Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 + 1395 word(s) 1395 2021-10-11 05:45:19 |
2 format correct Meta information modification 1395 2021-11-17 10:24:30 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Schmidt, M. Immunomodulating Therapies in Breast Cancer. Encyclopedia. Available online: https://encyclopedia.pub/entry/16005 (accessed on 18 June 2024).
Schmidt M. Immunomodulating Therapies in Breast Cancer. Encyclopedia. Available at: https://encyclopedia.pub/entry/16005. Accessed June 18, 2024.
Schmidt, Marcus. "Immunomodulating Therapies in Breast Cancer" Encyclopedia, https://encyclopedia.pub/entry/16005 (accessed June 18, 2024).
Schmidt, M. (2021, November 15). Immunomodulating Therapies in Breast Cancer. In Encyclopedia. https://encyclopedia.pub/entry/16005
Schmidt, Marcus. "Immunomodulating Therapies in Breast Cancer." Encyclopedia. Web. 15 November, 2021.
Immunomodulating Therapies in Breast Cancer
Edit

Review of the role of the immune system in breast cancer. It covers the prognostic and predictive impact of tumor-infiltrating lymphocytes. Furthermore therapeutic advances ranging from immune checkpoint inhibitors and personalized vaccination strategies are highlighted.

tumor infiltrating lymphocytes (TILs) immune checkpoint inhibitors (ICPis) mRNA vaccine tumor-associated antigens (TAA) neoantigens

1. Introduction

Breast cancer is the most common cancer and the leading cause of cancer death for women worldwide [1]. In 2015, breast vancer incidence was 2.4 million, with 523,000 breast cancer deaths. Invasive breast cancer can be divided in several molecular subgroups (e.g., luminal A, luminal B, HER2-positive, and triple-negative) which have different prognoses and different systemic therapeutic options (e.g., chemotherapy, endocrine therapy, anti-HER2 therapy) [2]. Early breast cancer has no distant metastases and is curable [2]. However, if distant metastases occur, the disease is treatable but incurable [3].

The role of the immune system in breast cancer has long been debated [4]. With the advent of modern techniques, such as mRNA sequencing data from The Cancer Genome Atlas (TCGA), it has been shown that high expression of T-cell and B-cell signatures predicts improved overall survival in many tumor types, including breast cancer [5]. In particular, triple-negative breast cancer (TNBC), which has a more pronounced immunogenic potential compared to other molecular subtypes, is of great interest. TNBC accounts for up to 20% of breast cancers and is associated with a significantly worse prognosis in the first 2 to 3 years after diagnosis compared with other breast cancer subtypes [6]. It is now generally accepted that TNBC is not a homogeneous disease. Instead, TNBC consists of multiple subtypes (e.g., basal-like 1 and 2, immunomodulatory, mesenchymal, mesenchymal stem-like, and luminal androgen receptor) [7]. In a comprehensive immunogenomic analysis of over 10,000 tumors using TCGA data, Thorsson and co-workers identified six stable and reproducible immune subtypes C1–C6 (i.e., wound-healing, IFN-γ-dominant, inflammatory, lymphocyte-depleted, immunologically quiet, and TGF-β-dominant) [8]. Interestingly, these immune subtypes include multiple tumor types, and are characterized by a dominance of either macrophage or lymphocyte signatures, T-helper phenotype, extent of intratumoral heterogeneity, and proliferative activity. Although these authors did not comment specifically on TNBC, it is likely that TNBC with a strong lymphocytic infiltrate belong to immune subtype C3. Using even more sophisticated techniques, such as single-cell sequencing, Wu and his collaborators have deconvoluted breast cancer cohorts and stratified them into nine clusters, called “ecotypes”, with unique cellular compositions and clinical outcomes that provide a comprehensive transcriptional atlas of breast cancer cellular architecture [9]. Significantly more somatic mutations and neoantigens are detected in TNBC than in other molecular subtypes, resulting in increased immunogenicity [10]. In a systematic review, Stanton and colleagues showed that the extent of tumor-infiltrating lymphocytes (TILs) varies within and between breast cancer subtypes, with TNBC having numerous TILs [11]. This may identify breast cancers that are more suitable for immunotherapy.

2. Prognostic and Predictive Significance of Tumor-Infiltrating Lymphocytes

Most studies that addressed the prognostic and/or predictive role of TILs in breast cancer focused on the cellular immune system, particularly cytotoxic T cells [12][13][14][15][16][17]. Overall, these studies showed that increased rates of tumor-infiltrating lymphocytes or T-cell transcripts were associated with improved prognosis in rapidly proliferating breast cancer such as TNBC.

In contrast, we primarily examined B cells and the humoral immune system and reported a strong positive prognostic impact of a B cell metagene on breast cancer prognosis [18]. This strong protective effect of a B cell/plasma cell signature was later confirmed by others [19][20]. Tumor-infiltrating plasma cells were identified by confocal microscopy as the source of immunoglobulin kappa C (IGKC) expression [21]. In this study, co-staining with anti-human IgG showed that IGKC was expressed in IgG-positive cells, a known feature of B-cell maturation and plasma cell differentiation after antigen contact. IGKC has been associated with favorable prognosis in untreated patients and with response to anthracycline-containing neoadjuvant chemotherapy in early breast cancer [21]. Indeed, in a comprehensive analysis of the prognostic landscape of genes and infiltrating immune cells in human cancers, Gentles et al. confirmed that plasma cell signatures, as well as plasma cells expressing IGKC, are associated with improved survival [20]. However, the strong dependence of the humoral immune system on T cells is examplified by C-X-C motif chemokine ligand 13 (CXCL13)-positive CD4+ follicular helper T (Tfh) cells, which are crucial for germinal center development and antigen-specific B cell maturation to high-affinity memory cells and antibody-secreting plasma cells [22]. In addition, CXCL13 has been associated with improved survival in TNBC [23].

Overall, these and other findings suggest that humoral immunity may be as important as cellular immunity in eliminating cancer [24]. These, initially retrospective, results were later confirmed in exploratory studies using archival tissue from randomized trials [23][25][26], as well as by histological evidence of TILs in archival tissue from randomized trials [15][27][28]. Recently, in the neoadjuvant EXPRESSION trial, we demonstrated that genes with significantly higher expression in pathologically complete responders are primarily related to the immune response, including immunoglobulins [29]. These results also support the predictive role of the humoral immune system in early breast cancer.

This significant association of tumor-infiltrating immune cells and TNBC is not surprising, considering that the overall mutational burden is highest in TNBC [10]. In addition, these authors found that mutational burden was highly correlated with neoepitope load (R2 = 0.86). A comprehensive analysis of immunogenic signatures in TNBC based on two sets of large-scale breast cancer genomic data showed that TNBC has the strongest immunogenicity among breast cancer subtypes [30]. Furthermore, these authors confirmed that TNBC also has higher levels of immune cell infiltration and higher expression of genes encoding immune checkpoints than non-TNBC. However, mutational and neoantigen load appear to incompletely explain the immune response in TNBC, as other studies have described an inverse relationship between immune infiltration and somatic copy number alterations [31][32]. Obviously, the exact relationship between immune infiltration, mutation burden, and neoantigen burden has not been fully elucidated. Nevertheless, TILs are widely used, especially in TNBC. To improve reproducibility, a standardized method for the evaluation of TILs has been defined to integrate this parameter into standard histopathological practice [33][34].

3. Immune Checkpoint Inhibitors

Important target structures in the immune system are “immune checkpoints”. Immune checkpoint inhibitors (ICPis) block the interaction of certain cell surface proteins that serve as “brakes” on immune responses. Currently, the most important immune checkpoint in breast cancer is the PD-1/PD-L1 axis [35][36]. The interaction between PD-1 and its ligand PD-L1 functions as an immune checkpoint against unrestrained cytotoxic T effector cell activity. Furthermore, it promotes peripheral T effector cell exhaustion and conversion of T effector cells to immunosuppressive Tregs [37]. Immune checkpoint inhibitors that block the PD-1/PD-L1 axis and reactivate cytotoxic T effector cell function increase immune cell activity against tumor cells.

Phase I/II evidence for ICPis in advanced breast cancer.

Abbreviations: AE, adverse events; HR, hormine receptor; ICPis, immune checkpoint inhibitors; m, months; ORR, objective response rate; OS, overall survival; PFS, progression-free survival; TMBC, triple-negative breast cancer; vs., versus.

Indeed, the vast majority of randomized trials using ICPi in early or advanced TNBC showed significant benefits over standard therapy alone. When combined with an acceptable safety profile, immune checkpoint inhibitors are a promising new therapeutic option in TNBC. Recently, the Society for Immunotherapy of Cancer (SITC) published a clinical practice guideline on immunotherapy for breast cancer [38]. Recommendations in this clinical practice guideline include diagnostic testing, treatment planning, immune-related adverse events, and patient quality of life considerations to provide guidance to the oncology community treating breast cancer patients with immunotherapies.

4. Predictive Markers for Immune Checkpoint Inhibitors

Currently, the only established predictive biomarker for response to ICPi in advanced TNBC is PD-L1 status. Recent analyses have shown a potential role of TMB in response to durvalumab in early TNBC [39]. In a recently published comprehensive genomic analysis of 3831 consecutive breast cancer samples, potential biomarkers (e.g., TMB, microsatellite instability [MSI], BRCA mutations) were assessed to guide the use of ICPIs in these patients [40]. Interferon-γ (IFN-γ) plays a crucial role in the regulation of anti-tumor immunity [41]. Upon ligand binding, IFN-y receptor 1 and 2 (IFNγR1 and IFNγR2) oligomerize and transphosphorylate, activating Janus-activated kinase (JAK) 1 and 2. Thereby, IFNγR1 is phosphorylated, creating a docking site for the signal transducer and activator of transcription (STAT) 1. Interferon-γ (IFN-γ) signaling signatures are associated with clinical response to treatment with ICPi [42]. Similarly, JAK/STAT pathways predict response to ICPi therapy [43]. In addition, cancer stem cells are a potential biomarker to predict the effectiviness of ICPis [44]. However, for all of these potential biomarkers, prospective randomized trials are needed to assess the predictive value in response to immune checkpoint inhibitors.

References

  1. Fitzmaurice, C.; Allen, C.; Barber, R.M.; Barregard, L.; Bhutta, Z.A.; Brenner, H.; Dicker, D.J.; Chimed-Orchir, O.; Dandona, R.; Dandona, L.; et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2017, 3, 524–548.
  2. Burstein, H.J.; Curigliano, G.; Thürlimann, B.; Weber, W.P.; Poortmans, P.; Regan, M.M.; Senn, H.J.; Winer, E.P.; Gnant, M. Customizing local and systemic therapies for women with early breast cancer: The St. Gallen International Consensus Guidelines for treatment of early breast cancer 2021. Ann. Oncol. 2021, 32, 1216–1235.
  3. Cardoso, F.; Paluch-Shimon, S.; Senkus, E.; Curigliano, G.; Aapro, M.S.; André, F.; Barrios, C.H.; Bergh, J.; Bhattacharyya, G.S.; Biganzoli, L.; et al. 5th ESO-ESMO international consensus guidelines for advanced breast cancer (ABC 5). Ann. Oncol. 2020, 31, 1623–1649.
  4. Berg, J.W. Inflammation and prognosis in breast cancer; a search for host resistance. Cancer 1959, 12, 714–720.
  5. Iglesia, M.D.; Parker, J.S.; Hoadley, K.A.; Serody, J.S.; Perou, C.M.; Vincent, B.G. Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types. J. Natl. Cancer Inst. 2016, 108, djw144.
  6. Metzger-Filho, O.; Tutt, A.; de Azambuja, E.; Saini, K.S.; Viale, G.; Loi, S.; Bradbury, I.; Bliss, J.M.; Azim, H.A., Jr.; Ellis, P.; et al. Dissecting the heterogeneity of triple-negative breast cancer. J. Clin. Oncol. 2012, 30, 1879–1887.
  7. Lehmann, B.D.; Bauer, J.A.; Chen, X.; Sanders, M.E.; Chakravarthy, A.B.; Shyr, Y.; Pietenpol, J.A. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin. Investig. 2011, 121, 2750–2767.
  8. Thorsson, V.; Gibbs, D.L.; Brown, S.D.; Wolf, D.; Bortone, D.S.; Ou Yang, T.-H.; Porta-Pardo, E.; Gao, G.F.; Plaisier, C.L.; Eddy, J.A.; et al. The Immune Landscape of Cancer. Immunity 2018, 48, 812–830.e14.
  9. 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.
  10. Narang, P.; Chen, M.; Sharma, A.A.; Anderson, K.S.; Wilson, M.A. The neoepitope landscape of breast cancer: Implications for immunotherapy. BMC Cancer 2019, 19, 200.
  11. Stanton, S.E.; Adams, S.; Disis, M.L. Variation in the Incidence and Magnitude of Tumor-Infiltrating Lymphocytes in Breast Cancer Subtypes: A Systematic Review. JAMA Oncol. 2016, 2, 1354–1360.
  12. Burugu, S.; Asleh-Aburaya, K.; Nielsen, T.O. Immune infiltrates in the breast cancer microenvironment: Detection, characterization and clinical implication. Breast Cancer 2016, 24, 3–15.
  13. Peng, G.-L.; Li, L.; Guo, Y.-W.; Yu, P.; Yin, X.-J.; Wang, S.; Liu, C.-P. CD8(+) cytotoxic and FoxP3(+) regulatory T lymphocytes serve as prognostic factors in breast cancer. Am. J. Transl. Res. 2019, 11, 5039–5053.
  14. Mahmoud, S.M.A.; Paish, E.C.; Powe, D.G.; Macmillan, R.D.; Grainge, M.J.; Lee, A.H.S.; Ellis, I.O.; Green, A.R. Tumor-infiltrating CD8+ lymphocytes predict clinical outcome in breast cancer. J. Clin. Oncol. 2011, 29, 1949–1955.
  15. Denkert, C.; Loibl, S.; Noske, A.; Roller, M.; Müller, B.M.; Komor, M.; Budczies, J.; Darb-Esfahani, S.; Kronenwett, R.; Hanusch, C.; et al. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J. Clin. Oncol. 2010, 28, 105–113.
  16. Alexe, G.; Dalgin, G.S.; Scanfeld, D.; Tamayo, P.; Mesirov, J.P.; DeLisi, C.; Harris, L.; Barnard, N.; Martel, M.; Levine, A.J.; et al. High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates. Cancer Res. 2007, 67, 10669–10676.
  17. Rody, A.; Holtrich, U.; Pusztai, L.; Liedtke, C.; Gaetje, R.; Ruckhaeberle, E.; Solbach, C.; Hanker, L.; Ahr, A.; Metzler, D.; et al. T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers. Breast Cancer Res. 2009, 11, R15.
  18. Schmidt, M.; Bohm, D.; von Torne, C.; Steiner, E.; Puhl, A.; Pilch, H.; Lehr, H.-A.; Hengstler, J.G.; Kolbl, H.; Gehrmann, M. The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res. 2008, 68, 5405–5413.
  19. Bianchini, G.; Qi, Y.; Alvarez, R.H.; Iwamoto, T.; Coutant, C.; Ibrahim, N.K.; Valero, V.; Cristofanilli, M.; Green, M.C.; Radvanyi, L.; et al. Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers. J. Clin. Oncol. 2010, 28, 4316–4323.
  20. Gentles, A.J.; Newman, A.M.; Liu, C.L.; Bratman, S.V.; Feng, W.; Kim, D.; Nair, V.S.; Xu, Y.; Khuong, A.; Hoang, C.D.; et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 2015.
  21. Schmidt, M.; Hellwig, B.; Hammad, S.; Othman, A.; Lohr, M.; Chen, Z.; Boehm, D.; Gebhard, S.; Petry, I.; Lebrecht, A.; et al. A comprehensive analysis of human gene expression profiles identifies stromal immunoglobulin kappa C as a compatible prognostic marker in human solid tumors. Clin. Cancer Res. 2012, 18, 2695–2703.
  22. Nutt, S.L.; Tarlinton, D.M. Germinal center B and follicular helper T cells: Siblings, cousins or just good friends? Nat. Immunol. 2011, 12, 472–477.
  23. Schmidt, M.; Weyer-Elberich, V.; Hengstler, J.G.; Heimes, A.-S.; Almstedt, K.; Gerhold-Ay, A.; Lebrecht, A.; Battista, M.J.; Hasenburg, A.; Sahin, U.; et al. Prognostic impact of CD4-positive T cell subsets in early breast cancer: A study based on the FinHer trial patient population. Breast Cancer Res. 2018, 20, 15.
  24. Whiteside, T.L.; Ferrone, S. For breast cancer prognosis, immunoglobulin kappa chain surfaces to the top. Clin. Cancer Res. 2012, 18, 2417–2419.
  25. Schmidt, M.; Edlund, K.; Hengstler, J.G.; Heimes, A.-S.; Almstedt, K.; Lebrecht, A.; Krajnak, S.; Battista, M.J.; Brenner, W.; Hasenburg, A.; et al. Prognostic Impact of Immunoglobulin Kappa C (IGKC) in Early Breast Cancer. Cancers 2021, 13, 3626.
  26. Denkert, C.; von Minckwitz, G.; Brase, J.C.; Sinn, B.V.; Gade, S.; Kronenwett, R.; Pfitzner, B.M.; Salat, C.; Loi, S.; Schmitt, W.D.; et al. Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers. J. Clin. Oncol. 2015, 33, 983–991.
  27. Denkert, C.; von Minckwitz, G.; Darb-Esfahani, S.; Lederer, B.; Heppner, B.I.; Weber, K.E.; Budczies, J.; Huober, J.; Klauschen, F.; Furlanetto, J.; et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: A pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol. 2018, 19, 40–50.
  28. Loi, S.; Michiels, S.; Salgado, R.; Sirtaine, N.; Jose, V.; Fumagalli, D.; Kellokumpu-Lehtinen, P.-L.; Bono, P.; Kataja, V.; Desmedt, C.; et al. Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: Results from the FinHER trial. Ann. Oncol. 2014, 25, 1544–1550.
  29. Edlund, K.; Madjar, K.; Lebrecht, A.; Aktas, B.; Pilch, H.; Hoffmann, G.; Hofmann, M.; Kolberg, H.-C.; Boehm, D.; Battista, M.; et al. Gene Expression-Based Prediction of Neoadjuvant Chemotherapy Response in Early Breast Cancer: Results of the Prospective Multicenter EXPRESSION Trial. Clin. Cancer Res. 2021, 27, 2148–2158.
  30. Liu, Z.; Li, M.; Jiang, Z.; Wang, X. A Comprehensive Immunologic Portrait of Triple-Negative Breast Cancer. Transl. Oncol. 2018, 11, 311–329.
  31. Karn, T.; Jiang, T.; Hatzis, C.; Sanger, N.; El-Balat, A.; Rody, A.; Holtrich, U.; Becker, S.; Bianchini, G.; Pusztai, L. Association Between Genomic Metrics and Immune Infiltration in Triple-Negative Breast Cancer. JAMA Oncol. 2017, 3, 1707–1711.
  32. Safonov, A.; Jiang, T.; Bianchini, G.; Gyorffy, B.; Karn, T.; Hatzis, C.; Pusztai, L. Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer. Cancer Res. 2017, 77, 3317–3324.
  33. Salgado, R.; Denkert, C.; Demaria, S.; Sirtaine, N.; Klauschen, F.; Pruneri, G.; Wienert, S.; van den Eynden, G.; Baehner, F.L.; Penault-Llorca, F.; et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: Recommendations by an International TILs Working Group 2014. Ann. Oncol. 2015, 26, 259–271.
  34. Hendry, S.; Salgado, R.; Gevaert, T.; Russell, P.A.; John, T.; Thapa, B.; Christie, M.; van de Vijver, K.; Estrada, M.V.; Gonzalez-Ericsson, P.I.; et al. Assessing Tumor-infiltrating Lymphocytes in Solid Tumors: A Practical Review for Pathologists and Proposal for a Standardized Method From the International Immunooncology Biomarkers Working Group: Part 1: Assessing the Host Immune Response, TILs in Invasive Breast Carcinoma and Ductal Carcinoma In Situ, Metastatic Tumor Deposits and Areas for Further Research. Adv. Anat. Pathol. 2017, 24, 235–251.
  35. Postow, M.A.; Callahan, M.K.; Wolchok, J.D. Immune Checkpoint Blockade in Cancer Therapy. J. Clin. Oncol. 2015, 33, 1974–1982.
  36. Ribas, A. Releasing the Brakes on Cancer Immunotherapy. N. Engl. J. Med. 2015, 373, 1490–1492.
  37. Swoboda, A.; Nanda, R. Immune Checkpoint Blockade for Breast Cancer. In Optimizing Breast Cancer Management; Gradishar, W.J., Ed.; Springer International Publishing: Cham, Switzerland, 2018; pp. 155–165. ISBN 978-3-319-70195-0.
  38. Emens, L.A.; Adams, S.; Cimino-Mathews, A.; Disis, M.L.; Gatti-Mays, M.E.; Ho, A.Y.; Kalinsky, K.; McArthur, H.L.; Mittendorf, E.A.; Nanda, R.; et al. Society for Immunotherapy of Cancer (SITC) clinical practice guideline on immunotherapy for the treatment of breast cancer. J. Immunother. Cancer 2021, 9.
  39. Karn, T.; Denkert, C.; Weber, K.E.; Holtrich, U.; Hanusch, C.; Sinn, B.V.; Higgs, B.W.; Jank, P.; Sinn, H.P.; Huober, J.; et al. Tumor mutational burden and immune infiltration as independent predictors of response to neoadjuvant immune checkpoint inhibition in early TNBC in GeparNuevo. Ann. Oncol. 2020, 31, 1216–1222.
  40. Sivapiragasam, A.; Ashok Kumar, P.; Sokol, E.S.; Albacker, L.A.; Killian, J.K.; Ramkissoon, S.H.; Huang, R.S.P.; Severson, E.A.; Brown, C.A.; Danziger, N.; et al. Predictive Biomarkers for Immune Checkpoint Inhibitors in Metastatic Breast Cancer. Cancer Med. 2020, 10, 53–61.
  41. Castro, F.; Cardoso, A.P.; Gonçalves, R.M.; Serre, K.; Oliveira, M.J. Interferon-Gamma at the Crossroads of Tumor Immune Surveillance or Evasion. Front. Immunol. 2018, 9, 847.
  42. Grasso, C.S.; Tsoi, J.; Onyshchenko, M.; Abril-Rodriguez, G.; Ross-Macdonald, P.; Wind-Rotolo, M.; Champhekar, A.; Medina, E.; Torrejon, D.Y.; Shin, D.S.; et al. Conserved Interferon-γ Signaling Drives Clinical Response to Immune Checkpoint Blockade Therapy in Melanoma. Cancer Cell 2020, 38, 500–515.
  43. Nishida, N. Role of Oncogenic Pathways on the Cancer Immunosuppressive Microenvironment and Its Clinical Implications in Hepatocellular Carcinoma. Cancers 2021, 13, 3666.
  44. Shi, X.; Liu, Y.; Cheng, S.; Hu, H.; Zhang, J.; Wei, M.; Zhao, L.; Xin, S. Cancer Stemness Associated With Prognosis and the Efficacy of Immunotherapy in Adrenocortical Carcinoma. Front. Oncol. 2021, 11, 651622.
More
Information
Subjects: Oncology
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
View Times: 280
Revisions: 2 times (View History)
Update Date: 29 Mar 2022
1000/1000
Video Production Service