Targeting Immune Checkpoints: Advancements in Cancer Immunotherapy
Received: 01-Mar-2024 / Manuscript No. icr-24-138302 / Editor assigned: 02-Mar-2024 / PreQC No. icr-24-138302 / Reviewed: 18-Mar-2024 / QC No. icr-24-138302 / Revised: 22-Mar-2024 / Manuscript No. icr-24-138302 / Published Date: 31-Mar-2024
Abstract
In recent years, cancer immunotherapy has emerged as a promising approach for treating various malignancies. Among the diverse strategies within this field, targeting immune checkpoints has gained significant attention due to its ability to unleash the body's immune system against tumor cells. This mini-review highlights the recent advancements in immune checkpoint inhibitors and novel strategies in cancer immunotherapy.
Keywords
Cancer immunotherapy; Immune checkpoints; CTLA- 4; PD-1/PD-L1; Combination therapy; Biomarkers; Personalized medicine
Introduction
Immune checkpoints are crucial regulators of the immune system, maintaining self-tolerance and preventing autoimmunity. However, cancer cells exploit these checkpoints to evade immune surveillance, leading to tumor progression and immune evasion [1,2]. Inhibiting immune checkpoints, such as cytotoxic T-lymphocyte antigen 4 (CTLA- 4) and programmed cell death protein 1 (PD-1), has revolutionized cancer treatment by restoring anti-tumor immunity. Here, we discuss the latest developments in targeting immune checkpoints for cancer immunotherapy [3].
Immune checkpoint inhibitors
Monoclonal antibodies targeting CTLA-4, such as ipilimumab, have demonstrated remarkable efficacy in treating melanoma and other malignancies [4]. Similarly, antibodies against PD-1 (e.g., pembrolizumab, nivolumab) and its ligand PD-L1 have shown significant clinical benefits across various cancer types, including lung cancer, renal cell carcinoma, and Hodgkin lymphoma. The approval of these immune checkpoint inhibitors has transformed the treatment landscape, offering durable responses and improved survival outcomes in patients with advanced cancer.
Combination therapies
While immune checkpoint inhibitors have shown impressive results, not all patients respond to monotherapy, necessitating the exploration of combination strategies [5]. Combinations of immune checkpoint inhibitors with other immunotherapeutic agents, chemotherapy, targeted therapy, or radiotherapy have been investigated to enhance antitumor immunity and overcome resistance mechanisms. For example, the combination of ipilimumab and nivolumab has demonstrated superior efficacy compared to monotherapy in melanoma and other tumor types.
Biomarkers and personalized medicine
Identifying predictive biomarkers is crucial for patient selection and treatment optimization in cancer immunotherapy. PD-L1 expression on tumor cells or immune cells has been widely used as a biomarker to predict response to PD-1/PD-L1 inhibitors [6]. However, the predictive value of PD-L1 expression varies across different cancer types and treatment regimens. Other potential biomarkers, such as tumor mutational burden (TMB) and immune gene signatures are being explored to improve patient stratification and personalize treatment strategies.
Novel immune checkpoints and targets
In addition to CTLA-4 and PD-1/PD-L1, emerging immune checkpoints and targets are being investigated to expand the repertoire of immunotherapy options [7,8]. Examples include lymphocyte activation gene 3 (LAG-3), T-cell immunoglobulin and mucindomain containing-3 (TIM-3), and indoleamine 2,3-dioxygenase 1 (IDO1). Preclinical and clinical studies evaluating inhibitors of these novel checkpoints alone or in combination with existing therapies are ongoing, with the aim of enhancing anti-tumor immune responses and overcoming resistance mechanisms.
Conclusion
Targeting immune checkpoints has revolutionized cancer immunotherapy, offering new hope for patients with advanced malignancies. Immune checkpoint inhibitors have demonstrated remarkable clinical benefits across a wide range of cancer types, leading to their widespread adoption in clinical practice. However, challenges such as resistance mechanisms and biomarker identification remain, highlighting the need for continued research and innovation in this field. Novel strategies targeting emerging immune checkpoints and personalized medicine approaches hold promise for further improving outcomes and advancing the era of precision cancer immunotherapy.
References
- Fine PE (1995)Variation in protection by BCG: implications of and for heterologous immunity. Lancet 346: 1339-1345.
- Fallahi-Sichani M, Flynn JL, Linderman JJ, Kirschner DE (2012)Differential risk of tuberculosis reactivation among anti-TNF therapies is due to drug binding kinetics and permeability and not apoptotic and cytolytic activities. J Immun J 188: 3169-3178.
- Marino S, El-Kebir M, Kirschner DE (2011)A hybrid multi-compartment model of granuloma formation and T cell priming in Tuberculosis.J Theor Biol 280: 50-62.
- Fallahi-Sichani M, El-Kebir M, Marino S, Kirschner DEK, Linderman J (2011)Multi-scale computational modeling reveals a critical role for TNF receptor 1 dynamics in tuberculosis granuloma formation. J Immun J 186: 3472-3483.
- Choi SH (2019)Hypoxia-induced rela/p65 derepresses slc16a3 (mct4) by downregulating zbtb7a. Biochim Biophys Acta Gene Regul Mech 1862: 771-785.
- Yang Y (2020)Enalapril overcomes chemoresistance and potentiates antitumor efficacy of 5-fu in colorectal cancer by suppressing proliferation, angiogenesis, and nf-kappab/stat3-regulated proteins. Cell Death Dis 11: 477.
- Liu Y (2018)Comparative molecular analysis of gastrointestinal adenocarcinomas33: 721-735.
- Berger AC (2018)A comprehensive pan-cancer molecular study of gynecologic and breast cancers33: 690-705.
Indexed at, Google Scholar, Crossref
Indexed at, Google Scholar, Crossref
Indexed at, Google Scholar, Crossref
Indexed at, Google Scholar, Crossref
Indexed at, Google Scholar, Crossref
Indexed at, Google Scholar, Crossref
Indexed at, Google Scholar, Crossref
Citation: Chukwuemeka M (2024) Targeting Immune Checkpoints: Advancementsin Cancer Immunotherapy. Immunol Curr Res, 8: 191.
Copyright: © 2024 Chukwuemeka M. This is an open-access article distributedunder the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided theoriginal author and source are credited.
Share This Article
Recommended Journals
Open Access Journals
Article Usage
- Total views: 120
- [From(publication date): 0-2024 - Nov 08, 2024]
- Breakdown by view type
- HTML page views: 88
- PDF downloads: 32