
==== Front
Clin Transl Oncol
Clin Transl Oncol
Clinical & Translational Oncology
1699-048X
1699-3055
Springer International Publishing Cham

38769215
3514
10.1007/s12094-024-03514-4
Research Article
High MAL2 expression predicts shorter survival in women with triple-negative breast cancer
Borowczak Jędrzej 1
Zdrenka Marek 1
Socha Weronika 2
Gostomczyk Karol 2
Szczerbowski Krzysztof 1
Maniewski Mateusz 123
Andrusewicz Hanna 1
Łysik-Miśkurka Joanna 1
Nowikiewicz Tomasz 45
Szylberg Łukasz 126
http://orcid.org/0000-0003-3748-1058
Bodnar Magdalena magdalena1bodnar@gmail.com

26
1 Department of Tumor Pathology and Pathomorphology, Oncology Centre, Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
2 https://ror.org/04c5jwj47 grid.411797.d 0000 0001 0595 5584 Department of Obstetrics, Gynaecology and Oncology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
3 https://ror.org/0102mm775 grid.5374.5 0000 0001 0943 6490 Doctoral School of Medical and Health Sciences, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
4 Clinical Department of Breast Cancer and Reconstructive Surgery, Oncology Center, Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
5 https://ror.org/04c5jwj47 grid.411797.d 0000 0001 0595 5584 Department of Surgical Oncology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
6 https://ror.org/05r81yb60 0000 0004 6063 7919 Chair of Pathology, University Hospital No. 2 im. Dr. Jan Biziel in Bydgoszcz, Ujejskiego 75, 85-168 Bydgoszcz, Poland
20 5 2024
20 5 2024
2024
26 10 25492558
3 3 2024
3 5 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/.
Introduction

Due to its lack of conventional surface receptors, triple-negative breast cancer (TNBC) is inherently resistant to most targeted therapies. MAL2 overexpression prompts endocytosis, conferring resistance to novel therapeutics. This study explores the role of MAL2 and PD-L1 in TNBC patients’ prognosis.

Methods

We performed immunohistochemical analysis on 111 TNBC samples collected from 76 patients and evaluated the expression of MAL2 and PD-1. We expanded the study by including The Cancer Genome Atlas (TCGA) cohort.

Results

MAL2 expression did not correlate with stage, grade, tumor size, lymph node invasion, metastasis, and PD-1 expression. Patients with high MAL2 had significantly lower 5-year survival rates (71.33% vs. 89.59%, p = 0.0224). In the tissue microarray cohort (TMA), node invasions, size, recurrence, and low MAL2 (HR 0.29 [CI 95% 0.087–0.95]; p < 0.05) predicted longer patients’ survival. In the TCGA cohort, patients with low MAL2 had significantly longer overall survival and disease-specific survival than patients with high MAL2. Older age and high MAL2 expression were the only independent predictors of shorter patient survival in the BRCA TCGA cohort.

Conclusion

High MAL2 predicts unfavorable prognosis in triple-negative breast cancer, and its expression is independent of PD-1 levels and clinicopathological features of TNBC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12094-024-03514-4.

Keywords

Breast cancer
Triple-negative
Prognosis
Survival
Biomarker
MAL2
PD-L1
issue-copyright-statement© Federación de Sociedades Españolas de Oncología (FESEO) 2024
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pmcIntroduction

Breast cancer is the most common female malignancy worldwide. Although overall survival has significantly increased over the past few years, it remains the second most prevalent cause of cancer-related deaths in women, accounting for 2.3 million new cases and 685,000 deaths in 2020 [1]. Triple-negative breast cancer (TNBC) accounts for approximately 15–20% of all breast cancer cases and lacks the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) [2]. This aggressive subtype of breast cancer affects predominantly young patients, especially those under 40 and African Americans, who rarely respond to standard targeted therapies and progress aggressively [2–4]. While early detection increases the chances of radical treatment and improves prognosis, current treatment modalities are limited to surgery, chemotherapy, radiation therapy, and only a few targeted therapies. Therefore, there exists a critical need to find new biomarkers and develop novel approaches to treat triple-negative breast cancer [5, 6].

Genetic and molecular factors play a key role in the development and progression of TNBC. Considering its inherent resistance to targeted therapies, developing novel prognostic markers became crucial to planning treatment, assessing disease progression, and stratifying patients’ risk [6]. Recently, T Cell Differentiation Protein 2 (MAL2) emerged as a potential driver of cancer immune escape and novel therapeutic target [7]. MAL2 was overexpressed in tumors and was associated with poor prognosis, and MAL2 knockdown improved major histocompatibility complex type 1 (MHC-1) recognition on tumor surfaces by CD8 + T cells, highlighting the role of MAL2 in immune regulation [8–11]. While the exact mechanism remains unknown, reducing MAL2 expression may be a novel therapeutic approach. More research is needed to fully understand the protein potential.

Targeting PD-1/PD-L1 has recently revolutionized patient care and started the era of immunotherapy in TNBC. PD-L1 is present on the surface of tumor cells and binds with Programmed Cell Death Protein 1 (PD-1) present on T cells, rendering them unable to induce antitumor response [12, 13]. Approximately 20% of TNBCs express Programmed Cell Death Ligand 1 (PD-L1), indicating their potential sensitivity to immune checkpoint inhibitors [14]. Blocking PD-1/PD-L1 interactions significantly improved the efficacy of cancer treatments in solid tumors, including breast cancer [15, 16]. While not all anti-PD-L1 regimens showed clinical benefits, blocking this interaction remains a promising treatment option [17]. Despite their distinct mechanisms of action, MAL2 and PD-1/PD-L1 expression suppress immune surveillance and facilitate cancer progression [9, 13]. Therefore, their concurrent targeting may hold the premise of improving therapeutic strategies to treat TNBC and prolong patient survival.

In this study, we explore the role of MAL2 in TNBC and investigate its interactions with PD-1 and PD-L1. Since both MAL2 and PD-L1 modulate tumor immune response, confirming their interactions may uncover the role of MAL2 as a novel prognostic marker and therapeutic target in TNBC.

Materials and methods

Sample acquisition

This study included 111 triple-negative breast cancer samples collected from 76 women in the Department of Pathomorphology between 2011 and 2015. Clinical data, including age, sex, overall survival, tumor differentiation (grade), stage T, lymph node invasion, metastasis, tumor size, and recurrence, were obtained and analyzed retrospectively (Table 1). The study followed the Declaration of Helsinki, and the protocol was approved by the Nicolaus Copernicus University Bioethics Committee (KB 746/2021). All 76 patients were treated by surgery, and the resection was radical in 75 cases. In 57 cases, it was breast-conserving surgery; in 19 cases, radical mastectomy was conducted. Three patients underwent neoadjuvant chemotherapy. Of 73 patients qualified for adjuvant therapy, 24 underwent chemotherapy, 47 chemotherapy and radiotherapy, while 2 were radiotherapy only. Three patients were disqualified from adjuvant therapy due to the high risk of adverse events relative to comorbidities. The high and low MAL2 expression groups (extracted from the tissue microarray (TMA), TCGA breast cancer, and TCGA TNBC cohorts) did not differ significantly in regard to administered treatment (p > 0.05).Table 1 Basic patient characteristics

Clinical data	n (%)	
Cases	76	
Samples	111	
Median age	59.5 years (range 32–85)	
Age	
  ≤ 60	41 (54.67%)	
  > 60	35 (45.33%)	
Sex	
 Female	76 (100%)	
 Male	0 (0%)	
HT/HRT	
 No	67 (90.54%)	
 Yes	7 (7.45%)	
Neoadjuvant chemotherapy	
 No	73 (96.05%)	
 Yes	3 (3.95%)	
Adjuvant therapy	
 No	3 (3.95%)	
 Yes	73 (96.05%)	
Nottingham grading score	
 G1	1 (1.32%)	
 G2	31 (40.79%)	
 G3	41 (53.95%)	
Stage	
 T1	16 (21.05%)	
 T2	56 (73.68%)	
 T3	2 (2.63)	
 T4	2 (2.63%)	
Lymph nodes invasion	
 N0	54 (71.05%)	
 N1–N3	22 (28.85%)	
Metastasis	
 M0	76 (100%)	
 M1	0 (0%)	
Tumor size	
  ≤ 25 mm	45 (59.21%)	
  > 25 mm	30 (40.79%)	
Recurrence	
 No recurrence	64 (84.21%)	
 Recurrence	12 (15.79%)	
Time to recurrence (mean)	20.5 months (range 8–66)	
Survival status	
 Alive	57 (75%)	
 Dead	19 (25%)	
Median follow-up time	72 months (range 2–72 months)	

Immunohistochemistry

A retrospective immunohistochemical analysis of MAL2 comprised 111 formalin-fixed, paraffin-embedded tissue blocks cut into 5 μm sections, attached to a glass slide, and incubated at 60 °C for 2 h. IHC staining was performed on the Ventana Benchmark Ultra platform according to NordiQC operating procedure. A primary MAL2 polyclonal antibody (ab75347, Abcam) was used for staining. The specimens were fixed in 10% buffered formalin for 24 h at room temperature.

Image acquisition and analysis

The pathologists evaluating the immunohistochemical expression of PD-L1 worked independently and were blinded to clinical and pathological data. Protein expression was evaluated using a light microscope at 20 × original objective magnification. All of the collected tissue samples were processed following the standard diagnostic protocol. For this study, representative material from diagnostic biopsies and matching resection specimens was selected for additional immunohistochemical studies. The immunohistochemical studies of PD-L1 expression were performed using an anti-PD-L1 antibody (clone SP142, Ventana Medical Systems) on material from diagnostic biopsies, and the original protocol provided by Ventana was followed. Finally, the brown color due to the histochemical reaction product was considered as observed in the site of the presence of the searched antigen. According to the guide provided by Ventana, tumors with PD-L1 expression ≥ 1% IC were considered PD-L1-positive [18].

Matching resection specimens were stained to assess MAL2 expression. For each sample, the expression of MAL2 was obtained by calculating the H-score in three representative areas of the tumor. The H-score was assigned using the formula [1 × (% cells low positive) + 2 × (% cells positive) + 3 × (%cells high positive)], obtaining a value from 0 to 300. The final result was the mean value of the three assessments [19] (Fig. 1).Fig. 1 Cross-section TNBC staining patterns: a H&E, b high MAL2 expression, c low MAL2 expression, d negative MAL2 expression, e negative PD-L1 expression, and f positive PD-L1 expression

In silico analysis

TCGA cohort clinical data were accessed through the cBioPortal [20]. USCS XENA was used to acquire normalized expression data of MAL2 and PD-1 [21]. FPKM gene expression was obtained through the Human Protein Atlas [22, 23]. Normalized gene expression was calculated as log2 (fpkm + 1). The cutoff was set at 126.57 FPKM for breast cancer and at 136.7 FPKM for TNBC.

Statistical analysis

All statistical analyses were performed using Statistica version 13.3 (Statsoft) and Microsoft Excel 2019. p value < 0.05 was considered statistically significant. Variables were tested for normality using the Kolmogorov–Smirnov test. The intergroup comparisons used the Mann–Whitney U test or the ANOVA Kruskal–Wallis test. The correlations between MAL2 expression and the clinicopathological features of triple-negative bladder cancer were evaluated using Spearman’s rank correlation coefficient. Univariate and multivariate analyses of potential predictors for overall survival implemented the Cox proportional hazard regression. The optimal cutoffs were calculated using Cutoff Finder [24] Results were expressed as hazard ratio (HR) and 95% confidence interval (CI). The estimation of survival differences between groups was based on the log-rank test.

Results

MAL2 expression is independent of clinical features of TNBC

First, we calculated the expression of MAL2 depending on the clinical features of TNBC (Table 2, Table S1). MAL2 did not correlate with stage, grade, tumor size, and lymph node invasion. Its expression was independent of age, PD-L1 status, Ki-67 status, progression, and recurrence. Although the PD-L1 status and lymph node invasions show a trend toward higher MAL2 expression, those correlations were not statistically significant. There was no correlation between MAL2 expression and received neoadjuvant or adjuvant therapy (p > 0.05).Table 2 MAL2 expression based on clinical features

Clinical data	Total (N)	Median MAL2 expression	Q1	Q3	p value	
 ≤ 60 years	59	110 (range 275–84)	84	185	0.772	
 > 60 years	52	111 (range 0–265)	77.5	167.5	
HRT	97	112 (range 0–275)	90	180	0.22	
No HRT	10	87 (range 0–260)	15	160	
IA	24	110 (range 0–265)	80	200	0.1	
IIA	72	110 (range 0–265)	75	162	
IIB	10	182 (range 70–275)	114	230	
IIIB	5	105 (range 0–127)	105	105	
T1	20	110 (range 0–265)	92	150	0.446	
T2	84	112 (range 0–275)	82	180	
T3	4	70 (range 32–112)	41	101	
T4	3	105 (range 105–127)	105	127	
N0	78	105 (range 0–265)	70	160	0.08	
N1–N3	33	114 (range 0–275)	101	200	
NGS1	2	95 (range 10–180)	10	180	0.742	
NGS2	48	110 (range 0–265)	77	170	
NGS3	56	112 (range 0–275)	88	187.5	
 ≤ 25 mm	62	119 (range 0–275)	86	185	0.268	
 > 25 mm	47	105 (range 0–260)	70	165	
No recurrence	91	110 (range 0–275)	80	180	0.9175	
Recurrence	20	111 (range 0-230_	95	139.5	
PD-1 negative	61	105 (range 0–255)	70	160	0.072	
PD-1 positive	49	125 (range 00–275)	95	190	
Low Ki-67	17	135 (range 5–275)	102	200	9.208	
High Ki-67	35	110 (range 5–240)	80	170	
NGS Nottingham grade score; T stage; N lymph node involvement; M distant metastasis; HRT history of hormone replacement therapy

MAL2 predicts shorter survival in TNBC

To examine the predictive value of MAL in patients with triple-negative breast cancer, we dichotomized the samples into low and high MAL2 expression groups, with the cutoff point set at 88 H-score. H-score ≤ 88 indicated low MAL2 expression, and H-score > 88 indicated high MAL2 expression. Patients with high MAL2 had significantly lower 5-year survival rates (71.33% vs. 89.59%, respectively; p = 0.0224) than patients with low MAL2 (Fig. 2). The Kaplan–Meier analysis of overall survival by quartiles showed significant differences in OS between patients in the first and second quartiles of MAL2 expression (p = 0.021). MAL2 expression did not predict disease-free survival in patients with TNBC (86.75% vs. 81.59% 5 years after diagnosis; p = 0.41).Fig. 2 Patients’ survival depends on MAL2 status (89.59% vs. 71.33% 5 years after diagnosis in low-MAL2 and high-MAL2 groups, respectively; p = 0.0244)

In univariate Cox regression analysis, lymph node invasions, size, recurrence, and low MAL2 expression (HR 0.29 [CI 95% 0.087–0.95]; p < 0.05) were associated with longer patients’ survival. In multivariate analysis, only disease recurrence and lymph node invasion retained statistical significance (Table 3). Given its significance in univariate analysis, we expand the analysis of MAL2 onto the TCGA cohort.Table 3 Univariate and multivariate analysis of survival prognosis factors in TNBC

Variable	Univariate analysis	Multivariate analysis	
HR	95% CI	p value	HR	95% CI	p value	
Age (≤ 60 vs. > 60)	1.11	0.53–2.35	0.78	–	–	–	
Stage (T1 vs. T2–T4)	0.295	0.07–1.24	0.097	–	–	–	
Nodes (N0 vs. N1–N3)	0.2	0.095–0.433	0.00004	0.358	0.158–0.815	0.014	
NGS (G1 vs. G2–G3)	0.787	0.36–1.71	0.55	–	–	–	
Size (≤ 25 mm vs. > 25 mm)	0.4	0.186–0.851	0.017	0.527	0.23–1.19	0.12	
Recurrence (no vs. yes)	0.1	0.046–0.22	0.0000001	0.18	0.07–0.435	0.00014	
PD-1 (negative vs. positive)	1.485	0.685–3.219	0.316	–	–	–	
Ki-67 (low vs. high)	1.128	0.385–3.3	0.826	–	–	–	
MAL2 (low vs. high)	0.29	0.087–0.95	0.041	0.417	0.124–1.406	0.158	
Sstatistically significant results are bolded

Fig. 3 MAL2 and PD-1 are overexpressed in cancers compared to normal adjacent tissue (p = 0.0001 and p = 0.0007, respectively)

In silico analysis

We assessed the expression of MAL2 and PDCD1 in the TCGA BRCA cohort using the XENA browser (Table 4) [22, 23, 25]. The normalized gene expression for MAL (5.09 vs. 6.4; p < 0.000001) and PDCD1 (0.29 vs. 0.37; p = 0.00049) was higher in tumors than in adjacent normal tissues. MAL2 and PDCD1 are overexpressed in breast cancer (Fig. 3).Table 4 Basic characteristics of the TCGA cohorts

Clinical data	Breast cancer (%)	TNBC (%)	
Cases	1052	169	
Median age	58 (range 26–90)	57 (range 29–90)	
Age	
  ≤ 60	586 (55.7%)	95 (56.21%)	
  > 60	466 (44.3%)	74 (43.79%)	
Sex	
 Female	1040 (98.86%)	165 (97.63%)	
 Male	12 (1.14%)	4 (2.37%)	
Race	
 White	743 (70.63%)	154 (91.12%)	
 Black or African-American	179 (17.02%)	10 (5.91%)	
 Asian	58 (5.51%)	5 (3.97%)	
Neoadjuvant therapy	
 Yes	6 (0.6%)	2 (1.18%)	
 No	1046 (99.4%)	167 (98.82%)	
Adjuvant radiotherapy	
 Yes	538 (51.14%)	72 (42.6%)	
 No	429 (40.78%)	84 (49.6%)	
 Unknown	85 (8.08%)	13 (7.69%)	
Stage	
 T1	117 (11.12%)	32 (18.93%)	
 T2	600 (57.03%)	91 (53.85%)	
 T3	239 (22.72%)	41 (24.26%)	
 T4	19 (1.8%)	3 (1.18%)	
Lymph nodes invasion	
 N0	497 (47.24%)	80 (47.34%)	
 N1–N3	536 (50.95%)	89 (52.66%)	
Metastasis	
 M0	872 (82.89%)	143 (84.62%)	
 M1	21 (2%)	4 (2.37%)	
Progression	
 No progression	907 (86.22%)	144 (85.21%)	
 Progression	145 (13.78%)	25 (14.79%)	
Progression-free survival (median)	25.78 months (range 003–281)	28 months (range 0–143)	
Follow-up time (median)	28.08 months (range 0.03–283)	30 months (range 0–130)	
Survival status	
 Alive	901 (85.65%)	144 (85.21%)	
 Dead	151 (14.35%)	25 (14.79%)	

Fig. 4 Overall survival in the TCGA cohort based on MAL2 expression (p = 0.0001)

We used the Ensembl gene ID from TCGA to map the TCGA RNA-seq data. The samples were dichotomized into low and high MAL2 expression groups based on the FPKMs value (number of fragments per kilobase of exon per Million reads).

Breast cancer patients with low MAL2 had significantly longer overall survival (p = 0.0001) and disease-specific survival (p = 0.0026) than patients with high MAL2 expression (Fig. 4). In multivariate COX regression analysis, older age and high MAL2 expression were the only independent predictors of shorter patient survival in the BRCA TCGA cohort (Supplementary materials).

Next, we analyzed the role of MAL2 in a subset of TNBC patients extracted from the TCGA database. In the TNBC TGCA cohort, patients with high MAL2 expression had significantly higher 5-year survival than patients with low MAL2 expression (87.25% vs. 55.45%, respectively) (Fig. 5). Furthermore, high MAL2 expression, older age, higher stage, and disease progression were independent prognostic factors of shorter TNBC patients survival (p < 0.05) (Table 5). The results obtained from the TCGA cohort are consistent with the findings from our tissue microarray (TMA) cohort.Fig. 5 Overall survival in the TCGA TNBC cohort based on MAL2 expression (p = 0.008)

Table 5 Univariate and multivariate analysis of the TCGA triple-negative breast cancer cohort

Variable	Univariate analysis	Multivariate analysis	
HR	95% CI	p value	HR	95% CI	p value	
Age (≤ 60 vs. > 60)	2.63	1.16–5.99	0.02	3.41	1.34–8.66	0.01	
Sex (male vs. female)	0.34	0.04–2.58	0.3	–	–	–	
Stage (T1–T2 vs. T3–T4)	3.4	1.52–7.6	0.003	4.5	1.28–15.83	0.01	
Nodes (N0 vs. N1–N3)	2.61	1.09–6.27	0.03	1.27	0.36–4.48	0.71	
Metastasis (M0 vs. M1)	6.83	2.28–20.49	0.0006	0.77	0.2–2.94	0.7	
Progression (no vs. yes)	9.48	4.05–22.23	0.00001	17.54	5.51–55.84	0.0001	
MAL2 (low vs. high)	3.01	1.33–7.22	0.008	8.72	2.64–28.87	0.0003	
Statistically significant results are bolded

Discussion

In our study, MAL2 overexpression was associated with shorter survival in triple-negative breast cancer. Although the results from the TCGA database analysis align with the results from our TMA cohort, MAL2 expression seems independent of PD-L1 status, tumor grade, stage, size, lymph node invasion, and metastasis. The lack of association with clinicopathological features of TNBC indicates that MAL2 independently contributes to cancer progression. However, the unclear relationship between MAL2, PD-1, and cancer immune escape needs to be addressed.

T Cell Differentiation Protein 2 (MAL2) is a 176 amino acid residue protein encoded by a gene located on chromosome 8 (8q24.12) [10, 11, 26]. It belongs to the MAL proteolipid family and encodes a transmembrane protein that participates in transcytosis [8, 27]. While the nature of the interactions between MAL2 signaling and breast cancer remains largely unexplored, recent studies have shed new light on its role in cancer immune escape [9]. Cancer cells interact with each other and modulate the adjacent stroma cells to form an immunosuppressive microenvironment (TME) where they dwell and proliferate [28]. In their niche, cells lose epithelial phenotype and acquire mesenchymal-like phenotype. This phenomenon, called epithelial–mesenchymal transition (EMT), is the key event that leads to cancer immune escape [29].

MAL2 is overexpressed in breast cancer, and its high expression correlates with poor prognosis [27]. The Bhandari et al. study showed that MAL2 knockdown decreased breast cancer cell proliferation, migration, and invasion. It was associated with increased E-cadherin and Vimentin levels, indicating that MAL2 drives disease progression by EMT. Those results seem to be confirmed by Yuan et al. study, in which MAL2 deletion in ovarian cancer cells reduced their proliferation, migration, invasion, and EMT [11].

The expression of PD-L1 on tumor cells increases along disease progression due to cancer immune escape, epithelial–mesenchymal transition, or the upregulation of the JAK-STAT, MAPK, and PI3K-AKT signaling pathways [30–32]. Contrary to MAL2, TNBC expressing PD-L1 seems to be associated with better outcomes and is more likely to respond to neoadjuvant chemotherapy [33]. While direct interactions between PD-1/PD-L1 and MAL2 are currently unknown, they seem to regulate T cell reactivity and anticancer immune response [9, 34]. By interacting with MHC-I and RAB, MAL2 caused the degradation of MHC-I, promoted tumor antigen endocytosis, and suppressed their presentation. MAL2 depletion in a mice xenograft suppressed breast tumor growth and enhanced tumor-infiltrating CD8 + T cells cytotoxicity, presumably by facilitating tumor antigen presentation [9].

While direct interaction between MAL2 and PD-1/PD-L1 remains unknown, their overexpression appears to reflect different mechanisms of therapeutic resistance. PD-1/PD-L1 affects the activation of T cells, while MAL2 renders them ineffective in recognizing malignant cells [13, 27]. Considering that we found no direct relationship between PD-1 and MAL2 expression, we hypothesize that both mechanisms occur separately and MAL2 overexpression may limit the efficacy of anti-PD-1/anti-PD-L1 therapy.

Conclusion

MAL2 is overexpressed in TNBC, and high MAL2 expression predicts shorter overall survival in triple-negative breast cancer in both the TMA and TCGA cohorts. MAL2 levels are independent of patients’ age, PD-1 status, Ki-67 status, hormonal therapy, progression, and recurrence status. We found no direct correlation between the expression of PD-L1 and MAL2, but both proteins partake in tumor immunoregulation and may be intertwined in mediating tumor immune escape. While targeting MAL2 appears promising, further trials are needed to examine its clinical applicability.

Supplementary Information

Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 15 kb)

Author contributions

Conceptualization: J.B. and K.S.; writing—original draft preparation: J.B., W.S., K.G., K.Sz.; formal analysis: J.Ł-M., H.A., M.M., J.B.; visualization: M.Z., Ł.S., M.B.; staining evaluation: M.Z., Ł.Sz.; investigation: J.B., M.M.; acquisition: J.Ł-M., H.A.; writing—review and editing: J.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study received no funding.

Data availability

The data presented in this study are available on request from the corresponding author. Due to ethical restrictions, they are not publicly available.

Declarations

Conflict of interest

The authors declare no conflicts of interest relevant to the article’s content.

Ethical approval

The study was conducted following the Declaration of Helsinki and approved by the Bioethics Committee (KB 746/2021).

Informed consent

The Bioethical Committee waived the requirement for patient consent due to the study’s retrospective nature.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Breast cancer. [cited 17 May 2023]. Available: https://www.who.int/news-room/fact-sheets/detail/breast-cancer. Accessed 24 Apr 2024.
2. Triple-negative breast cancer. [cited 3 May 2023]. Available: https://www.cancer.org/cancer/breast-cancer/about/types-of-breast-cancer/triple-negative.html. Accessed 24 Apr 2024.
3. Irvin WJ Jr Carey LA What is triple-negative breast cancer? Eur J Cancer 2008 44 2799 2805 10.1016/j.ejca.2008.09.034 19008097
Irvin WJ Jr, Carey LA. What is triple-negative breast cancer? Eur J Cancer. 2008;44:2799–805. 10.1016/j.ejca.2008.09.034.19008097
4. Smolarz B Nowak AZ Romanowicz H Breast cancer-epidemiology, classification, pathogenesis and treatment (review of literature) Cancers 2022 10.3390/cancers14102569 35626173
Smolarz B, Nowak AZ, Romanowicz H. Breast cancer-epidemiology, classification, pathogenesis and treatment (review of literature). Cancers. 2022. 10.3390/cancers14102569.35626173
5. Wahba HA El-Hadaad HA Current approaches in treatment of triple-negative breast cancer Cancer Biol Med 2015 12 106 116 10.7497/j.issn.2095-3941.2015.0030 26175926
Wahba HA, El-Hadaad HA. Current approaches in treatment of triple-negative breast cancer. Cancer Biol Med. 2015;12:106–16. 10.7497/j.issn.2095-3941.2015.0030.26175926
6. Li Y Zhan Z Yin X Fu S Deng X Targeted therapeutic strategies for triple-negative breast cancer Front Oncol 2021 11 731535 10.3389/fonc.2021.731535 34778045
Li Y, Zhan Z, Yin X, Fu S, Deng X. Targeted therapeutic strategies for triple-negative breast cancer. Front Oncol. 2021;11: 731535. 10.3389/fonc.2021.731535.34778045
7. Zhong Y Zhuang Z Mo P Shang Q Lin M Gong J Overexpression of MAL2 correlates with immune infiltration and poor prognosis in breast cancer Evid Based Complement Alternat Med 2021 2021 5557873 10.1155/2021/5557873 34567213
Zhong Y, Zhuang Z, Mo P, Shang Q, Lin M, Gong J, et al. Overexpression of MAL2 correlates with immune infiltration and poor prognosis in breast cancer. Evid Based Complement Alternat Med. 2021;2021:5557873. 10.1155/2021/5557873.34567213
8. Dersh D Yewdell JW Immune MAL2-practice: breast cancer immunoevasion via MHC class I degradation J Clin Invest 2021 10.1172/jci144344 33393509
Dersh D, Yewdell JW. Immune MAL2-practice: breast cancer immunoevasion via MHC class I degradation. J Clin Invest. 2021. 10.1172/jci144344.33393509
9. Fang Y Wang L Wan C Sun Y Van der Jeught K Zhou Z MAL2 drives immune evasion in breast cancer by suppressing tumor antigen presentation J Clin Invest 2021 10.1172/jci140837 34779411
Fang Y, Wang L, Wan C, Sun Y, Van der Jeught K, Zhou Z, et al. MAL2 drives immune evasion in breast cancer by suppressing tumor antigen presentation. J Clin Invest. 2021. 10.1172/jci140837.34779411
10. Byrne JA Maleki S Hardy JR Gloss BS Murali R Scurry JP MAL2 and tumor protein D52 (TPD52) are frequently overexpressed in ovarian carcinoma, but differentially associated with histological subtype and patient outcome BMC Cancer 2010 10 497 10.1186/1471-2407-10-497 20846453
Byrne JA, Maleki S, Hardy JR, Gloss BS, Murali R, Scurry JP, et al. MAL2 and tumor protein D52 (TPD52) are frequently overexpressed in ovarian carcinoma, but differentially associated with histological subtype and patient outcome. BMC Cancer. 2010;10:497. 10.1186/1471-2407-10-497.20846453
11. Yuan J Jiang X Lan H Zhang X Ding T Yang F Multi-omics analysis of the therapeutic value of MAL2 based on data mining in human cancers Front Cell Dev Biol 2021 9 736649 10.3389/fcell.2021.736649 35111745
Yuan J, Jiang X, Lan H, Zhang X, Ding T, Yang F, et al. Multi-omics analysis of the therapeutic value of MAL2 based on data mining in human cancers. Front Cell Dev Biol. 2021;9: 736649. 10.3389/fcell.2021.736649.35111745
12. Schütz F Stefanovic S Mayer L von Au A Domschke C Sohn C PD-1/PD-L1 pathway in breast cancer Oncol Res Treat 2017 40 294 297 10.1159/000464353 28346916
Schütz F, Stefanovic S, Mayer L, von Au A, Domschke C, Sohn C. PD-1/PD-L1 pathway in breast cancer. Oncol Res Treat. 2017;40:294–7. 10.1159/000464353.28346916
13. Gou Q Dong C Xu H Khan B Jin J Liu Q PD-L1 degradation pathway and immunotherapy for cancer Cell Death Dis 2020 11 955 10.1038/s41419-020-03140-2 33159034
Gou Q, Dong C, Xu H, Khan B, Jin J, Liu Q, et al. PD-L1 degradation pathway and immunotherapy for cancer. Cell Death Dis. 2020;11:955. 10.1038/s41419-020-03140-2.33159034
14. Mittendorf EA Philips AV Meric-Bernstam F Qiao N Wu Y Harrington S PD-L1 expression in triple-negative breast cancer Cancer Immunol Res 2014 2 361 370 10.1158/2326-6066.cir-13-0127 24764583
Mittendorf EA, Philips AV, Meric-Bernstam F, Qiao N, Wu Y, Harrington S, et al. PD-L1 expression in triple-negative breast cancer. Cancer Immunol Res. 2014;2:361–70. 10.1158/2326-6066.cir-13-0127.24764583
15. Lei Q Wang D Sun K Wang L Zhang Y Resistance mechanisms of anti-PD1/PDL1 therapy in solid tumors Front Cell Dev Biol 2020 8 672 10.3389/fcell.2020.00672 32793604
Lei Q, Wang D, Sun K, Wang L, Zhang Y. Resistance mechanisms of anti-PD1/PDL1 therapy in solid tumors. Front Cell Dev Biol. 2020;8:672. 10.3389/fcell.2020.00672.32793604
16. Cortes J Rugo HS Cescon DW Im S-A Yusof MM Gallardo C Pembrolizumab plus chemotherapy in advanced triple-negative breast cancer N Engl J Med 2022 387 217 226 10.1056/nejmoa2202809 35857659
Cortes J, Rugo HS, Cescon DW, Im S-A, Yusof MM, Gallardo C, et al. Pembrolizumab plus chemotherapy in advanced triple-negative breast cancer. N Engl J Med. 2022;387:217–26. 10.1056/nejmoa2202809.35857659
17. Miles D Gligorov J André F Cameron D Schneeweiss A Barrios C Primary results from IMpassion131, a double-blind, placebo-controlled, randomised phase III trial of first-line paclitaxel with or without atezolizumab for unresectable locally advanced/metastatic triple-negative breast cancer Ann Oncol 2021 32 994 1004 10.1016/j.annonc.2021.05.801 34219000
Miles D, Gligorov J, André F, Cameron D, Schneeweiss A, Barrios C, et al. Primary results from IMpassion131, a double-blind, placebo-controlled, randomised phase III trial of first-line paclitaxel with or without atezolizumab for unresectable locally advanced/metastatic triple-negative breast cancer. Ann Oncol. 2021;32:994–1004. 10.1016/j.annonc.2021.05.801.34219000
18. Ventana Medical Systems, Inc. and Roche Diagnostics International, Inc. Ventana PD-L1 (SP142) assay quick referene guide for ttiple-negative breast carcinoma (TNBC). 2022. Available: https://elabdoc-prod.roche.com/eLD/api/downloads/dba77a2d-da62-ea11-fa90-005056a772fd?countryIsoCode=be. Accessed 24 Apr 2024.
19. Nicholson RI Bouzubar N Walker KJ McClelland R Dixon AR Robertson JF Hormone sensitivity in breast cancer: influence of heterogeneity of oestrogen receptor expression and cell proliferation Eur J Cancer 1991 27 908 913 10.1016/0277-5379(91)90145-4 1834127
Nicholson RI, Bouzubar N, Walker KJ, McClelland R, Dixon AR, Robertson JF, et al. Hormone sensitivity in breast cancer: influence of heterogeneity of oestrogen receptor expression and cell proliferation. Eur J Cancer. 1991;27:908–13. 10.1016/0277-5379(91)90145-4.1834127
20. Cerami E Gao J Dogrusoz U Gross BE Sumer SO Aksoy BA The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data Cancer Discov 2012 2 401 404 10.1158/2159-8290.cd-12-0095 22588877
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2:401–4. 10.1158/2159-8290.cd-12-0095.22588877
21. Goldman MJ Craft B Hastie M Repečka K McDade F Kamath A Visualizing and interpreting cancer genomics data via the Xena platform Nat Biotechnol 2020 38 675 678 10.1038/s41587-020-0546-8 32444850
Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38:675–8. 10.1038/s41587-020-0546-8.32444850
22. Expression of MAL2 in breast cancer - the human protein atlas. [cited 26 Jan 2023]. Available: https://www.proteinatlas.org/ENSG00000147676-MAL2/pathology/breast+cancer. Accessed 24 Apr 2024.
23. Expression of PDCD1 in breast cancer - the human protein atlas. [cited 26 Jan 2023]. Available: https://www.proteinatlas.org/ENSG00000188389-PDCD1/pathology/breast+cancer. Accessed 24 Apr 2024.
24. Budczies J Klauschen F Sinn BV Győrffy B Schmitt WD Darb-Esfahani S Cutoff Finder: a comprehensive and straightforward Web application enabling rapid biomarker cutoff optimization PLoS ONE 2012 7 e51862 10.1371/journal.pone.0051862 23251644
Budczies J, Klauschen F, Sinn BV, Győrffy B, Schmitt WD, Darb-Esfahani S, et al. Cutoff Finder: a comprehensive and straightforward Web application enabling rapid biomarker cutoff optimization. PLoS ONE. 2012;7: e51862. 10.1371/journal.pone.0051862.23251644
25. UCSC Xena. [cited 30 Apr 2024]. Available: https://xenabrowser.net/datapages/?dataset=TCGA-BRCA.htseq_fpkm.tsv&host=https://gdc.xenahubs.net&removeHub=https://xena.treehouse.gi.ucsc.edu:443. Accessed 24 Apr 2024.
26. Zheng C Wang J Zhang J Hou S Zheng Y Wang Q Myelin and lymphocyte protein 2 regulates cell proliferation and metastasis through the Notch pathway in prostate adenocarcinoma Transl Androl Urol 2021 10 2067 2077 10.21037/tau-21-244 34159087
Zheng C, Wang J, Zhang J, Hou S, Zheng Y, Wang Q. Myelin and lymphocyte protein 2 regulates cell proliferation and metastasis through the Notch pathway in prostate adenocarcinoma. Transl Androl Urol. 2021;10:2067–77. 10.21037/tau-21-244.34159087
27. Bhandari A Shen Y Sindan N Xia E Gautam B Lv S MAL2 promotes proliferation, migration, and invasion through regulating epithelial-mesenchymal transition in breast cancer cell lines Biochem Biophys Res Commun 2018 504 434 439 10.1016/j.bbrc.2018.08.187 30195491
Bhandari A, Shen Y, Sindan N, Xia E, Gautam B, Lv S, et al. MAL2 promotes proliferation, migration, and invasion through regulating epithelial-mesenchymal transition in breast cancer cell lines. Biochem Biophys Res Commun. 2018;504:434–9. 10.1016/j.bbrc.2018.08.187.30195491
28. Fan Y He S The characteristics of tumor microenvironment in triple negative breast cancer Cancer Manag Res 2022 14 1 17 10.2147/cmar.s316700 35018117
Fan Y, He S. The characteristics of tumor microenvironment in triple negative breast cancer. Cancer Manag Res. 2022;14:1–17. 10.2147/cmar.s316700.35018117
29. Terry S Savagner P Ortiz-Cuaran S Mahjoubi L Saintigny P Thiery J-P New insights into the role of EMT in tumor immune escape Mol Oncol 2017 11 824 846 10.1002/1878-0261.12093 28614624
Terry S, Savagner P, Ortiz-Cuaran S, Mahjoubi L, Saintigny P, Thiery J-P, et al. New insights into the role of EMT in tumor immune escape. Mol Oncol. 2017;11:824–46. 10.1002/1878-0261.12093.28614624
30. Xu D Li J Li R Lan T Xiao C Gong P PD-L1 expression is regulated by NF-κB during EMT signaling in gastric carcinoma Onco Targets Ther 2019 12 10099 10105 10.2147/ott.s224053 31819504
Xu D, Li J, Li R, Lan T, Xiao C, Gong P. PD-L1 expression is regulated by NF-κB during EMT signaling in gastric carcinoma. Onco Targets Ther. 2019;12:10099–105. 10.2147/ott.s224053.31819504
31. Zhang L Xu L-J Zhu J Li J Xue B-X Gao J ATM-JAK-PD-L1 signaling pathway inhibition decreases EMT and metastasis of androgen-independent prostate cancer Mol Med Rep 2018 17 7045 7054 10.3892/mmr.2018.8781 29568923
Zhang L, Xu L-J, Zhu J, Li J, Xue B-X, Gao J, et al. ATM-JAK-PD-L1 signaling pathway inhibition decreases EMT and metastasis of androgen-independent prostate cancer. Mol Med Rep. 2018;17:7045–54. 10.3892/mmr.2018.8781.29568923
32. Quan Z Yang Y Zheng H Zhan Y Luo J Ning Y Clinical implications of the interaction between PD-1/PD-L1 and PI3K/AKT/mTOR pathway in progression and treatment of non-small cell lung cancer J Cancer 2022 13 3434 3443 10.7150/jca.77619 36313041
Quan Z, Yang Y, Zheng H, Zhan Y, Luo J, Ning Y, et al. Clinical implications of the interaction between PD-1/PD-L1 and PI3K/AKT/mTOR pathway in progression and treatment of non-small cell lung cancer. J Cancer. 2022;13:3434–43. 10.7150/jca.77619.36313041
33. Oner G Önder S Karatay H Ak N Tükenmez M Müslümanoğlu M Clinical impact of PD-L1 expression in triple-negative breast cancer patients with residual tumor burden after neoadjuvant chemotherapy World J Surg Oncol 2021 19 264 10.1186/s12957-021-02361-9 34474671
Oner G, Önder S, Karatay H, Ak N, Tükenmez M, Müslümanoğlu M, et al. Clinical impact of PD-L1 expression in triple-negative breast cancer patients with residual tumor burden after neoadjuvant chemotherapy. World J Surg Oncol. 2021;19:264. 10.1186/s12957-021-02361-9.34474671
34. Shi L Chen S Yang L Li Y The role of PD-1 and PD-L1 in T-cell immune suppression in patients with hematological malignancies J Hematol Oncol 2013 6 74 10.1186/1756-8722-6-74 24283718
Shi L, Chen S, Yang L, Li Y. The role of PD-1 and PD-L1 in T-cell immune suppression in patients with hematological malignancies. J Hematol Oncol. 2013;6:74. 10.1186/1756-8722-6-74.24283718
