Targeting breast cancer with a combination of DNT and LAG3 checkpoint blockage and its mechanism

Abstract Introduction The characteristics of the tumor immune microenvironment (TIME) are closely related to immunotherapy. Breast cancer can benefit from immunotherapy, and its TIME is still unclear. Methods We utilized mass cytometry to explore the immune cell heterogeneity in breast cancer. Double‐negative T cells (DNTs) from healthy volunteers (HBs) were enriched in vitro. Flow cytometry was used to detect the cell surface receptors of cancer cells and DNT cells. The correlation between immune checkpoints and the abundance of immune cells or prognosis of breast cancer was analyzed by the TCGA database. The messenger RNA (mRNA) expression of functional genes was performed by quantitative real‐time PCR. Results We found that the frequencies of Granzyme B (GZMB)+CD8+T and GZMB+DNT cells in cancer tissues (CA) of breast cancer were lower than those in blood samples of patients (PB), and the frequencies of programmed cell death protein 1 (PD1)+CD8+T and PD1+DNT cells in CA were higher than those in PB. DNTs from HBs had a cytotoxic effect on MDA‐MB‐231. LAG3Ab could upregulate the mRNA expression of interferon gamma and perforin by increasing T‐BET transcription to enhance the cytotoxicity of DNT cells in vitro. Conclusion Our study revealed the suppressive status of TIME in breast cancer and supported DNT cells had the potential to be applied as a novel adoptive cell therapy for TNBC either alone or combined with LAG3Ab.

(TIME) heterogeneity. 6,7 The abundance and states of T cells have a great effect on tumor progression. [8][9][10] Recently, immunotherapy has achieved remarkable effects in many tumors by improving host's immune response actively or passively. 7,11,12 Adoptive cell therapy (ACT) has shown an encouraging efficacy in solid tumors. 11,13,14 Double-negative T (DNT) cells, defined by expressing CD3, without CD4 and CD8, has attracted more and more attention as an emerging ACT. 15 Studies have shown that DNT cells have a significant antitumor efficacy in nonsmall cell lung cancer, pancreatic cancer, and acute myeloid leukemia. [15][16][17][18][19][20][21] Immune checkpoints, such as programmed cell death protein 1 (PD1), cytotoxic T lymphocyte antigen 4, and lymphocyte activation gene 3 (LAG3), are the typical characteristic of T cell dysfunction. [22][23][24] Immune checkpoint inhibitors (ICIs) have achieved great therapeutic outcomes by preventing or reversing T cell exhaustion in the TIME. 12,23,25 As a promising immune checkpoint, LAG3 has attracted more and more attention. LAG3 can bind to its ligands, such as MHC II expressed on tumor cells or antigen presenting cells, to transmit inhibitory signals. 26,27 Clinical trials of LAG3 monoclonal antibody (LAG3Ab) for various cancers are being proceeded in full swing. 28 Here we applied mass cytometry (cytometry by time of flight, CyTOF) to draw the TIME landscape of breast cancer, and depicted the frequency and distribution variation of T cells in cancer tissues (CA) and adjunct tissues (ADJ). We demonstrated the T cell cytotoxic effect on breast cancer cells in vitro, and reported the possibility of combining LAG3Ab and DNT to treat TNBC.

| Sample collection
CA and ADJ (2 cm away from tumor) were collected from 11 breast cancer patients who underwent surgical treatment in the General Surgery Department of Beijing Friendship Hospital, Capital Medical University. The sample volume is about 1 cm in diameter and weighs 100-200 mg. The specimen was stored in precooled RRMI-1640 (Corning) medium and transferred to the laboratory for prompt processing immediately. Ethylenediaminetetraacetic acid tubes were used to collect 4 ml paired peripheral blood of the patients (PB) within 4 h before surgery, and 5 peripheral blood samples were obtained from healthy volunteers (HBs). This study was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University, and all participants signed the written informed consent.
Single cells to be detected by CyTOF were stained with 0.5 μM cisplatin (Fluidigm), washed twice, fixed with 1.6% paraformaldehyde (Sigma) for 10 min at room temperature, then resuspended with cryopreserved solution containing 10% Dimethyl sulfoxide (Sigma) and cryopreserved.
Conjugated antibodies and multi-metal labeling kits were purchased from Fluidigm. Pure-antibodies were purchased from Biolegend and conjugated with multi-metal labeling kits (Fluidigm) in our laboratory according to the manufacturer's instructions. The CyTOF antibody list is shown in Table S1. The cryopreserved cells were thawed and rinsed twice with cell staining buffer (CSB; Biolegend), and stained with antibodies targeting cell surface receptors at room temperature for 30 min. After incubation, the samples were processed with nuclear antigen staining perm (Fluidigm) for washing and membrane breaking and then incubated with antibodies targeting intracellular molecules at room temperature for 30 min and washed twice by DPBS. Then cells were resuspended using Ir Intercalator (Fluidigm) and stored at 4°C overnight, washed twice with CSB and Distillation-Distillation H 2 O the next day, suspended with 10% EQ™ Four Element Calibration Beads (Fluidigm) for loading.

| Cancer cell
Humanized TNBC cell line MDA-MB-231 and HER2+ cell line SK-BR-3 were purchased from China Infrastructure of Cell Line Resource. The cells were cultured with RPMI-1640 medium containing 10% FBS in a constant temperature incubator at 37°C and 5% CO 2 . The cells were stored in a −80°C refrigerator or in liquid nitrogen using serumfree cryopreserved solution (Cellregen).

| Human DNT expansion
A total of 15 ml peripheral blood from HB was collected for DNT expansion. This study was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University (2021-P2-064). All participants have signed the written informed consent. PBMC was collected via Ficoll gradient centrifugation. CD4 + and CD8 + cells were removed with negative sorting kit (STEMCELL™ Technology) to enrich DNT cells from PBMC. 21 X-VIVO (Lonza) medium was used to purify and expand DNT cells. DNT cells were cultured in 24-well plates coated with purified anti-CD3 (5 μg/ml) (Biolegend) for 5 days, adding human recombinant interleukin (IL)-2 (25 ng/ml) (peprotech) and purified anti-CD28 (3 μg/ml) (Biolegend) at D0, D3, and D5 to the medium. Fresh X-VIVO medium containing IL-2 (25 ng/ml) and anti-CD3 (100 ng/ml) was added on D7, D10 and D13. DNT cells cultured for 7-14 days were used for subsequent experiments.

| Cytotoxicity assays
DNT cells and MDA-MB-231 or SK-BR-3 cells were cocultured in 96-well plates for 24 h with the effector to target (E:T) ratios of 0:1, 1:1, 2.5:1, 5:1, 10:1. RPMI-1640 complete medium was used in this experiment. In some assays, DNT was prestimulated with LAG3Ab (10 μg/ml) (offered by Innovent Company) for 1 h before coculture. The specific killing of DNTs against breast cancer cells was calculated by In coculture systems, the apoptotic breast cancer cells were defined as CD45−Annexin V + .

| Quantitative real-time PCR
DNT cells were cultured in X-VIVO medium with or without LAG3Ab (

| TCGA data analysis
TCGA data were calculated by some interactive web resources. Lag3 expression in subgroups of breast cancer were analyzed by UALCAN (http://ualcan.path.uab.edu/). 31 The clinical outcome relevance between lag3 expression and cancer subtypes was determined by "outcome module" of the tumor immune estimation resource (TIMER 2.0, http://timer.cistrome.org/). 32 Cox regression was used in this section, and the hazard ratio, p value for Cox model, and the log-rank p value for KM curve are shown on the KM curve plot. We also applied TIMER 2.0 to explore the correlation of LAG3 (lag3), PD1 (pdcd1), NKG2D (klrk1) expression with immune infiltration level in breast cancer. The partial Spearman's correlation was conducted to perform this association analysis. "Gene_Corr module" of TIMER 2.0 was used to explore the correlation between interested genes in TNBC. Gene expression profiling interactive analysis (http://gepia.cancer-pku.cn/) analyzed the correlation of the pair-wise gene expression in breast cancer. Pearson was utilized in this calculation. 33 Relations between abundance of tumor-infiltrating lymphocytes and lag3 expression in diverse cancer types were evaluated by TISIDB (http://cis.hku.hk/TISIDB/). 34

| Statistical analysis
Experimental data was calculated by Prism 8.0 software (GraphPad Software). Comparisons were made by the Student t test and one-way analysis of variance analysis. *p < .05; **p < .01; ***p < .001. Data represent SEM.

| T lymphocyte cells composition in breast cancer
To reveal the immune landscape of the TIME in breast cancer, we utilized 37 markers to analyze the immune cells in CA, ADJ, PB samples from breast cancer patients and HB samples from HBs by CyTOF. The immune cell clusters in PB from different breast cancer subtypes were shown in Figure 1A. 24 clusters were found in a HER2+ patient, 22 clusters in a Luminal B patient, and 24 clusters in a TNBC patient. The CD3, CD4, and CD8 expression variation were also found in breast cancer patients. We also observed that the frequency of T cells in CA was much higher than that in ADJ among total breast cancer patients, while the frequencies of CD4 + T, CD8 + T, and DNT cells in CA were comparable with those in ADJ. T cells were more enriched in CA than ADJ of HER2+ and Luminal B patients, which was not suitable for TNBC. The frequency of CD8 + T cells in CA was higher than that in ADJ, which was only observed in HER2+ patients. Interestingly, the frequency of DNT cells in PB was higher than that in HB among total breast cancer patients and Luminal B patients ( Figure 1B). To further explore the T cells status in the TIME, we analyzed the characteristics of CD8 + T and DNT cells. The results showed that in breast cancer population, the frequencies of Granzyme B (GZMB) + CD8 + T cells and GZMB + DNT cells in CA were lower than those in PB, while the frequencies of PD1 + CD8 + T cells and PD1 + DNT cells were higher than those in PB ( Figure 1C,D).

| Immune checkpoint expression on DNT
Immune checkpoints, such as LAG3, PD1, and NKG2D, were expressed on DNT ( Figure 3A), and its ligands could be detected in MDA-MB-231 and SK-BR-3 cells ( Figure 3B). TCGA database analysis showed that the expression of lag3 in breast cancer tissue was significantly higher than that in normal breast tissue, and with the highest expression in TNBC, especially in TNBC-IM. The expression of lag3 in TP53 mutant breast cancer was significantly higher than that in TP53 wild-type ( Figure 3C). In Luminal B, the high expression of LAG3 represents a good prognosis, while LAG3 expression was not significantly relevant to the prognosis of other subtypes of breast cancer ( Figure 3D). lag3, pdcd1, and klrk1 were positively correlated with CD8 + T and CD4 + T cells in breast cancer ( Figure 3E).

| LAG3Ab enhances the killing effect of DNT in vitro
Cocultured with MDA-MB-231 resulted in a significant increase of LAG3 + DNT and PD1 + DNT cells when compared with DNT cells cultured alone ( Figure 4A-C), which was consistent with the conclusion analyzed by CyTOF data that DNT cells in CA were in an exhausted state. stimulated with LAG3Ab resulted in a lower frequency of LAG3 + DNT cells and a higher frequency of PD1 + DNT cells ( Figure 4D-F). LAG3Ab blocks the binding between LAG3 expressed on DNT cells and its ligands, such as MHC-II, expressed on cancer cells to enhance the cytotoxicity of DNT cells ( Figure 4G). In the in vitro killing assays, stimulated with LAG3Ab (10 μg/ml) could enhance the cytotoxicity of DNT cells when compared with the control group ( Figure 4F).

| LAG3Ab enhances DNT mediated antitumor activities by upregulating T-BET
To explore the mechanism that LAG3Ab enhances DNT cells cytotoxicity in vitro, we determined whether T-BET (tbx21), a well-known transcription factor regulating effector T-cell activation, could be induced by LAG3Ab. Q-PCR confirmed that LAG3Ab increased T-BET transcription in DNT cells. IFNγ (ifng), perforin (prf1), and other cytokines transcription in DNT cells were also significantly increased after being stimulated with LAG3Ab ( Figure 5A). Database mining revealed T-BET was significantly correlative with IFNγ, perforin and other molecules in TNBC ( Figure 5B) and breast cancer ( Figure 5C). we also observed T-BET was positively correlated with active CD8 + T, Tfh and Th1 cells in multiple cancers. In breast cancer, T-BET was more positively correlated with effector memory CD8 + T and active CD8 + T cells ( Figure 5D).

| DISCUSSION
Immunotherapy has achieved a great success in malignant solid tumors, offering an unprecedented resolution in cancer treatment. 35,36 The TIME variation affects the immunotherapy efficacy. Exploring characteristics of TIME is helpful to understand the differences of immunotherapy efficacy and select patients who benefit most from immunotherapy. 37,38 In this study, CyTOF was to reveal the proportion and distribution variation of immune cells in breast cancer, which is consistent with the different responses of breast cancer patients to immunotherapy. 39 Although CD8 + T and DNT cells proportion variations were observed in CA of different subtype patients, both cells are in exhausted states ( Figure 1). This suggests that increasing the number of effector cells and restoring its activation in TIME is a promising way to approve the antitumor effect. Study showed that ACT could be promising in breast cancer. 13,40,41 DNT cells have achieved a remarkable efficacy in solid tumors and hematological tumors, and its cytotoxicity was comparable with CD8 + T cells. [15][16][17][18][19] In our study, DNT cells from HBs were purified, enriched, and expanded in vitro (Figure 2A,B). DNT would be purified again in some experiments. The frequency of MDA-MB-231 cell apoptosis was increased from 11.0% when cultured alone to 40.2% when cocultured with 10 times the number of DNT cells. And the average specifical killing ratio of DNT cells reached 20.34% when the E:T was 10:1 ( Figure 2C-E), suggesting that TNBC patients may benefit from DNT cell therapy. While the cytotoxicity of DNT to TNBC in vivo needs further exploration. But DNT cells barely had a killing effect on SK-BR-3 cells, a kind of HER2+ breast cancer cell line ( Figure 2F). Without the expression of CD4 and CD8 molecules, DNT cells cannot rely on the interaction of CD4 and MHC II or CD8 and MHC I to exert the antitumor effect. While according to the recent study, LAG3 expressed on DNT cells plays an important role in MHC II antigen recognition. 42,43 Our study shows that DNT cells from healthy donors expressed LAG3, and breast cancer cells expressed MHC II, which is also the main ligand of LAG3, suggesting that LAG3 makes a high contribution to the cytotoxicity of DNT cells to breast cancer. We also observed that breast cancer cells expressed a high level of MICA/MICB, which was a kind of ligands of NKG2D. DNT can depend on the connection between NKG2D receptors and its ligands to recognize cancer cells. 15,16 Expression of Granzyme B and perforin and incretion of IFNγ of DNT cells were also involved in the cytotoxicity to cancer cells, 16,17 which is a classical method to exert antitumor efficacy. The Fas/FasL pathway also plays an important role in DNT-mediated killing effect. 16,18 Immune checkpoint is the focus of immunotherapy research, and ICIs enhance the antitumor activities of immune effector cells by blocking inhibitory pathway. Whether DNTs can be regulated by immune checkpoints and the mechanism is still unknown. Database analysis showed that LAG3 expression in breast cancer tissue was significantly higher than that in normal breast tissue, with the highest expression in TNBC-IM; LAG3 expression in TP53 mutant breast cancer was higher than that in TP53 nonmutant breast cancer ( Figure 3C). In breast cancer, lag3, pdcd1, and klrk1 are positively correlated with the abundance of CD8 + T and CD4+T cells ( Figure 3E), suggesting that LAG3 may be a potential immunotherapeutic target in breast cancer, especially for TNBC and TP53 mutant breast cancer. Previous studies have also shown that TNBC-IM might benefit more from ICIs, 2 which is consistent with our conclusion. LAG3, PD1 and NKG2D molecules could be detected on DNTs ( Figure 3A), suggesting DNT cells might also be regulated by immune checkpoints. We observed that stimulating DNT cells with LAG3Ab would greatly improve the cytotoxicity of DNT to breast cancer cells ( Figure 4F). After prestimulation with LAG3Ab, the T-BET transcription of DNT cells was upregulated significantly. Furthermore, the mRNA expression of cytotoxicity-related cytokines, such as IFNγ, in DNTs were also increased after stimulation with LAG3Ab ( Figure 5A). Previous studies have shown that anti-PD1 could enhance the cytotoxic effect of T cells by upregulating the T-BET transcription, [44][45][46] which lays the foundation of cotargeting LAG3 and PD1 in cancer immunotherapy. We demonstrated that LAG3Ab increased the cytotoxicity of DNT cells in different ways. Other studies have found that LAG3Ab may also improve the antitumor effect by positively regulating the function of CD8 + T and CD4 + T cells, 47,48 which provides solid evidence to target LAG3 as a novel target of immunotherapy. Studies have found that LAG3 and PD1 are coexpressed in TNBC, 49 and the antitumor effect of DNT cells combined with LAG3Ab and anti-PD1 in TNBC is also worth further exploration.
In summary, we revealed that DNT and CD8 + T cells were mostly in exhausted states in the TIME in breast cancer. DNT cells, as a novel ACT, can increase the number of immune effector cells in the TIME. DNT does not cause graft-versus host reaction after DNT infusion, which enhances the possibility for the clinical application. 15,17 Expanded DNT cells were able to significantly kill TNBC cells in vitro in a dose dependent manner. LAG3Ab not only blocked the binding of LAG3 expressed in DNT cell surface to its ligands expressed in tumor cells, but also significantly enhanced the T-BET transcription in DNT cells, which lays a reliable foundation for the application of ACT combined with ICIs in breast cancer, especially in TNBC.
AUTHOR CONTRIBUTIONS Qin Li: established the hypotheses, supervised the studies, and cowrote the manuscript, and had full access to all the contents included in this study and took responsibility for the integrity of the data and the accuracy of the data analysis. Miao Wang: participated in performing the research, analyzing the data. Miao Wang and Hongzhong Li: initiating the original draft of the article. Yuhan Wei, Yingrui Li, Jiangtao Jin, and Yuting Lu: participated in performing the research and collecting the data. All list authors participated meaningfully in the study, and they have seen and approved the submission of this manuscript.

ACKNOWLEDGMENTS
This work was supported by the Research Foundation of Beijing Friendship Hospital (Grant No.: yyqdkt2019-40). The funders were not involved in the design of the study, the collection, analysis or interpretation of the data or the writing of the study. The authors would like to sincerely thank the open-access database for data sharing and processing.

CONFLICTS OF INTEREST
The authors declare no conflicts of interest.

DATA AVAILABILITY STATEMENT
All data generated or analyzed during this study are included.