Novel anti-PD-L1 peptide selected from combinatorial phage library inhibits tumor cell growth and restores T-cell activity

Natural peptides for immunological regulation in cancer therapy: Mechanism, facts and perspectives

Cancer incidence and mortality rates are increasing annually. Treatment with surgery, chemotherapy and radiation therapy (RT) is unsatisfactory because many patients have advanced disease at the initial diagnosis. However, the emergence of immunotherapy promises to be an effective strategy to improve the outcome of advanced tumors. Immune checkpoint antibodies, which are at the forefront of immunotherapy, have had significant success but still leave some cancer patients without benefit. For more cancer patients to benefit from immunotherapy, it is necessary to find new drugs and combination therapeutic strategies to improve the outcome of advanced cancer patients and achieve long-term tumor control or even eradication. Peptides are promising choices for tumor immunotherapy drugs because they have the advantages of low production cost, high sequence selectivity, high tissue permeability, low toxicity and low immunogenicity etc., and the adjuvant matching and technologies like nanotechnology can further optimize the effects of peptides. In this review, we present the current status and mechanisms of research on peptides targeting multiple immune cells (T cells, natural killer (NK) cells, dendritic cells (DCs), tumor-associated macrophages (TAMs), regulatory T cells (Tregs)) and immune checkpoints in tumor immunotherapy; and we summarize the current status of research on peptide-based tumor immunotherapy in combination with other therapies including RT, chemotherapy, surgery, targeted therapy, cytokine therapy, adoptive cell therapy (ACT) and cancer vaccines. Finally, we discuss the current status of peptide applications in mRNA vaccine delivery.

Tumor Suppression by PD-1/PD-L1 Interaction Blockage in Mice Model

Background

Overexpression of programmed cell death ligand 1 (PD-L1) in tumor cells and subsequent interaction with the programmed cell death protein 1 (PD-1) in tumor-infiltrating T cells cause an immune evasion of the tumor from cytotoxic T-cells. Therefore, inhibiting such interaction by a recombinant PD-1 can hinder tumor growth and extend the survival rate.

Methods

The mouse extracellular domain of PD-1 (mPD-1) was expressed in E. coli BL21 (DE3) strain and purified using nickel affinity chromatography. The binding ability of the purified protein to human PD-L1 was studied using ELISA. Finally, the tumor-bearing mice were used to evaluate the potential antitumor effect.

Results

The recombinant mPD-1 showed a significant binding capacity to human PD-L1 at the molecular level. The tumor size significantly decreased in the tumor-bearing mice after the intra-tumoral injections of mPD-1. Moreover, the survival rate increased significantly after eight weeks of monitoring. The histopathology revealed the necrosis in the tumor tissue of the control group compared to the mPD-1 received mice.

Conclusions

Our outcomes propose that interaction blockade between PD-1 and PD-L1 is a promising approach for targeted tumor therapy.

PDL1Binder: Identifying programmed cell death ligand 1 binding peptides by incorporating next-generation phage display data and different peptide descriptors

Monoclonal antibody drugs targeting the PD-1/PD-L1 pathway have showed efficacy in the treatment of cancer patients, however, they have many intrinsic limitations and inevitable drawbacks. Peptide inhibitors as alternatives might compensate for the drawbacks of current PD-1/PD-L1 interaction blockers. Identifying PD-L1 binding peptides by random peptide library screening is a time-consuming and labor-intensive process. Machine learning-based computational models enable rapid discovery of peptide candidates targeting the PD-1/PD-L1 pathway. In this study, we first employed next-generation phage display (NGPD) biopanning to isolate PD-L1 binding peptides. Different peptide descriptors and feature selection methods as well as diverse machine learning methods were then incorporated to implement predictive models of PD-L1 binding. Finally, we proposed PDL1Binder, an ensemble computational model for efficiently obtaining PD-L1 binding peptides. Our results suggest that predictive models of PD-L1 binding can be learned from deep sequencing data and provide a new path to discover PD-L1 binding peptides. A web server was implemented for PDL1Binder, which is freely available at http://i.uestc.edu.cn/pdl1binder/cgi-bin/PDL1Binder.pl.

GIMAP7 as a Potential Predictive Marker for Pan-Cancer Prognosis and Immunotherapy Efficacy

Background

Methods

Results

Conclusion