Immunotherapy holds tremendous potential in cancer therapy, in particular, when treatment regimens are combined to achieve synergy between pathways along the cancer immunity cycle. In previous works, we demonstrated that in situ vaccination with the plant virus cowpea mosaic virus (CPMV) activates and recruits innate immune cells, therefore reprogramming the immunosuppressive tumor microenvironment toward an immune-activated state, leading to potent anti-tumor immunity in tumor mouse models and canine patients. CPMV therapy also increases the expression of checkpoint regulators on effector T cells in the tumor microenvironment, such as PD-1/PD-L1, and we demonstrated that combination with immune checkpoint therapy improves therapeutic outcomes further. In the present work, we tested the hypothesis that CPMV could be combined with anti-PD-1 peptides to replace expensive antibody therapies. Specifically, we set out to test whether a multivalent display of anti-PD-1 peptides (SNTSESF) would enhance efficacy over a combination of CPMV and soluble peptide. Efficacy of the approaches were tested using a syngeneic mouse model of intraperitoneal ovarian cancer. CPMV combination with anti-PD-1 peptides (SNTSESF) resulted in increased efficacy; however, increased potency against metastatic ovarian cancer was only observed when SNTSESF was conjugated to CPMV, and not added as a free peptide. This can be explained by the differences in the in vivo fates of the nanoparticle formulation vs. the free peptide; the larger nanoparticles are expected to exhibit prolonged tumor residence and favorable intratumoral distribution. Our study provides new design principles for plant virus-based in situ vaccination strategies.
Cancer immunotherapy has emerged as a pillar of the cancer therapy armamentarium. Immune checkpoint therapy (ICT) is a mainstay of modern immunotherapy. Although ICT monotherapy has demonstrated remarkable clinical efficacy in some patients, the majority do not respond to treatment. In addition, many patients eventually develop resistance to ICT, disease recurrence, and toxicity from off-target effects. Combination therapy is a keystone strategy to overcome the limitations of monotherapy. With the integration of ICT and any therapy that induces tumor cell lysis and release of tumor-associated antigens (TAAs), ICT is expected to strengthen the coordinated innate and adap-
Designing novel functional proteins remains a slow and expensive process due to a variety of protein engineering challenges; in particular, the number of protein variants that can be experimentally tested in a given assay pales in comparison to the vastness of the overall sequence space, resulting in low hit rates and expensive wet lab testing cycles. In this paper, we propose a few-shot learning approach to novel protein design that aims to accelerate the expensive wet lab testing cycle and is capable of leveraging a training dataset that is both small and skewed (≈ 10^5 datapoints, < 1% positive hits). Our approach is composed of two parts: a semi-supervised transfer learning approach to generate a discrete fitness landscape for a desired protein function and a novel evolutionary Monte Carlo Markov Chain sampling algorithm to more efficiently explore the fitness landscape. We demonstrate the performance of our approach by experimentally screening predicted high fitness gene activators, resulting in a dramatically improved hit rate compared to existing methods. Our method can be easily adapted to other protein engineering and design problems, particularly where the cost associated with obtaining labeled data is significantly high. We have provided open source code for our method at github.com/SuperSecretBioTech/evolutionary_monte_carlo_search.
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