off values requires further validation.
Conclusions
This is the first study to demonstrate the association between inflammatory biomarkers in the peripheral blood and MPR in NSCLC patients receiving neoadjuvant chemoimmunotherapy. We found and validated that low on-treatment SII was independently associated with a high rate of MPR. Thus, on-treatment SII could be considered a valuable biomarker for predicting MPR. Furthermore, the predictive value was further improved by combining on-treatment SII with radiological tumor regression. Since SII is obtained from routine CBC, its use does not involve additional procedures or extra costs for healthcare providers. Moreover, we found that the underlying mechanism of inflammatory biomarkers in the peripheral blood predicting MPR may be related to the activation of the B cell-mediated immune response. Still, prospective studies with a larger patient cohort are needed for further validation.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We ae grateful to all the members in the multidisciplinary team for their efforts.
Authors Contributions
The study conducts and design: CL, JW, LJ, and LZ, QL, and CC; data acquisition: CL, JW, JH, YT, XL, LX, LH, MY, MM, CS, HZ, and HC; data analysis and interpretation: CL, JW, LJ, YZ, HE, PG, YS, and DX; drafting the manuscript or revising it: all authors; final approval of the manuscript: all authors.
Funding
This study was supported by National Key Research and Development Program of China (2021YFC2500904 and 2021YFC2500905), Shanghai Municipal Health Commission (202040322, 201940192, 2019SY072), Shanghai Hospital Development Center (SHDC22021217), National Natural Science Foundation of China (No. 81972176), and the Science Foundation of Shanghai (No.18ZR1435100).
Data availability
The data that support the finding of our study are available on request from the corresponding author.
Declarations
Conflict of interests
The authors declare no competing interests.
Ethics Approval
This study was approved by the Institutional Review Board of Shanghai Pulmonary Hospital (IRB number: L21-224). The IRB waived the patient’s informed consent as this was a non-interventional study using routinely collected data.
Consent for publication
Not required.
Footnotes
The original online version of this article was revised: Only Dong Xie is listed as the corresponding author. Authors Qingquan Luo (luoqingquan@hotmail.com) and Chang Chen (chenthoracic@163.com) should be listed as well.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Chongwu Li, Junqi Wu and Long Jiang have contributed equally to this work.
Change history
9/20/2022
A Correction to this paper has been published: 10.1007/s00262-022-03294-2
Contributor Information
Dong Xie, Email: kongduxd@163.com.
Qingquan Luo, Email: luoqingquan@hotmail.com.
Chang Chen, Email: chenthoracic@163.com.
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Supplementary Materials
Data Availability Statement
The data that support the finding of our study are available on request from the corresponding author.