| Literature DB >> 33391547 |
Ashu Shah1, Sanchita Rauth1, Abhijit Aithal1, Sukhwinder Kaur1, Koelina Ganguly1, Catherine Orzechowski1, Grish C Varshney1, Maneesh Jain1,2, Surinder K Batra1,3,2.
Abstract
Over the past three decades, monoclonal antibodies (mAbs) have revolutionized the landscape of cancer therapy. Still, this benefit remains restricted to a small proportion of patients due to moderate response rates and resistance emergence. The field has started to embrace better mAb-based formats with advancements in molecular and protein engineering technologies. The development of a therapeutic mAb with long-lasting clinical impact demands a prodigious understanding of target antigen, effective mechanism of action, gene engineering technologies, complex interplay between tumor and host immune system, and biomarkers for prediction of clinical response. This review discusses the various approaches used by mAbs for tumor targeting and mechanisms of therapeutic resistance that is not only caused by the heterogeneity of tumor antigen, but also the resistance imposed by tumor microenvironment (TME), including inefficient delivery to the tumor, alteration of effector functions in the TME, and Fc-gamma receptor expression diversity and polymorphism. Further, this article provides a perspective on potential strategies to overcome these barriers and how diagnostic and prognostic biomarkers are being used in predicting response to mAb-based therapies. Overall, understanding these interdependent parameters can improve the current mAb-based formulations and develop novel mAb-based therapeutics for achieving durable clinical outcomes in a large subset of patients. © The author(s).Entities:
Keywords: Antibodies; cancer; challenges; mechanisms of action; therapy
Mesh:
Substances:
Year: 2021 PMID: 33391547 PMCID: PMC7738893 DOI: 10.7150/thno.52614
Source DB: PubMed Journal: Theranostics ISSN: 1838-7640 Impact factor: 11.556
Distribution of Human and Mouse Fcγ receptors, their variants on effector cell subsets and antibody binding selectivity
| FcγR | Motif | Variantsa | Distributionb | Subclass selectivityc | Effector functiond |
|---|---|---|---|---|---|
| FcγRI (CD64) | ITAM | NA | Monocyte, Macrophages, Neutrophils (inducible), Dendritic cells (inducible) | IgG1, IgG3, IgG4, mIgG2a,**mIgG2b | ADCP |
| FcγRIIA (CD32A) | - | H131, R131 | Monocytes, Macrophages, Neutrophils, Dendritic cells | IgG1, IgG2, IgG3, IgG4 | ADCP |
| FcγRIIB (CD32B) | ITIM | I232, T232 | B cells, Dendritic cells, Monocytes, Macrophages | IgG1, IgG3, IgG4 | Immune inhibition |
| FcγRIIC (CD32C) | - | Q57, * | NK cells (inducible), Neutrophils, Monocytes | IgG1, IgG2, IgG3, IgG4 | ADCC |
| FcγRIIIA (CD16) | ITAM | V158, F158 | NK cells, Macrophages, Neutrophils, NK cells, Dendritic cells | IgG1, IgG2, IgG3, IgG4 | ADCC |
| FcγRIIIB (CD16B) | GPI anchor | **NA1(R36N65D82V106) | Neutrophils | IgG1, IgG3 | Immune inhibition |
| FcRn | NA | Endothelial cells, epithelial cells | IgG1, IgG2, IgG3, IgG4 | Antibody recycling & transport | |
| FcγRI (CD64) | ITAM | NA | DC, Monocytes, Macrophages | mIgG2a, mIgG2b | ADCP |
| FcγRIIB (CD32B) | ITIM | NA | B cells, DC, Neutrophils, Monocytes, Macrophages | mIgG1,mIgG2a, mIgG2b, IgG1 | Immune inhibition |
| FcγRIII | ITAM | NA | NK, DC, Monocytes, Macrophages, Neutrophils | mIgG1,mIgG2a, mIgG2b | ADCC |
| FcγRIV | ITAM | NA | DC, Monocytes, Macrophages, Neutrophils | mIgG2a, mIgG2b, IgG1 | ADCP |
| FcRn | NA | NA | Endothelial cells, epithelial cells | mIgG1,mIgG2a, mIgG2b, IgG3 | Antibody recycling & transport |
Notes: a. Data for the existence of FcγR variants is adapted from 79-82. b. FcγR distribution on different effector cells is compiled from 83-86. c. Antibody selectivity data is compiled from 85, 87, 88. d. Effector function data is compiled from 84-86. * Stop codon, *** NA1 and 2-neutrophil specific antigen 1 and 2, **m= denotes for mouse antibodies. NA, not available; ITAM, immunotyrosine based activation motif; ITIM, immunotyrosine based inhibitory motif; DC, dendritic cells; NK, natural Killer; ADCC, antibody dependent cell cytotoxicity; ADCP, antibody Dependent cell phagocytosis
Overview of strategies for enhancing the efficacy of antibody-based therapies
| Agents | Strategy | Cancers | Reference | |
|---|---|---|---|---|
| 1. | Anti-MICAL-1 antibodies | Reactivating NK cell function | Melanoma | |
| 2. | Cytokines IL-2 and IL-15 | Reactivating NK cell function | Head and Neck cancer | |
| 3. | Anti-KIR Antibody (Lirilumab) in combination with ICB | Reactivating NK cell function | Solid tumors | NCT01714739, NCT01750580 |
| 4. | IL-15 in Combination with ICB | Activation of immune system | Solid tumors | NCT03388632 |
| 5. | Anti-CD40 agonist antibodies (ChiLob7/4) | Direct cytotoxic effects on tumor cells | Solid tumors | |
| 6. | Anti-CSF-1 antibody | Sensitization to ICB | PC, BC, Melanoma, Ovarian, malignant neoplasms, RCC, NSCLC, Billiary tract cancer | |
| 7. | Antibodies against CD47-SIRPα axis | Acquired vasculogenic ability by the macrophages in the TME | Solid tumors | |
| 8. | CXCR2 inhibition in combination with ICB antibodies | Inhibit trafficking of MDSCs to tumor site | Pediatric sarcomas | |
| 9. | IL-2 in combination with anti-CTLA4 antibody | Expansion and differentiation of effector T cells | Melanoma | NCT01480323 |
| 10. | Anti-CD39/CD-73 antibodies in combination with ICB | Reversal of adenosine mediated T cell exhaustion | Solid tumors | |
| 11. | Anti-VEGF/VEGFR2 antibody in combination with ICB | Vascular normalization, high endothelial venules (HEVs) formation, immune stimulation and decreased recruitment of immunosuppressive Tregs | PC, BC, CRC |
Notes: This table summarizes the list of strategies being used to normalize tumor vasculature, decrease tumor-suppressive myeloid cells, and enhancing the cytotoxic activity of antibodies. PC, Pancreatic cancer; BC, Breast cancer; CRC, Colorectal cancer; RCC, Renal Cell Carcinoma; NSCLC, Non-small-cell lung carcinoma; IL-2, Interleukin-2; IL-15, Interleukin-15; NK, Natural Killer; DC, Dendritic cells; ADCC, Antibody-dependent cell cytotoxicity; ICB, Immune checkpoint blockade; APC, Antigen-presenting cell; MDSCs, Myeloid-derived suppressor cells; TME, Tumor microenvironment; CSF-1R, colony-stimulating factor receptor-1, CSF-1, colony stimulating factor-1; KIR, killer immunoglobulin receptor.