Comparative analyses of tumor immune microenvironment using paired samples from primary non-small cell lung cancer and brain metastases

doi:10.21203/rs.3.rs-8278469/v1

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Comparative analyses of tumor immune microenvironment using paired samples from primary non-small cell lung cancer and brain metastases

https://doi.org/10.21203/rs.3.rs-8278469/v1

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The efficacy of immune checkpoint inhibitors against brain metastases (BMs) from non-small cell lung cancer (NSCLC) often appears limited, potentially due to differences in the tumor immune microenvironment (TIME) between primary tumors (PTs) and BMs. We aimed to characterize the TIME of paired PTs and BMs and to investigate the impact of metastatic timing (synchronous BMs [sBMs] vs. metachronous BMs [mBMs]). We retrospectively analyzed 14 paired samples (7 sBMs, 7 mBMs) using immunohistochemistry for CD163, T-cell subsets (CD3, CD8, programmed cell death 1 [PD-1], forkhead box protein 3 [FoxP3]), programmed cell death ligand 1 (PD-L1), and peripheral lymph node addressin (PNAd). The densities of T-cell subsets and proportions of CD163-positive areas were quantitatively evaluated in intratumoral and stromal compartments using digital pathology. PD-L1 expression was assessed as the tumor proportion score. Compared to PTs, BMs exhibited a more immunosuppressive TIME, with a significantly larger CD163-positive area, lower densities of intratumoral T cells (CD3+, CD8+, PD-1+), and complete absence of PNAd + tumor-associated high-endothelial venules. PD-L1 expression was well-conserved between sites. Critically, TIMEs differed by metastatic timing. PTs of the sBM group showed higher intratumoral and stromal FoxP3/CD3 and lower stromal CD8/CD3 compared to the mBM group. Subsequently, sBMs displayed a distinct TIME with higher intratumoral CD8/CD3 and PD-1/CD3 than mBMs. BMs from NSCLC thus appear to harbor a distinct, immunosuppressive TIME. The timing of metastasis appears fundamentally associated with this TIME profile, with sBM groups showing a unique immunosuppressive signature originating from the PT. These findings provide a rationale for stratifying patients with BMs to guide personalized immunotherapy.

non-small cell lung cancer

brain metastasis

tumor immune microenvironment

tumor-associated macrophage

tumor-infiltrating lymphocyte

synchronous metastasis

metachronous metastasis

tumor-associated high-endothelial venule

paired-sample analysis

Although the clinical efficacy of immune checkpoint inhibitors (ICIs) against brain metastases (BMs) from melanoma or non-small cell lung cancer (NSCLC) has increasingly been reported, therapeutic response is not observed in all patients [12, 37, 39]. Clinically, we encounter cases in which BMs emerge even when the primary tumor (PT) and extracranial metastases have been well-controlled by ICIs. In such situations, subsequent therapies often prove ineffective. One potential reason for this therapeutic resistance is that the brain forms a potent and unique immunosuppressive environment due to its anatomical constraints [31], leading to disparities in tumor immune microenvironments (TIMEs) between BMs and PTs. This suggests that BMs may acquire distinct mechanisms of resistance to ICIs [13].

Further, BMs can be clinically categorized into synchronous BMs (sBMs), diagnosed within 1 year of initial diagnosis of the PT, and metachronous BMs (mBMs), diagnosed after more than 1 year. Historically, sBMs have been associated with poorer prognosis than mBMs [21], but recent advances in immunotherapy may be changing this landscape. Indeed, excellent responses to ICIs leading to prolonged overall survival are sometimes observed in NSCLC patients with sBMs [25, 35]. This clinical observation suggests that sBMs and mBMs may have different biological characteristics, particularly in terms of the TIME, but the details remain poorly understood.

This study aimed to identify differences in TIMEs between intra- and extracranial sites by conducting a comparative analysis of paired PT and BM samples from the same NSCLC patients. Further, by comparing the TIMEs of sBMs and mBMs, we sought to clarify differences in TIMEs based on the timing of metastasis, with the goal of discussing the stratification of patients for successful ICI therapy.

Case Collection and Design

This retrospective study included paired PT and BM samples from patients with NSCLC. Samples were collected at The Jikei University School of Medicine Daisan Hospital from 2014 to 2023 and at Kashiwa Hospital from 2006 to 2017.

Inclusion criteria for this study were: 1) histological confirmation of NSCLC; and 2) sufficient tumor area (≥1000 × 1000 µm²) of both PT and BM specimens. A total of 14 NSCLC cases were selected, yielding 28 formalin-fixed, paraffin-embedded (FFPE) blocks. Clinicopathological features were collected, including age, sex, and histological type. Results of genetic analysis were also collected where available (n = 6).

Cases were defined as having sBMs if the BMs were diagnosed concurrently with or within 1 year of the initial diagnosis of the PT. Cases were defined as having mBMs if the BMs were diagnosed more than 1 year after PT diagnosis.

Immunohistochemistry (IHC)

Serial sections with a thickness of 4 µm were prepared from FFPE blocks for IHC, which was then performed using the following monoclonal antibodies (see Supplementary Table 1 for details): CD163 (D61U1J), CD3 (SP7), CD8 (C8/144B), programmed cell death 1 (PD-1) (D4W2J), forkhead box protein 3 (FoxP3) (D2W8E), programmed cell death ligand 1 (PD-L1) (E1L3N), and peripheral lymph node addressin (PNAd) (MECA-79).

Digital Analysis of Tumor-Associated Macrophages (TAMs) and Tumor-Infiltrating Lymphocytes (TILs)

For the evaluation of TAMs and TILs, IHC slides for CD163, CD3, CD8, PD-1, and FoxP3 were scanned at 200× magnification to create whole-slide images (WSIs) using an Aperio ScanScopeAT2 scanner (Leica Biosystems, Deer Park, IL, USA). Annotations and quantification of positive areas and cell densities were performed using QuPath software (version 0.5.1) [2].

Distributions of TAMs were evaluated as follows. On each CD163-stained WSI, four random 500 × 500-µm² regions of interest (ROIs) were selected from viable tumor areas (Fig. 1A). These four ROIs were analyzed collectively for each patient. Within the selected areas, intratumoral (tumor cell nests) and stromal compartments were manually annotated (Fig. 1B). Using the pixel classifier function in QuPath, a color deconvolution algorithm was applied to separate the hematoxylin and 3,3'-diaminobenzidine (DAB) (for CD163) stains. A positive pixel was defined as any pixel with a DAB optical density exceeding a manually set, consistent threshold (Fig. 1C). The percentage of the CD163-positive area was then calculated for each compartment by dividing the summed area of positive pixels from all four ROIs by the summed total area of the respective compartments from all four ROIs. This provided a single representative value for iCD163 and sCD163 for each patient.

Distributions of TILs were evaluated as follows. For each case, a single 500 × 500-µm² ROI representing a lymphocyte infiltration "hotspot" was manually selected from a viable tumor area on the CD3-stained WSI (Fig. 1D). The coordinates of this ROI were then applied to all other TIL marker slides (CD8, PD-1, FoxP3) for the same case to ensure analysis of the identical region. Within the ROI, intratumoral and stromal compartments were manually annotated (Fig. 1E). Cell detection was performed in QuPath using a nucleus-based algorithm on the hematoxylin stain to identify all cells. Subsequently, a positive cell classifier was trained based on the DAB staining intensity within the cytoplasm or nucleus, depending on the marker. A cell was classified as "positive" if the mean DAB optical density exceeded a predefined, consistent threshold (Fig. 1F). The density of positive cells for each marker was then calculated for each compartment by dividing the total number of positive cells by the area of that compartment (e.g., iCD3, sCD3), and expressed as cells per square millimeter.

In addition, for CD8, PD-1, and FoxP3, the ratio of densities to the density of CD3 + cells was calculated (e.g., iCD8/iCD3).

Evaluation of PD-L1

The PD-L1 tumor proportion score (TPS) was evaluated by visual microscopic assessment as the percentage of viable tumor cells showing any membranous staining across the entire section. For values below 5%, the TPS was recorded as precisely as possible. For values of 5% or higher, the TPS was scored in 5% increments (e.g., 5%, 10%, 15%).

Evaluation of PNAd

The presence or absence of PNAd-positive vessels within the tumor, representing TA-HEVs, was evaluated. In addition, ectopic expression on tumor cells was assessed as a PNAd TPS using the same method as for PD-L1.

Statistical Analysis

Correlations were analyzed using Spearman's rank correlation coefficient. Paired comparisons between PTs and BMs were performed using the Wilcoxon signed-rank test for continuous variables and the McNemar test for categorical variables. Group comparisons between sBMs and mBMs were performed using the Mann–Whitney U test and Fisher's exact test. Values of p < 0.05 were considered statistically significant. All analyses were performed using JMP Pro version 18.0.2 (SAS Institute, Cary, NC, USA). Graphs were generated using JMP Pro version 18.0.2 or R software version 4.5.1.

Patient Characteristics

The study included 14 NSCLC patients (10 men, 4 women) who underwent surgical resection of BMs and had tissue available from the PT. The cohort was divided into 7 cases of sBM (sBM group) and 7 cases of mBM (mBM group). Median age of the overall cohort was 70 years (interquartile range [IQR], 62.5–76 years). No significant differences in age, sex, or histological type were apparent between the two groups. However, PTs in the sBM group were significantly more likely to have been sampled by biopsy rather than surgical resection. Information regarding ICI therapy during the entire clinical course was available for 8 of the 14 patients. Among these, two patients (2/8, 25%), both in the sBM group, had received ICIs (Table 1).

Table 1
Comparison of patient characteristics between sBM and mBM groups
Parameters	sBMs (n = 7)	mBMs (n = 7)	p
Age, years	68 [64–79]	73 [59–77]	0.90
Sex, women/men	2/5 (28.6%/71.4%)	2/5 (28.6%/71.4%)	1.00
Histology
Adenocarcinoma	4 (57.1%)	6 (85.7%)	0.56
Squamous cell carcinoma	3 (42.9%)	1 (14.3%)
Method of PT sampling
Biopsy	6 (85.7%)	1 (14.3%)	0.03
Surgical resection	1 (14.3%)	6 (85.7%)
Gene mutation profiles (n = 6)
EGFR mutation	3 (75%)	-
ALK fusion gene	-	1 (50%)
None detected	1 (25%)	1 (50%)
History of ICI therapy, yes/no (n = 8)	2/5 (40%)	0/3 (0%)

Data are presented as median [interquartile range] or n (%).

ALK, anaplastic lymphoma kinase; EGFR, epidermal growth factor receptor; ICI, immune checkpoint inhibitor; mBM, metachronous brain metastasis; sBM, synchronous brain metastasis.

Comparison of TIMEs between PTs and BMs

Paired analysis of PTs and BMs revealed that the CD163-positive area was significantly larger in BMs than in PTs for both iCD163 and sCD163 (Fig. 2). Conversely, iCD3, iCD8, and iPD-1 were significantly lower in BMs compared with PTs. Further, iFoxP3 showed a trend toward being lower in BMs (Fig. 2). TA-HEVs were detected in 10 of 14 PTs (71.4%, p < 0.05) but were absent in all 14 BMs (0%). No significant differences in ratios of TIL subsets, PD-L1 TPS, or PNAd TPS were observed between PTs and BMs.

Correlations among TIME factors in BMs

In BMs, iCD163 showed significant positive correlations with multiple TIL-related parameters, as depicted in the heatmap (Fig. 3A). Notably, iCD163 correlated significantly with iCD8 (ρ = 0.56, p = 0.04; Fig. 3B). Further, iPD-1/iCD3 and iFoxP3/iCD3 also correlated positively with iCD163. A strong positive correlation was observed between iCD8 and iFoxP3 (ρ = 0.78, p < 0.01; Fig. 3C).

Comparison of TIMEs between sBM and mBM Groups

To investigate whether the timing of metastasis influences the TIME, we compared immunological markers between sBM and mBM groups (Fig. 4). In PTs, the compositions of TIME exhibited distinct differences. PTs from the sBM group showed significantly higher iFoxP3/iCD3 and sFoxP3/sCD3 compared with the mBM group (medians: 4.8% vs. 0% and 10.6% vs. 0.3%, respectively). Conversely, sCD8 and sCD8/sCD3 were significantly lower in the sBM group (medians: 257.8 cells/mm² vs. 1965.8 cells/mm² and 20.0% vs. 40.1%, respectively). TA-HEVs were detected in 4 of 7 PTs (57.1%) in the sBM group, but 6 of the 7 PTs (85.7%) in the mBM group (p = 0.56).

In BMs, while the absolute densities of most TILs and the CD163-positive area did not differ significantly between groups, T-cell composition showed a clear distinction (Fig. 4). The iCD8/iCD3 was significantly higher in sBMs than in mBMs (median, 29.4% vs. 0%, respectively). Similarly, the iPD-1/iCD3 was also higher in the sBM group (median, 95.7% vs. 0%, respectively). In addition, the sFoxP3/sCD3 tended to be higher in the sBM group (median, 5.3% vs. 1.3%; p > 0.05).

In this exploratory study, we conducted a comprehensive quantitative analysis of immunological factors constituting the TIME using paired PT and BM samples from NSCLC patients. Our major findings suggested that: 1) BMs possess a more immunosuppressive TIME compared to PTs, characterized by an increase in CD163-positive macrophages and a decrease in T-cell infiltration; 2) the timing of metastasis (sBM vs. mBM) is associated with the TIME composition, particularly in terms of the ratios of T-cell subsets; and 3) while PD-L1 expression on tumor cells was well-conserved between PTs and BMs, tumor-associated TA-HEVs were completely absent in BMs. These results provide preliminary insights into the immunosuppressive mechanisms present in NSCLC brain metastases and may help generate hypotheses for overcoming therapeutic resistance.

First, the findings from this study suggest that BMs form an immunosuppressive TIME distinct from that of PTs. Direct comparison of paired samples revealed that the CD163-positive area was significantly larger in BMs in both intratumoral and stromal compartments. In contrast, iCD3, iCD8, and iPD-1 were significantly lower. This characteristic of BMs as an immunologically "cold" environment is consistent with multiple previous reports [11, 18–20, 23, 24, 26, 40, 42]. Interestingly, despite the overall low level of T-cell infiltration, positive correlations were identified between iCD163 and the densities of various T-cell subsets, as well as between the densities of CD8 + and FoxP3 + T cells. This suggests that the TIME of BM is a complex immune landscape characterized by the spatial co-existence of immunosuppressive cells, such as M2-like TAMs and FoxP3 + T cells (a known marker for regulatory T cells [Tregs] [7]), with CD8 + T cells. CD163-positive M2-like TAMs are known to play a central role in immunosuppression by inhibiting T-cell responses [10, 34], and reports of T-cell exhaustion being enhanced by TAM/CD8 + T-cell interactions [17] support the possibility that the co-localization we observed occurs in an immunosuppressive context. Further, most of the macrophages that increase in BMs from NSCLC have been suggested to be monocyte-derived rather than brain-resident microglia [20], and studies in glioma have associated monocyte-derived TAMs with poor prognosis [33]. These findings suggest that therapeutic strategies aimed at re-educating TAMs toward an anti-tumor phenotype may also be effective against BMs [28].

Second, a key hypothesis-generating aspect of this study was the exploration of immune status by comprehensively analyzing the distribution and numbers of immune cells in relation to therapeutic interventions for the primary cancer and the timing of BM detection. Studies on the TIME based on the timing of metastasis have been limited, and no consistent trends have been established [8, 19, 22]. Our analysis revealed that, compared to the mBM group, the sBM group had a lower sCD8/sCD3 and significantly higher iFoxP3/iCD3 and sFoxP3/sCD3 even in the PT. This suggests that tumors capable of early metastasis may establish a potent local immunosuppressive environment from an early stage by recruiting and accumulating FoxP3 + Tregs. Our focus on the ratios between these T-cell subsets, rather than absolute numbers alone, may be crucial. This approach is supported by findings from other brain tumors, such as glioblastoma, where the balance between different T-cell populations, such as CD8 + T cells and FoxP3 + Tregs, not simply the absolute count of any single cell type, was shown to represent a key determinant of clinical outcomes [32]. For BMs, the sBM group showed significantly higher iCD8/iCD3 and iPD-1/iCD3, and a trend toward a higher sFoxP3/sCD3 compared to the mBM group. The increase in the PD-1 + T-cell ratio requires careful interpretation, as these cells represent a functionally heterogeneous population [1] that could reflect the activation or exhaustion of CD8 + T cells in response to tumor antigens, or an increase in other cell lineages. In any case, whereas the TIME of mBMs may evolve through processes such as clonal changes due to therapeutic interventions, immunoediting [30], and immunosenescence [44], the TIME of sBMs may be based on an environment established from the early stages of carcinogenesis, similar to the PT. This suggests that cancer cells and immunoregulatory cells in sBMs and mBMs may exhibit different biological behaviors. This difference in the TIME could provide a potential rationale for stratifying patients for ICI therapy. Specifically, the high iCD8/iCD3 and iPD-1/iCD3 observed in sBMs might suggest that T cells infiltrate the tumor, inducing PD-1/PD-L1 expression. This leads to the hypothesis that these markers could offer good predictors of response to anti-PD-1/PD-L1 antibody therapy. However, the abundant presence of FoxP3 + Tregs, potentially carried over from the PTs in sBMs, could contribute to ICI resistance and diminished therapeutic effects [38]. This duality suggests that responses to ICIs may be heterogeneous even within the sBM group, and future personalization of therapeutic strategies might require consideration of combination therapies targeting Tregs, depending on the TIME profile. Thus, while further validation in larger cohorts is required, comparative analyses of the TIME before and after therapeutic intervention in the same patient have the potential to infer dynamic changes in the TIME based on the timing of metastasis and to identify important stratification factors for treatment strategies in patients with BMs.

Third, our study provided several noteworthy observations from the perspective of therapeutic targets and biomarkers. Regarding PD-L1 expression, while previous reports on the concordance between PTs and BMs have varied [3, 18, 22–24, 26, 40, 42, 43], our study found that levels of PD-L1 expression were well-conserved between paired sites, suggesting that BM specimens could offer reliable surrogates for PD-L1 testing. In addition, TA-HEVs, as a major route for lymphocyte infiltration, were present in many PTs but completely absent in BMs. This finding is consistent with a previous report [14] and, considering the multifaceted role of TA-HEVs in anti-tumor immunity [27], their absence is highly likely to contribute to the reduced T-cell infiltration and the establishment of an immunosuppressive environment in BMs. Studies in glioma have suggested that inducing TA-HEVs could improve the efficacy of cancer immunotherapy [29], and a similar approach might be promising for BMs. Further, as an additional finding, we discovered that the TA-HEV marker PNAd was ectopically expressed on some NSCLC tumor cells. To the best of our knowledge, this is the first report of such expression in NSCLC. Ectopic expression of PNAd has been reported in normal epithelia [9, 16] and various cancers [4–6, 15, 16, 36]. While some reports have found no association with prognosis [15, 16], others have linked ectopic expression to poor prognosis [5] or promotion of metastasis [6, 36, 40], suggesting that PNAd may have functions beyond being a mere marker of HEV. Although we found no significant difference in PNAd expression of tumor cells between sBM and mBM groups in our cohort, this could have been due to the small sample size. Further research is needed to elucidate the mechanisms and functional significance of ectopic PNAd expression in NSCLC.

This study showed several important limitations. As a retrospective, exploratory study with a small sample size, the statistical power of comparisons and the generalizability of the findings were limited. The different methods of PT sampling between sBM and mBM groups could have provided a potential source of bias. Further, this study was based on static analysis using IHC. Specifically, the evaluation of TILs was performed on a single hotspot region, which may not have fully represented the heterogeneity of lymphocyte infiltration across the entire tumor. Although the evaluation of TAMs was based on multiple random regions, these analyses still provide only a static snapshot of the TIME. The large number of comparisons made without correction for multiple testing also increases the risk of false-positive findings. A promising future direction would be to validate these findings in larger, prospective cohorts and to analyze associations between TIME profiles and clinical outcomes to determine optimal treatment schedules.

Our findings suggest that BMs from NSCLC exhibit a distinct, immunosuppressive TIME dominated by an abundance of M2-like TAMs. Further, we observed that TIMEs differ according to the timing of metastasis. Specifically, sBMs were characterized by an abundance of FoxP3 + T cells beginning at the PT, associated with a TIME in the BMs featuring higher proportions of CD8 + and PD-1 + T cells, suggestive of an immunosuppressive state. These differences in TIMEs between sBMs and mBMs may provide a preliminary basis for patient stratification and could potentially inform future studies on predicting the therapeutic efficacy of ICIs.

ALK

anaplastic lymphoma kinase

brain metastasis

DAB

3,3'-diaminobenzidine

EGFR

epidermal growth factor receptor

FFPE

formalin-fixed,paraffin-embedded

FoxP3

forkhead box protein 3

ICI

immune checkpoint inhibitor

IHC

immunohistochemistry

IQR

interquartile range

mBM

metachronous brain metastasis

NSCLC

non-small cell lung cancer

PD-1

programmed cell death 1

PD-L1

programmed cell death ligand 1

PNAd

peripheral lymph node addressin

primary tumor

ROI

region of interest

sBM

synchronous brain metastasis

TA-HEV

tumor-associated high-endothelial venule

TAM

tumor-associated macrophage

TIL

tumor-infiltrating lymphocyte

TIME

tumor immune microenvironment

TPS

tumor proportion score

Treg

regulatory T cell

WSI

whole-slide image.

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki (revised in 2013) and was approved by the ethics committee of The Jikei University School of Medicine (approval number: 27–239 (8124)).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

SUPPLEMENTARY INFORMATION

Supplementary_material.docx

Funding

This study is supported by the Jikei University Exploratory Collaboration Research Fund (2025-205-HK). The sponsor or funding organization had no role in the design or conduct of this research.

Author Contribution

MF, NF, YY, and TT, conceptualized, designed, and performed the study. Material preparation, data collection and analysis were performed by neurosurgeons (YY, AT, YA, TT) and pathologists (MF, NF, AI). The first draft of the manuscript was written by MF and NF. All authors commented on previous versions of the manuscript. TT assisted with writing the discussion and reviewed the manuscript. All authors read and approved the final manuscript.

Acknowledgement

Authors thank to Marina Minami in the department of pathology at Jikei University Kashiwa Hospital and other technicians in the department of pathology at Jikei University Daisan Hospital.

Data Availability

The data that support the findings of this study are available from the corresponding author on reasonable request.

Ando S, Araki K (2022) CD8 T-cell heterogeneity during T-cell exhaustion and PD-1-targeted immunotherapy. Int Immunol. 10.1093/intimm/dxac038
Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD et al (2017) QuPath: Open source software for digital pathology image analysis. Sci Rep. 10.1038/s41598-017-17204-5
Batur S, Dulger O, Durak S, Yumuk PF, Caglar HB, Bozkurtlar E et al (2020) Concordance of PD-L1 expression and CD8 + TIL intensity between NSCLC and synchronous brain metastases. Bosn J Basic Med Sci. 10.17305/bjbms.2019.4474
Bento DC, Jones E, Junaid S, Tull J, Williams GT, Godkin A et al (2015) High endothelial venules are rare in colorectal cancers but accumulate in extra-tumoral areas with disease progression. Oncoimmunology. 10.4161/2162402X.2014.974374
Boottanun P, Ino Y, Shimada K, Hiraoka N, Angata K, Narimatsu H (2021) Association between the expression of core 3 synthase and survival outcomes of patients with cholangiocarcinoma. Oncol Lett. 10.3892/ol.2021.13021
Cho YJ, Hwang I, Park S, Lee S, Kang SY, Kim MJ et al (2024) Prognostic Effect of Tertiary Lymphoid Structures in Epstein-Barr Virus-Associated Gastric Carcinomas Measured by Digital Image Analysis. Lab Invest. 10.1016/j.labinv.2024.102071
Deng G, Song X, Greene MI (2020) FoxP3 in T(reg) cell biology: a molecular and structural perspective. Clin Exp Immunol. 10.1111/cei.13357
Fujita K, Kimura G, Tsuzuki T, Kato T, Banno E, Kazama A et al (2022) The Association of Tumor Immune Microenvironment of the Primary Lesion with Time to Metastasis in Patients with Renal Cell Carcinoma: A Retrospective Analysis. Cancers (Basel). 10.3390/cancers14215258
Hoshino H, Ohta M, Ito M, Uchimura K, Sakai Y, Uehara T et al (2016) Apical membrane expression of distinct sulfated glycans represents a novel marker of cholangiolocellular carcinoma. Lab Invest. 10.1038/labinvest.2016.104
Hussain SF, Yang D, Suki D, Aldape K, Grimm E, Heimberger AB (2006) The role of human glioma-infiltrating microglia/macrophages in mediating antitumor immune responses. Neuro Oncol. 10.1215/15228517-2006-008
Ikarashi D, Okimoto T, Shukuya T, Onagi H, Hayashi T, Sinicropi-Yao SL et al (2021) Comparison of Tumor Microenvironments Between Primary Tumors and Brain Metastases in Patients With NSCLC. JTO Clin Res Rep. 10.1016/j.jtocrr.2021.100230
Ivetic J, Dedeic J, Milicevic S, Vidojevic K, Delic M (2025) Comparative Meta-Analysis of Survival, Risk, and Treatment Efficacy in Immunotherapy for Metastatic Melanoma Using Random-, Fixed-, and Mixed-Effects Models. J Clin Med. 10.3390/jcm14145017
Kang S, Jeong H, Park JE, Kim HS, Kim YH, Lee DH et al (2023) Central nervous systemic efficacy of immune checkpoint inhibitors and concordance between intra/extracranial response in non-small cell lung cancer patients with brain metastasis. J Cancer Res Clin Oncol. 10.1007/s00432-022-04251-3
Karpathiou G, Camy F, Dumollard JM, Peoc'h M (2021) Immunohistochemical analysis of L1 cell adhesion molecule and high endothelial venules in breast cancer brain metastasis. Pathol Res Pract. 10.1016/j.prp.2021.153484
Karpathiou G, Dumollard JM, Gavid M, Casteillo F, Vieville M, Prades JM et al (2021) High endothelial venules are present in pharyngeal and laryngeal carcinomas and they are associated with better prognosis. Pathol Res Pract. 10.1016/j.prp.2021.153392
Karpathiou G, Sramek V, Dagher S, Mobarki M, Dridi M, Picot T et al (2022) Peripheral node addressin, a ligand for L-selectin is found in tumor cells and in high endothelial venules in endometrial cancer. Pathol Res Pract. 10.1016/j.prp.2022.153888
Kersten K, Hu KH, Combes AJ, Samad B, Harwin T, Ray A et al (2022) Spatiotemporal co-dependency between macrophages and exhausted CD8(+) T cells in cancer. Cancer Cell. 10.1016/j.ccell.2022.05.004
Kim R, Keam B, Kim S, Kim M, Kim SH, Kim JW et al (2019) Differences in tumor microenvironments between primary lung tumors and brain metastases in lung cancer patients: therapeutic implications for immune checkpoint inhibitors. BMC Cancer. 10.1186/s12885-018-5214-8
Kleinberger M, Cifci D, Paiato C, Tomasich E, Mair MJ, Steindl A et al (2025) Density and entropy of immune cells within the tumor microenvironment of primary tumors and matched brain metastases. Acta Neuropathol Commun. 10.1186/s40478-025-01939-8
Kudo Y, Haymaker C, Zhang J, Reuben A, Duose DY, Fujimoto J et al (2019) Suppressed immune microenvironment and repertoire in brain metastases from patients with resected non-small-cell lung cancer. Ann Oncol. 10.1093/annonc/mdz207
Li J, Zhang X, Wang Y, Jin Y, Song Y, Wang T (2024) Clinicopathological characteristics and prognosis of synchronous brain metastases from non-small cell lung cancer compared with metachronous brain metastases. Front Oncol. 10.3389/fonc.2024.1400792
Li M, Hou X, Sai K, Wu L, Chen J, Zhang B et al (2022) Immune suppressive microenvironment in brain metastatic non-small cell lung cancer: comprehensive immune microenvironment profiling of brain metastases versus paired primary lung tumors (GASTO 1060). Oncoimmunology. 10.1080/2162402X.2022.2059874
Liu JS, Cai YX, He YZ, Xu J, Tian SF, Li ZQ (2024) Spatial and temporal heterogeneity of tumor immune microenvironment between primary tumor and brain metastases in NSCLC. BMC Cancer. 10.1186/s12885-024-11875-w
Lu BY, Gupta R, Aguirre-Ducler A, Gianino N, Wyatt H, Ribeiro M et al (2021) Spatially resolved analysis of the T cell immune contexture in lung cancer-associated brain metastases. J Immunother Cancer. 10.1136/jitc-2021-002684
Mahashabde R, Bhatti SA, Martin BC, Painter JT, Rodriguez A, Ying J et al (2023) Real-World Survival of First-Line Immune Checkpoint Inhibitor Treatment Versus Chemotherapy in Older Patients With Non-Small-Cell Lung Cancer and Synchronous Brain Metastases. JCO Oncol Pract. 10.1200/OP.23.00042
Mansfield AS, Aubry MC, Moser JC, Harrington SM, Dronca RS, Park SS et al (2016) Temporal and spatial discordance of programmed cell death-ligand 1 expression and lymphocyte tumor infiltration between paired primary lesions and brain metastases in lung cancer. Ann Oncol. 10.1093/annonc/mdw289
Milutinovic S, Gallimore A (2023) The link between T cell activation and development of functionally useful tumour-associated high endothelial venules. Discov Immunol. 10.1093/discim/kyad006
Quail DF, Joyce JA (2013) Microenvironmental regulation of tumor progression and metastasis. Nat Med. 10.1038/nm.3394
Ramachandran M, Vaccaro A, van de Walle T, Georganaki M, Lugano R, Vemuri K et al (2023) Tailoring vascular phenotype through AAV therapy promotes anti-tumor immunity in glioma. Cancer Cell. 10.1016/j.ccell.2023.04.010
Roerden M, Spranger S (2025) Cancer immune evasion, immunoediting and intratumour heterogeneity. Nat Rev Immunol. 10.1038/s41577-024-01111-8
Sampson JH, Gunn MD, Fecci PE, Ashley DM (2020) Brain immunology and immunotherapy in brain tumours. Nat Rev Cancer. 10.1038/s41568-019-0224-7
Sayour EJ, McLendon P, McLendon R, De Leon G, Reynolds R, Kresak J et al (2015) Increased proportion of FoxP3 + regulatory T cells in tumor infiltrating lymphocytes is associated with tumor recurrence and reduced survival in patients with glioblastoma. Cancer Immunol Immunother. 10.1007/s00262-014-1651-7
Silvin A, Qian J, Ginhoux F (2023) Brain macrophage development, diversity and dysregulation in health and disease. Cell Mol Immunol. 10.1038/s41423-023-01053-6
Skytthe MK, Graversen JH, Moestrup SK (2020) Targeting of CD163(+) Macrophages in Inflammatory and Malignant Diseases. Int J Mol Sci. 10.3390/ijms21155497
Sumiyoshi K, Yatsushige H, Shigeta K, Aizawa Y, Fujino A, Ishijima N et al (2024) Survival prognostic factors in nonsmall cell lung cancer patients with simultaneous brain metastases and poor performance status at initial presentation. Heliyon. 10.1016/j.heliyon.2024.e38128
Taga M, Hoshino H, Low S, Imamura Y, Ito H, Yokoyama O et al (2015) A potential role for 6-sulfo sialyl Lewis X in metastasis of bladder urothelial carcinoma. Urol Oncol. 10.1016/j.urolonc.2015.05.026
Tawbi HA, Forsyth PA, Algazi A, Hamid O, Hodi FS, Moschos SJ et al (2018) Combined Nivolumab and Ipilimumab in Melanoma Metastatic to the Brain. N Engl J Med. 10.1056/NEJMoa1805453
Tay C, Tanaka A, Sakaguchi S (2023) Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy. Cancer Cell. 10.1016/j.ccell.2023.02.014
Vilarino N, Bruna J, Bosch-Barrera J, Valiente M, Nadal E (2020) Immunotherapy in NSCLC patients with brain metastases. Understanding brain tumor microenvironment and dissecting outcomes from immune checkpoint blockade in the clinic. Cancer Treat Rev. 10.1016/j.ctrv.2020.102067
Wu J, Sun W, Yang X, Wang H, Liu X, Chi K et al (2022) Heterogeneity of programmed death-ligand 1 expression and infiltrating lymphocytes in paired resected primary and metastatic non-small cell lung cancer. Mod Pathol. 10.1038/s41379-021-00903-w
Zhang D, Zhang Y, Zou X, Li M, Zhang H, Du Y et al (2023) CHST2-mediated sulfation of MECA79 antigens is critical for breast cancer cell migration and metastasis. Cell Death Dis. 10.1038/s41419-023-05797-x
Zhang Y, Cheng R, Ding T, Wu J (2024) Discrepancies in PD-L1 expression, lymphocyte infiltration, and tumor mutational burden in non-small cell lung cancer and matched brain metastases. Transl Lung Cancer Res. 10.21037/tlcr-24-735
Zhou J, Gong Z, Jia Q, Wu Y, Yang ZZ, Zhu B (2018) Programmed death ligand 1 expression and CD8(+) tumor-infiltrating lymphocyte density differences between paired primary and brain metastatic lesions in non-small cell lung cancer. Biochem Biophys Res Commun. 10.1016/j.bbrc.2018.03.053
Zingoni A, Antonangeli F, Sozzani S, Santoni A, Cippitelli M, Soriani A (2024) The senescence journey in cancer immunoediting. Mol Cancer. 10.1186/s12943-024-01973-5

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Comparative analyses of tumor immune microenvironment using paired samples from primary non-small cell lung cancer and brain metastases

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Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

Case Collection and Design

Immunohistochemistry (IHC)

Digital Analysis of Tumor-Associated Macrophages (TAMs) and Tumor-Infiltrating Lymphocytes (TILs)

Evaluation of PD-L1

Evaluation of PNAd

Statistical Analysis

RESULTS

Patient Characteristics

Comparison of TIMEs between PTs and BMs

Correlations among TIME factors in BMs

Comparison of TIMEs between sBM and mBM Groups

DISCUSSION

CONCLUSIONS

Abbreviations

Declarations

Competing interests

SUPPLEMENTARY INFORMATION

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1