Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer
<p>Analysis of breast cancer patient subgroups derived from immune infiltration profiles. (<b>a</b>) Hierarchical clustering based on immune infiltration levels revealed patient groups in the TCGA dataset: high T cell abundance (S2), moderate infiltration (S1), and low infiltration (S3). (<b>b</b>) The moderate infiltration group (S1) showed the best survival rate, while the low infiltration group (S3) had the poorest survival. (<b>c</b>) Immune checkpoint genes <span class="html-italic">PDCD1</span>, <span class="html-italic">CD274</span>, and <span class="html-italic">CTLA4</span>, as well as genes associated with T cell response, <span class="html-italic">GZMB</span>, and <span class="html-italic">IFNG</span>, were significantly more highly expressed in the T cell abundance group (S2) than in the other two groups (S1 and S3). **** <math display="inline"><semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.0001</mn> </mrow> </semantics></math>. (<b>d</b>) The moderate infiltration group (S1) demonstrated the highest stromal score, while the low infiltration group (S3) had the highest tumor purity. The <span class="html-italic">p</span>-values were calculated using the Mann–Whitney U test.</p> "> Figure 2
<p>Analysis of immune infiltration and activity scores. (<b>a</b>) Comparison of infiltration levels of 24 innate and adaptive immune cells, along with activity scores for angiogenesis and antigen-presenting machinery (APM), across the three patient groups. The immune cells were ranked based on differences between the moderate infiltration (S1) and low infiltration (S3) groups, which were associated with favorable and poor patient survival, respectively. (<b>b</b>) Pearson correlation analysis of immune cell infiltration levels and activity levels of angiogenesis and APM.</p> "> Figure 3
<p>(<b>a</b>) Comparison of tumor burden across the three immune infiltration groups. Mann–Whitney tests revealed a significantly higher tumor burden in the high T cell abundance (S2) group compared to the moderate infiltration (S1) and the low infiltration (S3) groups. (<b>b</b>) Analysis of amplification regions in the genomes among patients in distinct groups. The moderate infiltration group (S1) exhibited significantly lower rates of amplification compared to the other groups, as determined by Mann–Whitney tests. (<b>c</b>) Analysis of deletion regions in the genomes among patients in distinct groups. The moderate infiltration group (S1) showed significantly lower deletion rates than the other two groups, as determined by Mann–Whitney tests. (<b>d</b>) Spearman correlation analysis between tumor burden and the infiltration of each immune cell type, as well as angiogenesis and APM activity scores. * <math display="inline"><semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.05</mn> </mrow> </semantics></math>, ** <math display="inline"><semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.01</mn> </mrow> </semantics></math>, *** <math display="inline"><semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.001</mn> </mrow> </semantics></math>. (<b>e</b>) Identification of the top mutated genes in the three immune infiltration groups. <span class="html-italic">TP53</span> was highly mutated in the high T cell abundance (S2) group, whereas <span class="html-italic">PIK3CA</span> was predominantly mutated in the moderate infiltration group (S1).</p> "> Figure 4
<p>Analysis of single-cell RNA sequencing data from breast cancer patients. (<b>a</b>) Visualization of major cell types in the tumor microenvironment (TME) using UMAP. (<b>b</b>) Hierarchical clustering analysis of the pseudo-bulk expression data revealed three distinct infiltration groups: SC1, SC2, and SC3. These groups, represented by red, yellow, and purple sidebars, respectively, closely mirror the groups identified in the TCGA bulk RNA sequencing data (S1, S2, and S3). (<b>c</b>) Distribution of the three infiltration groups across different cell populations. (<b>d</b>) Evaluation of stromal score and tumor purity across the three groups, demonstrating trends in SC1, SC2, and SC3 similar to those observed in S1, S2, and S3 from the TCGA dataset.</p> "> Figure 5
<p>Overview of the cell–cell communications in the immune-active (SC1) and immune-suppressed (SC3) groups. (<b>a</b>) The number of interactions (<b>left</b>) and interaction strength (<b>right</b>) within each group. (<b>b</b>) Changes in the number (<b>left</b>) and strength (<b>right</b>) of cell–cell communications between the immune-active and -suppressed groups. Blue arrows indicate a decrease in the number or strength of interactions in the immune-suppressed group compared to the immune-active group, while red arrows indicate an increase in these metrics in the immune-suppressed group. In the weighted directed graphs, the arrows represent the signaling pathways, pointing from the signal-originating cell type (ligand-producing) to the target cell type (signal-receiving). The width of the arrows reflects the differences in the number or strength of communication interactions between the two groups. (<b>c</b>) Detailed analysis of differential cell–cell communication numbers (<b>left</b>) and communication strength (<b>right</b>) between the immune-active and -suppressed groups. Cell types are plotted on the x-axis as receivers and on the y-axis as senders of communication signals. The color gradient from red to blue indicates decreasing values in the number or strength of interactions in the immune-suppressed group relative to the immune-active group.</p> "> Figure 6
<p>Cell–cell communication patterns coordinated by various cell types and signal pathways. This figure illustrates four communication patterns (P1, P2, P3, and P4) linking cell types and signaling pathways within the TME of the immune-active and -suppressed groups. The color gradient from red to blue indicates the probability of communication, ranging from 1 (high) to 0 (low). (<b>a</b>) Incoming signals in the immune-active group: The left panel displays signal-receiving cell types, while the right panel shows the pathways involved in these communication patterns. (<b>b</b>) Outgoing signals in the immune-active group: The left panel illustrates signal-sending cell types, with the right panel depicting the pathways involved. (<b>c</b>) Incoming signals in the immune suppressive group: The left panel presents signal-sending cell types, and the right panel shows the pathways involved in these communication patterns. (<b>d</b>) Outgoing signals in the immune-suppressed group: The left panel features signal-receiving cell types, while the right panel displays the associated pathways.</p> "> Figure 7
<p>Pathways in the immune-active (SC1) and immune-suppressed (SC3) groups: (<b>a</b>) Functional similarity clusters of signaling pathways in the immune-active and immune-suppressed groups. (<b>b</b>) Overlapping pathways ranked based on functional differences. (<b>c</b>) Overlapping pathways ranked based on structural differences. (<b>d</b>) Heatmap illustrating the relative significance of each cell type derived from network centrality measures within the IGF signaling network in the immune-active group (top panel) and immune-suppressed group (bottom panel). (<b>e</b>) Ranking of signaling pathways based on their involvement in the immune-active (SC1) and immune-suppressed (SC3) groups. (<b>f</b>) Interactions of the SPP1 and EGF pathways with various cell types in the immune-active (SC1) and immune-suppressed (SC3) groups. (<b>g</b>) Top panel: Contribution of ligand–receptor pairs in the SPP1 pathway. Bottom panel: Contribution of ligand–receptor pairs in the EGF pathway.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Breast Cancer Patient Cohorts
2.2. Assessment of Tumor Immune Infiltration
2.3. Computing Tumor Mutation Burden
2.4. Copy Number Variation Identification
2.5. Survival Analysis
2.6. Single-Cell RNA Sequencing Data Process
2.7. Cell Communication Network Inference
3. Results
3.1. Immune-Infiltration Decomposition in Breast Cancer
3.2. Exploring Infiltration Patterns across Patient Groups
3.3. Relation of Genetic Mutations with Immune Infiltration
3.4. Breast Cancer Patient Clusters Revealed by scRNA-seq Data
3.5. Deciphering Cellular Interactions in Tumor Microenviroment
3.6. Major Cell Communication Patterns within the TME
3.7. Signaling Pathway Alterations Associated with Immune Activation and Suppression
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
TME | Tumor microenviroment |
TMB | Tumor mutation burden |
Breast cancer | BC |
References
- Abdel-Rahman, O. Incidence and predictors of 1-year mortality among 9236 breast cancer patients referred for adjuvant chemotherapy. Future Oncol. 2018, 14, 2335–2341. [Google Scholar] [CrossRef]
- Guo, L.; Kong, D.; Liu, J.; Zhan, L.; Luo, L.; Zheng, W.; Zheng, Q.; Chen, C.; Sun, S. Breast cancer heterogeneity and its implication in personalized precision therapy. Exp. Hematol. Oncol. 2023, 12, 3. [Google Scholar] [CrossRef]
- Emens, L.A. Breast cancer immunotherapy: Facts and hopes. Clin. Cancer Res. 2018, 24, 511–520. [Google Scholar] [CrossRef]
- Debien, V.; De Caluwé, A.; Wang, X.; Piccart-Gebhart, M.; Tuohy, V.K.; Romano, E.; Buisseret, L. Immunotherapy in breast cancer: An overview of current strategies and perspectives. NPJ Breast Cancer 2023, 9, 7. [Google Scholar] [CrossRef] [PubMed]
- Pan, K.; Farrukh, H.; Chittepu, V.C.S.R.; Xu, H.; Pan, C.x.; Zhu, Z. CAR race to cancer immunotherapy: From CAR T, CAR NK to CAR macrophage therapy. J. Exp. Clin. Cancer Res. 2022, 41, 119. [Google Scholar] [CrossRef]
- Zhang, Y.; Zhang, Z. The history and advances in cancer immunotherapy: Understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell. Mol. Immunol. 2020, 17, 807–821. [Google Scholar] [CrossRef] [PubMed]
- Stanton, S.E.; Disis, M.L. Clinical significance of tumor-infiltrating lymphocytes in breast cancer. J. Immunother. Cancer 2016, 4, 1–7. [Google Scholar] [CrossRef] [PubMed]
- Brummel, K.; Eerkens, A.L.; de Bruyn, M.; Nijman, H.W. Tumour-infiltrating lymphocytes: From prognosis to treatment selection. Br. J. Cancer 2023, 128, 451–458. [Google Scholar] [CrossRef]
- Li, K.; Shi, H.; Zhang, B.; Ou, X.; Ma, Q.; Chen, Y.; Shu, P.; Li, D.; Wang, Y. Myeloid-derived suppressor cells as immunosuppressive regulators and therapeutic targets in cancer. Signal Transduct. Target. Ther. 2021, 6, 362. [Google Scholar] [CrossRef]
- Bianchini, G.; De Angelis, C.; Licata, L.; Gianni, L. Treatment landscape of triple-negative breast cancer—Expanded options, evolving needs. Nat. Rev. Clin. Oncol. 2022, 19, 91–113. [Google Scholar] [CrossRef]
- Conti, L.; Ruiu, R.; Barutello, G.; Macagno, M.; Bandini, S.; Cavallo, F.; Lanzardo, S. Microenvironment, oncoantigens, and antitumor vaccination: Lessons learned from BALB-neuT mice. BioMed Res. Int. 2014, 1, 534969. [Google Scholar] [CrossRef]
- Neophytou, C.M.; Panagi, M.; Stylianopoulos, T.; Papageorgis, P. The role of tumor microenvironment in cancer metastasis: Molecular mechanisms and therapeutic opportunities. Cancers 2021, 13, 2053. [Google Scholar] [CrossRef]
- De Visser, K.E.; Joyce, J.A. The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell 2023, 41, 374–403. [Google Scholar] [CrossRef]
- Buffa, F.M.; Camps, C.; Winchester, L.; Snell, C.E.; Gee, H.E.; Sheldon, H.; Taylor, M.; Harris, A.L.; Ragoussis, J. microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer. Cancer Res. 2011, 71, 5635–5645. [Google Scholar] [CrossRef]
- Wu, S.Z.; Al-Eryani, G.; Roden, D.L.; Junankar, S.; Harvey, K.; Andersson, A.; Thennavan, A.; Wang, C.; Torpy, J.R.; Bartonicek, N.; et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 2021, 53, 1334–1347. [Google Scholar] [CrossRef]
- Bindea, G.; Mlecnik, B.; Tosolini, M.; Kirilovsky, A.; Waldner, M.; Obenauf, A.C.; Angell, H.; Fredriksen, T.; Lafontaine, L.; Berger, A.; et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 2013, 39, 782–795. [Google Scholar] [CrossRef]
- Chtanova, T.; Newton, R.; Liu, S.M.; Weininger, L.; Young, T.R.; Silva, D.G.; Bertoni, F.; Rinaldi, A.; Chappaz, S.; Sallusto, F.; et al. Identification of T cell-restricted genes, and signatures for different T cell responses, using a comprehensive collection of microarray datasets. J. Immunol. 2005, 175, 7837–7847. [Google Scholar] [CrossRef]
- Hyrcza, M.D.; Kovacs, C.; Loutfy, M.; Halpenny, R.; Heisler, L.; Yang, S.; Wilkins, O.; Ostrowski, M.; Der, S.D. Distinct transcriptional profiles in ex vivo CD4+ and CD8+ T cells are established early in human immunodeficiency virus type 1 infection and are characterized by a chronic interferon response as well as extensive transcriptional changes in CD8+ T cells. J. Virol. 2007, 81, 3477–3486. [Google Scholar] [CrossRef]
- Wendt, K.; Wilk, E.; Buyny, S.; Buer, J.; Schmidt, R.E.; Jacobs, R. Gene and protein characteristics reflect functional diversity of CD56dim and CD56bright NK cells. J. Leukoc. Biol. 2006, 80, 1529–1541. [Google Scholar] [CrossRef]
- Masiero, M.; Simões, F.C.; Han, H.D.; Snell, C.; Peterkin, T.; Bridges, E.; Mangala, L.S.; Wu, S.Y.Y.; Pradeep, S.; Li, D.; et al. A core human primary tumor angiogenesis signature identifies the endothelial orphan receptor ELTD1 as a key regulator of angiogenesis. Cancer Cell 2013, 24, 229–241. [Google Scholar] [CrossRef]
- Wieczorek, M.; Abualrous, E.T.; Sticht, J.; Álvaro-Benito, M.; Stolzenberg, S.; Noé, F.; Freund, C. Major histocompatibility complex (MHC) class I and MHC class II proteins: Conformational plasticity in antigen presentation. Front. Immunol. 2017, 8, 248429. [Google Scholar] [CrossRef]
- Barbie, D.A.; Tamayo, P.; Boehm, J.S.; Kim, S.Y.; Moody, S.E.; Dunn, I.F.; Schinzel, A.C.; Sandy, P.; Meylan, E.; Scholl, C.; et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 2009, 462, 108–112. [Google Scholar] [CrossRef]
- Hänzelmann, S.; Castelo, R.; Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 2013, 14, 1–15. [Google Scholar] [CrossRef]
- Mayakonda, A.; Lin, D.C.; Assenov, Y.; Plass, C.; Koeffler, H.P. Maftools: Efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018, 28, 1747–1756. [Google Scholar] [CrossRef]
- Cerami, E.; Gao, J.; Dogrusoz, U.; Gross, B.E.; Sumer, S.O.; Aksoy, B.A.; Jacobsen, A.; Byrne, C.J.; Heuer, M.L.; Larsson, E.; et al. The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012, 2, 401–404. [Google Scholar] [CrossRef]
- Mermel, C.H.; Schumacher, S.E.; Hill, B.; Meyerson, M.L.; Beroukhim, R.; Getz, G. GISTIC2. 0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011, 12, 1–14. [Google Scholar] [CrossRef]
- Satija, R.; Farrell, J.A.; Gennert, D.; Schier, A.F.; Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 2015, 33, 495–502. [Google Scholar] [CrossRef]
- Jin, S.; Guerrero-Juarez, C.F.; Zhang, L.; Chang, I.; Ramos, R.; Kuan, C.H.; Myung, P.; Plikus, M.V.; Nie, Q. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 2021, 12, 1088. [Google Scholar] [CrossRef]
- Krug, K.; Mertins, P.; Zhang, B.; Hornbeck, P.; Raju, R.; Ahmad, R.; Szucs, M.; Mundt, F.; Forestier, D.; Jane-Valbuena, J.; et al. A curated resource for phosphosite-specific signature analysis*[S]. Mol. Cell. Proteom. 2019, 18, 576–593. [Google Scholar] [CrossRef]
- Dhatchinamoorthy, K.; Colbert, J.D.; Rock, K.L. Cancer immune evasion through loss of MHC class I antigen presentation. Front. Immunol. 2021, 12, 636568. [Google Scholar] [CrossRef]
- Chen, F.; Li, Y.; Qin, N.; Wang, F.; Du, J.; Wang, C.; Du, F.; Jiang, T.; Jiang, Y.; Dai, J.; et al. RNA-seq analysis identified hormone-related genes associated with prognosis of triple negative breast cancer. J. Biomed. Res. 2020, 34, 129. [Google Scholar] [CrossRef]
- Yoshihara, K.; Shahmoradgoli, M.; Martínez, E.; Vegesna, R.; Kim, H.; Torres-Garcia, W.; Treviño, V.; Shen, H.; Laird, P.W.; Levine, D.A.; et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 2013, 4, 2612. [Google Scholar] [CrossRef]
- Revkov, E.; Kulshrestha, T.; Sung, K.W.K.; Skanderup, A.J. PUREE: Accurate pan-cancer tumor purity estimation from gene expression data. Commun. Biol. 2023, 6, 394. [Google Scholar]
- Shaul, M.E.; Levy, L.; Sun, J.; Mishalian, I.; Singhal, S.; Kapoor, V.; Horng, W.; Fridlender, G.; Albelda, S.M.; Fridlender, Z.G. Tumor-associated neutrophils display a distinct N1 profile following TGFβ modulation: A transcriptomics analysis of pro-vs. antitumor TANs. Oncoimmunology 2016, 5, e1232221. [Google Scholar] [CrossRef]
- Zhou, S.L.; Zhou, Z.J.; Hu, Z.Q.; Huang, X.W.; Wang, Z.; Chen, E.B.; Fan, J.; Cao, Y.; Dai, Z.; Zhou, J. Tumor-associated neutrophils recruit macrophages and T-regulatory cells to promote progression of hepatocellular carcinoma and resistance to sorafenib. Gastroenterology 2016, 150, 1646–1658. [Google Scholar] [CrossRef]
- Eruslanov, E.B.; Bhojnagarwala, P.S.; Quatromoni, J.G.; Stephen, T.L.; Ranganathan, A.; Deshpande, C.; Akimova, T.; Vachani, A.; Litzky, L.; Hancock, W.W.; et al. Tumor-associated neutrophils stimulate T cell responses in early-stage human lung cancer. J. Clin. Investig. 2014, 124, 5466–5480. [Google Scholar] [CrossRef]
- Jablonska, J.; Leschner, S.; Westphal, K.; Lienenklaus, S.; Weiss, S. Neutrophils responsive to endogenous IFN-β regulate tumor angiogenesis and growth in a mouse tumor model. J. Clin. Investig. 2010, 120, 1151–1164. [Google Scholar]
- Si, Y.; Merz, S.F.; Jansen, P.; Wang, B.; Bruderek, K.; Altenhoff, P.; Mattheis, S.; Lang, S.; Gunzer, M.; Klode, J.; et al. Multidimensional imaging provides evidence for down-regulation of T cell effector function by MDSC in human cancer tissue. Sci. Immunol. 2019, 4, eaaw9159. [Google Scholar] [CrossRef]
- Wang, T.t.; Zhao, Y.l.; Peng, L.s.; Chen, N.; Chen, W.; Lv, Y.p.; Mao, F.y.; Zhang, J.y.; Cheng, P.; Teng, Y.s.; et al. Tumour-activated neutrophils in gastric cancer foster immune suppression and disease progression through GM-CSF-PD-L1 pathway. Gut 2017, 66, 1900–1911. [Google Scholar]
- Wculek, S.K.; Cueto, F.J.; Mujal, A.M.; Melero, I.; Krummel, M.F.; Sancho, D. Dendritic cells in cancer immunology and immunotherapy. Nat. Rev. Immunol. 2020, 20, 7–24. [Google Scholar]
- Szpor, J.; Streb, J.; Glajcar, A.; Frączek, P.; Winiarska, A.; Tyrak, K.E.; Basta, P.; Okoń, K.; Jach, R.; Hodorowicz-Zaniewska, D. Dendritic cells are associated with prognosis and survival in breast cancer. Diagnostics 2021, 11, 702. [Google Scholar] [CrossRef]
- Derakhshani, A.; Vahidian, F.; Alihasanzadeh, M.; Mokhtarzadeh, A.; Nezhad, P.L.; Baradaran, B. Mast cells: A double-edged sword in cancer. Immunol. Lett. 2019, 209, 28–35. [Google Scholar] [CrossRef] [PubMed]
- Varricchi, G.; Galdiero, M.R.; Loffredo, S.; Marone, G.; Iannone, R.; Marone, G.; Granata, F. Are mast cells MASTers in cancer? Front. Immunol. 2017, 8, 261015. [Google Scholar] [CrossRef]
- Wolf, N.K.; Kissiov, D.U.; Raulet, D.H. Roles of natural killer cells in immunity to cancer, and applications to immunotherapy. Nat. Rev. Immunol. 2023, 23, 90–105. [Google Scholar] [CrossRef] [PubMed]
- Le, L.; Tokumaru, Y.; Oshi, M.; Asaoka, M.; Yan, L.; Endo, I.; Ishikawa, T.; Futamura, M.; Yoshida, K.; Takabe, K. Th2 cell infiltrations predict neoadjuvant chemotherapy response of estrogen receptor-positive breast cancer. Gland Surg. 2021, 10, 154. [Google Scholar] [CrossRef]
- Bielenberg, D.R.; Zetter, B.R. The contribution of angiogenesis to the process of metastasis. Cancer J. 2015, 21, 267–273. [Google Scholar] [CrossRef]
- Al-Ostoot, F.H.; Salah, S.; Khamees, H.A.; Khanum, S.A. Tumor angiogenesis: Current challenges and therapeutic opportunities. Cancer Treat. Res. Commun. 2021, 28, 100422. [Google Scholar] [CrossRef] [PubMed]
- Wellenstein, M.D.; de Visser, K.E. Cancer-cell-intrinsic mechanisms shaping the tumor immune landscape. Immunity 2018, 48, 399–416. [Google Scholar] [CrossRef]
- Melo, C.M.; Vidotto, T.; Chaves, L.P.; Lautert-Dutra, W.; Reis, R.B.d.; Squire, J.A. The role of somatic mutations on the immune response of the tumor microenvironment in prostate cancer. Int. J. Mol. Sci. 2021, 22, 9550. [Google Scholar] [CrossRef]
- Xie, N.; Shen, G.; Gao, W.; Huang, Z.; Huang, C.; Fu, L. Neoantigens: Promising targets for cancer therapy. Signal Transduct. Target. Ther. 2023, 8, 9. [Google Scholar]
- Şenbabaoğlu, Y.; Gejman, R.S.; Winer, A.G.; Liu, M.; Van Allen, E.M.; de Velasco, G.; Miao, D.; Ostrovnaya, I.; Drill, E.; Luna, A.; et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 2016, 17, 1–25. [Google Scholar] [CrossRef]
- Kang, S.; Bader, A.G.; Zhao, L.; Vogt, P.K. Mutated PI 3-kinases: Cancer targets on a silver platter. Cell Cycle 2005, 4, 571–574. [Google Scholar] [CrossRef]
- Amzel, L.M.; Huang, C.H.; Mandelker, D.; Lengauer, C.; Gabelli, S.B.; Vogelstein, B. Structural comparisons of class I phosphoinositide 3-kinases. Nat. Rev. Cancer 2008, 8, 665–669. [Google Scholar] [CrossRef] [PubMed]
- Bachman, K.E.; Argani, P.; Samuels, Y.; Silliman, N.; Ptak, J.; Szabo, S.; Konishi, H.; Karakas, B.; Blair, B.G.; Lin, C.; et al. The PIK3CA gene is mutated with high frequency in human breast cancers. Cancer Biol. Ther. 2004, 3, 772–775. [Google Scholar] [CrossRef] [PubMed]
- Samuels, Y.; Wang, Z.; Bardelli, A.; Silliman, N.; Ptak, J.; Szabo, S.; Yan, H.; Gazdar, A.; Powell, S.M.; Riggins, G.J.; et al. High frequency of mutations of the PIK3CA gene in human cancers. Science 2004, 304, 554. [Google Scholar] [CrossRef]
- Ligresti, G.; Militello, L.; Steelman, L.S.; Cavallaro, A.; Basile, F.; Nicoletti, F.; Stivala, F.; McCubrey, J.A.; Libra, M. PIK3CA mutations in human solid tumors: Role in sensitivity to various therapeutic approaches. Cell Cycle 2009, 8, 1352–1358. [Google Scholar]
- Kastenhuber, E.R.; Lowe, S.W. Putting p53 in context. Cell 2017, 170, 1062–1078. [Google Scholar] [CrossRef] [PubMed]
- The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012, 490, 61–70. [Google Scholar]
- Jiang, Y.; Chen, M.; Nie, H.; Yuan, Y. PD-1 and PD-L1 in cancer immunotherapy: Clinical implications and future considerations. Hum. Vaccines Immunother. 2019, 15, 1111–1122. [Google Scholar]
- Berx, G.; Becker, K.F.; Höfler, H.; Van Roy, F. Mutations of the human E-cadherin (CDH1) gene. Hum. Mutat. 1998, 12, 226–237. [Google Scholar] [CrossRef]
- Berx, G.; Cleton-Jansen, A.M.; Strumane, K.; De Leeuw, W.; Nollet, F.; Van Roy, F.; Cornelisse, C. E-cadherin is inactivated in a majority of invasive human lobular breast cancers by truncation mutations throughout its extracellular domain. Oncogene 1996, 13, 1919–1925. [Google Scholar]
- Stephens, P.J.; Tarpey, P.S.; Davies, H.; Van Loo, P.; Greenman, C.; Wedge, D.C.; Nik-Zainal, S.; Martin, S.; Varela, I.; Bignell, G.R.; et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 2012, 486, 400–404. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.; Su, M.A.; Wan, Y.Y. An essential role of the transcription factor GATA-3 for the function of regulatory T cells. Immunity 2011, 35, 337–348. [Google Scholar] [CrossRef]
- Zhu, J.; Min, B.; Hu-Li, J.; Watson, C.J.; Grinberg, A.; Wang, Q.; Killeen, N.; Urban, J.F.; Guo, L.; Paul, W.E. Conditional deletion of Gata3 shows its essential function in TH1-TH2 responses. Nat. Immunol. 2004, 5, 1157–1165. [Google Scholar] [CrossRef]
- He, F.; Chen, H.; Probst-Kepper, M.; Geffers, R.; Eifes, S.; Del Sol, A.; Schughart, K.; Zeng, A.P.; Balling, R. PLAU inferred from a correlation network is critical for suppressor function of regulatory T cells. Mol. Syst. Biol. 2012, 8, 624. [Google Scholar] [CrossRef] [PubMed]
- Stein, J.V.; López-Fraga, M.; Elustondo, F.A.; Carvalho-Pinto, C.E.; Rodríguez, D.; Gómez-Caro, R.; De Jong, J.; Martínez-A, C.; Medema, J.P.; Hahne, M.; et al. APRIL modulates B and T cell immunity. J. Clin. Investig. 2002, 109, 1587–1598. [Google Scholar] [CrossRef] [PubMed]
- Salminen, A.; Kaarniranta, K.; Kauppinen, A. Insulin/IGF-1 signaling promotes immunosuppression via the STAT3 pathway: Impact on the aging process and age-related diseases. Inflamm. Res. 2021, 70, 1043–1061. [Google Scholar] [CrossRef]
- Smith, T.J. Insulin-like growth factor-I regulation of immune function: A potential therapeutic target in autoimmune diseases? Pharmacol. Rev. 2010, 62, 199–236. [Google Scholar] [CrossRef]
- Li, G.; Zhu, X.; Liu, C. Characterization of immune infiltration and construction of a prediction model for overall survival in melanoma patients. Front. Oncol. 2021, 11, 639059. [Google Scholar] [CrossRef]
- Kmiecik, J.; Poli, A.; Brons, N.H.; Waha, A.; Eide, G.E.; Enger, P.Ø.; Zimmer, J.; Chekenya, M. Elevated CD3+ and CD8+ tumor-infiltrating immune cells correlate with prolonged survival in glioblastoma patients despite integrated immunosuppressive mechanisms in the tumor microenvironment and at the systemic level. J. Neuroimmunol. 2013, 264, 71–83. [Google Scholar] [CrossRef]
- Gonzalez, H.; Hagerling, C.; Werb, Z. Roles of the immune system in cancer: From tumor initiation to metastatic progression. Genes Dev. 2018, 32, 1267–1284. [Google Scholar] [CrossRef] [PubMed]
- Labani-Motlagh, A.; Ashja-Mahdavi, M.; Loskog, A. The tumor microenvironment: A milieu hindering and obstructing antitumor immune responses. Front. Immunol. 2020, 11, 525594. [Google Scholar] [CrossRef] [PubMed]
- Melssen, M.M.; Sheybani, N.D.; Leick, K.M.; Slingluff, C.L., Jr. Barriers to immune cell infiltration in tumors. J. Immunother. Cancer 2023, 11, e006401. [Google Scholar] [CrossRef]
- Zhang, X.; Shi, M.; Chen, T.; Zhang, B. Characterization of the immune cell infiltration landscape in head and neck squamous cell carcinoma to aid immunotherapy. Mol. Ther.-Nucleic Acids 2020, 22, 298–309. [Google Scholar] [CrossRef]
- Blank, C.U.; Haining, W.N.; Held, W.; Hogan, P.G.; Kallies, A.; Lugli, E.; Lynn, R.C.; Philip, M.; Rao, A.; Restifo, N.P.; et al. Defining ‘T cell exhaustion’. Nat. Rev. Immunol. 2019, 19, 665–674. [Google Scholar] [CrossRef] [PubMed]
- McGoverne, I.; Dunn, J.; Batham, J.; Tu, W.J.; Chrisp, J.; Rao, S. Epitherapy and immune checkpoint blockade: Using epigenetic reinvigoration of exhausted and dysfunctional T cells to reimburse immunotherapy response. BMC Immunol. 2020, 21, 22. [Google Scholar] [CrossRef]
- Voskoboinik, I.; Whisstock, J.C.; Trapani, J.A. Perforin and granzymes: Function, dysfunction and human pathology. Nat. Rev. Immunol. 2015, 15, 388–400. [Google Scholar] [CrossRef]
- Cao, X.; Cai, S.F.; Fehniger, T.A.; Song, J.; Collins, L.I.; Piwnica-Worms, D.R.; Ley, T.J. Granzyme B and perforin are important for regulatory T cell-mediated suppression of tumor clearance. Immunity 2007, 27, 635–646. [Google Scholar] [CrossRef]
- Pinkoski, M.J.; Hobman, M.; Heibein, J.A.; Tomaselli, K.; Li, F.; Seth, P.; Froelich, C.J.; Bleackley, R.C. Entry and trafficking of granzyme B in target cells during granzyme B-perforin–mediated apoptosis. Blood J. Am. Soc. Hematol. 1998, 92, 1044–1054. [Google Scholar]
- Mendoza, J.L.; Escalante, N.K.; Jude, K.M.; Sotolongo Bellon, J.; Su, L.; Horton, T.M.; Tsutsumi, N.; Berardinelli, S.J.; Haltiwanger, R.S.; Piehler, J.; et al. Structure of the IFNγ receptor complex guides design of biased agonists. Nature 2019, 567, 56–60. [Google Scholar] [CrossRef]
- Tau, G.Z.; Cowan, S.N.; Weisburg, J.; Braunstein, N.S.; Rothman, P.B. Regulation of IFN-γ signaling is essential for the cytotoxic activity of CD8+ T cells. J. Immunol. 2001, 167, 5574–5582. [Google Scholar] [CrossRef] [PubMed]
- Maimela, N.R.; Liu, S.; Zhang, Y. Fates of CD8+ T cells in tumor microenvironment. Comput. Struct. Biotechnol. J. 2019, 17, 1–13. [Google Scholar] [CrossRef] [PubMed]
- Angelova, M.; Charoentong, P.; Hackl, H.; Fischer, M.L.; Snajder, R.; Krogsdam, A.M.; Waldner, M.J.; Bindea, G.; Mlecnik, B.; Galon, J.; et al. Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol. 2015, 16, 1–17. [Google Scholar] [CrossRef] [PubMed]
- Zaidi, M.R.; Davis, S.; Noonan, F.P.; Graff-Cherry, C.; Hawley, T.S.; Walker, R.L.; Feigenbaum, L.; Fuchs, E.; Lyakh, L.; Young, H.A.; et al. Interferon-γ links UV to melanocyte activation and promotes melanomagenesis. Nature 2011, 469, 548. [Google Scholar] [CrossRef]
- Hellmann, M.D.; Nathanson, T.; Rizvi, H.; Creelan, B.C.; Sanchez-Vega, F.; Ahuja, A.; Ni, A.; Novik, J.B.; Mangarin, L.M.; Abu-Akeel, M.; et al. Genomic features of response to combination immunotherapy in patients with advanced non-small-cell lung cancer. Cancer Cell 2018, 33, 843–852. [Google Scholar] [CrossRef]
- Hamid, A.A.; Gray, K.P.; Shaw, G.; MacConaill, L.E.; Evan, C.; Bernard, B.; Loda, M.; Corcoran, N.M.; Van Allen, E.M.; Choudhury, A.D.; et al. Compound genomic alterations of TP53, PTEN, and RB1 tumor suppressors in localized and metastatic prostate cancer. Eur. Urol. 2019, 76, 89–97. [Google Scholar] [CrossRef] [PubMed]
- Bezzi, M.; Seitzer, N.; Ishikawa, T.; Reschke, M.; Chen, M.; Wang, G.; Mitchell, C.; Ng, C.; Katon, J.; Lunardi, A.; et al. Diverse genetic-driven immune landscapes dictate tumor progression through distinct mechanisms. Nat. Med. 2018, 24, 165–175. [Google Scholar] [CrossRef]
- Kaur, H.B.; Lu, J.; Guedes, L.B.; Maldonado, L.; Reitz, L.; Barber, J.R.; De Marzo, A.M.; Tomlins, S.A.; Sfanos, K.S.; Eisenberger, M.; et al. TP53 missense mutation is associated with increased tumor-infiltrating T cells in primary prostate cancer. Hum. Pathol. 2019, 87, 95–102. [Google Scholar] [CrossRef]
- Nusrat, M.; Roszik, J.; Katkhuda, R.; Menter, D.; Raghav, K.P.S.; Morris, V.K.; Sharma, P.; Allison, J.P.; Blando, J.M.; Maru, D.M.; et al. Association of PIK3CA mutations (mut) with immune engagement and clinical benefit from immunotherapy in microsatellite stable (MSS) colorectal cancer (CRC) patients (pts). J. Clin. Oncol. 2019, 37, 15. [Google Scholar] [CrossRef]
- Zhang, J.M.; An, J. Cytokines, inflammation, and pain. Int. Anesthesiol. Clin. 2007, 45, 27–37. [Google Scholar] [CrossRef]
- Kay, J.; Thadhani, E.; Samson, L.; Engelward, B. Inflammation-induced DNA damage, mutations and cancer. DNA Repair 2019, 83, 102673. [Google Scholar] [CrossRef]
- Sumimoto, H.; Imabayashi, F.; Iwata, T.; Kawakami, Y. The BRAF–MAPK signaling pathway is essential for cancer-immune evasion in human melanoma cells. J. Exp. Med. 2006, 203, 1651. [Google Scholar] [CrossRef] [PubMed]
- Dumont, A.G.; Dumont, S.N.; Trent, J.C. The favorable impact of PIK3CA mutations on survival: An analysis of 2587 patients with breast cancer. Chin. J. Cancer 2012, 31, 327. [Google Scholar] [CrossRef] [PubMed]
- Kalinsky, K.; Jacks, L.M.; Heguy, A.; Patil, S.; Drobnjak, M.; Bhanot, U.K.; Hedvat, C.V.; Traina, T.A.; Solit, D.; Gerald, W.; et al. PIK3CA mutation associates with improved outcome in breast cancer. Clin. Cancer Res. 2009, 15, 5049–5059. [Google Scholar] [CrossRef]
- Maruyama, N.; Miyoshi, Y.; Taguchi, T.; Tamaki, Y.; Monden, M.; Noguchi, S. Clinicopathologic analysis of breast cancers with PIK3CA mutations in Japanese women. Clin. Cancer Res. 2007, 13, 408–414. [Google Scholar] [CrossRef]
- Barbareschi, M.; Buttitta, F.; Felicioni, L.; Cotrupi, S.; Barassi, F.; Del Grammastro, M.; Ferro, A.; Dalla Palma, P.; Galligioni, E.; Marchetti, A. Different prognostic roles of mutations in the helical and kinase domains of the PIK3CA gene in breast carcinomas. Clin. Cancer Res. 2007, 13, 6064–6069. [Google Scholar] [CrossRef]
- Pérez-Tenorio, G.; Alkhori, L.; Olsson, B.; Waltersson, M.A.; Nordenskjöld, B.; Rutqvist, L.E.; Skoog, L.; Stål, O. PIK3CA mutations and PTEN loss correlate with similar prognostic factors and are not mutually exclusive in breast cancer. Clin. Cancer Res. 2007, 13, 3577–3584. [Google Scholar] [CrossRef] [PubMed]
- Li, S.Y.; Rong, M.; Grieu, F.; Iacopetta, B. PIK3CA mutations in breast cancer are associated with poor outcome. Breast Cancer Res. Treat. 2006, 96, 91–95. [Google Scholar] [CrossRef]
- Lerma, E.; Catasus, L.; Gallardo, A.; Peiro, G.; Alonso, C.; Aranda, I.; Barnadas, A.; Prat, J. Exon 20 PIK3CA mutations decreases survival in aggressive (HER-2 positive) breast carcinomas. Virchows Arch. 2008, 453, 133–139. [Google Scholar] [CrossRef]
- Lai, Y.L.; Mau, B.L.; Cheng, W.H.; Chen, H.M.; Chiu, H.H.; Tzen, C.Y. PIK3CA exon 20 mutation is independently associated with a poor prognosis in breast cancer patients. Ann. Surg. Oncol. 2008, 15, 1064–1069. [Google Scholar] [CrossRef]
- Loi, S.; Haibe-Kains, B.; Majjaj, S.; Lallemand, F.; Durbecq, V.; Larsimont, D.; Gonzalez-Angulo, A.M.; Pusztai, L.; Symmans, W.F.; Bardelli, A.; et al. PIK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor–positive breast cancer. Proc. Natl. Acad. Sci. USA 2010, 107, 10208–10213. [Google Scholar] [CrossRef] [PubMed]
- Michelucci, A.; Di Cristofano, C.; Lami, A.; Collecchi, P.; Caligo, A.; Decarli, N.; Leopizzi, M.; Aretini, P.; Bertacca, G.; Porta, R.P.; et al. PIK3CA in breast carcinoma: A mutational analysis of sporadic and hereditary cases. Diagn. Mol. Pathol. 2009, 18, 200–205. [Google Scholar] [PubMed]
- Boyault, S.; Drouet, Y.; Navarro, C.; Bachelot, T.; Lasset, C.; Treilleux, I.; Tabone, E.; Puisieux, A.; Wang, Q. Mutational characterization of individual breast tumors: TP53 and PI3K pathway genes are frequently and distinctively mutated in different subtypes. Breast Cancer Res. Treat. 2012, 132, 29–39. [Google Scholar] [CrossRef]
- Saal, L.H.; Holm, K.; Maurer, M.; Memeo, L.; Su, T.; Wang, X.; Yu, J.S.; Malmström, P.O.; Mansukhani, M.; Enoksson, J.; et al. PIK3CA mutations correlate with hormone receptors, node metastasis, and ERBB2, and are mutually exclusive with PTEN loss in human breast carcinoma. Cancer Res. 2005, 65, 2554–2559. [Google Scholar] [CrossRef]
- Stemke-Hale, K.; Gonzalez-Angulo, A.M.; Lluch, A.; Neve, R.M.; Kuo, W.L.; Davies, M.; Carey, M.; Hu, Z.; Guan, Y.; Sahin, A.; et al. An integrative genomic and proteomic analysis of PIK3CA, PTEN, and AKT mutations in breast cancer. Cancer Res. 2008, 68, 6084–6091. [Google Scholar] [CrossRef] [PubMed]
- Fillbrunn, M.; Signorovitch, J.; André, F.; Wang, I.; Lorenzo, I.; Ridolfi, A.; Park, J.; Dua, A.; Rugo, H.S. PIK3CA mutation status, progression and survival in advanced HR+/HER2-breast cancer: A meta-analysis of published clinical trials. BMC Cancer 2022, 22, 1002. [Google Scholar] [CrossRef] [PubMed]
- Mosele, F.; Stefanovska, B.; Lusque, A.; Dien, A.T.; Garberis, I.; Droin, N.; Le Tourneau, C.; Sablin, M.P.; Lacroix, L.; Enrico, D.; et al. Outcome and molecular landscape of patients with PIK3CA-mutated metastatic breast cancer. Ann. Oncol. 2020, 31, 377–386. [Google Scholar]
- Micke, P.; Strell, C.; Mattsson, J.; Martín-Bernabé, A.; Brunnström, H.; Huvila, J.; Sund, M.; Wärnberg, F.; Ponten, F.; Glimelius, B.; et al. The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types. EBioMedicine 2021, 65, 103269. [Google Scholar] [CrossRef]
- Folkman, J. Tumor angiogenesis: Therapeutic implications. N. Engl. J. Med. 1971, 285, 1182–1186. [Google Scholar]
- Khosravi Shahi, P.; Soria Lovelle, A.; Pérez Manga, G. Tumoral angiogenesis and breast cancer. Clin. Transl. Oncol. 2009, 11, 138–142. [Google Scholar] [CrossRef]
- Zhong, M.; Li, N.; Qiu, X.; Ye, Y.; Chen, H.; Hua, J.; Yin, P.; Zhuang, G. TIPE regulates VEGFR2 expression and promotes angiogenesis in colorectal cancer. Int. J. Biol. Sci. 2020, 16, 272. [Google Scholar] [CrossRef] [PubMed]
- Hall, R.D.; Le, T.M.; Haggstrom, D.E.; Gentzler, R.D. Angiogenesis inhibition as a therapeutic strategy in non-small cell lung cancer (NSCLC). Transl. Lung Cancer Res. 2015, 4, 515. [Google Scholar]
- Zimna, A.; Kurpisz, M. Hypoxia-inducible factor-1 in physiological and pathophysiological angiogenesis: Applications and therapies. BioMed Res. Int. 2015, 2015, 549412. [Google Scholar] [CrossRef]
- Ebos, J.M.; Kerbel, R.S. Antiangiogenic therapy: Impact on invasion, disease progression, and metastasis. Nat. Rev. Clin. Oncol. 2011, 8, 210–221. [Google Scholar] [CrossRef] [PubMed]
- Liu, Z.L.; Chen, H.H.; Zheng, L.L.; Sun, L.P.; Shi, L. Angiogenic signaling pathways and anti-angiogenic therapy for cancer. Signal Transduct. Target. Ther. 2023, 8, 198. [Google Scholar] [CrossRef] [PubMed]
- Wu, C.L.; Zhang, S.M.; Lin, L.; Gao, S.S.; Fu, K.F.; Liu, X.D.; Liu, Y.; Zhou, L.J.; Zhou, P.K. BECN1-knockout impairs tumor growth, migration and invasion by suppressing the cell cycle and partially suppressing the epithelial-mesenchymal transition of human triple-negative breast cancer cells. Int. J. Oncol. 2018, 53, 1301–1312. [Google Scholar] [CrossRef]
- Sharma, A.; Singh, K.; Mazumder, S.; Hill, B.; Kalaycio, M.; Almasan, A. BECN1 and BIM interactions with MCL-1 determine fludarabine resistance in leukemic B cells. Cell Death Dis. 2013, 4, e628. [Google Scholar] [CrossRef]
- Liang, X.H.; Jackson, S.; Seaman, M.; Brown, K.; Kempkes, B.; Hibshoosh, H.; Levine, B. Induction of autophagy and inhibition of tumorigenesis by beclin 1. Nature 1999, 402, 672–676. [Google Scholar] [CrossRef]
- Qu, X.; Yu, J.; Bhagat, G.; Furuya, N.; Hibshoosh, H.; Troxel, A.; Rosen, J.; Eskelinen, E.L.; Mizushima, N.; Ohsumi, Y.; et al. Promotion of tumorigenesis by heterozygous disruption of the beclin 1 autophagy gene. J. Clin. Investig. 2003, 112, 1809–1820. [Google Scholar] [CrossRef]
- Yue, Z.; Jin, S.; Yang, C.; Levine, A.J.; Heintz, N. Beclin 1, an autophagy gene essential for early embryonic development, is a haploinsufficient tumor suppressor. Proc. Natl. Acad. Sci. USA 2003, 100, 15077–15082. [Google Scholar] [CrossRef]
- Laddha, S.V.; Ganesan, S.; Chan, C.S.; White, E. Mutational landscape of the essential autophagy gene BECN1 in human cancers. Mol. Cancer Res. 2014, 12, 485–490. [Google Scholar] [CrossRef] [PubMed]
- Tang, H.; Sebti, S.; Titone, R.; Zhou, Y.; Isidoro, C.; Ross, T.S.; Hibshoosh, H.; Xiao, G.; Packer, M.; Xie, Y.; et al. Decreased BECN1 mRNA expression in human breast cancer is associated with estrogen receptor-negative subtypes and poor prognosis. EBioMedicine 2015, 2, 255–263. [Google Scholar] [CrossRef] [PubMed]
- Moo, T.A.; Sanford, R.; Dang, C.; Morrow, M. Overview of breast cancer therapy. PET Clin. 2018, 13, 339–354. [Google Scholar] [CrossRef] [PubMed]
- Akbay, E.A.; Koyama, S.; Carretero, J.; Altabef, A.; Tchaicha, J.H.; Christensen, C.L.; Mikse, O.R.; Cherniack, A.D.; Beauchamp, E.M.; Pugh, T.J.; et al. Activation of the PD-1 pathway contributes to immune escape in EGFR-driven lung tumors. Cancer Discov. 2013, 3, 1355–1363. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, W.; Lee, A.; Tiwari, A.K.; Yang, M.Q. Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer. Cells 2024, 13, 1518. https://doi.org/10.3390/cells13181518
Zhang W, Lee A, Tiwari AK, Yang MQ. Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer. Cells. 2024; 13(18):1518. https://doi.org/10.3390/cells13181518
Chicago/Turabian StyleZhang, Wenjuan, Alex Lee, Amit K. Tiwari, and Mary Qu Yang. 2024. "Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer" Cells 13, no. 18: 1518. https://doi.org/10.3390/cells13181518