Association of systemic inflammatory markers and tertiary lymphoid structures with pathological complete response in patients with advanced lung adenocarcinoma receiving preoperative treatment: a retrospective cohort study

Introduction

Treatments before surgery, such as conversion or neoadjuvant therapy, have been frequently used to manage advanced lung adenocarcinoma as it can downstage tumors, increase the R0 resection rate, and elevate the radical resection rate (1,2). Pathological complete response (pCR), a crucial measure of the complete eradication of lung cancer cells during preoperative treatment, may be an alternative marker of long-term survival (3-5). pCR differs from partial pathological or clinical treatment response in patients, and it has attracted broad interest from thoracic surgeons because patients with pCR are suitable candidates for surgical treatment de-escalation according to multidisciplinary medicine (6). However, the predictors of pCR for patients with advanced lung adenocarcinoma have remained largely unexplored.

Inflammation serves as an indicator of tumor genesis and development (7), and a systemic inflammatory response to cancer is associated with treatment response (8). Systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) have been extensively applied in the preoperative evaluation of partial response to treatment among patients with advanced lung adenocarcinoma. However, the relationship between the combination of systemic immune biomarkers (SII + NLR) and pCR is not clear (9-11).

Chronic inflammatory signal exposure may result in the formation of tertiary lymphoid structures (TLSs) within the local tumor microenvironment (TME), indicating the antitumor active immune response region (12). Although TLSs are reflected by a variety of features and can predict pCR rates among patients with gastric (13), breast (14,15), or colorectal cancer (16,17), the relationship between TLSs and pCR among patients with advanced lung adenocarcinoma remains unknown.

In this study, we investigated the relationship between the immune response and pCR among patients with advanced lung adenocarcinoma, with a consideration of systemic immune status and the TLSs within the local TME. The associations between SII + NLR, TLS components, TLS density, and pCR were analyzed. This study yielded insights into the relation of immune responses with pCR, which may assist clinicians in making informed decisions regarding the treatment of patients with advanced lung adenocarcinoma. We present this article in accordance with the STROBE reporting checklists (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1550/rc).

Methods

Ethical statement

The Ethics Committee of Beijing Shijitan Hospital, Capital Medical University approved the study on June 19, 2022 [approval No. sjtkyll-lx-2022(35)]. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Informed consent was taken from all individual participants.

Patients

We obtained the records of patients with lung adenocarcinoma who underwent chemotherapy (regardless of immunotherapy and radiotherapy) before curative resection (R0 resection: no cancer cells could be found at the cutting edge under the microscope, and no cancer cells remained either visually or microscopically; the lesion was completely removed) between July 2020 and April 2024. The exclusion criteria were as follows: patients with additional histological subtypes, including squamous cell carcinoma, large cell carcinoma, and adenosquamous carcinoma; concurrent primary malignant disease of other organs; R1/R2 resection; and an unclear preoperative treatment regimen. Ultimately, 216 patients were included in the study, among whom 19 achieved pCR and 197 did not (non-pCR).

Blood parameters

We obtained venous blood test results following after preoperative treatment and before surgical resection. The SII was calculated as follows: (neutrophil count) × (platelet)/(lymphocyte count). Meanwhile, the NLR was calculated as follows: (neutrophil count)/(lymphocyte count). The classification was based on the best cut-off value of receiver operating characteristic (ROC) and was divided into high-expression and low-expression groups.

pCR

pCR was defined as a lack of residual tumor component inside the primary tumor or regional lymph nodes harvested (18).

Pathological analysis

Surgically obtained samples (peripheral tumor samples or regressing tumor samples from patients who achieved pCR) were embedded in paraffin and sectioned into 4-µm-thick sections.

CD8 (D263403; Sangon Biotech, Shanghai, China), CD19 (90176S; Cell Signaling Technology, Danvers, MA, USA), FOXP3 (ab215206; Abcam, Cambridge, UK), and PD1 (ab137132; Abcam) antigens were detected through immunohistochemistry (IHC) and multiplex immunofluorescence (mIF) staining. The tissue sections were deparaffinized and rehydrated, which was followed by antigen retrieval, with endogenous peroxidase blocking and crosslinking occurring during IHC and mIF, respectively. After blocking, the sections were incubated overnight with primary antibodies at 4 ℃ for IHC or for 1 h under ambient temperature for mIF. Subsequently, the sections were washed and probed with horseradish peroxidase-labeled secondary antibody (Servicebio, Wuhan, China). In IHC, sections were stained with 3,3'-diaminobenzidine solution (Dako, Agilent Technologies, Santa Clara, CA, USA) and counter-stained with hematoxylin. For mIF, the sections were stained with a fluorescent staining amplification solution (Absin Bioscience Inc., Shanghai, China).

Hematoxylin and eosin (HE) and IHC/mIF staining were quantitatively analyzed via QuPath software (v.0.4.3) on whole-slide images (WSIs).

The TLSs were identified according to the morphologies observed on HE-stained slides. We assessed the mature TLS [maturation classification II/III (19)] and divided them into two groups based on location: including tumor bed or adjacent non-carcinoma tissue. We defined the tumor bed as a region that included the tumor stroma, residual tumor, and regression bed. The TLS quantity in cm² of the entire slide area was determined as the TLS density. The presence of over 5 TLS in one WSI led to a random selection of 5 TLS in the analysis of the components. When there were <5 TLS, all were incorporated for analyses. The positive cell proportion inside the TLS was analyzed via the cell detection function in QuPath software.

Statistical analysis

Categorical and nonparametric data are represented by absolute numbers and percentages and as median and interquartile range, respectively. The optimal thresholds for SII and NLR were determined via the ROC curve, and patients were categorized into high and low groups. The patients were further stratified based on the combination of SII and NLR into high SII + NLR (either one or both variables were high) and low SII + NLR (both variables were low). Differences in the nonparametric numeric data were evaluated with the Mann-Whitney test, whereas those in the categorical data were analyzed with the Fisher exact test or Pearson χ² test. Overall survival (OS) was determined to be the period from the first diagnosis (pathologically confirmed) and all-cause mortality or the final follow-up and was analyzed via Kaplan-Meier curves, whereas the log-rank test was used to analyze the differences.

A logistic regression model was applied in both univariate and multivariate regression analyses. Statistically significant variables obtained from univariate regression were incorporated into multivariate regression. The odds ratio (OR) and 95% confidence interval (CI) were obtained.

Next, we assessed the relation between TLS and pCR and determined the mean TLS parameters on each WSI slide to analyze the TLS components and subsequently conduct comparisons. P<0.05 (two-sided) was considered significant. R v.4.1.2 (The R Foundation for Statistical Computing, Vienna, Austria) and SPSS v.26.0 (IBM, Armonk, NY, USA) were employed for statistical analysis.

Results

Baseline features

Figure 1 displays the flowchart of the study. In all, 216 patients who underwent R0 lobectomy after preoperative treatment were enrolled from July 2020 to April 2024 (Table 1).

Figure 1 Flowchart of the patient screening process in this study. pCR, pathological complete response.

Patients were placed into a SII-high (≥550, 72.2%) or low (<550, 27.8%) group and an NLR-high (≥2.67, 73.1%) or low (<2.67, 26.9%) group. Subsequently, SII was combined with NLR to form a SII + NLR grade-high (79.6%) or low (20.4%) group (Table 1).

Of the 216 cases, 19 (8.8%) attained pCR following the preoperative treatment. The median OS of non-pCR cases was 33 months, whereas that of pCR cases was not reached. The OS of pCR cases was remarkably superior [hazard ratio (HR): 1.981; 95% CI: 1.09–4.00; P=0.02] (Figure 2).

Figure 2 Kaplan-Meier curves for the OS of pCR and non-pCR cases. In all, 216 cases were enrolled for analysis, including 19 in the pCR group and 197 in the non-pCR group. The non-pCR group had a median survival of 46 months, whereas this measure was unavailable in the pCR group. The OS in pCR cases was higher than that in non-pCR cases (log-rank test P=0.02). OS, overall survival; pCR, pathological complete response.

Patient and tumor features before treatment and pCR

Univariate regression (Table 2) revealed that prognostic factors including combination with immunotherapy were correlated with pCR. Cases administered immunotherapy (OR: 4.13; 95% CI: 2.06–7.44; P=0.02) demonstrated an increased pCR rate as compared to patients without immunotherapy, which remained independently predictive of pCR in the multivariate regression analysis (OR: 4.25; 95% CI: 2.71–6.81; P=0.02) (Table 3).

pCR and peripheral blood biomarkers after treatment

Univariate analysis of peripheral blood biomarkers after treatment indicated that pCR was associated with neutrophil count (OR: 0.61; 95% CI: 0.54–0.89; P=0.04), lymphocyte count (OR: 0.45, 95% CI: 0.22–0.68; P=0.03), and platelet count (OR: 0.97; 95% CI: 0.96–0.99; P=0.02) (Table 2). We examined the SII and NLR levels to further characterize these factors. The pCR rates were higher among cases with decreased SII (OR: 3.12; 95% CI: 1.46–4.32; P=0.009) and NLR (OR: 1.99, 95% CI: 1.25–3.64; P=0.02) (Table 2). The combination of SII with NLR achieved a superior prediction performance compared to either indicator used alone. A lower SII + NLR grade was a significant predictor of pCR in the univariate regression (OR: 2.69; 95% CI: 1.58–4.68; P=0.01) (Table 2) and multivariate regression (OR: 3.26; 95% CI: 1.79–6.58; P=0.04) analyses (Table 3).

TLS and pCR

Tumor bed TLS of pCR cases, but not that non-pCR cases, exhibited a difference to immunity. In addition, the tumor bed TLS density was higher in pCR cases than in non-pCR cases (P=0.003) (Figure 3A). TLS components were also analyzed (Figure 3B), revealing a reduced PD1⁺ cell proportion in the TLS of pCR cases, whereas among non-PCR cases, a reduced CD8⁺ cell proportion in the TLS was only found within the tumor bed (P=0.04). The FOXP3⁺ and CD19⁺ cell proportions were not significantly different between pCR and non-pCR cases. Figure 3C presents the typical IHC staining for TLS.

Figure 3 TLS features measured through IHC. (A) TLS density was analyzed according to TLS quantity in cm² of the whole-slide area and compared between paired pCR and non-pCR cases. (B) The PD1⁺, CD8⁺, FOXP3⁺, and CD19⁺ cell proportions in TLSs were analyzed via IHC staining and were compared between the pCR and non-pCR groups. One empty circle represents the mean proportion in one patient, as determined based on the WSI. (C) Typical IHC images showing PD1⁺, CD8⁺, FOXP3⁺, and CD19⁺ cells inside the TLSs of pCR and non-pCR cases. Magnification is 10×. *, P<0.05; **, P<0.01; ns, not significant, P>0.05. IHC, immunohistochemistry; pCR, pathological complete response; TLS, tertiary lymphoid structure; WSI, whole-slide image.

We conducted an mIF analysis on an IHC subset (nine pairs) (Figure 4A) to verify the IHC findings. The PD1⁺ and CD8⁺ cell proportions in the tumor bed TLS in pCR cases were lower than that in non-pCR cases (P=0.02 and P=0.03) (Figure 4B).

Figure 4 TLS features as measured through immunofluorescence. (A) The TLSs observed via mIF staining. The expression of markers is shown in images on the left. Magnification is 10×. (B) PD1⁺, CD8⁺, FOXP3⁺, and CD19⁺ proportions inside tumor bed TLSs and inside peripheral normal tissue were examined. One empty circle represents the mean proportion in one patient as determined based on the WSI. *, P<0.05; ns, not significant, P>0.05. mIF, multicolor immunofluorescence; pCR, pathological complete response; TLS, tertiary lymphoid structure; WSI, whole-slide image.

Preoperative treatment and pCR

According to Table 3, immunotherapy in preoperative treatment was independently associated with an increased pCR probability (OR: 3.84; 95% CI: 2.16–5.69; P=0.03).

Discussion

Local and systemic immune responses can determine the outcome in patients with complex diseases, including emerging cancers (20-22). The current retrospective study explored the relation between SII + NLR as well as TLS within local TME and pCR in patients with advanced lung adenocarcinoma.

The relationship between systemic immune response and treatment response may critically determine the effect of overall immune system activity on treatment efficacy (23,24). Peripheral blood markers are cost-effective and expedient biomarkers for systemic immune status. Lowered SII and NLR levels after treatment are related to the treatment response of different cancers (25-28), yet their thresholds remain inconsistent due to a lack of uniform standards. Our results showed that a decreased SII + NLR grade after preoperative treatment was an independent predictor of pCR (Table 3). Neutrophilia can regulate immunosuppression and enhance angiogenesis (29), and circulating lymphocyte count is associated with antitumor T-cell response and tumor-infiltrating lymphocyte level (30). Moreover, platelets can shield tumor cells against immune surveillance (31). Thus, SII and NLR reflect the balance between tumor-promoting inflammation and antitumor immunity, and their decreased levels after treatment may indicate a lower systemic inflammatory status, suggesting efficient tumor eradication and a higher probability of pCR.

In this study, a distinct local immune profile, particularly related to TLS features, could be detected in pCR cases (Figure 3). TLSs contribute to the histological and functional assembly of secondary lymphoid organs and the destruction of cancer cells (32,33). An increased TLS density results in a better treatment response and prognostic outcome (34). Both TLS emergence and position are associated with prognostic prediction ability (35). Similar to previous work (36-38), this study demonstrated a relation between tumor bed TLS density and an increased rate of pCR, whereas the nontumoral TLS density was similar in both pCR and non-pCR cases (Figure 3A). Immune cell interactions in TLSs exert a strong influence on treatment response in the tumor, and therefore the analysis of TLS composition has been emphasized in research (39). The PD1⁺ and CD8⁺ cell proportions in the tumor bed TLS of non-pCR cases were higher than those of pCR cases (Figure 3B,3C,4B), consistent with the different effects of PD1 in the tumor. The expression of PD1 on T cells can indicate tumor antigen-mediated activation. Although the level of PD1 is still high upon persistent exposure to tumor antigens, it declines upon the clearance of activating antigens (40). Furthermore, PD1 contributes considerably to the exhaustion of T cells, leading to poor tumor control (41). In this study, the tumor antigens were eradicated in the pCR cases; therefore, TLSs in these patients had decreased PD1⁺ and CD8⁺ T cell populations. The increased PD1⁺ cell infiltration within the TLSs of non-pCR cases indicated the presence of an immunosuppressive microenvironment, which contributed to poorer treatment response.

We integrated the systemic and local immune response indicators to establish an immune profile associated with pCR. This method is based on the concept of the cancer-immunity cycle (20), in which cancer is considered a condition of interrelated systemic and local immunity; therefore, evaluating systemic and local immune responses can predict tumor eradication. With the deepening understanding of immunity and cancer, the advent of immunotherapy has changed the tumor treatment model; in immunotherapy, the immune response is stimulated and tumor-induced immune deficiency reversed, and in comparison with conventional radiotherapy and chemotherapy, this provides improved treatment for those with cancer (42-44). A phase III clinical study (45) reported that immunotherapy plus preoperative multimodal treatment yielded a >23% pCR rate among patients with lung adenocarcinoma, results from which definitive conclusions could not be drawn. In our study, the administration of immunotherapy was associated with a higher pCR rate (Table 3). A preliminary analysis of the role of immunotherapy in systemic and local immunity suggests that achieving pCR through immunotherapy may be different in nature from doing so with conventional radiotherapy and chemotherapy. Immunotherapy has been previously suggested to affect systemic immunity (46) and can regulate TLS generation and activity in a number of cancers (47,48). Nonetheless, such research has primarily concentrated on the functions of immunotherapy as a single treatment or on patients with partial response. Studies concerning its influence as one component of preoperative treatment or on pCR among patients with advanced lung adenocarcinoma are scarce. Thus, more research aimed at comprehensively clarifying the role of immunotherapy in determining therapeutic efficacy and immune response is warranted.

This study has certain limitations that should be addressed. First, the results should be further validated due to the retrospective nature and single-center design. In addition, there might have been clinical setting bias, especially as it pertains to the immunotherapy combination treatments. Immunotherapy is more often recommended and likely more beneficial for patients with deficient mismatch repair, higher tumor mutation burden, and higher PD-L1 scores and may provide a higher pCR rate among these patients. The above-mentioned bias could not be addressed as there were insufficient clinical data related to the molecular pathological diagnosis. In addition, the SII, NLR, and TLS data were obtained after treatment, precluding an analysis of the dynamic alterations of such parameters and the drawing of reliable conclusions concerning the causal relation between the immune profile and pCR rate. Further prospective and animal model studies are necessary to generate reliable evidence and elucidate the associated mechanisms.

Despite these limitations, this study provides insights into the effect of immunity on attaining pCR. A high number of patients with pCR were reviewed over a 5-year span, allowing us to concentrate on these patients as opposed to pooling this patient group with a partial response group with residual tumors. Our identified immune-related factors were obtained through routine clinical tests, such as computed tomography-guided needle biopsy or bronchoscopy and peripheral blood tests. The minimally invasive method for collecting evidence for pCR before surgical treatment may help shift the current thinking regarding surgery for select patients with lung adenocarcinoma and may help increase the pCR rate among those with advanced disease.

Conclusions

This finding from this retrospective study suggests that the immune profile is related to pCR. The decreased systemic inflammation level, as evidenced by a lower SII + NLR grade, could independently predict pCR. The higher TLS density and lower PD1⁺ and CD8⁺ cell proportions within the tumor bed TLS in the local TME were found to be associated with pCR. In summary, local and systemic immune responses should be considered in the management of patients with lung adenocarcinoma who receive preoperative treatment.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1550/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1550/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1550/prf

Funding: This study was supported by the National Natural Science Foundation of China (No. 62176166).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1550/coif). A.B. received fees for speaker honoraria and advisory board role with AstraZeneca, BMS, MSD, and Roche. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The Ethics Committee of Beijing Shijitan Hospital, Capital Medical University approved the study on June 19, 2022 [approval No. sjtkyll-lx-2022(35)]. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Informed consent was taken from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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