BCAT2 Shapes A Noninflamed Tumor Microenvironment And Induces Resistance To Anti-PD-1/PD-L1 Immunotherapy By Negatively Regulating Proinflammatory Chemokines And Anticancer Immunity

To improve the response rate of monotherapy of immune checkpoint blockade (ICB), it is necessary to find an emerging target in combination therapy. Through analyzing tumor microenvironment (TME)-related indicators, it is validated that BCAT2 shapes a noninflamed TME in bladder cancer. The outcomes of multiomics indicate that BCAT2 has an inhibitory effect on cytotoxic lymphocyte recruitment by restraining activities of proinflammatory cytokine/chemokine-related pathways and T-cell-chemotaxis pathway. Immunoassays reveal that secretion of CD8+T-cell-related chemokines keeps a robust negative correlation with BCAT2, generating a decreasing tendency of CD8+T cells around BCAT2+ tumor cells from far to near. Cotreatment of BCAT2 deficiency and anti-PD-1 antibody has a synergistic effect in vivo, implying the potential of BCAT2 in combination therapy. Moreover, the value of BCAT2 in predicting the efficacy of immunotherapy is validated in multiple immunotherapy cohorts. Together, as a key molecule in TME, BCAT2 is an emerging target in combination with ICB and a biomarker of guiding precision therapy.

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1. Introduction

Bladder cancer (BLCA) is one of the most common malignancies in the urinary system.[1] It is estimated that over 430,000 patients are diagnosed worldwide every year.[2] In spite of radical surgery treatment, almost half of patients with muscle-invasive bladder cancer occur metastasis.[3] Hence, systemic therapy plays an important role in advanced bladder cancer. With the development of immunotherapy, especially immune checkpoint blockade (ICB), accumulating evidence indicated that ICB has an excellent performance in eliminating tumor burden.[4] However, there are still a large number of patients who fail to respond to monotherapy of ICB by reason of primary resistance or acquired resistance.[5] For resolving the unmet clinical need, the combination of other rational therapies with ICB provides a brand-new insight into monotherapy resistance. The tumor microenvironment (TME) is a complicated system and has a profound impact on the efficacy of immunotherapy.[6] With insufficient infiltration level of cytotoxic T lymphocytes (CTLs), noninflamed TME was considered as a critical factor in failing to generate a potent antitumor immune response during immunotherapy.[7] Therefore, it is vital to find a key molecule that can remodel noninflamed TME into inflamed TME and has the potential to be a combination therapy target. Branched-chain aminotransferase 2 (BCAT2) is a core enzyme in the process of sulfur amino acid metabolism.[8] Li et al. found that BCAT2 was essential for the development of pancreatic cancer by mediating branched-chain amino acids (BCAAs) catabolism.[9] Lee et al. demonstrated that BCAT2 deficiency inhibited tumor growth of pancreatic ductal adenocarcinoma (PDAC) by regulating lipid metabolism.[10] Although there were several researches documented that BCAT2 directly influenced the biological process of tumors by regulating metabolic-related pathways, its role in regulating TME immune status has never been explored. In our study, through a comprehensive analysis of TME-related indicators in the Xiangya BLCA cohort and multiple public BLCA cohorts, we screened that BCAT2 probably shapes an immunosuppressive TME in BLCA. For exploring molecular mechanisms, we further performed single-cell RNA sequencing (siRNA seq) and bulk-RNA seq, revealing that BCAT2 plays a repressive part in the recruitment of CTLs into TME, by inhibiting activities of cytokine/chemokine-associated signal pathways and Tcell-chemotaxis signal pathways. In vitro, multi-immunoassays demonstrated that secretion of CTL-related chemokines has stable negative correlations with expression of BCAT2. According to panoramic analysis of tissue microarray (TMA), we found a mutually exclusive relationship between CTLs and BCAT2+ tumor cells in spatial distribution. In vivo, a cooperative effect was revealed in combination therapy of BCAT2 loss and ICB. More importantly, its role in forecasting the curative effect of immunotherapy was validated in the Xiangya BLCA immunotherapy cohort.

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2. Results

2.1. BCAT2 Negatively Correlates with Anticancer Immunity of TME in BLCA

In most cancer types, the expression of BCAT2 is higher in cancer tissues than in normal tissues (Figure S1A, Supporting Information) and its expression pattern in BLCA was validated in Xiangya BLCA Cohort (Figure S1B, Supporting Information). Besides, BCAT2 is widely expressed in various cancer cell lines (Figure S1C, Supporting Information). Further, the outcomes of comprehensive pan-cancer analysis on chemokine system, MHCs, immunostimulators and TIICs indicated that BCAT2 has significant immunosuppressive effects in a couple of cancer types, including bladder cancer (BLCA), breast cancer (BRCA), kidney renal papillary cell carcinoma (KIRP), pancreatic adenocarcinoma (PAAD), sarcoma (SARC) and thyroid carcinoma (THCA) (Figure S2A, B, Supporting Information). In addition, negative correlations between BCAT2 and common inhibitory immune checkpoints (PD- 1, PD-L1, CTLA-4, and LAG-3) were also found (Figure S2C–F, Supporting Information). Through comprehensive pan-cancer analysis, we found that BCAT2 has the most profound immunosuppressive effect in BLCA. Hence, we further explored its immunological role in BLCA. On the basis of the median expression value of BCAT2, we divided individuals into the high-BCAT2 group and low-BCAT2 group in the TCGA-BLCA cohort. Obviously, a series of chemokines, chemokine receptors, MHC-related molecules, and immunostimulators were down-expressed in the high-BCAT2 group and over-expressed in the low-BCAT2 group (Figure 1A). A similar outcome was found in the cancer immunity cycle, which covers multiple essential steps in recruiting and infiltrating immune cells (Figure 1B). Moreover, we demonstrated robust negative connections between infiltration levels of immune cells (CD8+T cell, CD4+T cell, nature killer (NK) cell, and dendritic (DC) cell) and BCAT2 in different algorithms (Figure 1C). Likewise, lower expression of immune cell’s effector genes (CD8+T cell, NK cell, Macrophage, T helper 1 (Th1) cell, and DC cell) were found in the high-BCAT2 group compared to the low-BCAT2 group (Figure 1D). More importantly, when correlated BCAT2 with TIS score (Figure S3, Supporting Information) and inhibitory immune checkpoints (Figure 1E), obvious negative relationships were revealed between them. Hence, we speculated that BCAT2 may negatively regulate the TME immune response and affect the efficacy of immunotherapy profoundly. Furthermore, the above results were well validated in nine independent BLCA cohorts (GSE31684, GSE32894, GSE48075, GSE48276, GSE69795, GSE83586, GSE86411, GSE87304, and GSE128702) (Figures S4– S12, Supporting Information). At last, we verified the association between BCAT2 and TME again in Xiangya BLCA Cohort. Consistently, the expression of BCAT2 has negative relations to the activity of the cancer immunity cycle, infiltration levels of TIICs, TIS score, and expression levels of immune checkpoints (Figure 2A–C). Collectively, we revealed that high expression of BCAT2 forms a noninflamed TME in BLCA and low expression of BCAT2 shapes an inflamed TME in BLCA.

2.2. Exploring the Mechanism of BCAT2 in Shaping TME by Bulk RNA-seq and siRNA-seq

In the TCGA-BLCA cohort, DEGs between high/low BCAT2 groups, high/low immune score groups, and high/low stromal score groups were identified and took intersection (Figure S13A, Supporting Information). Interestingly, BCAT2 positive-related DEGs had no intersection with immune and stromal score positive-related DEGs (Figure 2D).Meanwhile, the same phenomenon occurred in negative-related EGs among them (Figure S13B, Supporting Information). These findings indicated that BCAT2 probably negatively regulates immune immune-related pathways. Coincidently, the results of GO and KEGG analyses revealed that BCAT2-related DEGs were most significantly enriched in the pathways of leukocyte migration, T cell activation, and cytokine-–cytokine receptor interaction (Figure 2E, F). Hence, we speculated that BCAT2 shapes a noninflamed TME in BLCA by inhibiting the activities of cytokine/chemokine-related pathways. However, the characteristic of bulk NA sequencing determines its limitation, whose expression level of the gene is calculated by the mean value of all cells in the tissue. To overcome the limitation, siRNA was performed on three BLCA samples. More than 19 000 cells were analyzed and classified into six categories: epithelial cell, fibroblast cell, T/NK cell, endothelial cell, B cell, and myeloid cell (Figure 3A), refer to recognized biomarkers (EPCAM, LYZ, CD3D, COL1A1, CD79A, CD19, PECAM1, and VWF).[11] Strikingly, BCAT2 was mainly expressed in epithelial cells (EPCAM+), rather than endothelial cells and immune cells. On the basis of CNV accumulation, EPCAM+ epithelial cells were regarded as malignant urothelial cells. To validate the expression pattern of BCAT2, two external siRNA cohorts were included in the study. In the GSE135337 scRNA cohort, more than 36,000 cells were processed and divided into five clusters, The primarily expressed cell subtype of BCAT2 was still a malignant bladder urothelial cell (Figure 3B). A similar expression pattern reappeared in the GSE145137 siRNA cohort (Figure 3C). Therefore, the pattern of majority expression on malignant cells inferred that BCAT2 acts as an immunoregulator part in TME mainly by means of varying characteristics of cancer cells. Based on the expression level of BCAT2, malignant urothelial cells were divided into a high-BCAT2 group and a low-BCAT2 group. GSEA analysis of GO terms in GSE135337 and GSE145137 siRNA cohorts revealed that a bunch of cytokine/chemokine-related pathways were significantly downregulated in the high-BCAT2 group, including chemokine production, chemokine secretion, regulation of chemokine mediated signaling pathway, response to chemokine, chemokine receptor binding, cytokine activity, cytokine binding and cytokine receptor binding (Figure 3D–G; Figure S14B, Supporting Information). Meanwhile, leukocyte chemotaxis, lymphocyte chemotaxis, T cell chemotaxis, and their regulatory pathways had significantly negative correlations to BCAT2 (Figure 3E–I; Figure S14A, Supporting Information). GSEA analysis of KEGG terms indicated that pathways of cytokine–cytokine receptor interaction and antigen processing and presentation were obviously downregulated in the high-BCAT2 group (Figure 3F). Together, high expression of BCAT2 represses the activities of proinflammatory cytokines and chemokines-related pathways, leading to a decreased infiltration level of TIICs, which shapes a noninflamed TME ultimately.

Figure 1. BCAT2 negatively correlates with anticancer immunity in TME of BLCA. A) Expression pattern of chemokines, chemokine receptors, MHC molecules, and immunostimulators in high and low BCAT2 groups. B) Activity of cancer immunity cycle in high and low BCAT2 groups. Seven colors represent seven steps in the cycle. *p < 0.05; **p < 0.01; ***p < 0.001. C) Correlations between BCAT2 and multiple types of immune cell (CD8+T cell, CD4+T cell, DC cell, and NK cell) in six independent algorithms. D) Expression patterns of multiple subtypes of immune cell (CD8+T cell, NK cell, Macrophage, Th1 cell, and DC cell) related effector genes in high and low BCAT2 groups. E) Correlations between BCAT2 and ICB related effector genes. Number in the circle means correlation coefficient.

Figure 2. Validation the function of BCAT2 by Xiangya BLCA cohort. A) Correlations between BCAT2/cancer immunity cycle (left) and BCAT2/TIICs (right). Solid and dotted lines mean positive and negative relationships, thickness of the lines mean coefficient of correlations, lines with different colors represent p-value of correlations. B) Correlations between BCAT2 and ICB related effector genes. C) Correlations between BCAT2 and TIS score related effector genes. Number in the circle represents correlation coefficient. D) Intersection of BCAT2, immune score and stromal score positive-related genes. E,F) Top 20 enrichment signaling pathways of GO and KEGG analyses in the BCAT2-related genes.

Figure 3. Exploring the mechanism of BCAT2 in shaping TME by bulk RNA-seq and scRNA-seq. A, B) tSNE plots of the single-cell expression pattern of BCAT2 in Xiangya siRNA cohort and GSE135337 cohort. C) Violin plot of the single-cell expression pattern of BCAT2 in the GSE145137 cohort. D, E) GSEA analysis of GO term indicates activities of cytokine/chemokine-related pathways and immune cells chemotaxis-related pathways between different BCAT2 groups in the GSE135337 cohort. F) GSEA analysis of KEGG term indicates activities of antigen presentation and interaction of cytokine and cytokine receptor pathway between different BCAT2 groups in the GSE145137 cohort. G–I) GSEA analysis of GO term indicates activities of cytokine/chemokine-related pathways, immune cells chemotaxis-related pathways, and immune cells migration-related pathways between different BCAT2 groups in the GSE145137 cohort. J, K) GO and KEGG analysis of DEGs between BCAT2 OE and BCAT2 KD cell lines.

2.3. BCAT2 Varies Expression Patterns of CD8+T-Cell-Related Chemokines and Inhibits Cytotoxic Capacity of CTLs

To validate the enrichment pathways mentioned above, BCAT2 overexpression (BCAT2 OE) and BCAT2 knockdown (BCAT2 KD) human (T24)/murine (MB49) bladder cancer cell lines were constructed successfully (Figure S15A–D, Supporting Information) and were tested with high-throughput RNA sequencing. Expectedgy, GO analysis of T24 cell lines revealed that DEGs were significantly enriched in the pathways of chemokine-mediated signaling, response to chemokine, leukocyte migration, and leukocyte migration (Figure 3J). KEGG pathway analysis of T24 cell lines showed that the chemokine signaling pathway, cytokine-–cytokine receptor interaction pathway, and bladder cancer pathway were significantly enriched (Figure 3K). Similarly, several critical cytokine/chemokine-related pathways were found in GO and KEGG analyses of MB49 cell lines (Figure S16A, B, Supporting Information). Self-evidently, various cytokines and chemokines play critical roles in regulating TME. Therefore, it is necessary to screen cytokines/chemokines which are specifically regulated by BCAT2. Hence, ProcartaPlex multiple immunoassays were applied to identify secretion variation of cytokines/chemokines in human and mouse bladder cancer cell lines. Surprisingly, secretion levels of CCL3, CCL4, CCL5, and CXCL10 upregulated in human and murine BCAT2-KD cell lines and downregulated in human and murine BCAT2-OE cell lines (Figure 4A, B; Figure S16C, D, Supporting Information). In previous studies, CCL3, CCL4, CCL5, CXCL9, and CXCL10 were regarded as crucial chemokines for recruiting CD8+T cells into TME.[12] Consequently, for a better comprehensive analysis of CD8+T-cell-related chemokines, all of them were included for further validation. Strikingly, RNA expression levels of CCL3, CCL4, CCL5, CXCL9, and CXCL10 were significantly downregulated in BCAT2 OE cells and significantly upregulated in BCAT2 KD cells (Figure 4C). Similarly, outcomes of ELISA showed that secreted protein levels also had negative correlations with an expression of BCAT2 (Figure 4D). These findings indicated that overexpression of BCAT2 inhibits transcriptome and protein levels of CD8+T-cell-related chemokines, including CCL3, CCL4, CCL5, CXCL9, and CXCL10. More importantly, the chemotaxis assay revealed that the BCAT2 OE cell line significantly inhibited the chemotaxis ability of CD8+T cells (Figure 4E, left). Conversely, cell supernatants of the BCAT2 KD cell line were capable of attracting more CD8+T cells than its negative control (Figure 4E, right). These findings indicated that BCAT2 can affect the chemotaxis capacity of CD8+T cells by regulating mRNA and protein levels of related chemokines. Furthermore, a T-cell-mediated cancer cell-killing assay was used to explore the cytotoxicity variation of T cells after direct contact with tumor cells expressing a different level of BCAT2. As shown in Figure 4F and Figure S17A, B (Supporting Information), overexpression of BCAT2 on tumor cells directly suppressed the cytotoxic function of T cells and knockdown of BCAT2 on tumor cells significantly reversed the tendency. Flow cytometry analysis of collected T cells found that proportions of CD8+TNF-𝛼+ T cells and CD8+IFN-𝛾+ T cells had significant differences in different coculture systems, which meant a massive change in the activity of CD8+T cells (Figure 4G; Figure S17C, D, Supporting Information). Collectively, BCAT2 is capable of inhibiting the recruitment ability of CD8+T cells by regulating related chemokines levels as well as suppressing activities of CTLs by direct contact. Additionally, the outcome of the T-cell-mediated cancer cell killing assay (without T cells group) can infer that BCAT2 plays an oncogenic role in bladder cancer. Therefore, plate colony assay and transwell migration/invasion assay were utilized to validate this hypothesis. As anticipated, overexpression of BCAT2 significantly enhanced the proliferation, migration, and invasion ability of tumor cells, and knockdown of BCAT2 markedly suppressed these behaviors (Figure S18A–F, Supporting Information).

2.4. Validation of Exclusive Spatial Relationship between BCAT2+ Tumor Cell and CD8+T Cell by TMA of Xiangya BLCA Cohort

According to outcomes of bulk RNA-seq, scRNA-seq, and vitro experiments, we demonstrated that BCAT2 plays an immunosuppressive role in BLCA by suppressing recruitment and cytotoxicity of CD8+T cells. However, the interaction of BCAT2+ tumor cells and CD8+T cells is still unknown at the human tissue level. In the Xiangya BLCA cohort, representative images and an overall score of IHC revealed a negative correlation between BCAT2 and CD8 (R = −0.38, p = 0.0038) (Figure 5A, B). Multicolor IF staining of BCAT2+ tumor cells (BCAT2+CK19+) and BCAT2+CD8+T cells was conducted in TMA and semiautomatic analyzed using the TissueFAXS panoramic quantification platform. As shown in Figure 5C, the expression scope of BCAT2 and CK19 was largely overlapped. Conversely, the expression scope of BCAT2 and CD8 was distinct and separate. Detailed coexpression proportions of BCAT2+CK19+ cells and BCAT2+CD8+T cells were 87.29% and 5.09% (Figure 5D, E), which was consistent with the expression pattern of BCAT2 in scRNA-seq. In overall TMA, there was still a significant difference in coexpression proportion between them (Figure S19A, Supporting Information). More importantly, in multidimensional distance gradient analysis (0–25, 25–50, 50–100, and 100–150 μm) around BCAT2+ tumor cells, counts of CD8+T cells were gradually increased from near to far (Figure 5F; Figure S19B, Supporting Information). Together, we demonstrated that on the human tissue level, BCAT2 is also mainly expressed in tumor cells, and the BCAT2+ tumor cell has an exclusive spatial relationship with CD8+T cell.

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2.5. Loss of BCAT2 Enhances Efficacy of Anti-PD-1 Therapy

With a prominently adverse impact on TME and a negative close correlation to inhibitory checkpoint blockade, it arouses great interest to explore the synergistic effect of BCAT2 loss and anti-PD- 1 treatment. Ahead of immunotherapy, a subcutaneous bladder cancer model was built by BCAT2 KD and control murine cell lines. Then, anti-PD-1 therapy and control therapy were applied to tumor-bearing mice (Figure 6A). Consistently with its oncogenic role and regulation mechanism on chemokines in vitro, BCAT2 deficiency inhibited tumor growth and prolonged survival time in vivo, by upregulating CD8+T related chemokines (Figure 6B–E; Figure S20A, Supporting Information). In the aspect of immunotherapy efficacy, although anti-PD-1 monotherapy could partly decrease tumor burden, cotreatment of BCAT2 KD and anti-PD-1 monoclonal antibody (mab) indicated a better tumor suppression effect and gained more survival benefit. On the scheduled day, tumors were harvested and prepared for further analysis. A part of them was digested into single-cell suspension and conducted flow cytometry analysis. With a similar population of leukocytes and T cells (Figure S20B–E, Supporting Information) in the tumor, a higher proportion of CD8+T cells was shown in the BCAT2 deficiency group than in the negative control group. Similarly, the combination therapy group had a more massive infiltration of CTLs than the monotherapy group (Figure 6F–H). More importantly, cytotoxicity indicators (GZMB, IFN-𝛾, TNF-𝛼, and Perforin) of CTLs were also reinforced in murine tumors with BCAT2 loss and combination treatment compared to corresponding control (Figure 6G, I–L). The other parts of the tumors were made into frozen sections and implemented IF staining. As expected, the density of CD8+T cells in the region of interest (ROI) showed the same tendency with flow cytometry analysis (Figure 6M, N). These findings indicated that BCAT2 deficiency on tumor cells can shape an inflamed TME and have great efficacy with the treatment of immunotherapy. To figure out the role of CD8+T cells in the synergistic effect of cotreatment, CD8𝛼 mab was applied to antagonize them in immune-competent mice (Figure S21A, Supporting Information) and validated its depletion effect by flow cytometry and IF (Figure S21B, C, Supporting Information). Obviously, after depletion, tumor burden and survival time were significantly reversed (Figure S21D–G, Supporting Information). These findings indicated that CD8+T cells play an indispensable part in the synergistic effect of combination therapy.

Figure 4. BCAT2 varies expression patterns of CD8+T-cell-related chemokines and inhibits cytotoxic capacity of CTLs. A,B) Heatmaps of ProcartaPlex multiple immunoassays display secretion variation of common cytokines and chemokines in BCAT2 OE, BCAT2 KD and negative control groups (n = 3 per group). C,D) Histograms show normalized mRNA expression levels and protein secretion concentrations of CCL3, CCL4, CCL5, CXCL9, and CXCL10 in BCAT2 OE (left), BCAT2 KD (right) and negative control groups (n = 3 per group). *p < 0.05; **p < 0.01; ***p < 0.001. E) Chemotaxis assay indicates different chemotaxis ability of CTLs in BCAT2 OE, BCAT2 KD, and negative control groups (n = 3 per group). *p < 0.05; **p < 0.01. F) T-cell-mediated cancer cell killing assay indicates different killing abilities of T cells cocultured with BCAT2 OE, BCAT2 KD, and negative control cell lines (n = 3 per group). G) Flow cytometry analysis indicates different activities of CD8+T cells in different coculture groups (n = 3 per group).

Figure 5. Validation of exclusive spatial relationships of BCAT2+ tumor cells and CD8+T cells by TMA of Xiangya BLCA cohort. A) IHC image of BCAT2 and CD8 in inflamed and noninflamed types of TME. Scale bar: 50 μm. B) Correlation between BCAT2 and CD8 on the basis of IHC scores of them in overall TMA. C) Multicolor IF image of BCAT2, CK19, CD8, and combination index in inflamed and noninflamed types of TME. BCAT2+ cell (pink), CK19+ cell (cyan), CD8+T cell (orange), and cell nucleus (blue). Scale bar: 50 μm. D,E) Detailed coexpression rates of BCAT2+CK19+ and BCAT2+CD8+ cells in the typical sample. F) Distance gradient analysis (0–25, 25–50, 50–100, and 100–150 μm) of CD8+T cells around BCAT2+CK19+ cells in the typical sample.

Figure 6. Loss of BCAT2 enhances the efficacy of anti-PD-1 therapy. A) Flow diagram of the treatment plan. B) Harvested tumors of different therapy regimens (n = 5 per group). C–E) Quantification of tumor volume, body weight, and survival time in different therapy regimens (n = 5 per group). ns: no significance; *p< 0.05; **p < 0.01; ***p < 0.001. F) Contour plots indicate the proportion of CD8+T cells; G) proportions of GZMB+CD8+T cells, IFN-𝛾+CD8+T cells, TNF- 𝛼+CD8+T cells, and Perforin+CD8+T cells in different therapy regimens. H) Quantified scatter plots exhibit proportion of CD8+T cells; I–L) proportions of GZMB+ CD8+T cells, IFN-𝛾+ CD8+T cells, TNF-𝛼+ CD8+T cells, and Perforin+ CD8+T cells in different therapy regimens (n = 5 per group). ns: no significance; *p<0.05; **p < 0.01; ***p < 0.001. M, N) IF image and quantified histogram show densities of CD8+T cells in different therapy regimens (n = 3 per group). Scale bar: 20 μm. CD8+T cell (green) and cell nucleus (blue). ns: no significance, *p<0.05; **p < 0.01; ***p < 0.001.

Furthermore, alongside tumor cells, a small proportion of BCAT2 is also expressed in CD8+T cells. Hence, we further explored whether the different expression level of BCAT2 on CD8+T cells can affect their activities. After staining single-cell suspension of the murine spleen with BCAT2 and T cell-related fluorescent antibodies, we compared proportions of CD8+ TNF-𝛼+T and CD8+ IFN-𝛾+T cells between BCAT2+CD8+ and BCAT2−CD8+ groups. Interestingly, we found that the proportions were similar between the two groups, revealing that the activity of the CD8+T cell is independent of its expression level of BCAT2 (Figure S22A–C, Supporting Information).

2.6. BCAT2 Predicts Response to Immunotherapy in Several Real-World Immunotherapy Cohorts

The above findings have demonstrated an immunosuppressive role of BCAT2 in TME and the synergetic effect of combining BCAT2 loss with anti-PD-1 therapy. However, its predictive potential on immunotherapy efficacy is unclear. Therefore, in the TCGA-BLCA cohort, enrichment scores of immunotherapy-related pathways were compared between high and low BCAT2 groups. As shown in Figure S23A (Supporting Information), the low-BCAT2 group had more active genes in immunotherapy-related pathways, which can infer that low expression of BCAT2 represents a more sensitive state to immunotherapy. For further validation, 58 muscle-invasive bladder cancer (MIBC) patients who accepted neoadjuvant anti-PD-1 therapy in our hospital were included in the Xiangya BLCA immunotherapy cohort. Representative IHC, IF, and CT images of the responder and nonresponder implied that the expression level of BCAT2 has a close relation to the efficacy of immunotherapy—an individual with a low BCAT2 expression level is more likely to respond to immunotherapy than an individual with a high BCAT2 expression level (Figure 7A–C). Moreover, the IHC score of the Xiangya BLCA immunotherapy cohort and mRNA expression matrix of the IMvigor210 cohort indicated a robust negative correlation between BCAT2 and PD-L1 (R = −0.4, p = 0.002; R = −0.41, p < 0.001) (Figure S23B, C, Supporting Information). Hence, we directly compared the efficacy of immunotherapy between high and low BCAT2 groups in the Xiangya BLCA immunotherapy cohort and found that the low BCAT2 group had a significantly higher proportion of responders than the high BCAT2 group (p = 0.001) (Figure 7D). Moreover, we assessed the prediction values of BCAT2, PD-L1, and combination index (BCAT2+PD-L1) on pathological response to immunotherapy and found the great performance of combination index (BCAT2+PD-L1) on prediction accuracy (Figure 7E). Inspiringly, in the aspect of survival outcome, the low BCAT2 group had a longer disease-free survival (DFS) than the high BCAT2 group (p = 0.032) (Figure 7F), demonstrating the prognosis value of BCAT2 in BLCA immunotherapy. In clinical, individuals with desert TME are more likely to have a limited immunotherapy efficacy than individuals with inflamed TME, further highlighting the importance of forecasting indicators. Consequently, we selected all the individuals with desert TME in the IMvigor210 cohort and conducted the comprehensive assessment. In a direct comparison of the expression level of BCAT2 among different response groups, the CR group (responder) had a significantly lower expression level of BCAT2 than the SD and PD groups (nonresponder) (Figure 7G). More importantly, the combination index (BCAT2+PD-L1) also had a terrific performance on prediction accuracy (Figure 7H). Although there was no significant difference in overall survival (OS) between high and low BCAT2 groups in the desert type of IMvigor210 cohort, prognosis tendency was still similar to the outcome of the Xiangya BLCA immunotherapy cohort (Figure 7I). Apart from PD-L1, microsatellite instability (MSI) is the other essential predictor of the efficacy of immunotherapy. The status of mismatch repair (MMR)—proficient MMR (pMMR) and defi- cient MMR (dMMR) keep high conformity with low-frequency MSI (MSI-L) and high-frequency MSI (MSI-H).[13] Hence, we conducted a comprehensive evaluation of all the individuals in the Xiangya BLCA immunotherapy cohort based on the staining results of four MMR marker genes (MLH1, MSH2, MSH6, and PMS2). The outcome revealed a significantly higher proportion of dMMR (MSI-H) in the low BCAT2 group compared to the high BCAT2 group (p = 0.02) (Figure S23D, E, Supporting Information). Besides, we evaluated the predictive values of MMR, BCAT2, and combination index (MMR+BCAT2) on immunotherapy efficacy and found that the combination index (MMR+BCAT2) had an eligible performance on prediction accuracy (Figure S23F, Supporting Information). Together, these findings demonstrated that BCAT2 is qualified to act as a complementary predictor of the efficacy of BLCA immunotherapy. Finally, the predictive value of BCAT2 in response to immunotherapy was explored in various cancer types. We found that individuals with high expression of BCAT2 showed a significantly poorer response rate (p = 0.04) in the GSE35640 cohort (melanoma) (Figure 7J). Although there were no significant differences in response rate between high and low BCAT2 groups in GSE173839 (breast cancer), GSE135222 (NSCLC), and Gide 2019 cohort (melanoma), patients with low expression of BCAT2 were still more likely to have a positive response to immunotherapy (Figure 7K–M).

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2.7. The Value of BCAT2 in Forecasting Molecular Subtype and Guiding Precision Therapy

With great heterogeneity existing in traditional histology classification, increasing evidence showed that molecular subtype is able to provide more accurate classification on the basis of transcriptome profile in BLCA.[14] Furthermore, with similar immunolog ical characteristics in the same subtype, molecular classification has great potential to predict TME and guide precision therapy in clinical.[15] Through comprehensive analysis of different molecular subtype systems in the TCGA-BLCA cohort, we found that individuals with low expression of BCAT2 are more likely classified into a basal subtype, accompanied by activated pathways of basal differentiation, EMT differentiation, immune differentiation, interferon response, and so on. Individuals with high expression of BCAT2 are mainly classified into luminal subtypes, accompanied by active pathways of luminal differentiation, urothelial differentiation, and Ta (Figure S24A, Supporting Information). According to the consensus of molecular subtype, the basal subtype has more infiltration level of effector lymphocytes and a more active IFN-𝛾 signaling pathway than the luminal subtype,[16] which implies a better efficacy of immunotherapy. Then, AUC was utilized to evaluate the accuracy of prediction. Amazingly, except Baylor subtype system (AUC = 0.76), most of the AUC was beyond 0.90 in other systems (Figure S24B, Supporting Information), which means a robust prediction accuracy of BCAT2 in the molecular subtype. To further explore its role in predicting the efficacy of other therapies, we compared the activity of epidermal growth factor receptor (EGFR) target therapy and radiotherapy-related pathways between high and low BCAT2 groups. With obvious up-regulated activities, patients in the low-BCAT2 group are more likely to have a positive response to EGFR-target therapy and radiotherapy (Figure S24C, Supporting Information). For enhancing prediction reliability on molecular subtype and treatment sensitivity, eight BLCA cohorts (Xiangya BLCA cohort, GSE31684, GSE69795, GSE48075, GSE128702, GSE83586, GSE52329, and E-MTAB-1803) and one BLCA immunotherapy cohort (IMvigor210) were applied for external validation. Along with excellent accuracy of prediction, similar findings were found in these cohorts (Figures S25–S27, Supporting Information). Hyperprogressive disease (HPD) is an extremely insensitive state to immunotherapy. For exploring the influence of BCAT2 on HPD, HPD-associated biomarkers were collected from previous studies[17] and the CNV of biomarkers were compared between low and high BCAT2 groups. Except for EGFR, CNV amplification rates of other HPD-positive associated biomarkers (red symbol) were higher in the high-BCAT2 group, especially MDM2 (p = 0.0116). CNV deletion rates of HPD-negative associated biomarkers (green symbol) were lower in the high-BCAT2 group (Figure S24D, Supporting Information). The outcome indicated that patients with high expression of BCAT2 have a potential risk of HPD during immunotherapy. Neoadjuvant chemotherapy (NAC) is a crucial therapy in BLCA. Markers forecasting response to NAC in BLCA were collected from a review of Buttigliero et al.[18] and their mutation rates were compared between the two groups. Based on higher overall mutation rates of biomarkers and more frequent mutation of RB1 in the low-BCAT2 group, we speculated that individual with low expression of BCAT2 has a better efficacy of NAC in clinical (Figure S24E, Supporting Information). Hence, the Drugbank database was utilized to compare the efficacies of several common chemotherapy regimens between two groups and found that individuals with low expression of BCAT2 are more likely to response to chemotherapy. Additionally, the low-BCAT2 group also showed better efficacies in common regimens of immunotherapy and ERBB therapy. Unexpectedly, patients with high expression of BCAT2 probably gain better curative effects in antiangiogenic therapy (Figure S24F, Supporting Information). Collectively, individuals with low expression of BCAT2 belong to an inflamed molecular subtype, basal subtype, which means a more positive response to immunotherapy, chemotherapy, radiotherapy, EGFR-target therapy, and ERBB therapy. Unfortunately, patients with high expression of BCAT2 have a poor response to these treatments. However, antiangiogenic therapy may be a glimmer of light to them.

Figure 7. BCAT2 predicts response to immunotherapy in several real-world immunotherapy cohorts. A) IHC image of BCAT2 and PD-L1 between responder and nonresponder in Xiangya BLCA immunotherapy cohort. Scale bar: 50 μm. B) Multicolor IF image of BCAT2 and PD-L1 between responder and nonresponder in Xiangya BLCA immunotherapy cohort. Scale bar: 50 μm. BCAT2+cell (green), PD-L1+cell (yellow), and cell nucleus (blue). C) CT image of difference in immunotherapy efficacy between individuals with high and low expression levels of BCAT2. The red arrow points to the tumor area. D) Overall difference in response rate between high and low BCAT2 groups in Xiangya BLCA immunotherapy cohort. E) Prediction accuracy of BCAT2, PD-L1, and combination index (BCAT2+PD-L1) on response to immunotherapy in Xiangya BLCA immunotherapy cohort. F) Difference in disease-free survival (DFS) between high and low BCAT2 groups in Xiangya BLCA immunotherapy cohort. G) Differences in the expression level of BCAT2 among CR, PR, SD, and PD groups in the desert type of IMvigor210 cohort. *p < 0.05; ns: no significance. H) Prediction accuracy of BCAT2, PD-L1, and combination index (BCAT2+PD-L1) on response to immunotherapy in a desert type of IMvigor210 cohort. I) Difference in overall survival (OS) between high and low BCAT2 groups in a desert type of IMvigor210 cohort. J–M) Differences in response to immunotherapy between high and low BCAT2 groups in GSE35640, GSE173839, GSE135222, and Gide2019 cohorts.

3. Discussion

As a key enzyme in the catabolism process of BCAAs, previous research mainly focused on the metabolic-related role of BCAT2 in the diseases, such as obesity, diabetes, and arrhythmia. Ma et al. demonstrated that knocking out BCAT2 in adipose tissue can accelerate adipose browning and thermogenesis, which leads to a reduced obesity rate in mice.[19] Through detecting blood samples of more than 2000 individuals, Gerszten et al. depicted a diabetes-related metabolite profile and found that BCAT2-transformed BCAAs play a crucial part in the development of disease.[20] Portero et al. revealed that nonsense mutations of BCAT2p.Q300*/p.Q300* result in elevated levels of BCAAs and induce arrhythmias in mice.[21] Recently, a couple of studies found that BCAT2 has a close relationship with cancer. Lei et al. revealed that acetylation-mediated degradation of BCAT2 can retard the development of pancreatic ductal adenocarcinoma (PDAC) by downregulating the metabolism level of BCAAs.[22] Wang et al. considered that restrained transcription level of BCAT2 leads to a decreased intracellular glutamate level, which stimulates ferroptosis of hepatoma cells.[23] However, all of the existing research emphasized the influence of BCAT2-mediated BCAA catabolism on the biological process of cancer rather than exploring a brand-new mechanism. In this study, we revealed the immunosuppressive role of BCAT2 in TME for the first time. Application of immunotherapy rede- fines cancer treatment, with terrific living quality and prolonged survival time. Nonetheless, on account of the heterogeneity of the tumor immune ecological system, just a portion of individuals gain satisfactory efficacy. TME is a complicated system, composed of tumor cells, immune cells, stromal cells, and capillaries.[24] According to the infiltration level of CTLs, TME can be classified into inflamed and noninflamed types in general.[25] Growing evidence indicated that inflamed TME plays a positive role in eliciting effective antitumor immune responses during therapy.[26] Hence, we comprehensively analyzed multiple TME-related indicators and found that high expression of BCAT2 shapes a noninflamed TME, with impeded cancer immunity cycle, repressive expression pattern of chemokine profile, MHC molecules, and insufficient infiltration level of TIICs. In addition, BCAT2 is negatively related to TIS and effector genes of ICB, which implies that individuals with high expression of BCAT2 are more likely not to respond to immunotherapy. To validate our hypothesis, we compared the response rate between the high and low BCAT2 groups in the Xiangya BLCA immunotherapy cohort and other immunotherapy cohorts. Surprisingly, there were significant differences in multiple cohorts, which indicated that BCAT2 is also capable of playing a predictor of immunotherapy’s efficacy, especially in BLCA. Furthermore, scRNA-seq was used for exploring the regulating mechanism of BCAT2 in TME and indicated that activity of cytokine/chemokine related signaling pathways, T cell chemotaxis signaling pathway, and T cell migration signaling pathway have significant negative correlations with BCAT2. As a critical regulation network, cytokines and chemokines are indispensable for trafficking various immune cells into TME. The chemokine system has four superfamilies: C, CC, CXC, and CX3C, with considerable redundancy in the pairing of ligands and receptors.[27] Cytokine can be divided into Th1, Th2, and Th17 subfamilies based on their biological function.[28] Through the secretion of different levels of them, the wrestle between tumor cells and immune cells is enough to reprogram the characteristics of TME.[29] Nonetheless, among a bulk of cytokine and chemokine, it is requisite to screen out the most valuable cluster. Using a 34-cytokine and chemokine immunoassay panel, we revealed that chemokines including CCL3, CCL4, CCL5, CXCL9, and CXCL10, are robustly negatively correlated with the expression of BCAT2 in the human and murine bladder cancer cell. It is well known that CXCL9 and CXCL10 are responsible for recruiting CD8+T cells into TME.[30] To ascertain the function of CCL3, CCL4, and CCL5, we looked up previous research. Harlin et al. made use of protein arrays and qPCR to reveal that the upregulation of CCL3, CCL4, and CCL5 is capable of promoting the migration of CD8+T cells into melanoma.[31] Noman et al. found that an increased secretion population of CCL5 assists in establishing a proinflammatory TME by trafficking CTLs into colorectal cancer tissue.[32] Through IHC and RNA-seq of chemokine profile in multiple cohorts, Romero et al. demonstrated that CCL4 and CCL5 have a strong association with infiltration level of CD8+T cells in pancreatic cancer.[33] Hence, we considered that CD8+T-cell-related chemokines are major chemokines regulated by BCAT2 in bladder cancer. Although we demonstrated the robust negative correlations between BCAT2 and CD8+T cell-related chemokines, the underlying regulatory mechanism between them still needs to be further explored. The study of Peterson et al. demonstrated that activation of the MAP kinase (MAPK) signaling pathway can induce the production of CX3CL1.[34] Xu et al. revealed that the JAK-STAT signaling pathway regulated the secretion of Th1-related chemokines, causing a decreased infiltration level of TILs.[35] Recently, Peng et al. found that demethylase JMJD3 can inhibit the expression of CD4+Tcell-related chemokines and reduce the infiltration level of cytotoxicity T cells in the TME, representing a crucial role of epigenetic modification in regulating chemokine expression.[36] Mayo et al. also revealed that an epigenetic inhibitor, histone deacetylase 1, has a great capacity to suppress the expression of CXCL8 via activation of the nuclear factor-𝜅B (NF-𝜅B) signaling pathway.[37] Moreover, as symbols of tumor cell metabolism, aerobic glycolysis and reactive oxygen species (ROS), indicating great performances on inducing expression of CXCL8 and CXCL14 by elevating activities of signaling pathways of NF-𝜅B and transcription factor activator protein 1 (AP-1).[38] Interestingly, in our study, signaling pathways of NF-𝜅B, STAT, and MAPK were significantly enriched between high and low BCAT2 cell lines (Figure 3K; Figure S16A, B, Supporting Information). Hence, combining with the above studies, we will further explore the key molecule in the regulation mechanism of BCAT2 and CD8+T-related chemokines. The limited objective response rate of monotherapy with ICB prompts the advent of a combination therapy strategy. For converting nonimmunologic TME into immunologic TME, Wolchok et al. applied cotreatment of nivolumab (anti-PD-1 therapy) and ipilimumab (anti-CTLA-4 therapy) in patients with melanoma and found an encouraging curative effect, ascribing to synchronous enhanced priming, activation and killing of CTLs.[39] More than combining with another type of ICB, chemotherapy plus immunotherapy is s alternative therapy option. As a first-line treatment regimen for advanced urothelial carcinoma, a couple of studies found that cisplatin-based chemotherapy is capable of shaping a proinflammatory TME by increasing the expression level of MHC I class on dendritic cells and damaging MDSCs and Tregs.[40] Coincidentally, Homma et al. revealed that another common chemotherapy drug for bladder cancer— gemcitabine—can improve the infiltration quantity of CD8+T cells and impair immunosuppressive immune cells in TME.[41] In our preclinical animal experiment, cotreatment of BCAT2 loss and anti-PD-1 mab displayed a more distinct antitumor efficacy compared to monotherapy of anti-PD-1 mab in immune-competent mice, which provides an innovative treatment strategy for ICB resistance patients. Meanwhile, through flow cytometry analysis and IF, we demonstrated that the knocking down of BCAT2 not only recruits more population of CTLs into TME but also enhances the killing activity of CTLs. Similarly, Peng et al. found that overexpression of LGALS2 decreases the quantity of infiltrating CTLs as well as cytotoxic biomarkers in breast cancer-bearing mice.[42] Yang et al. illustrated that CXCL13 is capable of heightening response to immunotherapy in ovarian cancer mouse models by increasing the infiltration level of CD8+T cells and secretion level of GZMB, IFN-𝛾, and IL-2.[43] Accompanying stronger anticancer response, combination therapy further upsets the existing immune feedback loop, which probably leads to severe side effects. According to the expression pattern of BCAT2 at the siRNA level, specifically expressed in tumor cells rather than in immune cells and endothelial cells, we can preliminarily infer that BCAT2 loss or inhibitor of BCAT2 is less likely to result in terrible adverse effects. However, the optimal dosage and interval time of BCAT2 inhibitor and anti-PD-1 mab in combination therapy still need to be explored.

For guiding precision therapy in individuals, we correlated BCAT2 to the molecular subtype of BLCA and critical biomarkers of various therapies. Based on credible predictions in multiple cohorts, we found that immunotherapy, chemotherapy, radiotherapy, and EFGR-target therapy are suitable for patients with low expression of BCAT2. Antiangiogenic therapy is recommended for patients with high expression of BCAT2. Different from the complicated detection process of molecular subtypes, BCAT2 is a portable predictor for precision therapy. Inevitably, there are some limitations in our studies. First, the sample sizes and the follow-up time of the Xiangya BLCA cohort and Xiangya BLCA immunotherapy cohort were limited. We need to further enlarge the sample size and continue standardized follow-up. Second, as a real-world cohort, the Xiangya BLCA immunotherapy cohort contained different surgery options which probably caused a potential bias. We will further enlarge our cohort and conduct subgroup analysis based on the same surgery option. Third, the cooperative effect of combination therapy in vivo needs to be further validated using the system application of BCAT2 inhibitor. In summary, as an immunosuppressive role in TME of BLCA, BCAT2 is an emerging target in combination therapy of ICB and an accurate biomarker of precision therapy.

Benefits of cistanche tubulosa-strengthen immune syste

4. Experimental Section

cohort: As illustrated in a previous study,[44] 56 eligible BLCA patients accepted surgical treatment (TURBT (transurethral resection of bladder tumor) or radical cystectomy) in Xiangya Hospital. All the samples were conducted through high throughput RNA sequencing (RNA-seq) to acquire transcriptome information. Then, data on RNA-seq, clinicopathologic features, and follow-up information of these patients were utilized to construct the Xiangya BLCA cohort (Table S1, Supporting Information). Xiangya BLCA immunotherapy cohort: 58 MIBC patients were included in the Xiangya BLCA immunotherapy cohort. All the samples were obtained by diagnostic TURBT before the implementation of neoadjuvant anti-PD-1 therapy. After at least two cycles standard neoadjuvant immunotherapy (NAI), TURBT, or radical cystectomy (RC) were conducted based on treatment response and patients’ willingness. 17 individuals with complete response (CR) and 16 individuals with partial response (PR) were classified into responders, 17 individuals with stable disease (SD), and 8 individuals with progressive disease (PD) were classified into nonresponders. The detailed clinicopathological characteristics are listed in Table S2 (Supporting Information). Xiangya scRNA cohort: Three samples of muscle-invasive bladder cancer were implemented scRNA-seq, constituting the Xiangya scRNA cohort. Detailed preparation of single-cell suspension, data transformation, and cell quality control have been elaborated in previous articles.[45] In simple terms, three tumor samples were loaded on a Chromium Single Cell Controller instrument (10×Genomics, USA) to produce single-cell gel beads in suspension. Seurat R package was applied to transform the count matrix into Seurat format. Four standards were set up for excluding low-quality cells in the matrix: unique molecular identifier (UMI) amount < 1000, gene quantity < 200, log10GenesPerUMI < 0.70, and mitochondrial-originated UMI counts > 20%. After integrating the samples based on the top 3000 variable traits, principal component analysis (PCA) and the Findclusters algorithm were employed to identify main cell clusters. Based on the level of copy number variation (CNV) in epithelial cells, malignant bladder carcinoma cells were selected from clusters. The study plan was approved by the ethics committee of Xiangya Hospital, Central South University (Item number: 2021101175). All the specific men were collected complying with informed consent rights. The Cancer Genome Atlas (TCGA) Database: RNA expression matrix, survival outcome, and CNV of 33 types of carcinomas were acquired from the UCSC Xena database. Log2 transformation was employed to normalize RNA-seq data and the GISTIC algorithm was applied to process CNV data. Gene Expression Omnibus (GEO) Database: Transcriptome data of 1740 samples from nine BLCA cohorts: GSE31684 (93 samples), GSE48075 (142 samples), GSE69795(61 samples), GSE32894 (308 samples), GSE48276 (116 samples), GSE83586 (307 samples), GSE86411 (132 samples), GSE52329 (20 samples), GSE87304 (305 samples), and GSE128702 (256 samples) were obtained from GEO database. RNA-seq data of three immunotherapy cohorts: GSE35640 (14 samples, melanoma, and MAGE-A3 therapy), GSE173839 (105 samples, breast cancer, and anti-PD-L1 therapy), and GSE135222 (27 samples, nonsmall cell lung carcinoma (NSCLC), and anti-PD-1/PD-L1 therapy) were downloaded from GEO database. Single-cell transcriptomic characterization of two BLCA scRNA cohorts: GSE135337 (8 samples) and GSE145137 (3 samples) was obtained from the GEO database. Data processing was similar to the Xiangya siRNA cohort. Other Databases: RNA expression matrix and clinicopathologic characteristics of immunotherapy cohorts: IMvigor210 (348 samples, bladder cancer, and anti-PD-1 therapy) and Gide2019 (58 samples, melanoma, anti-PD-1, or anti-CTLA-4 therapy) were collected from http://researchpub.gene.com/IMvigor210CoreBiologies and TIDE database (http://tide. dfci.harvard.edu/). Data of RNA-seq and clinical characteristics of a BLCA cohort—E-MTAB-1803 (85 samples)—was downloaded from ArrayExpress (https://www.ebi.ac.uk/arrayexpress/). BCAT2 expression levels in various types of cancer cell lines were downloaded from the Cancer Cell Line Encyclopedia (CCLE) database. Specific clinicopathologic and follow-up information from public databases were listed in our previous studies.[44,46] Assessment of Immunological Identity of TME in BLCA: As depicted in our previous study,[44] a comprehensive evaluation of the immunological trait of TME in BLCA was summarized. The tracking tumor immunophenotype (TIP) website (http://biocc.hrbmu.edu.cn/TIP/) was used to assess the activities of cancer immunity cycles, which reflected the effectiveness of antitumor immunity. It is separated into seven concrete steps: release and presentation of tumor antigen (steps 1 and 2), startup and activation of effector T cells (step 3), trafficking and assisting diverse immune cells into TME (steps 4 and 5), recognition and killing cancer cells (steps 6 and 7).[47] Six independent algorithms (TIMER, CIBERSORT, CIBERSORT-ABS, MCP-COUNTER, TISIDB, and XCELL) were employed to calculate the infiltration levels of tumor-infiltrating immune cells (TIICs).[48] Furthermore, effector genes of immune cells, ligands and receptors of chemokines, major histocompatibility complex (MHC) molecules, and immunostimulators were collected for evaluating immunological characteristics of TME multi-dimensionally. T cell-inflamed score (TIS) and markers of ICB were utilized to assess the host sensitivity state to immunotherapy.[49] Enrichment Pathways Analysis: Limma R package with empirical Bayesian function was employed to screen differentially expressed genes (DEGs) between high and low expression of BCAT2 in the RNA expression matrix.[50] Screening thresholds were |log (fold change) (log FC) |> 1.5 and the adjusted p-value < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted on identified DEGs by ClusterProfiler R package.[51] Seurat R package with Findmarker algorithm was applied to calculate the expression level of BCAT2 on epithelial cells in scRNA-seq. On the basis of varied gene expression ranking by fold change (FC) value, gene set enrichment analysis (GSEA) was implemented for judging positive and negative correlations. Identification of Immune-Related DEGs: The ESTIMATE R package was employed to compute immune and stromal scores in the TCGA-BLCA cohort. According to the median value of two scores and the expression level of BCAT2, the Limma R package was applied to identify positive/negative immune-related and stromal-related DEGs. Venn diagram R package was used to take the intersection of various groups and make certain the common DEGs. Classification of Individuals Using Molecular Subtypes of BLCA: Seven molecular subtypes of BLCA (UNC, Baylor, TCGA, MDA, Lund, CIT, and Consensus) were widely applied in clinical for assessing characteristics of TME and efficacies of various therapies.[52] In consideration of the complex interaction of various subtypes, we collectively divided them into two main categories—basal and luminal subtypes.[16] R packages of Consensus MIBC and BLCAsubtyping were employed to classify individuals into specific subtypes. After normalization, the area under the ROC curve (AUC) was utilized to assess the reliability of BCAT2 in predicting classification. Precision Therapy Assessment of Patients: As illustrated in our previous study,[53] a series of critical gene signatures, that can predict the efficacy of chemotherapy, radiotherapy, targeted therapy, and immunotherapy, were gathered from different studies and databases.[16,54] The ssGSEA algorithm was used to reckon the enrichment scores of gene signatures.[55] Cells Lines: Human bladder cancer cells (T24) and murine bladder cancer cells (MB49) were purchased from Procell Life Science & Technology (Wuhan, China) and Meisen CTCC (Jinhua, China), respectively. They were maintained in DMEM medium (BasalMedia, China) with 10% fetal bovine serum (FBS) (BI, Israel), 1% penicillin and streptomycin (NCM Biotech, China), and cultured in an incubator at 37 °C temperature containing 5% CO2. Construction and RNA-seq of Stable Transfection Cells: Lentiviral vector plenti-BCAT2-flag-puromycin (GV341) was designed and constructed by GeneChem (Shanghai, China) for stable BCAT2-overexpression (BCAT2 OE) cell line and its negative control (oe-vector). In addition, BCAT2- short hairpin RNA (shRNA) was cloned into GV112-lentiviral vector by GeneChem (Shanghai, China) for a stable BCAT2-knock down (BCAT2 KD) cell line. Target sequences of shRNA were listed in Table S3 (Supporting Information). Then, recombinant BCAT2 lentiviruses were transfected into objective cells according to the manufacturer’s instructions. 2 μg mL−1 puromycin (Amersco, USA) was employed to filter stable transfection cells for three days. Western blot and quantitative reverse-transcription PCR (qRT-PCR) were applied to validate the efficacy of transfection. Eventually, a stable transfection cell with the best efficacy was picked for RNA-seq and further experiments. Three duplicate samples of each cell line were sent to the BGI RNA-seq platform (Shenzhen, China). Criteria for selecting DEGs and analysis of enrichment pathways were the same as depicted in 2.3 (enrichment pathways analyses). ProcartaPlex Multiple Immunoassays for Detecting Secretion Level of Chemokines and Cytokines of Cancer Cell Lines: Human ProcartaPlex immunoassay panel (Cat: EPX340-12167-901) and mouse ProcartaPlex immunoassay panel (Cat: EPX360-26092-901) were purchased from ThermoFisher Scientific (Massachusetts, USA) for detecting protein expression levels of more than 30 crucial chemokines and cytokines in stable transfection cancer cells. In brief, cell culture supernatants were collected from a 24-well plate and centrifugation to remove cells and cell debris. Then, clarifying supernatants were incubated with beads in the panel following the manufacturer’s instructions. Luminex detection platform (ThermoFisher Scientific, USA) was used to perform quantitative analysis on each sample. Undetected indicators were excluded from the analysis. qRT-PCR: Total RNA was isolated from cells with Cell Total RNA Isolation Kit and Animal Total RNA Isolation Kit (Foregene, China) according to the manufacturer’s protocol. cDNA was synthesized using UeIris II RTPCR System for First-Strand cDNA Synthesis (US Everbright, China). qRTPCR was performed using SYBR Green qPCR Master Mix (US Everbright, China) on CFX Connect System (Bio-Rad, USA). GAPDH was used as internal standard control. The primers were designed and synthesized by Sangon Biotech (Shanghai, China) and detailed primer sequences were listed in Table S4 (Supporting Information). ELISA: Concentrations of CCL3, CCL4, CCL5, CXCL9 (MIG), and CXCL10 (IP10) in human bladder cancer cell culture supernatants were determined by human ELISA kits following the manufacturer’s protocol (Proteintech, USA). A biotech microplate reader (ThermoFisher Scientific, USA) was employed to measure optical density (OD) values. In view of the huge diversity of secretion levels among different cytokines and chemokines, we conducted data standardization (log 2 transformation) before analysis. Western Blot: RIPA buffer (NCM biotech, China) added with a 1% Protease Inhibitor Cocktail (NCM biotech, China) was used to lyse cells. BCA Protein Assay Kit (NCM biotech, China) was employed to determine protein concentrations. After being transferred onto PVDF membranes, cell proteins were incubated with primary antibodies and specific HRP-conjugated secondary antibodies successively. Signals were captured by the XRS imaging system (Bio-Rad, USA). Primary antibodies include an anti-BCAT2 antibody (Cat: ab95976, Abcam, USA) and an anti-GAPDH antibody (Cat: ab8245, Abcam, USA). Secondary antibodies include HRP goat anti-rabbit IgG (Cat: 7074, Cell Signaling Technology, USA) and HRP goat anti-mouse IgG (Cat: 7076, Cell Signaling Technology, USA). T Lymphocyte-Mediated Cancer Cell-Killing Assay and Coculture Assay: Blood samples were collected from 10 healthy donors in our hospital. According to the manufacturer’s instructions, gradient centrifugation was employed to extract peripheral blood mononuclear cells (PBMCs) by Lymphoprep (Cat: 07851, StemCell Technologies, USA). In addition, Red Blood Cell Lysis Buffer (Solarbio, China) was used to eliminate red cells mixed with PBMCs. In order to activate T cells, PBMCs were cultured in DMEM medium (Gibco, USA), supplementing Recombinant Human IL- 2 (10 ng mL−1, Cat: 202-1L-050, R&D, USA), and ImmunoCult Human CD3/CD28/CD2 T cell activator (25 μL mL−1, Cat: 10970; STEMCELL Technologies, USA) for 1 week. At the ratio of 1:5, human bladder cancer cells (BCAT2-OE, BCAT2-KD, and their negative controls) were cocultured with activated T cells in DMEM medium with anti-CD3 antibody (100 ng mL−1, Thermo Scientific, USA) and IL-2 (10 ng mL−1) from one donator in 12- well plate for 72 h. Then, T cells were collected for flow cytometry analysis. The detailed gate strategy of flow analysis is shown in Figure S28 (Supporting Information). The remaining cancer cells were stained with crystal violet and measured OD value at 570 nm by a microplate reader. Antibodies for flow cytometry analysis include Zombie Aqua Fixable Viability Kit (Cat: 423101, Biolegend, USA), APC/ Cy7 anti-human CD45 (Cat: 368516, Biolegend, USA), Pacific Blue anti-human CD3 Antibody (Cat: 300329, Biolegend, USA), PerCP/Cy5.5 anti-human CD4 Antibody (Cat: 317427, Biolegend, USA), FITC anti-human CD8a Antibody (Cat: 301006, Biolegend, USA), PE/Dazzle 594 anti-human TNF-𝛼 Antibody (Cat: 502946, Biolegend, USA), and Brilliant Violet 711 anti-human IFN- 𝛾 Antibody (Cat: 502540, Biolegend, USA). Chemotaxis Assay: Chemotaxis assay was implemented in a 24-well transwell plate with 3 μm core diameter (Corning, USA). A human CD8+T Cell Isolation Kit (Cat: 480012, Biolegend, USA) was used to extract CD8+T cells from PBMC. 1 × 105 isolated cells in 200 μL volume were added into the upper chamber and 600 μL supernatants of different stable transfection cell lines were added into the lower chamber. After incubation for 6 h at 37 °C, cells migrated into the lower chamber and were harvested and counted by flow cytometry. Cell Proliferation, Migration, and Invasion Assays: A plate colony formation assay was used to assess the ability of cell proliferation. BCAT2-OE, BCAT2-KD, and negative control human bladder cancer cells were cultured into 6-well plates per well. After incubating in a 37 °C humidified atmosphere for 2 weeks, colonies were fixed and dyed with paraformaldehyde and crystal violet, respectively. Transwell chambers with 8.0 μm pore polycarbonate membrane inserts (Corning, USA) were employed to evaluate the capacity of cell migration and invasion in vitro. 1 × 105 stable transfected cells were suspended in serum-free medium and seeded into the top chamber with or without Matrigel (Corning, USA), for invasion and migration assay separately. After incubating for 24 h (migration assay) and 48 h (invasion assay), cells adhering to the lower surface of the membrane were fixed and stained. Cells that remained in the top chamber were removed by swabs. Five random fields were selected under a microscope to calculate cell numbers. Immunofluorescence (IF) and Immunohistochemistry (IHC): Multicolor IF on TMAs of Xiangya BLCA Cohort and Xiangya BLCA immunotherapy Cohort was implemented by multiple fluorescent immunohistochemical staining kit (Absin, China). In brief, after dewaxing, rehydrating, and antigen renovating, TMA was incubated with primary antibodies and secondary antibodies which prompts the binding of different luciferins by tyramide signal amplification (TSA). Following wiping off unbound antibodies with citrate buffer, the process of incubation was repeated twice. DAPI was employed to counterstain cell nuclei and images were scanned by the Pannoramic MIDI platform (3DHISTECH, Hungary). Primary antibodies on TMA of Xiangya BLCA cohort include anti-BCAT2 (Cat: ab95976, Abcam, USA), anti-Cytokeratin 19 (CK19) (Cat: ab52625, Abcam, USA), anti-CD8 (Cat: ab237709, Abcam, USA) and DAPI (Invitrogen, USA). Secondary antibodies and fluorochrome on TMA of Xiangya BLCA cohort include HRP Goat Anti-Rabbit/Mouse IgG, Absin 520 TSA Plus, Absin 570 TSA Plus, Absin 650 TSA Plus (abs50012, Absin). Primary antibodies on TMA of Xiangya BLCA immunotherapy cohort include anti-BCAT2 (Cat: ab95976, Abcam, USA), anti-PD-L1 (Cat: ab213524, Abcam, USA), and DAPI (Invitrogen, USA). Secondary antibodies and fluorochrome on TMA of Xiangya BLCA immunotherapy cohort include HRP Goat Anti-Rabbit/Mouse IgG, Absin 520 TSA Plus, and Absin 650 TSA Plus (abs50012, Absin). IF on murine tumor tissues was incubated with primary antibody—anti-CD8 (Cat: 372902, BioLegend, USA)—and secondary antibody—anti-mouse Alexa Fluor 488 dye conjugate (Invitrogen, USA) sequentially. DAPI was applied to the visualized cell nucleus. Five random fields were selected under a microscope to calculate positive staining cell numbers. IHC on TMAs of Xiangya BLCA Cohort and Xiangya BLCA immunotherapy Cohort was conducted by SP-9000 Kit (ZSGB-BIO, China). After antigen retrieval and blocking, primary antibodies, and secondary antibodies were incubated with tumor tissues in turn. Then, diaminobenzidine (DAB) was applied to dye target antigens. Brown signal was regarded as positive staining. Score of staining intensity (absent = 0, weak = 1, moderate = 2, and strong = 3) multiplying score of positive percentage (no staining = 0, staining cells less than 25% = 1, 25%–50% staining cells = 2, 50%–75% staining cells = 3, and staining cells more than 75% = 4) was ultimate IHC score of samples. The scores of BCAT2/CD8/PD-L1 less than six points were classified as low expression, the scores of them greater than or equal to six points were classified as high expression. For judging an individual’s status on MMR, all the samples from the Xiangya BLCA immunotherapy cohort were assessed based on staining outcomes of four MMR marker genes (MLH1, MSH2, MSH6, and PMS2). As depicted in previous studies,[56] when all four marker genes were positive staining, the individual was marked as MMR. When any of the four marker genes had negative staining, the individual was marked as dMMR. Two independent pathologists were invited to conduct the assessment. Antibodies include: anti-BCAT2 (Cat: ab95976, Abcam, USA), anti-CD8 (Cat: ab4055, Abcam, USA), anti-PD-L1 (Cat: ab213524, Abcam, USA), anti-MLH1(Cat: A4858, Abcolonal, China), anti-MSH2 (Cat: A22177, Abcolonal, China), anti-MSH6 (Cat: A16381, Abcolonal, China), anti-PMS2 (Cat: A4577, Abcolonal, China), and HRP Goat Anti-Rabbit/Mouse IgG (ZSGB-BIO, China). TissueFAXS Panoramic Analyses of Spatial Interaction in TME: For evaluating spatial interaction of BCAT2+ malignant cells and effector T cells, TissueFAXS panoramic platform (Tissue Gnostics, Austria) was utilized to scan and semiautomatically analyze objective cells stained by multicolor IF on TMA of Xiangya BLCA Cohort. The TMA was constituted of 1.5 mm core biopsies from paraffin-embedded specimens of tumor tissues. In detail, BCAT2+ cells, malignant cells, and CD8+T cells were stained by specific primary antibodies and secondary antibodies. DAPI (Invitrogen, USA) was used to stain the cell nucleus. Detailed steps have been described in the study of Makarevic et al.[57] In brief, according to fluorescence intensities of different indicators and physical properties of cells, positive cells were marked and quantified. More important, spatial distributions of CD8+T cells around BCAT2+CK19+ cells were depicted by scatter plots on the basis of space distance (0–25, 25–50, 50–100, and 100–150 μm). Two experienced pathologists were invited to check the decision outcome of the TissueFAXS panoramic platform. Animal Experiments: Female C57BL/6 mice (6–7 weeks) were obtained from the Department of Laboratory Animal, Central South University. All operations on mice were checked and approved by the Animal Care and Use Committee of Xiangya Hospital, Central South University (Item number: 2021101175). Above all, to investigate the role of BCAT2 on tumor growth in vivo, BCAT2 KD MB49 cells (5 × 105) and its control cells in 100 μL medium volume were subcutaneously injected into the right flank of mice. Moreover, after constructing a subcutaneous tumor model successfully (tumor volume up to 100 mm3), 100 μg InVivomAb anti-mouse PD-1 (Cat: BE0146, Bioxcell, USA) and IgG2a isotype control (Cat: BE0089, Bioxcell, USA) were intraperitoneally injected into per mouse, for assessing the synergistic effect of BCAT2 loss and anti-PD-1 therapy. Anti-PD-1 therapy was implemented on mice every 3 days and lasted up to five courses. Further, for judging whether the cooperative effect of cotreatment was dependent on CD8+T cells. Accompanying combination therapy,100 μg InVivoPlus anti-mouse CD8𝛼 (Cat: BP0117, Bioxcell, USA) and IgG2b isotype control (Cat: BP0090, Bioxcell, USA) were employed to deplete CD8+T cells in mice. The body weights of mice were recorded every three days. On the scheduled date, tumors were harvested, measured, and prepared for flow cytometry analysis, IF staining, and qRT-PCR. For exploring the interaction between the expression level of BCAT2 on CD8+T cells and the activity of CD8+T cells, spleens of female C57BL/6 mice (6– 7 weeks) without tumor-bearing were dissociated and prepared for single-cell suspension for further analysis. Flow Cytometry Analysis: Murine tumors were ground and digested into single-cell suspension by Collagenase IV (Cat: C5138, Sigma, USA), hyaluronidase (Cat: H3506, Sigma, USA), and DNase I (Cat: DN25, Sigma, USA). 70 μm cell strainers (BIOFIL, China) were used to filter out impurities and a cell counter (Countstar, China) was employed to gain (3–5) × 106 cells in each sample. After identifying live cells using Zombie Aqua Fixable Viability Kit (Cat: 423101, Biolegend, USA) and blocking Fc receptor with anti-mouse CD16/32 antibody (Cat: 156603, Biolegend, USA), Cell membrane antigens were stained by APC-Cy7 anti-mouse CD45 (Cat: 103116, Biolegend, USA), BV421 anti-mouse CD3 (Cat: 100341, Biolegend, USA), BV605 anti-mouse CD8a (Cat: 100744, Biolegend, USA) and PerCPCy5.5 anti-mouse CD4 (Cat: 100540, Biolegend, USA). Then, the bioscience Foxp3/Transcription Factor Staining Buffer Set (Cat: 2400632, Invitrogen, USA) was used for fixing and permeabilizing the cell and nucleus membrane. Cytotoxic effect related antigens were dyed by PE anti-human/mouse Granzyme B (Cat: 372208, Biolegend, USA), PE-Cy7 anti-mouse TNF-𝛼 (Cat: 506324, Biolegend, USA), PE-Dazzle 594 anti-mouse IFN-𝛾 (Cat: 505846, Biolegend, USA), APC anti-mouse Perforin (Cat: 154304, Biolegend, USA). The gating strategy of flow cytometry analysis is shown in Figure S29 (Supporting Information). Anti-human/mouse BCAT2 (Cat: A7426, Abclonal, China) was incubated with Flexible 488 antibody labeling kit (Cat: KFA001, Proteintech, USA) for synthesize personalized BCAT2 fluorescent antibody for flow cytometry analysis. Then, BCAT2 antibody and T-cell-related antibodies were mixed and added into single-cell suspension of murine spleen for exploring the interaction between the expression level of BCAT2 on CD8+T cell and activity of CD8+T cell. The gating strategy of flow cytometry analysis is shown in Figure S30 (Supporting Information). Stained samples were detected on Cytek DxpAthena Flow cytometry (Cytek Biosciences, USA) and data were analyzed by Flowjo version software (BD Biosciences, USA). Statistical Analysis: Data were presented as mean ± SD. t-test with or without Welch correction was utilized to compare continuous variables between two groups. One-way ANOVA analysis with or without Brown–Forsythe and Welch tests was used to compare continuous variables between multiple groups. Chi-squared test or Fisher’s exact test was applied to compare dichotomous variables. Pearson or Spearman correlation coefficients test was employed to estimate the intensity of correlation between different variables. Kaplan–Meier survival curve was employed to show prognostic analyses of dichotomous variables, and the log-rank test was used to judge statistical differences. Two-sided p < 0.05 was defined as the threshold of significance. R software (version 4.0) and GraphPad Prism8 were applied to process data in the study.

Reference

[1] R. L. Siegel, K. D. Miller, H. E. Fuchs, A. Jemal, CA: Cancer J. Clin. 2021, 71, 7. 

[2] V. G. Patel, W. K. Oh, M. D. Galsky, Ca-Cancer J. Clin. 2020, 70, 404.

[3] R. Nadal, J. Bellmunt, Cancer Treat. Rev. 2019, 76, 10. 

[4] a) M. D. Galsky, A. Arija JÁ, A. Bamias, I. D. Davis, M. De Santis, E. Kikuchi, X. Garcia-Del-Muro, U. De Giorgi, M. Mencinger, K. Izumi, S. Panni, M. Gumus, M. Özgüro˘glu, A. R. Kalebasty, S. H. Park, B. Alekseev, F. A. Schutz, J. R. Li, D. Ye, N. J. Vogelzang, S. Bernhard, D. Tayama, S. Mariathasan, A. Mecke, A. Thåström, E. Grande, Lancet 2020, 395, 1547; b) M. D. Galsky, A. Mortazavi, M. I. Milowsky, S. George, S. Gupta, M. T. Fleming, L. H. Dang, D. M. Geynisman, R. Walling, R. S. Alter, M. Kassar, J. Wang, S. Gupta, N. Davis, J. Picus, G. Philips, D. I. Quinn, G. K. Haines 3rd, N. M. Hahn, Q. Zhao, M. Yu, S. K. Pal, J. Clin. Oncol. 2020, 38, 1797; c) T. Powles, S. H. Park, E. Voog, C. Caserta, B. P. Valderrama, H. Gurney, H. Kalofonos, S. Radulovi´c, W. Demey, A. Ullén, Y. Loriot, S. S. Sridhar, N. Tsuchiya, E. Kopyltsov, C. N. Sternberg, J. Bellmunt, J. B. Aragon-Ching, D. P. Petrylak, R. Laliberte, J. Wang, B. Huang, C. Davis, C. Fowst, N. Costa, J. A. Blake-Haskins, A. di Pietro, P. Grivas, N. Engl. J. Med. 2020, 383, 1218. 

[5] a) R. W. Jenkins, D. A. Barbie, K. T. Flaherty, Br. J. Cancer 2018, 118, 9; b) A. J. Schoenfeld, M. D. Hellmann, Cancer Cell 2020, 37, 443.

[6] a) D. S. Chen, I. Mellman, Nature 2017, 541, 321; b) T. F. Gajewski, L. Corrales, J. Williams, B. Horton, A. Sivan, S. Spranger, Adv. Exp. Med. Biol. 2017, 1036, 19; c) D. C. Hinshaw, L. A. Shevde, Cancer Res. 2019, 79, 4557. 

[7] a) S. M. Vareki, J. Immunother. Cancer 2018, 6, 157; b) B. Zhang, Q. Wu, B. Li, D. Wang, L. Wang, Y. L. Zhou, Mol. Cancer 2020, 19, 53. 

[8] J. R. Mayers, M. E. Torrence, L. V. Danai, T. Papagiannakopoulos, S. M. Davidson, M. R. Bauer, A. N. Lau, B. W. Ji, P. D. Dixit, A. M. Hosios, A. Muir, C. R. Chin, E. Freinkman, T. Jacks, B. M. Wolpin, D. Vitkup, M. G. Vander Heiden, Science 2016, 353, 1161. 

[9] J. T. Li, M. Yin, D. Wang, J. Wang, M. Z. Lei, Y. Zhang, Y. Liu, L. Zhang, S. W. Zou, L. P. Hu, Z. G. Zhang, Y. P. Wang, W. Y. Wen, H. J. Lu, Z. J. Chen, D. Su, Q. Y. Lei, Nat. Cell Biol. 2020, 22, 167. 

[10] J. H. Lee, Y. R. Cho, J. H. Kim, J. Kim, H. Y. Nam, S. W. Kim, J. Son, Exp. Mol. Med. 2019, 51, 146.

[11] a) H. Lai, X. Cheng, Q. Liu, W. Luo, M. Liu, M. Zhang, J. Miao, Z. Ji, G. N. Lin, W. Song, L. Zhang, J. Bo, G. Yang, J. Wang, W. Q. Gao, Int. J. Cancer 2021, 149, 2099; b) D. Lambrechts, E. Wauters, B. Boeckx, S. Aibar, D. Nittner, O. Burton, A. Bassez, H. Decaluwé, A. Pircher, K. Van den Eynde, B. Weynand, E. Verbeken, P. De Leyn, A. Liston, J. Vansteenkiste, P. Carmeliet, S. Aerts, B. Thienpont, Nat. Med. 2018, 24, 1277.

[12] N. Nagarsheth, M. S. Wicha, W. Zou, Nat. Rev. Immunol. 2017, 17, 559.

[13] a) E. Stelloo, A. M. L. Jansen, E. M. Osse, R. A. Nout, C. L. Creutzberg, D. Ruano, D. N. Church, H. Morreau, V. Smit, T. van Wezel, T. Bosse, Ann. Oncol. 2017, 28, 96; b) A. S. Tjalsma, A. Wagner, W. N. M. Dinjens, P. C. Ewing-Graham, L. S. M. Alcalá, M. E. R. de Groot, K. E. Hamoen, A. C. van Hof, W. Hofhuis, L. N. Hofman, K. J. Hoogduin, J. Kaijser, A. C. F. Makkus, S. J. J. Mol, G. M. Plaisier, K. Schelfhout, H. P. M. Smedts, R. A. Smit, P. J. Timmers, P. Vencken, B. Visschers, A. A. M. van der Wurff, H. C. van Doorn, Gynecol. Oncol. 2021, 160, 771.