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Abstract

Introduction

Tertiary lymphoid structures (TLSs) have been associated with the prognosis of various solid tumors. However, the association between TLSs and the prognosis of invasive lung adenocarcinoma (IAC) remains unclear in terms of location, density, and maturity.

Methods

We retrospectively reviewed the clinicopathological characteristics of 750 patients with IAC. The density of TLSs in various tumor regions, as well as their maturation status, was examined by pathologists. The X-tile software was employed to determine the optimal cut-off values for the intratumoral and peritumoral TLS density based on overall survival. A threshold of 1.33 TLSs/cm² was used to distinguish between low- and high-density intratumoral TLSs, while a threshold of 1.39 TLSs/cm² was applied for peritumoral TLSs. Tissue slides exhibiting no or early TLSs demonstrated low maturation levels, while those with at least one lymphoid follicle indicated high maturation. We analyzed the correlation between TLS characteristics and clinicopathological parameters and assessed the impact of multiple clinicopathological factors on patient prognosis using Cox regression and Kaplan–Meier analyses. A multivariate logistic regression analysis model was used to explore predictive factors for the density of intratumoral TLSs and TLS maturity in patients with IAC.

Results

Lower intratumoral TLS density, increased TLS maturation, lymph node metastasis, and the presence of solid (≥30%) and micropapillary (≥5%) pathological subtypes were identified as poor independent prognostic factors for overall survival in patients with IAC. Solid (≥30%) and micropapillary (≥5%) pathological subtypes were predictive factors for lower intratumoral TLS density (hazard ratio [HR] = 0.434, 95% confidence interval [CI] = 0.267-0.706, P = 0.001), while higher TLSs maturity was associated with a smoking history or pleural invasion (HR = 1.655, 95% CI = 1.048-2.613, P = 0.031; HR = 1.933, 95% CI = 1.054-3.546, P = 0.033).

Conclusions

Both intratumoral TLS density and TLS maturation are independent prognostic factors for IAC. Additionally, higher TLS density predicted better prognosis, whereas greater TLS maturity predicted worse prognosis.

Keywords

tertiary lymphoid structures invasive lung adenocarcinoma tumor microenvironment clinicopathological characteristics prognosis

Introduction

Lung adenocarcinoma (LUAD) is the most prevalent form of lung cancer.¹ In recent decades, surgical resection has remained the first choice of treatment for early-stage LUAD.² Nonetheless, patients with invasive lung adenocarcinoma (IAC) exhibit a high postoperative recurrence rate along with poor prognosis.³ Therefore, investigating new indicators influencing the prognosis of patients with IAC is crucial.

Tertiary lymphoid structures (TLSs), also known as third lymphoid organs or ectopic lymphoid structures, are organized immune cell aggregates that develop in non-lymphoid tissues under pathological conditions, such as inflammation or tumors.⁴ TLSs encompass T cells, B cells, fibroblastic reticular cell networks, high endothelial venules, and follicular dendritic cells.⁵ By modulating immune cell trafficking and responses, TLSs influence the immune microenvironment and are associated with improved prognosis and enhanced immunotherapeutic response in several solid malignancies, including melanoma, colorectal cancer, and LUAD.^6-9 Moreover, LUAD is believed to progress sequentially from adenocarcinoma in situ to minimally invasive adenocarcinoma (MIA), and ultimately to fully invasive adenocarcinoma.¹⁰ Recent research has identified the presence of TLS as an independent favorable prognostic factor in patients with early-stage LUAD,^11,12 and has demonstrated that MIA tumors exhibit reduced CD8⁺ T cell density within TLSs.¹³ However, the significance of TLSs in the IAC remains unclear. In this study, we investigated the relationship between TLSs and the clinicopathological characteristics and prognosis of patients with IAC.

Materials and Methods

Patient Cohort

The reporting of this observational study follows the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.¹⁴ We retrospectively enrolled 809 patients with IAC who underwent resection at the Zhoushan Hospital (Zhejiang, China) between January 2018 and December 2020. The inclusion criteria were as follows: (1) diagnosis of a single primary IAC; (2) tumor, node, and metastases (TNM) stage IA to IIIB; (3) anatomical resection in combination with lymphadenectomy (systematic lymph node (LN) sampling or systematic LN dissection); and (4) adequate resected tissues for hematoxylin and eosin (H&E) staining.

The exclusion criteria were as follows: (1) multiple lesions, (2) small cell lung cancer or non-invasive lung cancer, (3) diagnostic biopsy before surgical resection, (4) preoperative neoadjuvant therapy, and (5) presence of severe infectious or autoimmune diseases. Finally, 750 patients with eligible tissue slides for TLS examination were included in the study cohort (Figure 1). The median patient age was 64 years. Of these patients, 42.0% were male and 58.0% were female. The tissue slides of the resected samples were examined by two experienced pathologists. This study was approved by the Institutional Ethics Committee of Zhoushan Hospital (approval No: 237/2024). Informed consent was obtained before surgery for the use of surgical specimens and related clinical data.

Figure 1.

Flowchart of Participant Selection

Follow-up

Patient survival data were obtained through telephone interviews and follow-up visits until January 1, 2023. Patients who declined follow-up or could not be contacted for any reason were classified as lost to follow-up.

Quantification of TLSs

The TLSs were assessed by two experienced pathologists who were blinded to the clinicopathological data. If any disagreements occurred, a joint session was conducted to reach a consensus. TLS maturity and location were evaluated in H&E-stained sections obtained from patients with IAC. Stained slides were scanned using a Digital Pathology Scanner PRO (KF-PRO-005), and images were analyzed using K-Viewer. As illustrated in Figure 2A, TLSs were divided into intratumoral and peritumoral TLSs based on their location relative to the tumor invasive margins. The density of intratumoral or peritumoral TLSs was calculated as the number of TLSs per cm² of the tissue area. X-tile software (version 3.6.1, Yale University, USA) was employed to obtain the optimal cut-off values for the density of intra- and peritumoral TLSs based on overall survival (OS). A threshold of 1.33 TLSs/cm² was used to classify low- and high-density intratumoral TLSs, while 1.39 TLSs/cm² served as the threshold to classify low- and high-density peritumoral TLSs.

Figure 2.

Classification of Tumor Region and the Maturity Status of Tertiary Lymphoid Structures (TLSs) Within the Tissue Area. The Region Enclosed by the Blue Line is the Intratumoral Region (A), While That Denoted by the Red Line is the Peritumoral Region (4 × Magnification, Scale bar 2.5 mm) (B). Representative Images of Hematoxylin and Eosin Staining of Cases With no TLSs (C), Early TLSs (D), and Lymphoid Follicles (E) (20 × Magnification, Scale bar 200 μm)

TLS maturation was divided into three categories: no TLSs: tumors lacking any TLSs; early TLSs: dense lymphocyte aggregates without follicular dendritic cells; lymphoid follicle TLSs: structures exhibiting characteristic morphology of proliferating centroblasts (Figure 2B).¹⁴ TLS maturation was considered high if at least one TLS exhibited the characteristic morphology of a lymphoid follicle TLS. Tissue slides with no or early TLSs were classified as having low TLS maturation.

Statistical Analysis

SPSS software (version 20.0) was utilized for data analysis. Continuous variables were expressed as mean ± standard deviation; meanwhile, categorical variables were expressed as numbers and proportions. The t test or Mann–Whitney U test was used to compare differences in the count data. Pearson’s χ² or Fisher’s exact tests were used to analyze differences in categorical variables across maturation stages of TLSs. The optimal cut-off values for continuous variables were obtained using X-tile software (version 3.6.1, Yale University, USA). The Kaplan–Meier method was employed for survival analysis, while the log-rank approach was utilized to analyze survival differences. In the multivariate analysis, the Cox proportional hazards regression model was used to evaluate the independent risk factors for OS. Multivariate logistic regression analysis was conducted to identify independent predictive factors for the intratumoral TLS density and TLS maturation. Two-tailed P-values <.05 were considered statistically significant for all tests.

Results

Association of TLSs and Clinicopathologic Characteristics of Patients With IAC

We retrospectively collected clinical data from 750 patients with IAC and analyzed their TLS features, as presented in Table 1. The data revealed that an increased TLS density was associated with a small tumor size (P < 0.01), absence of LN metastasis (P = 0.01), lower TNM stage (P = 0.01), and pathological subtyping (P = 0.01). Concurrently, an increased density of peritumoral TLSs was mainly observed in older patients (P = 0.01) with larger tumors (P < 0.01), LN metastasis (P < 0.01), advanced TNM stage (P < 0.01), pleural invasion (P = 0.00), and solid and micropapillary pathological subtypes (P < 0.01). Moreover, higher TLS maturation stages correlated with a history of smoking (P = 0.02), larger tumor size (P = 0.01), LN metastasis (P < 0.01), advanced TNM stage (P < 0.01), pleural invasion (P = 0.001), and solid and micropapillary pathological subtypes in patients with IAC (P = 0.04).

Table 1.

Relationship Between Clinicopathologic Characteristics and TLS Density and Maturation in Patients With Invasive Lung Adenocarcinomas (n = 750)

Characteristics	Numbers (%)	Density of Intra-Tumoral TLSs (/cm²)	P-value	Density of Peri-Tumoral TLSs (/cm²)	P-value	Maturation Stages of TLSs		χ ²	P-value
Characteristics	Numbers (%)	Density of Intra-Tumoral TLSs (/cm²)	P-value	Density of Peri-Tumoral TLSs (/cm²)	P-value	No TLSs or Early TLSs (n = 634)	Lymphoid Follicles TLSs (n = 116)	χ ²	P-value
Age (years)			0.31		0.01			0.23	0.63
<65	379 (50.5%)	5.37 ± 6.38		0.25±0.49		318	61
≥65	371 (49.5%)	4.94 ± 5.63		0.35 ± 0.60		316	55
Gender			0.60		0.46			0.12	0.73
Male	315 (42.0%)	5.03 ± 5.97		0.32 ± 0.55		268	47
Female	435 (58.0%)	5.27 ± 6.06		0.29 ± 0.55		366	69
Smoking			0.14		0.13			5.82	0.02
No	593 (79.1%)	5.00 ± 5.84		0.29 ± 0.54		511	82
Yes	157 (20.9%)	5.80 ± 6.65		0.36 ± 0.58		123	34
Tumor size			<0.01		<0.01			6.58	0.01
<3 cm	666 (88.8%)	5.40 ± 6.24		0.25 ± 0.46		571	95
≥3 cm	84 (11.2%)	3.33 ± 3.29		0.69 ± 0.91		63	21
Lymph node metastasis			0.01		<0.01			14.21	<0.01
No	696 (92.8%)	5.34 ± 6.13		0.25 ± 0.47		598	98
Yes	54 (7.2%)	2.90 ± 3.65		1.01 ± 0.86		36	18
Stage			0.01		<0.01			14.39	<0.01
I- II	720 (96.0%)	5.28 ± 6.09		0.26 ± 0.50		616	104
III	30 (4.0%)	2.37 ± 2.57		1.14 ± 0.95		18	12
Pleural invasion			0.07		<0.01			10.14	0.001
No	680 (90.7%)	5.29 ± 6.16		0.26 ± 0.48		584	96
Yes	70 (9.3%)	3.94 ± 4.21		0.68 ± 0.89		50	20
Pathological subtyping			0.01		<0.01			10.30	0.04
Lepidic	230 (30.7%)	5.28 ± 6.27		0.13 ± 0.25		204	26
Acinar	264 (35.2%)	5.62 ± 6.09		0.29 ± 0.48		227	37
Papillary	149 (19.9%)	5.51 ± 5.72		0.26 ± 0.48		121	28
Solid patterns (≥30%)	46 (6.1%)	2.91 ± 4.59		0.77 ± 0.87		34	12
Micropapillary (≥5%)	61 (8.1%)	3.64 ± 5.97		0.77 ± 0.92		48	13

Density of Intratumoral TLSs and TLS Maturation as Independent Prognostic Factors for OS in IAC

Univariate Cox regression analysis (Table 2) demonstrated that gender (hazard ratio [HR] = 2.444, 95% confidence interval [CI] = 1.348-4.432, P = 0.003), smoking (HR = 2.572, 95% CI = 1.436-4.607, P = 0.001), tumor size (HR = 4.344, 95% CI = 2.348-8.036, P < 0.01), LN metastasis (HR = 13.426, 95% CI = 7.534-23.923, P < 0.01), TNM stage (HR = 15.031, 95% CI = 8.123-27.814, P < 0.01), pleural invasion (HR = 3.263, 95% CI = 1.693-6.290, P < 0.01), pathological subtyping (HR = 2.023, 95% CI = 1.637-2.499, P < 0.01), maturation stages of TLSs (HR = 2.700, 95% CI = 1.462-4.986, P = 0.002), density of peritumoral TLSs (HR = 0.428, 95% CI = 0.242-0.759, P = 0.004) and density of intratumoral TLSs (HR = 6.200, 95% CI = 3.078-12.492, P < 0.01) were linked to the prognosis of patients with IAC. However, age was not correlated with patient prognosis. Subsequently, prognostic factors were included in multivariate Cox regression analysis. Moreover, LN metastasis, pathological subtyping, density of intratumoral TLSs, and maturation stage of TLSs served as independent prognostic factors for OS (HR = 3.106, 95% CI = 1.142-8.454, P = 0.026; HR = 1.359, 95% CI = 1.037-1.780, P = 0.026; HR = 0.478, 95% CI = 0.242-0.946, P = 0.034; HR = 1.540, 95% CI = 1.034-2.294, P = 0.034).

Table 2.

Multivariate Cox Analysis of Factors Associated With Overall Survival of Invasive Lung Adenocarcinomas

Variables	Univariate Analysis		Multivariate Analysis
Variables	HR (95% CI)	P-value	HR (95% CI)	P-value
Age, years (<65 vs ≥65)	1.183 (0.667-2.098)	0.57
Gender (female vs male)	2.444 (1.348-4.432)	0.003	1.564 (0.734-3.334)	0.247
Smoking (no vs yes)	2.572 (1.436-4.607)	0.001	1.769 (0.831-3.765)	0.139
Tumor size, cm (<3 vs ≥3)	4.344 (2.348-8.036)	P < 0.01	1.035 (0.473-2.264)	0.932
Lymph node metastasis ( no vs yes)	13.426 (7.534-23.923)	P < 0.01	3.106 (1.142-8.454)	0.026
Stage (I-II vs III)	15.031 (8.123-27.814)	P < 0.01	1.917 (0.717-5.127)	0.195
Pleural invasion	3.263 (1.693-6.290)	P < 0.01	1.259 (0.624-2.542)	0.521
Pathological subtyping	2.023 (1.637-2.499)	P < 0.01	1.359 (1.037-1.780)	0.026
Maturation stages of TLSs	2.700 (1.462-4.986)	0.002	1.540 (1.034-2.294)	0.034
Density of intratumoral TLSs (high vs low)	0.428 (0.242-0.759)	0.004	0.478 (0.242-0.946)	0.034
Density of peritumoral TLSs (high vs low)	6.200 (3.078-12.492)	P < 0.01	1.725 (0.736-4.045)	0.210

CI, confidence interval; HR, hazard ratio.

Multivariate logistic regression analysis demonstrated that solid (≥30%) and micropapillary (≥5%) pathological subtypes were independent predictive factors for the density of intratumoral TLSs (HR = 0.434, 95% CI = 0.267-0.706, P = 0.001). Furthermore, smoking history (HR = 1.655, 95% CI = 1.048-2.613, P = 0.031) and pleural invasion (HR = 1.933, 95% CI = 1.054-3.546, P = 0.033) served as independent predictive factors for TLS maturity, as presented in Table 3.

Table 3.

Multivariate Logistic Regression Analysis Model for the Density of Intratumoral TLSs and TLS Maturation in Patients With Invasive Lung Adenocarcinomas

Variables	Multivariate Analysis for the Density of Intratumoral TLSs		Multivariate Analysis for TLS Maturation
Variables	HR (95% CI)	P-value	HR (95% CI)	P-value
Smoking (no vs yes)	1.137(0.766-1.689)	0.524	1.655(1.048-2.613)	0.031
Tumor size, cm (<3 vs ≥3)	0.875(0.523-1.467)	0.613	1.381(0.747-2.556)	0.303
Lymph node metastasis (no vs yes)	1.198(0.477-3.011)	0.701	1.727(0.601-4.967)	0.311
Stage (I–II vs III)	0.468(0.153-1.436)	0.184	1.759(0.523-5.916)	0.361
Pleural invasion (no vs yes)			1.933(1.054-3.546)	0.033
Pathological subtyping (Lepidic, acinar, and papillary vs solid patterns (≥30%) and micropapillary (≥5%))	0.434(0.267-0.706)	0.001	0.993(0.523-1.884)	0.983

CI, confidence interval; HR, hazard ratio.

Prognostic Significance of TLS Density and Maturation Levels in IAC

We investigated the prognostic value of TLSs using Kaplan–Meier curves. The average OS time was 58.2 and 55.7 months for high-density and low-density intratumoral TLS cases, respectively. High intratumoral TLS density was significantly associated with improved survival compared to low TLS density (log-rank test P = 0.003, Figure 3A). As illustrated in Figure 3B, the OS was significantly higher in the low TLS maturation group than in the high TLS maturation group (log-rank test, P = 0.001). The average OS in the low TLS maturation group was 58.0 months, compared to 54.6 months in the high maturation group.

Figure 3.

Kaplan–Meier Estimates of Overall Survival in Invasive Lung Adenocarcinoma according to the Density of Intratumoral Tertiary Lymphoid Structures (TLSs) (A) and Maturation Stages of TLSs (B)

Discussion

TLSs are regarded as key sites for coordinating both cellular and humoral immune responses against tumor cells, and are associated with favorable clinical outcomes in various solid tumor types.^15,16 To our knowledge, the prognostic value of TLSs in patients with IAC remains unknown. In this study, analysis of TLSs based on their density, location, and maturity revealed that high intratumoral TLS density was an independent low-risk prognostic factor for IAC, while advanced TLS maturation was an independent high-risk factor. In addition, pathological subtype was identified as an independent predictor of the density of intratumoral TLSs. The density of intratumoral TLSs was low in solid and microcapillary IAC subtypes. Patients with IAC and a history of smoking or pleural invasion are more likely to exhibit higher TLS maturation.

Patients with LUAD exhibiting high TLS characteristics tend to have a favorable immune microenvironment and improved prognosis.¹⁷ Another study revealed that the number of TLSs was significantly lower in stage III patients compared to those at stage II.¹⁸ Consistent with the findings of previous studies,^19,20 our results revealed that the low density of intratumoral TLSs was attributed to larger tumors (≥3 cm), LN metastasis, advanced TNM stage, and pathological subtype. These findings support the notion that during cancer progression, tumor cells evade the immune response, influencing TLS formation.²¹ A high abundance of T follicular helper cells within intra-tumoral TLSs may underlie the association with a favorable prognosis.²² Therefore, further exploration of gene mutations and transcriptome profiles of intratumoral TLSs in advanced stages of cancer is warranted.

Previous studies have demonstrated that the abundance of intratumoral TLSs served as an effective predictor of a favorable prognosis for intrahepatic cholangiocarcinoma, whereas the presence of peritumoral TLSs was associated with dismal outcomes.²³ The dual functions of spatially different TLSs have been recognized in hepatocellular carcinoma and breast cancer.^15,24 Although in this study we discovered that peritumoral TLS density was not an independent risk factor for IAC prognosis, the high peritumoral TLS density was related to older age (≥65), larger tumor size (≥3 cm), LN metastasis, advanced TNM stage, pleural invasion, and specific pathological subtypes. Previous studies have demonstrated that peritumoral TLSs reflect an inflammatory context that supports tumor growth and can serve as a niche for the migration of malignant cells.¹⁵ The frequency of Treg cells in intratumoral TLSs increases significantly with the greater abundance of peritumoral TLSs.²² Therefore, the role and special cellular composition of TLSs around tumors in the IAC require further exploration.

Lepidic-predominant subtype has the best prognosis, followed by acinar - and papillary-predominant LUAD, while the worst prognoses are of micropapillary and solid-predominant LUAD with mucus secretion.^25,26 Previous studies have suggested that patients with predominantly micropapillary LUAD exhibit high postoperative recurrence and LN metastasis rates, which is likely related to their unique structure.^27,28 The formation of micropapillary structures is induced by the proliferation of basal tumor cells, which reverses cell polarity.²⁹ Therefore, clarifying the involvement of the tumor microenvironment, especially the TLSs, in these pathological processes warrants further investigation. In this study, we identified the pathological subtype as an independent predictive factor for the density of intratumor TLSs in IAC tissues through multivariate logistic regression analysis. The density of intratumoral TLSs in solid and micropapillary IAC was relatively lower than that observed in other pathological subtypes, whereas the density of peritumoral TLSs was elevated in solid and micropapillary IAC subtypes. Hence, the low immune response resulting from sparse TLS infiltration may be a key factor contributing to the poor prognosis of the solid and micropapillary subtypes of IAC.

TLSs were classified as fully mature if their morphological analysis revealed a clear germinal center.³⁰ Additionally, TLS formation is a complex process that relies on the production of chemokines in response to various inflammatory stimuli.³¹ Previous research revealed that tumor-invaded LNs in lung cancer can inhibit the maturation of intratumoral TLSs, which may be a result of reduced memory B cells and interferon gamma signaling.³² Another study demonstrated that cigarette smoke can induce lung inflammation accompanied by pulmonary tertiary lymphoid tissue formation.³³ Visceral pleural invasion is a poor prognostic factor contributing to the upstaging of early lung cancers.³⁴ In the present study, we revealed that smoking and pleural invasion were independent predictive factors for TLSs maturation. The aforementioned results suggest that patients with a history of smoking and pleural infiltration are likely to experience TLS maturation; however, the mechanism needs to be further explored.

A recent study demonstrated that immature intratumoral TLSs were associated with improved prognosis in non-small cell lung cancer³⁵ and that TLS maturation was greater in patients with advanced tumor stages. Although Siliņa K et al.¹⁶ indicated that the maturation of intratumoral TLSs was associated with better survival in lung squamous cell carcinoma. In this study, we demonstrated that the maturation stage of the TLSs was an independent risk factor for IAC prognosis. Higher TLS maturation stages were observed in IAC patients with a history of smoking, larger tumor size (≥3 cm), LN metastasis, advanced TNM stage, pleural invasion, and pathological subtypes associated with poor prognosis. The presence of TLSs is regarded as a predictive indicator of a favorable response to immunotherapy.³⁶ Thus, the enhanced efficacy of immunotherapy noted in some advanced-stage tumors may be attributed to the existence of mature TLSs within the tumor tissues.

This study has certain limitations, including a patient cohort predominantly limited to early-stage IAC (with only 4% at stage III and no stage IV cases) and its single-center, retrospective design.

Conclusion

In summary, this study demonstrated that the density of intratumoral TLSs, TLS maturation, LN metastasis, and pathological subtype were independent risk factors affecting the prognosis of patients with IAC. A high density of intratumoral TLSs was associated with an improved prognosis, whereas an increased TLS maturation was linked to a worse IAC prognosis. The density of intratumoral TLSs was reduced in the solid and microcapillary subtypes of the IAC. Smokers and those with pleural invasion may exhibit higher TLS maturation in IAC.

Footnotes

Ethical Considerations

This research was conducted after obtaining approval from the Clinical Research Ethics Committee of Zhoushan Hospital (approval No.: 237/2024).

Informed Consent

Informed consent was obtained before surgery for the use of surgical specimens and related clinical data.

Author contributions

Clinical and pathological analysis, YYH, FH, XYZ; Follow up data collection, CYL and JJL; Funding acquisition, YYH and LYX; Statistical analysis, YYH, FH, XYZ and CYL; Writing-original draft, YYH, FH and XYZ; Writing-review and editing, YYH, FH, XYZ, CYL, JJL and LYX; Study design, LYX, YYH, FH, and XYZ. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by Natural Science Foundation of Zhejiang Province (LQ20H160003), Clinical Research Expert Training Program Project of Zhoushan Hospital (YJZJ-C21), and the Scientific Research Foundation of Ministry of Public Health of China/Major Science and Technology Program of Medicine and Health of Zhejiang Province of China (grant no. WKJ-ZJ-2451).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Ettinger

Wood

Aisner

Non-Small Cell Lung Cancer , et al.. Version 3.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Cancer Netw. 2022;20(5):497-530. doi:10.6004/jnccn.2022.0025

Ganti

Klein

Cotarla

Seal

Chou

. Update of incidence, prevalence, survival, and initial treatment in patients with non-small cell lung cancer in the US. JAMA Oncol. 2021;7(12):1824-1832. doi:10.1001/jamaoncol.2021.4932

Huang

Deng

Tin

, et al. Distribution, risk factors, and temporal trends for lung cancer incidence and mortality: a global analysis. Chest. 2022;161(4):1101-1111. doi:10.1016/j.chest.2021.12.655. Epub 2022 Jan 11.

Colbeck

Ager

Gallimore

Jones

. Tertiary lymphoid structures in cancer: drivers of antitumor immunity, immunosuppression, or bystander sentinels in disease? Front Immunol. 2017;8:1830. doi:10.3389/fimmu.2017.01830

Helmink

Reddy

Gao

, et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature. 2020;577(7791):549-555. doi:10.1038/s41586-019-1922-8. Epub 2020 Jan 15.

Cabrita

Lauss

Sanna

, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020;577(7791):561-565. doi:10.1038/s41586-019-1914-8. Epub 2020 Jan 15. Erratum in: Nature. 2020 Apr;580(7801):E1. doi: 10.1038/s41586-020-2155-6.

Bergomas

Grizzi

, et al. Occurrence of tertiary lymphoid tissue is associated with T-cell infiltration and predicts better prognosis in early-stage colorectal cancers. Clin Cancer Res. 2014;20(8):2147-2158. doi:10.1158/1078-0432.CCR-13-2590. Epub 2014 Feb 12.

Feng

Yang

Qiao

, et al. Prognostic significance of gene signature of tertiary lymphoid structures in patients with lung adenocarcinoma. Front Oncol. 2021;11:693234. doi:10.3389/fonc.2021.693234

Kang

Feng

Luo

, et al. Tertiary lymphoid structures in cancer: the double-edged sword role in antitumor immunity and potential therapeutic induction strategies. Front Immunol. 2021;12:689270. doi:10.3389/fimmu.2021.689270

10.

Vinayanuwattikun

Le Calvez-Kelm

Abedi-Ardekani

, et al. Elucidating genomic characteristics of lung cancer progression from in situ to invasive adenocarcinoma. Sci Rep. 2016;6:31628. doi:10.1038/srep31628

11.

Gao

Duan

, et al. Clinical implications and molecular features of tertiary lymphoid structures in stage I lung adenocarcinoma. Cancer Med. 2023;12(8):9547-9558. doi:10.1002/cam4.5731. Epub 2023 Mar 6.

12.

Xie

Gao

, et al. The radiological characteristics, tertiary lymphoid structures, and survival status associated with EGFR mutation in patients with subsolid nodules like stage I-II LUAD. BMC Cancer. 2024;24(1):372. doi:10.1186/s12885-024-12136-6

13.

Wang

Jiang

Zheng

, et al. Tertiary lymphoid structure and decreased CD8+ T cell infiltration in minimally invasive adenocarcinoma. iScience. 2022;25(3):103883. doi:10.1016/j.isci.2022.103883

14.

Von

Altman

Egger

, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med. 2007;147:573-577.

15.

Sofopoulos

Fortis

Vaxevanis

, et al. The prognostic significance of peritumoral tertiary lymphoid structures in breast cancer. Cancer Immunol Immunother. 2019;68(11):1733-1745. doi:10.1007/s00262-019-02407-8. Epub 2019 Oct 9.

16.

Siliņa

Soltermann

Attar

, et al. Germinal centers determine the prognostic relevance of tertiary lymphoid structures and are impaired by corticosteroids in lung squamous cell carcinoma. Cancer Res. 2018;78(5):1308-1320. doi:10.1158/0008-5472.CAN-17-1987. Epub 2017 Dec 26.

17.

Goc

Germain

Vo-Bourgais

, et al. Dendritic cells in tumor-associated tertiary lymphoid structures signal a Th1 cytotoxic immune contexture and license the positive prognostic value of infiltrating CD8+ T cells. Cancer Res. 2014;74(3):705-715. doi:10.1158/0008-5472.CAN-13-1342. Epub 2013 Dec 23.

18.

Rakaee

Kilvaer

Jamaly

, et al. Tertiary lymphoid structure score: a promising approach to refine the TNM staging in resected non-small cell lung cancer. Br J Cancer. 2021;124(10):1680-1689. doi:10.1038/s41416-021-01307-y. Epub 2021 Mar 15.

19.

Schumacher

Thommen

. Tertiary lymphoid structures in cancer. Science. 2022;375(6576):eabf9419. doi:10.1126/science.abf9419. Epub 2022 Jan 7.

20.

Deguchi

Tanaka

Suzuki

, et al. Clinical relevance of tertiary lymphoid structures in esophageal squamous cell carcinoma. BMC Cancer. 2022;22(1):699. doi:10.1186/s12885-022-09777-w

21.

Pimenta

Barnes

. Role of tertiary lymphoid structures (TLS) in anti-tumor immunity: potential tumor-induced cytokines/chemokines that regulate TLS formation in epithelial-derived cancers. Cancers (Basel). 2014;6(2):969-997. doi:10.3390/cancers6020969

22.

Gu-Trantien

Migliori

Buisseret

, et al. CXCL13-producing TFH cells link immune suppression and adaptive memory in human breast cancer. JCI Insight. 2017;2(11):e91487. doi:10.1172/jci.insight.91487

23.

Ding

Yun

, et al. Distribution and density of tertiary lymphoid structures predict clinical outcome in intrahepatic cholangiocarcinoma. J Hepatol. 2022;76(3):608-618. doi:10.1016/j.jhep.2021.10.030. Epub 2021 Nov 15.

24.

Finkin

Yuan

Stein

, et al. Ectopic lymphoid structures function as microniches for tumor progenitor cells in hepatocellular carcinoma. Nat Immunol. 2015;16(12):1235-1244. doi:10.1038/ni.3290. Epub 2015 Oct 26.

25.

Yanagawa

Shiono

Abiko

Ogata

Sato

Tamura

. New IASLC/ATS/ERS classification and invasive tumor size are predictive of disease recurrence in stage I lung adenocarcinoma. J Thorac Oncol. 2013;8(5):612-618. doi:10.1097/JTO.0b013e318287c3eb

26.

Wang

Wei

, et al. Prognostic value of the new international association for the study of lung cancer/American thoracic society/European respiratory society classification in stage IB lung adenocarcinoma. Eur J Surg Oncol. 2015;41(10):1430-1436. doi:10.1016/j.ejso.2015.06.004. Epub 2015 Jun 23.

27.

Zhao

Wang

Shen

, et al. Minor components of micropapillary and solid subtypes in lung adenocarcinoma are predictors of lymph node metastasis and poor prognosis. Ann Surg Oncol. 2016;23(6):2099-2105. doi:10.1245/s10434-015-5043-9. Epub 2016 Feb 2.

28.

Hung

Jeng

Chou

, et al. Prognostic value of the new international association for the study of lung cancer/American thoracic society/European respiratory society lung adenocarcinoma classification on death and recurrence in completely resected stage I lung adenocarcinoma. Ann Surg. 2013;258(6):1079-1086. doi:10.1097/SLA.0b013e31828920c0

29.

Nassar

. Carcinomas with micropapillary morphology: clinical significance and current concepts. Adv Anat Pathol. 2004;11(6):297-303. doi:10.1097/01.pap.0000138142.26882.fe

30.

J Gunderson

Rajamanickam

Bui

, et al. Germinal center reactions in tertiary lymphoid structures associate with neoantigen burden, humoral immunity and long-term survivorship in pancreatic cancer. OncoImmunology. 2021;10(1):1900635. doi:10.1080/2162402X.2021.1900635

31.

Dieu-Nosjean

Goc

Giraldo

Sautès-Fridman

Fridman

. Tertiary lymphoid structures in cancer and beyond. Trends Immunol. 2014;35(11):571-580. doi:10.1016/j.it.2014.09.006. Epub 2014 Oct 22.

32.

Cai

, et al. Intratumoral tertiary lymphoid structure (TLS) maturation is influenced by draining lymph nodes of lung cancer. J Immunother Cancer. 2023;11(4):e005539. doi:10.1136/jitc-2022-005539

33.

Morissette

Jobse

Thayaparan

, et al. Persistence of pulmonary tertiary lymphoid tissues and anti-nuclear antibodies following cessation of cigarette smoke exposure. Respir Res. 2014;15(1):49. doi:10.1186/1465-9921-15-49

34.

Jiang

Liang

Shen

, et al. The impact of visceral pleural invasion in node-negative non-small cell lung cancer: a systematic review and meta-analysis. Chest. 2015;148(4):903-911. doi:10.1378/chest.14-2765

35.

Xiaoxu

Min

Chengcheng

. Immature central tumor tertiary lymphoid structures are associated with better prognosis in non-small cell lung cancer. BMC Pulm Med. 2024;24(1):155. doi:10.1186/s12890-024-02970-6

36.

Sautès-Fridman

Petitprez

Calderaro

Fridman

. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat Rev Cancer. 2019;19(6):307-325. doi:10.1038/s41568-019-0144-6

Density and Maturity of Tertiary Lymphoid Structures Predict Clinical Outcome in Invasive Lung Adenocarcinoma

Abstract

Introduction

Methods

Results

Conclusions

Keywords

Introduction

Materials and Methods

Patient Cohort

Follow-up

Quantification of TLSs

Statistical Analysis

Results

Association of TLSs and Clinicopathologic Characteristics of Patients With IAC

Density of Intratumoral TLSs and TLS Maturation as Independent Prognostic Factors for OS in IAC

Prognostic Significance of TLS Density and Maturation Levels in IAC

Discussion

Conclusion

Footnotes

Ethical Considerations

Informed Consent

Author contributions

Funding

Declaration of Conflicting Interests

References