Targeting Breast Cancer with a Combination of DNT and LAG3 Checkpoint Blockage and its Mechanism

doi:10.21203/rs.3.rs-1128606/v1

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Targeting Breast Cancer with a Combination of DNT and LAG3 Checkpoint Blockage and its Mechanism

https://doi.org/10.21203/rs.3.rs-1128606/v1

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Background

The characteristics of the tumor immune microenvironment (TIME) are closely related to immunotherapy. Breast cancer is a highly heterogeneous tumor, and its TIME is still unclear.

Methods

We utilized mass cytometry to explore the immune cell heterogeneity in breast cancer. DNTs from healthy volunteers 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 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 healthy volunteers had the cytotoxicity on MDA-MB-231. LAG3Ab could upregulate the mRNA expression of IFNγ and perforin by increasing T-BET transcription to enhance cytotoxic effect 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.

Cancer Biology

Oncology

Double negative T cells

Breast cancer

Tumor immune microenvironment

LAG3Ab

Mass cytometry

International agency for research on cancer (https://gco.iarc.fr/) published that breast cancer has become the most common cancer among worldwide in 2020 (Ferlay, Colombet et al. 2021). Triple-negative breast cancer (TNBC), accounting for 10%-20% of breast cancer is more heterogeneous and invasive, and has the metastasis and recurrence tendencies (Dent, Trudeau et al. 2007, Foulkes, Smith et al. 2010, Malorni, Shetty et al. 2012, Jiang, Ma et al. 2019). A large proportion breast cancer patients failed to response to current treatment due to tumor immune microenvironment (TIME) heterogeneity (Sørlie, Perou et al. 2001, Pilipow, Darwich et al. 2021). The abundance and states of T cell have a great effect on tumor progression (Crespo, Sun et al. 2013, Fleming, Morrissey et al. 2017, van der Leun, Thommen et al. 2020).

Recently, immunotherapy has achieved remarkable effects in many tumors by improving host's immune response actively or passively (Rotte 2019, Sommaggio, Cappuzzello et al. 2020, Pilipow, Darwich et al. 2021). Adoptive cell therapy (ACT) is a treatment that immune cells expanded in ex vivo are delivered back into patients. ACT has shown an encouraging efficacy in solid tumors (Rosenberg and Restifo 2015, Kverneland, Pedersen et al. 2020, Sommaggio, Cappuzzello et al. 2020). Double-negative T (DNT) cells, defined by expressing CD3, without CD4 and CD8, has attracted more and more attention as an emerging ACT (Lee, Kang et al. 2019). Studies have shown that DNT cells have a significant anti-tumor efficacy in non-small cell lung cancer, pancreatic cancer, and acute myeloid leukemia (Merims, Li et al. 2011, Chen, Lee et al. 2018, Lee, Minden et al. 2018, Chen, Hu et al. 2019, Fang, Ly et al. 2019, Lee, Kang et al. 2019, Yao, Ly et al. 2019).

Immune checkpoints, such as programmed cell death protein 1 (PD1), cytotoxic T lymphocyte antigen 4 (CTLA4), and lymphocyte activation gene 3 (LAG3), are characteristic of T cell dysfunction(He, Rivard et al. 2016, Marin-Acevedo, Dholaria et al. 2018, Rowshanravan, Halliday et al. 2018). Immune checkpoint inhibitors (ICIs) have achieved great therapeutic outcomes by preventing or reversing T cell exhaustion in the TIME (Marin-Acevedo, Dholaria et al. 2018, Rotte 2019, Lin, Luo et al. 2020). 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 (Ruffo, Wu et al. 2019, Wang, Du et al. 2021). Clinical trials of LAG3 monoclonal antibody (LAG3Ab) for various cancers are being proceeded in full swing (Qin, Xu et al. 2019).

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 by experienced operators 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 pre-cooled RRMI-1640 (Corning) medium and transferred to the laboratory for prompt processing immediately. EDTA tubes were applied to collect 4 ml paired peripheral blood of the patients (PB) within 4 hours before surgery, and 5 peripheral blood samples were obtained from healthy volunteers (HB). This study was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University, and all participants signed the written informed consent.

Sample preparation

Tissues were processed into 1 mm³ fragments by surgical scissors, mixed with HBSS (Solarbio Science &Technology Co. Ltd., Beijing, China) containing 0.03% type IV collagenase (Sigma), 0.01% DNase I (Sigma), 10% fetal bovine serum (FBS) (Gibco), and digested by Gentle-MACS dissociator with B-01 mode at 37 ° C for 45 minutes. Then 10ml Dulbecco’s phosphate buffered saline (DPBS) containing 2%FBS was added to neutralize digestive enzymes. Tissues were filtered through 50-mesh and 70-mesh strainers, washed with DPBS containing 2%FBS, centrifuged at 300g for 4 minutes to collect precipitates. Peripheral blood mononuclear cells (PBMC) were prepared by Ficoll (DAKEWE) gradient centrifugation.

Single cells for CyTOF were stained with 0.5uM cisplatin (Fluidigm), washed twice, fixed with 1.6% paraformaldehyde (Sigma) for 10 minutes 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 Maxpar multi-metal labeling kits 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 minutes. 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 minutes and washed twice by DPBS. Then cells were resuspended using Ir Intercalator (Fluidigm) and stored at 4℃ overnight, washed twice with CSB and Distillation-Distillation H₂O the next day, suspended with 10% EQ^TM Four Element Calibration Beads (Fluidigm) for loading.

CyTOF data acquisitions and analysis

CyTOF data were obtained by a Helios^TM mass cytometer (Fluidigm) from the Beijing Institute of Hepatology and analyzed visually using viSNE (Amir el, Davis et al. 2013). viSNE is a dimensional-reduction method implemented by Barnes-Hut acceleration of the T-SNE algorithm (Kobak and Berens 2019). Cytobank (http://www.cytobank.org/), FlowJo Software V10 (Treestar, Ashland, OR, USA), and R3.6.1(http://www.R-project.org) were applied for data analysis.

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℃ and 5%CO2. The cells were stored in a -80℃ refrigerator or in liquid nitrogen using serum-free cryopreserved solution (Cellregen).

Human DNT expansion

15 ml peripheral blood from healthy volunteer 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 has signed the written informed consent. PBMC was collected via Ficoll gradient centrifugation. CD4 and CD8 cells were removed with negative sorting kit (STEMCELL^TM Technology) to enrich DNT cells from PBMC (Merims, Li et al. 2011). 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 (5ug / mL) (Biolegend) for 5 days, adding human recombinant IL-2 (25ng / mL) (peprotech) and purified anti-CD28 (3ug / mL) (Biolegend) at D0, D3, and D5 to the medium. Fresh X-VIVO medium containing IL-2 (25ng / mL) and anti-CD3 (100ng / mL) was added on D7, D10 and D13. DNT cells cultured for 7 to 14 days were used for subsequent experiments.

Flow cytometry

All fluorochrome-conjugated anti-human antibodies against CD45 (Cat:304062), CD3 (Cat:300306), CD4 (Cat: 317444; Cat: 301051), CD8 (Cat:300539), LAG3 (Cat:369206), PD1 (Cat: 329918), NKG2D (Cat:320808), HLA-DR (Cat307604:), CD274 (Cat: 329708), MICA / MICB (Cat: 320906), Annexin V (AV) (Cat: 422201), Human TruStain FcX™ (Cat: 422302) were purchased from Biolegend company. Data were collected using the Aria II Flow Cytometer (BD Biosciences), Attune NxT (Thermo), or Cytoflex (Beckman) and analyzed by FlowJo Software V10.

Cytotoxicity assays

DNT cells and MDA-MB-231 or SK-BR-3 cells were co-cultured in 96-well plates for 24h 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 pre-stimulated with LAG3Ab (10ug / mL) (offered by Innovent Company) for 1 hour prior to co-culture. The specific killing of DNTs against breast cancer cells was calculated by:. In co-culture 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 (10ug / ml) for 48 hours, and washed twice with DPBS. Total RNA was extracted using Trizol (Sigma) reagent and cDNA was synthesized by Prime script^TM RT Reagent Kit (Perfect Real Time) (TAKARA) according to the manufacturer’s instructions. Quantitative real-time PCR was performed by ABI 7500 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). mRNA relative expression was calculated by . The genes and primer sequences are shown in Table 1.

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/) (Chandrashekar, Bashel et al. 2017). 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/) (Li, Fu et al. 2020). 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 (GEPIA, http://gepia.cancer-pku.cn/) analyzed the correlation of the pair-wise gene expression in breast cancer. Pearson was utilized in this calculation (Tang, Li et al. 2017). Relations between abundance of tumor-infiltrating lymphocytes and lag3 expression in diverse cancer types were evaluated by TISIDB (http://cis.hku.hk/TISIDB/) (Ru, Wong et al. 2019).

Statistical analysis

Experimental data was calculated by Prism 8.0 software (GraphPad Software, San Diego, CA, USA). Comparisons were made by the student’s t test and one-way ANOVA analysis. *p < 0.05; **p < 0.01; ***p < 0.001. 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 healthy volunteers by CyTOF. The immune cell clusters in PB from different breast cancer subtypes was 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).

Expanded DNT has a cytotoxic effect on TNBC cell in vitro

The purity of DNT (CD3+CD4-CD8-) cells obtained from healthy volunteers was above 90% (Figure 2a). DNTs expanded in vitro (Figure 2b) were co-cultured with MDA-MB-231 or SK-BR-3 cells in vitro at the E:T ratios of 0:1, 1:1, 2.5:1, 5:1, and 10:1. The results showed that under different culture conditions, the apoptosis rates of MDA-MB-231 were 11.0%, 18.6%, 26.9%, 30.5%, and 40.2%, respectively. The specific killing increased with the upregulation of E:T ratio (Figure 2c-e). However, DNT cells barely had a cytotoxic effect on SK-BR-3 (Figure 2f).

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

Co-culture 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 ug / ml) could enhance the cytotoxicity of DNT cells when compared with the control group (Figure 4f).

LAG3Ab enhances DNT mediated anti-tumor 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).

Immunotherapy has achieved a great success in malignant solid tumors, offering an unprecedented resolution in cancer therapy (Yang 2015, Pan, Liu et al. 2020). The TIME variation affects the immunotherapy efficacy (Dieterich and Bikfalvi 2020, Picard, Verschoor et al. 2020, Wang, Zhang et al. 2021). Exploring characteristics of TIME is helpful to understand the differences of immunotherapy efficacy, to screen proper patients who benefit most from immunotherapy (Gajewski, Woo et al. 2013, Frankel, Lanfranca et al. 2017). In this study, CyTOF revealed the proportion and distribution variation of immune cells in breast cancer, which is consistent with the different responses of breast cancer patients to immunotherapy (Basu, Ramamoorthi et al. 2019). Although CD8⁺T and DNT cells proportion variations were observed in CA of different subtype patients, both cells are in an exhausted state (Figure 1). This suggests that increasing the number of effector cells and restoring its activation in TIME is a possible way to exert anti-tumor effect.

Study showed that adoptive cell therapy could also be promising in breast cancer (Mahoney, Rennert et al. 2015, Rosenberg and Restifo 2015, Leon, Ranganathan et al. 2020). DNT cells, a novel adoptive cell therapy, has achieved a remarkable efficacy in solid tumors and hematological tumors and its cytotoxicity was comparable with CD8⁺T cells (Lee, Minden et al. 2018, Chen, Hu et al. 2019, Fang, Ly et al. 2019, Lee, Kang et al. 2019, Yao, Ly et al. 2019). In this study, DNT cells from healthy volunteers were purified, enriched, and expanded in vitro (Figure 2a-b). And DNT would be purified again in some experiments. We found that expanded DNT cells were able to significantly kill MDA-MB-231 in vitro in a dose dependent manner (Figure 2c-e), but barely had a killing effect on SK-BR-3 (Figure 2f), suggesting that DNT cell therapy may bring a greater benefit to TNBC patients.

Immune checkpoint is the focus of immunotherapy research, and ICIs enhance the anti-tumor activities of immune effector cells by blocking inhibitory pathway (Turnis, Andrews et al. 2015, Ribas and Wolchok 2018). Whether DNTs are regulated by immune checkpoints and the mechanism still need further exploration. 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 non-mutant breast cancer (Figure 3c). In breast cancer, lag3, pdcd1 and klrk1 are significantly positively correlated with the abundance of CD8⁺T and CD4+T cells (Figure 3e), suggesting that LAG3 may be a potential immunotherapeutic target for breast cancer, especially for TNBC and TP53 mutant breast cancer. Previous studies have also shown that TNBC-IM might benefit more from ICIs, which is consistent with our conclusion (Jiang, Ma et al. 2019). LAG3, PD1 and NKG2D could be detected on DNTs (Figure 3a), suggesting DNT cells might be regulated by immune checkpoints. We observed that stimulating DNT cells with LAG3Ab prior to co-culture would greatly improve the cytotoxicity of DNT on MDA-MB-231 (Figure 4f). LAG3Ab not only blocked the binding of LAG3 to its ligands (Figure 4g), but also significantly upregulated the T-BET transcription in DNT cells. Furthermore, the mRNA expression of cytotoxicity-related cytokines, such as IFNγ, in DNTs was also increased after stimulation with LAG3Ab (Figure 5a). Previous studies have shown that anti-PD1 could enhance the cytotoxic effect of T cells by up-regulating the T-BET transcription (Zhu, Ju et al. 2010, Berrien-Elliott, Yuan et al. 2015, Rudd, Chanthong et al. 2020), which is like our study. We demonstrated that LAG3Ab restored DNT activation and increased the cytotoxicity of DNT cells. Other studies have found that LAG3Ab may also exert a strong and lasting anti-tumor effect by positively regulating the function of CD8⁺T and CD4⁺T cells (Grosso, Kelleher et al. 2007, Matsuzaki, Gnjatic et al. 2010). The combination of DNT cells and LAG3Ab will synergistically exert anti-tumor activities, which is a promising treatment for TNBC. Studies have found that LAG3 and PD1 are co-expressed in TNBC (Du, Yi et al. 2020), and the anti-tumor effect of DNT cells combined with LAG3Ab and anti-PD1 in TNBC is also worth further exploration.

In summary, DNT, as an adoptive cell therapy, can increase the number of immune effector cells in the TIME. It does not cause graft-versus host reaction after DNT infusion, which enhances the possibility for the clinical application (Fang, Ly et al. 2019, Lee, Kang et al. 2019). Combination of DNT cells and LAG3Ab may greatly increase the host’s anti-tumor function, which lays a reliable foundation for the application of adoptive cell therapy combined with ICIs in breast cancer, especially in TNBC.

This study revealed the TIME variation in different breast cancer subtypes, and found that DNT and CD8⁺T cells were mostly in an exhausted state in the TIME. Expanded DNTs from healthy volunteers had a cytotoxicity on TNBC cells. LAG3Ab not only blocks the binding of LAG3 and its ligands, but also upregulates T-BET transcription to enhance the killing effect of DNTs, which supports the possibility of a novel combination immunotherapy consisting of DNT cells and LAG3Ab for TNBC.

Abbreviations	Full name
ACT	adoptive cell therapy
ADJ	adjacent tissues
AV	Annexin V
CA	cancer tissues
CSB	cell staining buffer
CTLA4	cytotoxic T lymphocyte antigen 4
CyTOF	cytometry by time of flight
DNT	double negative T
DPBS	Dulbecco’s phosphate buffered saline
E:T	effector to target
FBS	fetal bovine serum
GEPIA	Gene expression profiling interactive analysis
GZMB	granzyme B
HB	peripheral blood of healthy volunteers
ICI	immune checkpoint inhibitor
LAG3	lymphocyte activation gene 3
LAG3Ab	LAG3 monoclonal antibody
PB	peripheral blood of patients
PBMC	peripheral blood mononuclear cell
PD1	programmed cell death protein 1
prf	perforin
TIME	tumor immune microenvironment
TIMER	tumor immune estimation resource
TNBC	triple negative breast cancer

Funding

This work was supported by the Research Foundation of Beijing Friendship Hospital (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.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Authors’ contributions

QL 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. MW participated in performing the research, analyzing the data. MW and HZL initiating the original draft of the article. YHW, YRL, JTJ, and YTL 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.

Availability of data and materials

All data generated or analyzed during this study are included.

Ethical approval and consent to participate

This study was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University (2021-P2-064), and all participants signed the written informed consent.

Consent for publication

All authors approved the final manuscript for publication.

Acknowledgements

The authors would like to sincerely thank the open-access database for data sharing and processing.

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Table 1. Primer sequences used for Quantitative real-time PCR.

Gene name	Sequence (5'→3')
Gapdh	Forward: GGAGCGAGATCCCTCCAAAAT Reverse: GGCTGTTGTCATACTTCTCATGG
Lag3	Forward: GCCTCCGACTGGGTCATTTT Reverse: CTTTCCGCTAAGTGGTGATGG
Klrk1	Forward: TTTTTCAACACGATGGCAAAAGC Reverse: GGGCCACAGTAACTTTCGGT
Perforin 1	Forward: GACTGCCTGACTGTCGAGG Reverse: TCCCGGTAGGTTTGGTGGAA
Granzyme b	Forward: TACCATTGAGTTGTGCGTGGG Reverse: GCCATTGTTTCGTCCATAGGAGA
IFNγ	Forward: TCGGTAACTGACTTGAATGTCCA Reverse: TCGCTTCCCTGTTTTAGCTGC
Tbx21	Forward: GTCCAACAATGTGACCCAGAT Reverse: ACCTCAACGATATGCAGCCG

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Targeting Breast Cancer with a Combination of DNT and LAG3 Checkpoint Blockage and its Mechanism

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