
==== Front
NPJ Breast Cancer
NPJ Breast Cancer
NPJ Breast Cancer
2374-4677
Nature Publishing Group UK London

691
10.1038/s41523-024-00691-x
Article
Characterization and spatial distribution of infiltrating lymphocytes in medullary, and lymphocyte-predominant triple negative breast cancers
Alfaro A. alexia.alfaro@gustaveroussy.fr

1
Catelain C. 1
El-Masri H. 2
Rameau P. 1
http://orcid.org/0000-0002-6641-8536
Lacroix-Triki M. 3
Scoazec JY. 3
Marty V. 3
http://orcid.org/0000-0001-7841-2900
Mosele F. 24
http://orcid.org/0000-0002-9184-7199
Pistilli B. 2
1 grid.14925.3b 0000 0001 2284 9388 Gustave Roussy, UMS AMMICa, CNRS UAR 3655, INSERM US23, 114 rue Edouard Vaillant, Villejuif, F-94805 France
2 grid.14925.3b 0000 0001 2284 9388 Gustave Roussy, Department of Medical Oncology- Breast Cancer Unit U981, 114 rue Edouard Vaillant, Villejuif, F-94805 France
3 grid.14925.3b 0000 0001 2284 9388 Gustave Roussy, Department of Pathology, 114 rue Edouard Vaillant, Villejuif, F-94805 France
4 grid.14925.3b 0000 0001 2284 9388 INSERM U981, Gustave Roussy, Villejuif, France
14 9 2024
14 9 2024
2024
10 818 12 2023
3 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Medullary carcinoma of the breast (MedBC) is a rare histological type that accounts for less than 5% of all invasive breast cancers. Here, we performed an exploratory study aimed to determine whether imaging mass cytometry (IMC) can be used to characterize the immune infiltration and the spatial distribution heterogeneity in the rare subtype of MedBC compared to atypical MedBC and TNBC-TILS+ tumors. In both MedBC and TNBC-TILs+, there was a notable enrichment of immune cells in the peripheral regions of the tumors, whereas in atypical MedBC, the immune cells exhibited a central enrichment pattern. This distribution of infiltrated cells reflects an active immune recruitment correlated to more favorable prognosis. In MedBC, spatial analysis shows that immune cells are localized at a greater distance from the tumor cells. IMC highlights the heterogeneity of immune microenvironment across three main subtypes of breast tumors and could help to define distinct immune patterns.

Subject terms

Breast cancer
Cancer imaging
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Medullary carcinoma of the breast (MedBC) is a rare entity, accounting for 1–7% of all breast cancer (BC) subtypes1. The majority (92%) are categorized as triple negative breast cancer, (TNBC) while the remaining are HER2-positive. MedBCs often occur at a young age and are more frequently diagnosed in women with BRCA1 mutations, accounting for approximately 13% of breast cancers associated with BRCA1 mutations2.

Although MedBCs display distinctive histological and genomic features associated with aggressiveness and a basal-like phenotype, paradoxically, they tend to exhibit a more favorable prognosis in comparison to other BC subtypes, particularly other forms of TNBC3,4. Indeed, at the histologic level, these tumors are characterized by high mitotic index, enriched cytoplasm, syncytial growth pattern exceeding 75%, large nuclei, and prominent nucleoli. They also overexpress basal cytokeratins, such as CK5/6 and CK17, as well as markers of myoepithelial cells in the breast. However, the presence of an extensive lymphocytic infiltrate within the tumor and its surrounding region3,5,6, could explain their favorable prognosis. At the genomic level, MedBCs present marked genome instability with frequent TP53 mutations. In general, the diagnosis of MedBC is subject of significant inter-pathologist variability, depending on the classification system employed. For instance, a study conducted at the University of Virginia Health Sciences found consensus diagnoses of MedBC ranging from 70% to 96% using different histologic criteria7. The 2012 World Health Organization (WHO) classification categorizes these cancers as carcinomas with medullary features, encompassing medullary carcinomas, atypical medullary carcinomas and carcinomas of no special type with medullary features8. In the 2019 edition of the WHO classification, carcinoma with medullary features is now recognized as tumor-infiltrating lymphocytes (TIL)-rich invasive BC of no special type (TIL-rich IBC-NST)9.

In the last decade, innovative technologies have been developed to overcome the limitations of simple immunohistochemistry (IHC). Novel multiplex protein analytical methods enable simultaneous detection of multiple proteins and may provide spatial analysis and show interaction patterns of these proteins10. Among them, the Imaging Mass Cytometry (IMC), first described in 2014, which employs metal-tagged antibodies or probes (up to 40 antibodies) that bind to specific targets. This tool has enabled a detailed characterization of the tumor microenvironment and the identification of novel BC subgroups associated with distinct clinical outcomes11,12.

So far, the diagnosis of MedBC and atypical MedBC is only based on histological features13 making it sometimes difficult to distinguish them from lymphocyte-predominant TNBC (LPBC). Despite the favorable prognosis associated with MedBC, in general the current therapeutic approach for this subtype remains similar to that of TNBC. This raises an important question regarding the potential overtreatment of patients with MedBC. We performed this study with the specific aim of exploring whether imaging mass cytometry (IMC) on tissue samples allows to characterize the relative proportion and spatial distribution of intratumoral lymphocytes in MedBC and atypical MedBC, in comparison to LPBC. Our ultimate goal was to identify potential immune biomarkers that merit further exploration in larger studies to assess their usefulness in clinical practice for distinguishing these distinct subtypes and refining their prognostic characterization.

Results

Patients’ characteristics

The baseline characteristics of the patients included in the study are summarized in Table 1. All patients were female and diagnosed with TNBC TILS+ according to the last version of the ASCO/CAP guidelines ASCO 201814. All patients had early-stage BC1,15. Eight patients (32%) were diagnosed with stage I disease, 13 (52%) patients were stage II while the remaining 4 patients (16%) were stage III. They were all treated with chemotherapy either in the neoadjuvant (20%) or in the adjuvant (80%) setting, surgery (100%), and adjuvant radiotherapy (100%). All patients had a minimum level of TILS of 40% (range: 40–90%). Additionally, two (8%) patients were identified to have a BRCA1 mutation, one in the MedBC group and the other in the atypical MedBC group. BRCA status was missing in 10 patients (40%). At the median follow-up of 89 months, 24 (96%) are alive with no evidence of disease, while survival data is missing for one patient in the MedBC group. One (4%) patient, diagnosed with MedBC, experienced a locoregional relapse (lymph nodes) at 48 months of diagnosis, treated with chemotherapy and local radiotherapy and is now in complete remission at 100 months of follow up.Table 1 Patients characteristics

	Histological subtype	
All patients
n = 25	MedBC
n = 15	Atypical MedBC
n = 5	TNBC-TILS + 
n = 5	
Age at diagnosis (years)	
 Median (range)	59 (33–75)	59 (33–75)	56 (46–64)	59 (58–74)	
Sex	
 Female	25 (100%)	15 (100%)	5 (100%)	5 (100%)	
Phenotype					
 TNBC	25 (100%)	15 (100%)	5 (100%)	5 (100%)	
TILS score* (range)	70 (40–90)	80 (50–90)	70 (70–90)	50 (40–60)	
Stage at Diagnosis	
 I	8 (32%)	3 (20%)	2 (40%)	3 (60%)	
 II	13 (52%)	9 (60%)	2 (40%)	2 (40%)	
 III	4 (16%)	3 (20%)	1 (20%)	0	
BRCA mutation status	
 BRCA1 mutated	2 (8%)	1 (6.6%)	1 (20%)	0	
 BRCA1 wild type	13 (52%)	7 (46.7%)	2 (40%)	4 (80%)	
 Missing data	10 (40%)	7 (46.7%)	2 (40%)	1 (20%)	
Treatment	
 Surgery	25 (100%)	15 (100%)	5 (100%)	5 (100%)	
Chemotherapy in any setting	
 Yes	25 (100%)	15 (100)	5 (100)	5 (100)	
 Neoadjuvant	5 (20%)	2 (13.3%)	2 (40%)	1 (20%)	
 Adjuvant	20 (80%)	13 (86.7%)	3 (60%)	4 (80%)	
 Radiotherapy	25 (100%)	15 (100%)	5 (100%)	5 (100%)	
Clinical outcome	
 Complete remission	23 (92%)	14 (93.3%)	4 (80%)	5 (100%)	
 Local relapse	1 (4%)	1 (6.7%)	0	0	
 Missing data	1 (4%)	0	1 (20)	0	
MedBC medullary breast cancer, TNBC TILS+ triple negative cancer enriched with tumor infiltrating lymphocytes.

*Scoring of TILs was performed using the standardized method that was introduced by the International Immuno-Oncology Biomarker Working Group on Breast Cancer (tilsinbreastcancer.org).

Development of a multiplexed antibody panel to characterize the microenvironment of BC

Upon pathologist review, the stained tissue specimens were processed and analyzed through the IMC technique. IMC provided valuable insights regarding the spatial distribution of tumor and immune cells within the tumor. Figure 1 shows the distribution of immune cells, highlighting different subsets of T-lymphocytes, (CD4+ KI67+) helper proliferative T-lymphocytes, (CD4+ FOXP3+ and CD8+ FOXP3+) regulatory T cells, (CD8+ KI67+) proliferative cytotoxic T-lymphocytes, (CD3+) T cells and (CD20+) B cells. (Fig. 1A). Along with epithelial cells, detected through (PANK+) Pan-keratin and (E-CAD+) E-Cadherin staining (Fig. 1B). The IMC analysis also allowed to assess immune cell infiltration within the tumor, identifying BCs with a heavily immune cells infiltrate characterized by (CD20+) B-cells and (CD8+) cytotoxic T lymphocytes among (E-Cadherin+ and Ki67+) tumor cells and on the opposite, BCs with less intratumoral immune infiltrate (Fig. 2C). Figure 1D show cases of interactions between different cell types within the tumor. Additionally, Fig. 1E demonstrates the interactions between immune cells and (alpha-smooth muscle actin+ and collagen type 1+) stroma.Fig. 1 Multiplexed CyTOF IMC marker panel to characterize the breast tumor microenvironment.

Color overlay of representative images of Immune cells (A), Epithelial cells (B), Lymphocytic infiltration inside the tumor (C), Cells interaction (D) and Extra-cellular interaction (E) in Breast cancer sample. Scale bar in upper right image.

Fig. 2 Difference in cell proportions according to tumor type and region of interest (ROI).

Heatmap of the Immune, Epithelial, Interaction cell proportions using all identity marker in the 25 patients. Ratio of proportions is calculated per cell and tumor type. Each proportion level was scaled through all ROI samples and an empirical percentile transformation (percentize) was applied on the ratio scores to normalize the color scale between 0 and 1 in order to accentuate the differences between the values. The vertical axis represents surface markers on cell phenotypes. A Difference in cell proportions according to ROI. B Difference in cell proportions according to tumor type. C Difference in cell proportions according to ROI and tumor type.

Cell proportions according to ROI

We examined the cell proportions within different ROIs of all 25 samples, including the periphery and the central region of the tumor, and the surrounding normal tissue. The heatmap in Fig. 2A illustrates the proportion of cell types across the ROI, highlighting the clustering of the peripheral and central tumor areas in relation to the normal tissue area. The healthy region is predominantly composed of epithelial cells, with lower proportions of immune cells as compared to the tumor zones, whether central or peripheral. Within the tumor zones, a higher abundance of immune cells is observed, particularly in the peripheral area. Notable immune cell populations in the tumor periphery included (CD8+) cytotoxic T-lymphocytes, (CD4+) helper T-lymphocytes, (CD4+ FOXP3+) regulatory T-lymphocytes, (CD68+) macrophages and dendritic cells and (CD20+) B lymphocytes. Conversely, in the central region, (CD8+ Ki67+) proliferating cytotoxic T lymphocytes was the predominant subtype. Furthermore, cell interactions were prominently observed in the peripheral zone of the tumor, involving not only interactions among immune cells but also between immune cells and the stroma. This suggests a complex interplay between immune cells and the tumor microenvironment.

Cell proportions according to breast cancer subtype

We further investigated the cell proportions within each specific BC subtype (Fig. 2B). MedBC is characterized by a relatively low but uniform proportion of all the cell types (tumor, immune cells, and stroma). The predominant immune cells are (CD68+) macrophages and (CD20+) B lymphocytes. The TILS observed in this BC subtype may correspond to plasma cells (Fig. 2B). The TNBC TILs+ subgroup was characterized by (CD4+) helper lymphocytes and (CD4+ FOXP3+) T regulator lymphocytes, suggesting an active regulatory immune response within these tumors. Additionally, (CD68+) macrophages are predominant in this subgroup as well as naïve T lymphocytes (Fig. 2B). On the other hand, the atypical MedBC subgroup exhibits predominantly (CD20+) B-lymphocytes along with an increased proportion of (CD8+ KI67+). cytotoxic T lymphocytes Interestingly, this group also displays higher levels of immune cell interactions observed by a purple color bar compared to the other subgroups, except for T-helper cells and cytotoxic T lymphocytes with CD68+ cells, which are more pronounced in the TNBC TILs subgroup (Fig. 2B).

Cell proportions according to ROI in each subtype

We conducted a detailed analysis of cell proportions according to the ROI (central, peripheral, normal tissue) within each subgroup of cancers (Fig. 2C). Consistent with our previous observations, immune cells were found to be more abundant in the tumor region as compared to the surrounding normal tissue across all three BC subtypes. In both MedBC and TNBC TILs, the immune cells were predominant in the peripheral region of the tumors. This pattern was observed for various immune cell types, such as (CD8+) cytotoxic T-lymphocytes, (CD4+) helper T-lymphocytes, (CD4+FOXP3+) regulatory T-lymphocytes, (CD68+) macrophages and dendritic cells. However, (CD4+ Ki67+) proliferating helper T-lymphocytes and (CD8+ Ki67+) proliferating cytotoxic T-lymphocytes were predominant in the central tumoral region of MedBC. In contrast, the Atypical MedBC group exhibited immune cells in the central region except for (CD4+) helper T-cells and (CD68+) macrophages and dendritic cells, which were more preponderant in the peripheral region of the tumor. The proliferating subtype (positive for Ki67+) of CD4+ and CD8+ T-cells were consistently predominant in the central region of both MedBC and atypical MedBC tumors (Fig. 2C).

Spatial analysis

To gain a better understanding of the relationship between immune cells and tumor cells, we examined the spatial distribution of cell phenotypes. Figure 3A illustrates the distances between immune cells and PANK+ cells in the regions of interest across all tumor types combined. Interestingly, no important difference in distance was observed between PANK+ cells and immune cells in the tumor regions. However, in the healthy tissue, immune cells appeared to be in closer proximity to epithelial cells, suggesting a potential sign of active immune surveillance that warrants further exploration. To delve deeper into the specific characteristics of each BC subtype, we analyzed the distance between immune cells and PANK+ cells within each tumor category, as depicted in Fig. 3B. Notably, in TNBC TILS+ and atypical MedBC tumors, a higher percentage of immune cells were found to be in close proximity to PANK+ cells. This finding indicates that those two types of tumors are enriched with immune populations that establish direct contact with the tumor cells, often referred to as intraepithelial lymphocytes. In contrast, the MedBC tumor group, displayed a more homogeneous distribution of immune infiltrates. In this group, immune cells were scattered on both sides of the syncytial patches, rather than exhibiting distinct proximity to PANK+ cells (Fig. 3C).Fig. 3 Spatial analysis: repartition of immune cells according to ROI.

For each ROI, the distance of each cell phenotype was measured from the pankeratin positive cells. The range of scale is divided into 3 measurements: 0–10 µm/10–100 µm/ > 100 µm. The X axis represents the percentage of immune cell population. A The graph illustrates the distance of the different cell types in each region of interest (ROI). B The graph illustrates the distance of the different cell types in each tumor type. C Color (up) and schematic (down) overlay of representative images of lymphocytic infiltration inside the tumor for each tumor type.

Discussion

Immune infiltration, specifically TILs, has gained significant attention in the evaluation of solid tumors, particularly in TNBC TILS, as researchers strive to understand its relationship with response to neoadjuvant therapy and clinical outcomes16. In our study, we aimed to investigate immune infiltration in the rare subtype of MedBC compared to atypical MedBC and TNBC TILs tumors. Several studies have characterized the role of immune cells in MedBC, particularly their spatial distribution and interaction with tumor cells, in order to elucidate their biological characteristics and discriminate among the different MedBC histological types17. For instance, Igari et al. by using multiplexed fluorescent immunohistochemistry (IHC) found a higher proportion of stromal and intratumoral CD8+ TILs in MedBC as compared to lymphocyte-predominant TNBC TILS (LPBC)18. Using IHC, Nurlaila et al, focused on the relative enrichment of MedBC with B lymphocytes in close contact with tumor cells as compared to atypical MedBC19. In these analyses we used IMC, which offers advantages over conventional methods such as IHC and multiplexed fluorescent IHC due to its ability to analyze a larger number of markers simultaneously. While IMC has been employed in characterizing the tumor microenvironment of various BC subtypes11,12, its application in characterizing MedBC, atypical MedBC and TNBC TILs, which are now all grouped under one category in the recent 5th WHO classification in 20199, has not been explored. By utilizing IMC, we not only examined the diverse cellular components within the tumor and peritumoral tissue but also analyzed their spatial distribution and interactions in these three BC subtypes.

First, in our study, MedBC exhibited a unique distribution pattern characterized by a homogeneous and relatively low proportion of immune cells, with a predominance of CD20+ cells that may correspond to (TILs) enriched with plasma cells. This finding contrasts with previous studies that reported a predominance of (CD8+) T-lymphocytes, particularly cytotoxic T-lymphocytes, in MedBC. It is important to note that these previous studies were conducted on small population sizes due to the rarity of the disease, used different methodologies and techniques, analyzed different cell populations and classified BC subtypes differently18,20–22. However, earlier studies also described the presence of plasma cell infiltration in MedBC, emphasizing the role of humoral immune responses13,23,24. In our analysis, TNBC TILs tumors demonstrated enrichment of (CD4+) helper cells, (FOXP3+) regulatory cells, and (CD68+) macrophages. This observation aligns with previous studies that reported a higher proportion of (FOXP3+) cells and a lower (CD8+/FOXP3+) ratio in TNBC TILs compared to MedBC20.

Second, we observed a consistent predominance of immune cells within the tumor regions compared to the surrounding healthy tissue in all three groups of TNBC TILS analyzed. In both MedBC and TNBC TILS, there was a notable enrichment of immune cells in the peripheral regions of the tumors, whereas in atypical MedBC, the immune cells exhibited a central enrichment pattern. However, the proliferating subtype (positive for Ki67+) of (CD4+) and (CD8+) T-cells were consistently predominant in the central region of both MedBC and atypical MedBC tumors. This observation highlights the substantial infiltration of immune cells in these tumor types, which may contribute to their eventual prognosis. This spatial distribution may indicate an active immune surveillance mechanism aimed at eliminating tumor cells in these areas and reflects distinct mechanisms of immune cell recruitment and localization within the tumor microenvironment.

Third, our study revealed a notable difference in the interaction between infiltrating lymphocytes and tumor cells among the different breast cancer types. In the case of TNBC-TILS and atypical MedBC tumor type, we observed that the lymphocytes were closely adhering to and in direct contact with the tumor cells, which contrasts with the findings in MedBC where the immune cells appeared located at a greater distance from the tumor cells, despite this tumor is in general associated to a more favorable prognosis. Interestingly, the close association between infiltrating lymphocytes and tumor cells in TNBC TILS and atypical MedBC raises the possibility of a suppression effect, whereby the immune response fails to effectively surveil and eliminate the tumor cells. This is in contrast to the spatial analysis of TNBC TILS and atypical MedBC, which exhibited a different immune cell distribution pattern, determined by the humoral immune response (prevalence of CD20+ lymphocytes).

In summary, we showed that MedBC exhibits a distinct pattern of immune cell infiltration that sets it apart from the other two subtypes studied. Despite their differences, all three subtypes are currently grouped together under the 2019 5th edition of WHO classification as TIL-rich invasive breast cancer of no special type (TIL-rich IBC-NST)9. This classification reflects the commonality of immune cell infiltration observed in these subtypes regardless of clinical outcomes and the variability of immune cells subtypes. However, the distinct immune signature observed in medullary breast cancer (MedBC) using the innovative IMC technique, along with its well-known favorable outcome, raises important considerations regarding the development of tailored treatment approaches different from those of TNBC TILS. While our study did not draw definitive conclusions on clinical outcomes due to limitations such as the small sample size, short follow-up period, and the exploratory nature of the study, these findings call into question the utility of grouping MedBC with other subtypes under the same classification. Future research with larger sample sizes, comprehensive clinical data and pattern of immune infiltration is warranted to thoroughly evaluate the prognostic implications and treatment responses of MedBC compared to other subtypes. Such insights would pave the way for personalized treatment strategies of MedBC and potentially of other unique subtypes.

Methods

Cases and sample selection

Following approval from the Ethics Committee at Gustave Roussy, we conducted a retrospective study using archived formalin-fixed paraffin-embedded (FFPE) tumor sections obtained from 25 patients, diagnosed with BC between January 2015 and September 2021 and treated at Gustave Roussy. The slides underwent a thorough second reading and review conducted by an expert pathologist from Gustave Roussy (MLT). This process aimed to validate the initial diagnosis and classify the tumors into the selected three distinct categories: 15 patients with MedBC, 5 patients with atypical MedBC and 5 patients with TNBC and TILs ≥10% (TNBC TILS). All patients whose tumors did not fit into any of these categories were excluded from further analysis to ensure the accuracy and relevance of the study. At the time of their initial diagnosis, all patients provided consent for the utilization of any leftover tumor samples for scientific research.

Selection of antibodies panel

Each tissue sample underwent staining using the Maxpar Human Immuno-Oncology Kit (Standard BioTools reference: 201508), which consists of 17 pathologist-verified antibodies and a nucleic acid stain (Supplementary Table 1). The antibodies selected target various antigens involved in tissue architecture, such as Alpha smooth muscle actin expressed by myofibroblasts and pericytes, collagen type 1 in the connective tissue, E-cadherin on epithelial cells, histone H3 by nucleated cells, and Vimentin by mesenchymal cells. Additionally, the panel includes antibodies targeting antigens associated with immune activation, such as Granzyme B expressed by Natural Killer cells and T-lymphocytes, Ki-67 on cycling cells, PD-1 on T follicular helper cells and activated T cells and PD-L1 expressed by activated macrophages, dendritic cells and activated T and B lymphocytes. Furthermore, markers specific of tumor-infiltrating lymphocytes are included in the panel, such as CD3 expressed by T-lymphocytes, CD4 by helper T lymphocytes, CD8a corresponding to cytotoxic T-lymphocytes, CD20 specific for B-lymphocytes, CD45RO for memory lymphocytes, CD68 expressed by dendritic cells, macrophages, and granulocytes, FOXP3 by regulatory T lymphocytes and Pan-Keratin by keratinocytes.

Tissue labeling before IMC acquisition

For downstream analyses, all samples were cut at 5 mm and placed on Superfrost® Plus slides (Thermo Scientific, Saint-Herblain, France). With successive xylene baths, coverslips were removed and the mounting medium was washed. The staining process followed established IHC protocols, which involve dewaxing the sample in xylene, hydrating it in ethanol, incubating it in an antigen retrieval buffer, blocking with 3% Bovine serum albumin (BSA), performing primary incubation with diluted antibodies in PBS/BSA with overnight staining, and finally conducting secondary incubation with an iridium intercalating stain. To facilitate a comprehensive analysis, the pathologist marked the regions of interest (ROI) on each slide, specifically delineating the central and peripheral parts of the tumor region, as well as the surrounding healthy peritumoral tissue. Hence, 3 ROIs were identified on each slide and then delineated using the IMC (Fig. 4).Fig. 4 Image analysis pipeline.

A Visualization and pre-processing analyses: highly multiplexed images are generated using ablated regions of interest (ROI) and visualized by MCD viewer. The nuclei on the iridium channel were segmented with QuPath software and a cell mask was generated with FiJi. B Cell type content analysis: Cell mask from each ROI was used by HistoCAt to generate FCS file. Phenograph clustering was performed using 14 markers and dimensionality reduction was performed using UMAP to visualize the classification of cell type. Also, the proportion of cell type were calculated using a gating strategy on FlowJO software. C Spatial analysis: an Euclidian distance map (EDM) of the pankeratin positive cells were performed on Fiji software. Then, the distances between each cell type and the nearest pankeratin positive cells on the EDM were calculated.

IMC acquisition process

Before the acquisition, the Hyperion mass cytometry system IMC was autotuned using a 3-element tuning slide according to the tuning protocol provided by Standard BioTools. ROIs with sizes of 1 mm2 (1000 × 1000 µm) were ablated and acquired at 200 Hz. For each sample, one to 4 ROI were defined for the acquisition on Hyperion. The data obtained from each scanned spot, where each antibody corresponded to a single image per sample, was processed. These individual images were then combined to create a multi-image stack.

Image analysis pipeline

Raw data were visualized and converted to tiff format using the Standard BioTools MCDTM viewer. Next, cell segmentation was performed on the iridium channel for each ROI of each patient using the Qupath module Cell detection and Fiji software. We defined a minimum pixel size at 5 µm, a maximum at 200 µm with a cell expansion of 5 µm in order to detect all nuclei. This segmentation process allowed the identification of individual cells and the extraction of single-cell distribution information. Subsequently, the ROI were converted to Flow Cytometry Standard (FCS) file using HistoCAT software and a Phenograph clustering based on all identity markers were performed in R. Cell type labelling was performed using a multiple gate strategy on FlowJO software, in order to get the proportion of each cell type present in the samples. Results were expressed, for each phenotype, as the number of cell type by ROI. Additionally, spatial analysis was conducted to explore the interaction between immune cells and tumor cells. The distance between each cell and the nearest Pan-keratin positive cells was computed using Fiji software. The range of scale was chosen arbitrarily and divided into 3 measurements: 0 to 10 µm to discriminate direct contact cells interactions, 10 to 100 µm to represent the proximity of less than 10 cells and more than 100 µm to cover all the ROI. This analysis aimed to uncover any spatial patterns or associations between these cell populations within the tumor microenvironment (Fig. 4).

Statistical analysis

No formal statistical analysis was planned for this study due to its retrospective, descriptive, and exploratory nature. Additionally, the small population size further limited the feasibility of conducting comprehensive statistical analyses. Instead, the study primarily focused on providing a descriptive overview of the cellular composition and spatial distribution within the tumor microenvironment. The findings were intended to serve as preliminary insights and lay the groundwork for future research with larger sample sizes that would allow for more robust statistical analysis.

Supplementary information

Supplementary materials

Supplementary information

The online version contains supplementary material available at 10.1038/s41523-024-00691-x.

Acknowledgements

We thank the members of the Gustave Roussy platforms, including PETRA Core Facility for tissue slide preparation and PFIC Core Facility for Imaging Mass Cytometry. This work was supported by grants from Gustave Roussy Institute.

Author contributions

A.A. and C.C. were responsible for the image analysis pipeline and the analysis of data; C.C. and P.R. were responsible for the selection of antibodies panel, the tissue labeling and the IMC acquisition process; H.E, M.LT. F.M. and B.P. were responsible for the cases and sample selection; V.M. and JY.S. were responsible for the slides preparation before tissue labeling; all authors participated in the manuscript preparation, the interpretation of data analysis results, and critical review/revision of the manuscript for important intellectual content. All authors read and approved the final manuscript. All authors agreed to be accountable for all aspects of the work.

Data availability

Data are available upon request. All experiments and analysis details are described thoroughly in the “Materials and Methods” sections.

Competing interests

BP: Consulting/Advisor: Novartis, Astra Zeneca, Daiichi -Sankyo, MSD, Gilead; Personal fees: Novartis, AstraZeneca, MSD Oncology, Pfizer, Daiichi-Sankyo; Research funding: Daiichi-Sankyo, Novartis, Pfizer, AstraZeneca, MSD, Amgen, FM: Consulting fees: Novartis; Personal fees: Pegascy. No potential conflicts of interest were disclosed by the other authors.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: A. Alfaro, C. Catelain.
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