Tumor immune microenvironment analysis in different pathologic responses to neoadjuvant immunotherapy combined with chemotherapy in non-small cell lung cancer

Introduction

Lung cancer remains one of the most prevalent and deadly cancers globally, with non-small cell lung cancer (NSCLC) being the most common subtype (1,2). For resectable NSCLC, surgery remains the primary curative treatment. However, the 5-year survival rate for early-stage NSCLC patients remains low (3). In this context, perioperative therapies have emerged. Multiple clinical trials have demonstrated the unprecedented success of neoadjuvant immunotherapy combined with chemotherapy (IO-CT) in tumor reduction and overall prognosis improvement, and it is now applied in clinical practice (4,5).

Currently, pathologic complete response (pCR) rate and major pathologic response (MPR) rate are widely used as surrogate endpoints for evaluating neoadjuvant therapy (6). Numerous studies have shown significant differences in the benefits of neoadjuvant therapy among patient groups with varying responses. Exploratory analysis from the CheckMate 816 study suggests that pathological response may serve as a biomarker for survival outcomes (7). Additionally, a meta-analysis on resectable NSCLC demonstrated that patients achieving MPR or pCR following neoadjuvant IO-CT had significantly better event-free survival compared to those with non-pCR or non-MPR (8). Approximately 30–53% of patients undergoing neoadjuvant IO-CT achieve MPR (4,9-11). However, while most non-MPR patients also benefit from neoadjuvant IO-CT, they often experience shorter relapse times, disease progression, and relatively poorer survival outcomes. Therefore, identifying the reasons why these patients fail to achieve optimal benefit remains an urgent priority.

To investigate the factors influencing the success or failure of neoadjuvant IO-CT, we collected paired pre- and post-treatment tumor samples from stage II–IIIB NSCLC patients who underwent resection following neoadjuvant chemotherapy or neoadjuvant programmed death 1 (PD-1) combined with chemotherapy. Using multiplex immunofluorescence (mIF), we characterized the tumor immune microenvironment (TIME) before and after neoadjuvant treatment to identify biomarkers associated with major pathological response (MPR). This study reveals potential mechanisms by which neoadjuvant immune checkpoint inhibitors (ICIs) combined with chemotherapy sustain and activate anti-tumor immune cell populations, providing insights into variations in treatment response among patients and laying a scientific foundation for future personalized treatment strategies. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-17/rc).

Methods

Study design and participants

This study is a single-center, single-arm observational research project that included 31 patients with stage IIA to IIIB NSCLC who were diagnosed at Jinling Hospital, Nanjing University, between June 8, 2017 and May 20, 2021. All patients received at least one cycle of neoadjuvant therapy prior to surgery, which was performed within 4 to 6 weeks. Patients with adenocarcinoma harboring epidermal growth factor receptor (EGFR) mutations or anaplastic lymphoma kinase (ALK) rearrangements were excluded. We retrospectively collected clinical and pathological data from the patients, including age, sex, histological type, clinical stage, Eastern Cooperative Oncology Group performance status (ECOG PS), and details of the neoadjuvant therapy, such as the type and number of cycles. Radiological objective response assessments included complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Disease-free survival (DFS) was defined as the time until the earliest occurrence of recurrence, progression, secondary tumors, or death from any cause following surgery.

This study obtained approval from the Ethics Committee of Jinling Hospital, Nanjing University School of Medicine (No. 2022NZKY-043-02). Given its retrospective design, the committee waived the requirement for informed consent from individual patients. The study was conducted in strict accordance with the Declaration of Helsinki and its subsequent amendments.

Pathology and immunohistochemistry

The first pathological diagnosis of the patient and surgical specimens after neoadjuvant therapy were completed by the Department of Pathology, Jinling Hospital Affiliated to Nanjing University. Postoperative pathological results were evaluated according to the multidisciplinary recommendations for pathological assessment of lung cancer after neoadjuvant therapy published by the International Association for the Study of Lung Cancer (IASLC) in 2020, and independently interpreted by two professional pathologists. The viable tumors less than or equal to 10% were defined as MPR, and no evidence of viable tumor cells was defined as pCR. Programmed death-ligand 1 (PD-L1) testing was performed using the Ventana SP263 assay by immunohistochemistry.

mIF staining of TIME

mIF staining was carried out on formalin-fixed paraffin-embedded (FFPE) tissue sections using the OPAL Polaris 7-color IHC kit (NEL871001KT, Akoya Biosciences, Marlborough, USA). Following deparaffinization and rehydration with the automated BOND RX system (Leica Biosystems, Wetzlar, Germany), the tissue sections were subjected to sequential staining with a panel of primary antibodies targeting immune and structural markers. These included: CD163 (Abcam, Cambridge, UK; ab182422, 1:500), CD68 (Abcam, ab213363, 1:1,000), PD-1 (Cell Signaling Technology, Danvers, MA, USA; D4W2J, 86163S, 1:200), PD-L1 (CST, E1L3N, 13684S, 1:400), CD3 (Dako, Glostrup, Denmark; A0452, 1:1), CD4 (Abcam, ab133616, 1:100), CD8 (Abcam, ab178089, 1:200), CD56 (Abcam, ab75813, 1:1,000), CD20 (Dako, L26, IR604, 1:1), FOXP3 (Abcam, ab20034, 1:100), pan-cytokeratin (pan-CK; Abcam, ab7753, 1:100), and S100 (Abcam, ab52642, 1:200).

After primary antibody incubation, sections were treated with secondary antibodies conjugated to Opal fluorophores, and nuclei were counterstained using DAPI. To control for background signal, negative control slides were prepared by applying both primary and secondary antibodies without fluorophore labeling to assess autofluorescence levels.

Image acquisition was performed on the Vectra Polaris Quantitative Pathology Imaging System (Akoya Biosciences). Spectral scanning was conducted at 20 nm intervals across a wavelength range of 440–780 nm under a fixed magnification of ×200. Multispectral data were subjected to spectral unmixing and merged into composite images.

Image analysis was performed using the AP-TIME software (developed by 3D Medicines). Tumor epithelium and stromal regions were automatically segmented based on pan-CK expression. The abundance of immune cell subsets was quantified by calculating the density (positive cells per mm²) and their relative proportion among all nucleated cells.

Statistical analysis

We investigated the relationship between different treatment regimens or pathological responses and the TIME. Continuous variables are expressed as mean ± standard deviation. For independent continuous variables that follow a normal distribution, we used the t-test for group comparisons. For independent continuous variables that do not follow a normal distribution, the Mann-Whitney U test was employed. Paired continuous variables that conform to a normal distribution were analyzed using the paired t-test, while the Wilcoxon signed-rank test was used for the others. Additionally, DFS was estimated using the Kaplan-Meier method. A P value of <0.05 was considered statistically significant for all analyses. Statistical analyses were performed using PRISM 10 (GraphPad Software, San Diego, CA, USA).

Results

Clinical characteristics and survival analysis of patients treated with neoadjuvant immunochemotherapy

We retrospectively collected paired FFPE tissues from 21 patients with resectable NSCLC who received neoadjuvant IO-CT, as well as from 10 patients who received only neoadjuvant CT. The flow chart for the inclusion and exclusion of NSCLC patients is shown in Figure 1. And the clinical and pathological characteristics of the patients are presented in Table 1. After receiving neoadjuvant treatment, the best imaging response in patients showed that no patients achieved CR, while 58.1% (N=18) reached PR and 35.5% (N=11) had SD. Additionally, 2 patients experienced PD after treatment (Table 2 and Figure 2A).

Figure 1 Flow chart for the inclusion and exclusion of NSCLC patients. ALK, anaplastic lymphoma kinase; EGFR, epidermal growth factor receptor; NSCLC, non-small cell lung cancer.

Figure 2 A waterfall plot of best radiological and pathological responses (A), and survival analysis for all patients (B,C) and the immunotherapy-combined chemotherapy group (D-F). MPR, major pathologic response; pCR, pathologic complete response; PD, progressive disease; PR, partial response; SD, stable disease.

According to the postoperative assessment of pathological response, 38.1% (N=8) of patients in the neoadjuvant chemotherapy combined with immunotherapy group achieved MPR, with 23.8% (N=5) reaching pCR. In the neoadjuvant chemotherapy group, only 10.0% (N=1) of patients achieved MPR, and no patients reached pCR (Table 3).

We further investigated the relationship between the immune combination chemotherapy group, imaging or pathological response, presence or absence of lymph node metastasis, and DFS. The median follow-up time for the cohort was 797 days. Among the MPR/pCR group, 3 patients experienced recurrence or progression, while the non-MPR group had 15 cases. In the overall population, patients with imaging evaluation indicating PD after treatment had longer DFS than those with SD + PD (P=0.059), while positive lymph node metastasis indicated shorter DFS (P=0.04) (Figure 2B,2C). In patients receiving neoadjuvant IO-CT, imaging evaluation indicating PR and negative lymph node metastasis were both associated with longer DFS (P=0.01, P=0.004) (Figure 2D,2E). Additionally, the MPR/pCR group showed a trend toward longer DFS (P=0.09) (Figure 2F).

Changes in TIME of NSCLC before and after neoadjuvant therapy

To explore the characteristics of immune cell infiltration in the tumor and stromal microenvironment, we employed mIF to analyze various immune cell types, including CD8⁺ cytotoxic T cells (CTL), CD3⁺ T cells, CD3⁺ CD4⁺ T cells, CD3⁺ CD4⁺ FoxP3⁺ Treg cells, PD-1⁺ CD8⁺ T cells, CD20⁺ B cells, CD68⁺ CD163⁻ M1 macrophages, CD68⁺ CD163⁺ M2 macrophages, PD-L1⁺ CD68⁺ macrophages, CD56dim NK cells, CD56bright NK cells, and tertiary lymphoid structures (TLS). The immune microenvironment before and after neoadjuvant IO-CT and neoadjuvant chemotherapy is shown in Figure S1.

Our study results indicate that compared to IO-CT, the number and spatial proportion of CD3⁺ T cells, CD3⁺ CD4⁺ T cells, CD8⁺ T cells, CD56dim NK cells, and CD8⁺ PD-1⁺ T cells within the tumor decreased more significantly after chemotherapy (P=0.03, P=0.003, P=0.03, P=0.01, P=0.057) (Figure 3A-3E). In contrast, the IO-CT group exhibited a more pronounced increasing trend in TLS after treatment (P=0.07) (Figure 3F).

Figure 3 Comparison of changes in the tumor immune microenvironment of non-small cell lung cancer before and after neoadjuvant chemotherapy and neoadjuvant immunotherapy combined with chemotherapy. I + C, immunotherapy combined with chemotherapy; C, chemotherapy. *, P<0.05; **, P <0.01. PD-1, programmed death 1; TLS, tertiary lymphoid structures.

Additionally, an analysis of the distribution percentages of different markers within the tumor and tumor stroma revealed a more significant decreasing trend in the spatial proportions of CD3⁺ T cells, CD3⁺ CD4⁺ T cells, CD8⁺ T cells, and CD56dim NK cells within the tumor following chemotherapy compared to the IO-CT group (P=0.03, P=0.048, P=0.049, P=0.04) (Figure S2A-S2D).

Differences in TIME between MPR/pCR patients and other patients

Next, we examined the differences in the changes of the immune microenvironment before and after treatment between the pathological response group and the chemotherapy group. Compared to the CT group, the IO-CT (MPR or pCR) group showed a downward trend in the number and proportion of CD20⁺ B cells in the stroma after treatment (P=0.07, Figure 4A), while the number of TLS increased significantly (P=0.02, Figure 4B). Compared to a similar group receiving IO + CT treatment but not achieving MPR/pCR, the MPR or pCR group also exhibited a significant increase in TLS numbers (P=0.02, Figure 4C).

Figure 4 Comparison of tumor immune microenvironment changes between non-small cell lung cancer patients achieving pathological response and others after neoadjuvant immunotherapy combined with chemotherapy. MPR or pCR, patients who achieved major pathologic response or pathologic complete response; non-MPR, patients who did not achieve major pathologic response; C, chemotherapy. *, P<0.05. MPR, major pathologic response; pCR, pathologic complete response; TLS, tertiary lymphoid structures.

Comparison of TIME in paired specimens of NSCLC before and after neoadjuvant IO-CT

After receiving IO + CT treatment, the number of CD56bright NK cells and CD56dim NK cells in the stromal area significantly increased compared to before treatment (P=0.002, P=0.02, Figure 5A,5B), while the number of Foxp3⁺ T cells in the stromal area showed a decreasing trend (P=0.07, Figure 5C). Additionally, after IO + CT treatment, a significant increase in the number of detected TLS was observed (P<0.001, Figure 5D). Furthermore, we found that the proportion of CD56bright NK cells and CD68⁺CD163⁻ M1 macrophages in the stromal area was significantly higher than before treatment, and the proportion of cells within the TLS relative to the total cell count also increased significantly (P=0.04, P=0.08, P=0.02, Figure 5E-5G). The proportion of CD56dim NK cells in the tumor also showed an increasing trend (P=0.09, Figure 5H).

Figure 5 Paired comparison of tumor immune microenvironment in non-small cell lung cancer before and after neoadjuvant immunotherapy combined with chemotherapy. *, P<0.05; **, P<0.01; ****, P<0.0001. I + C, immunotherapy combined with chemotherapy. TLS, tertiary lymphoid structures.

Comparison of pre-treatment TIME in different pathological responses to neoadjuvant IO-CT

As shown in Figure 6, we then investigated the relationship between pre-treatment immune microenvironment and achieving MPR/pCR versus non-MPR in neoadjuvant immunotherapy. In the tumor tissues of the MPR/pCR group, the pre-treatment infiltration of CD3⁺ T cells, CD8⁺ T cells, CD20⁺ B cells, PD-1⁺ T cells, CD8⁺ PD-1⁺ T cells, and CD68⁺ CD163⁺ M2 macrophages were significantly higher compared to the non-MPR group (P=0.09, P=0.02, P=0.02, P=0.03, P=0.01, P=0.06). Their relative proportions were also greater in the MPR/pCR group than in the non-MPR group (P=0.08, P=0.03, P=0.03, P=0.03, P=0.02, P=0.09). Meanwhile, the number of CD68⁺ CD163⁻ M1 macrophages per unit area was higher in the pre-treatment phase of the MPR/pCR group compared to the non-MPR group (P=0.06). However, no significant difference was observed in PD-L1 tumor proportion score (TPS) between the two groups. The TIME of a patient who achieved pCR pre-treatment is clearly more active compared to that of a non-MPR patient (Figure 6I).

Figure 6 Comparison of tumor immune microenvironment before treatment in patients with pathological response and non-pathological response after neoadjuvant immunotherapy combined with chemotherapy. MPR or pCR, patients who achieved major pathologic response or pathologic complete response; non-MPR, patients who did not achieve major pathologic response. Multiplex immunofluorescence staining was performed using the OPAL 7-color kit. Images were acquired with the Vectra Polaris system at ×200 magnification. Tumor and stroma were identified by pan-CK staining. Scale bar: 50 µm (I). *, P<0.05. MPR, major pathologic response; pCR, pathologic complete response; PD-1, programmed death 1; PD-L1, programmed death ligand 1; TPS, tumor proportion score.

Discussion

Our study provides an in-depth analysis of changes in the TIME in early-stage NSCLC patients following neoadjuvant therapy, addressing some gaps in the current literature. By comparing paired samples before and after treatment, we revealed the significant advantage of IO-CT in maintaining immune cell populations. Additionally, we explored the crucial role of TLS formation in the immune response. Our findings offer new insights into the mechanisms underlying the varying sensitivities of patients to IO-CT, adding an important dimension to existing theories on treatment mechanisms.

Our analysis underscores the importance of enhancing tumor sensitivity to neoadjuvant IO-CT. Patients exhibiting radiological and pathological responses demonstrated a trend toward prolonged DFS. Thus, understanding the mechanisms of TIME reprogramming in patients with varying therapeutic outcomes is essential for maximizing the benefits of neoadjuvant IO-CT and improving patient survival. When comparing MPR/pCR cases to non-MPR cases, we observed that the former had a higher baseline infiltration of CD20⁺ B cells, and there was a significant increase in TLS density post-treatment, accompanied by a decrease in both the number and spatial proportion of CD20⁺ B cells. This change suggests that TLS may serve as a predictive marker for more effective anti-tumor immune responses following IO-CT treatment, aligning with previous research findings (12-15). The efficacy observed in the MPR/pCR group may be associated with the high levels of CD20⁺ B cell infiltration in tumors prior to IO-CT treatment (16). In certain cancers, such as melanoma and renal cell carcinoma, studies have shown that B cell-related gene expression profiles are enriched in responders to immune checkpoint blockade (13,17,18). CD20⁺ B cells in the stroma decreased after treatment in the MPR/pCR group, which may be related to chemotherapy toxicity, the formation of TLS, and the various roles of B cells in anti-tumor immunity. The sustained efficacy in the MPR/pCR group may not only be attributed to the high levels of B cell infiltration prior to treatment but also to their potential tendency to form TLS or differentiate into plasma cells post-treatment, thereby continuing to support anti-tumor immunity, which contributes to the reduction of CD20⁺ markers (13). Additionally, the reduction of CD20⁺ B cells may reflect changes in the immune dynamics of tumor remission areas. These regions have now transitioned to T cell-dominated zones, characterized by ongoing interactions between T cells and dendritic cells (DCs) and the dissolution of germinal centers. This suggests a restoration of immune balance, which helps prevent excessive immune responses and protects surrounding tissues (19,20). Further research is needed in the future to validate these observations and to deepen our understanding of the role of B cells in anti-tumor immunity.

In addition to CD20⁺ B cells, increased infiltration of other pre-existing immune cells within the tumor has also emerged as a potential predictive factor for MPR/pCR. Compared to non-responders, patients who responded to IO-CT exhibited higher baseline infiltration of cytotoxic T lymphocytes (CTLs), CD20⁺ B cells, and both M1 and M2 macrophages. This pre-treatment state of “immune activation”, characterized by enhanced immune cell infiltration, may prepare the tumor for a more effective response to IO-CT (21). Additionally, high levels of PD-1⁺ T cells and CD8⁺ PD-1⁺ T cell infiltration within the tumor may indicate a better response to IO-CT therapy. In the appropriate immunotherapy context, these cells are more likely to be reactivated after being released from inhibition, restoring their anti-tumor function (22). However, there was no significant difference in the TPS of PD-L1 between the two groups, indicating that the responsiveness to IO-CT is not always correlated with PD-L1 expression levels (23).

Our data indicate that, compared to the significant reduction of key immune cells following chemotherapy alone, IO-CT treatment markedly enhances the retention and infiltration of CD3⁺ T cells, CD4⁺ T cells, CD8⁺ T cells, and CD8⁺ PD-1⁺ T cells within the tumor. This suggests that while chemotherapy exerts cytotoxic effects, it also diminishes immune cell presence and activity. In contrast, combining ICI can alleviate tumor-induced T cell suppression and recruit more lymphocytes into the tumor area for anti-tumor effects (24). Furthermore, the decrease in Foxp3⁺ T cells in the stroma suggests a weakening of the immunosuppressive environment, further supporting the role of IO-CT in reprogramming the TIME. Additionally, the increase in TLS observed in the IO combination group suggests a beneficial reorganization of the TIME. TLS plays a vital role in local immune activation and the interaction between B and T cells, potentially contributing to the maintenance of the immune-inflammatory response in tumors following treatment (12). In contrast to other studies, our research highlights the recruitment and retention of NK cells, crucial for tumor cell killing, in the tumor microenvironment following ICI combination therapy. This phenomenon suggests that some exhausted anti-tumor NK cells may regain activity due to PD-1 inhibitor treatment, enhancing their infiltration within the tumor (25).

There are limitations to this study due to its retrospective single-center design, relatively small sample size, and gender imbalance (only 3 female participants). Therefore, the findings should be interpreted with caution and require validation in large-scale, prospective multi-center cohorts. Additionally, the lack of comprehensive molecular profiling, including P53 status, is another limitation. Due to incomplete molecular testing in some cases, P53 mutation status was not systematically available and thus could not be incorporated into the current analysis. Future studies should aim to include key genetic alterations, such as P53, to better understand their roles in immunotherapy response (26). Moreover, limitations also exist at the technical level. In this study, we primarily relied on mIF to analyze immune cell infiltration in the tumor microenvironment. While this method provides valuable spatial and phenotypic insights, it alone is insufficient for capturing the full complexity of immune dynamics. The use of a single-platform approach restricts the comprehensive assessment of immune cell heterogeneity and functional states. Furthermore, as a retrospective study, potential selection biases remain unavoidable. To address these limitations, future research should incorporate prospective designs and multi-omics technologies, such as single-cell sequencing and spatial transcriptomics, to unravel the mechanisms driving immune modulation and improve personalized treatment strategies. A recent spatial transcriptomic study focusing on TLS showed that SELENOP⁺ macrophages are enriched in TLS regions of responders and co-localize with cancer-associated fibroblasts and T cells, suggesting that structured immune cell interactions within TLS may play a key role in treatment response (27).

Conclusions

Compared to chemotherapy alone, neoadjuvant chemotherapy combined with PD-1 inhibitors effectively maintains an immune-active state within tumors. High levels of CD8⁺ T cells, CD20⁺ B cells, and CD8⁺ PD-1⁺ T cell infiltration at baseline may be key factors in predicting responses of NSCLC patients to neoadjuvant IO-CT. Additionally, immune therapy can induce the formation of TLS, which can serve as a biomarker to improve the accuracy of patient prognosis assessments. These findings highlight the significance of the tumor microenvironment in treatment responses, laying the foundation for personalized treatment strategies.

Acknowledgments

The authors would like to thank all the reviewers who participated in the review for their assistance during the preparation of this manuscript.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-17/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-17/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-17/prf

Funding: This study was supported by the National Natural Science Foundation of China (Nos. 82172728 and 82370096), and Jinling Hospital Management Project (Nos. 22LCYY-XH2 and 22LCYY-XH3).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-17/coif). Y.S. serves as the Editor-in-Chief of Translational Lung Cancer Research. 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. This study obtained approval from the Ethics Committee of Jinling Hospital, Nanjing University School of Medicine (No. 2022NZKY-043-02). The study was conducted in strict accordance with the Declaration of Helsinki and its subsequent amendments.

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/.

References

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
2. Zhang Y, Vaccarella S, Morgan E, et al. Global variations in lung cancer incidence by histological subtype in 2020: a population-based study. Lancet Oncol 2023;24:1206-18. [Crossref] [PubMed]
3. Uramoto H, Tanaka F. Recurrence after surgery in patients with NSCLC. Transl Lung Cancer Res 2014;3:242-9. [Crossref] [PubMed]
4. Forde PM, Spicer J, Lu S, et al. Neoadjuvant Nivolumab plus Chemotherapy in Resectable Lung Cancer. N Engl J Med 2022;386:1973-85. [Crossref] [PubMed]
5. Provencio M, Nadal E, Insa A, et al. Neoadjuvant chemotherapy and nivolumab in resectable non-small-cell lung cancer (NADIM): an open-label, multicentre, single-arm, phase 2 trial. Lancet Oncol 2020;21:1413-22. [Crossref] [PubMed]
6. Hines JB, Cameron RB, Esposito A, et al. Evaluation of Major Pathologic Response and Pathologic Complete Response as Surrogate End Points for Survival in Randomized Controlled Trials of Neoadjuvant Immune Checkpoint Blockade in Resectable in NSCLC. J Thorac Oncol 2024;19:1108-16. [Crossref] [PubMed]
7. Deutsch JS, Cimino-Mathews A, Thompson E, et al. Association between pathologic response and survival after neoadjuvant therapy in lung cancer. Nat Med 2024;30:218-28. [Crossref] [PubMed]
8. Marinelli D, Nuccio A, Di Federico A, et al. Improved Event-Free Survival After Complete or Major Pathologic Response in Patients With Resectable NSCLC Treated With Neoadjuvant Chemoimmunotherapy Regardless of Adjuvant Treatment: A Systematic Review and Individual Patient Data Meta-Analysis. J Thorac Oncol 2025;20:285-95. [Crossref] [PubMed]
9. Wakelee H, Liberman M, Kato T, et al. Perioperative Pembrolizumab for Early-Stage Non-Small-Cell Lung Cancer. N Engl J Med 2023;389:491-503. [Crossref] [PubMed]
10. Cascone T, Awad MM, Spicer JD, et al. Perioperative Nivolumab in Resectable Lung Cancer. N Engl J Med 2024;390:1756-69. [Crossref] [PubMed]
11. Provencio M, Nadal E, González-Larriba JL, et al. Perioperative Nivolumab and Chemotherapy in Stage III Non-Small-Cell Lung Cancer. N Engl J Med 2023;389:504-13. [Crossref] [PubMed]
12. Sun X, Liu W, Sun L, et al. Maturation and abundance of tertiary lymphoid structures are associated with the efficacy of neoadjuvant chemoimmunotherapy in resectable non-small cell lung cancer. J Immunother Cancer 2022;10:e005531. [Crossref] [PubMed]
13. Helmink BA, Reddy SM, Gao J, et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature 2020;577:549-55. [Crossref] [PubMed]
14. Elfving H, Yu H, Fessehatsion KK, et al. Spatial distribution of tertiary lymphoid structures in the molecular and clinical context of non-small cell lung cancer. Cell Oncol (Dordr) 2025;48:801-13. [Crossref] [PubMed]
15. Liu X, Lv W, Huang D, et al. The predictive role of tertiary lymphoid structures in the prognosis and response to immunotherapy of lung cancer patients: a systematic review and meta-analysis. BMC Cancer 2025;25:87. [Crossref] [PubMed]
16. Fridman WH, Meylan M, Pupier G, et al. Tertiary lymphoid structures and B cells: An intratumoral immunity cycle. Immunity 2023;56:2254-69. [Crossref] [PubMed]
17. Cabrita R, Lauss M, Sanna A, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 2020;577:561-5. [Crossref] [PubMed]
18. Petitprez F, de Reyniès A, Keung EZ, et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature 2020;577:556-60. [Crossref] [PubMed]
19. Teillaud JL, Houel A, Panouillot M, et al. Tertiary lymphoid structures in anticancer immunity. Nat Rev Cancer 2024;24:629-46. [Crossref] [PubMed]
20. Schumacher TN, Thommen DS. Tertiary lymphoid structures in cancer. Science 2022;375:eabf9419. [Crossref] [PubMed]
21. Liu R, Yang F, Yin JY, et al. Influence of Tumor Immune Infiltration on Immune Checkpoint Inhibitor Therapeutic Efficacy: A Computational Retrospective Study. Front Immunol 2021;12:685370. [Crossref] [PubMed]
22. Guo W, Qiao T, Li H, et al. Peripheral CD8(+)PD-1(+) T cells as novel biomarker for neoadjuvant chemoimmunotherapy in humanized mice of non-small cell lung cancer. Cancer Lett 2024;597:217073. [Crossref] [PubMed]
23. Yi L, Xu Z, Ma T, et al. T-cell subsets and cytokines are indicative of neoadjuvant chemoimmunotherapy responses in NSCLC. Cancer Immunol Immunother 2024;73:99. [Crossref] [PubMed]
24. Forde PM, Chaft JE, Smith KN, et al. Neoadjuvant PD-1 Blockade in Resectable Lung Cancer. N Engl J Med 2018;378:1976-86. [Crossref] [PubMed]
25. Myers JA, Miller JS. Exploring the NK cell platform for cancer immunotherapy. Nat Rev Clin Oncol 2021;18:85-100. [Crossref] [PubMed]
26. Carlsen L, Zhang S, Tian X, et al. The role of p53 in anti-tumor immunity and response to immunotherapy. Front Mol Biosci 2023;10:1148389. [Crossref] [PubMed]
27. Cui X, Liu S, Song H, et al. Single-cell and spatial transcriptomic analyses revealing tumor microenvironment remodeling after neoadjuvant chemoimmunotherapy in non-small cell lung cancer. Mol Cancer 2025;24:111. [Crossref] [PubMed]