Matching Items (13)

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Production and functional testing of a recombinant fusion protein immunotherapy for glioblastoma

Description

Fusion protein immunotherapies such as the bispecific T cell engager (BiTE) have displayed promising potential as cancer treatments capable of engaging the immune system against tumor cells. It has been

Fusion protein immunotherapies such as the bispecific T cell engager (BiTE) have displayed promising potential as cancer treatments capable of engaging the immune system against tumor cells. It has been shown that chlorotoxin, a 36-amino peptide found in the venom of the deathstalker scorpion (Leiurus quinquestriatus), binds specifically to glioblastoma (GBM) cells without binding healthy tissue, making it an ideal GBM cell binding moiety for a BiTE-like molecule. However, chlorotoxin’s four disulfide bonds pose a folding challenge outside of its natural context and impede production of the recombinant protein in various expression systems, including those relying on bacteria and plants. To overcome this difficulty, we have engineered a truncated chlorotoxin variant (Cltx∆15) that contains just two of the original eight cystine residues, thereby capable of forming only a single disulfide bond while maintaining its ability to bind GBM cells. We further created a BiTE (ACDClx∆15) which tethers Cltx∆15 to a single chain ⍺-CD3 antibody in order to bring T cells into contact with GBM cells. The gene for ACDClx∆15 was cloned into a pET-11a vector for expression in Escherichia coli and isolated from inclusion bodies before purification via affinity chromatography. Immunoblot analyses confirmed that ACDClx∆15 can be expressed in E. coli and purified with high yield and purity; moreover, flow cytometry indicated that ACDClx∆15 is capable of binding GBM cells. These data warrant further investigation into the ability of ACDClx∆15 to activate T cells against GBM cells.

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Agent

Created

Date Created
  • 2019-05

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Differential Relationships Among Autoantibody Responses to P53 Family Proteins in Late Stage Colorectal Cancer

Description

Colorectal cancer (CRC) is one of the most highly diagnosed cancers in the United States and accounts for 9.5% of all new cancer cases worldwide. With a 50% five-year prognosis,

Colorectal cancer (CRC) is one of the most highly diagnosed cancers in the United States and accounts for 9.5% of all new cancer cases worldwide. With a 50% five-year prognosis, it is the second highest cancerous cause of death in the U.S. CRC tumors express antigens that are capable of inducing an immune response. The identification of autoantibodies (AAb) against tumor-associated antigens (TAA) may facilitate personalized tumor treatment in the form of targeted immunotherapy. The objective of this study was to observe the AAb expression raised against a 2000 human gene survey in late-stage colorectal cancer using the Nucleic Acid Programmable Protein Arrays (NAPPA). AAbs from serum samples were collected from 80 patients who died within 24 months of their last blood draw and 80 age and gender matched healthy control were profiled using NAPPA. TAA p53, a well-established protein that is one of the most highly mutated across a variety of cancers, was one of the top candidates based on statistical analysis, which, along with its family proteins p63 and p73 (which showed inverse AAb response profiles) warranted further testing via RAPID ELISA. Statistical analysis from these results revealed an inverse differential relationship between p53 and p63, in which p53 seropositivity was higher in patients than in controls, while the opposite was unexpectedly the case for p63. This study involving the AAb immunoprofiling of advanced stage CRC patients is one of the first to shed light on the high-throughput feasibility of immunoproteomic experiments using protein arrays as well as the identification of immunotherapy targets in a more rapid move towards specialized treatment of advanced CRC.

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Agent

Created

Date Created
  • 2014-12

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Immune Blockade Therapy in Metastatic Osteosarcoma

Description

Since Metastatic Osteosarcoma is unresponsive to most of the current standards of care currently available, and yields a survival rate of 20%, it is pertinent that novel approaches to treating

Since Metastatic Osteosarcoma is unresponsive to most of the current standards of care currently available, and yields a survival rate of 20%, it is pertinent that novel approaches to treating it be undertaken in scientific research. Past studies in our lab have used a The Immune Blockade Therapy, utilizing α-CTLA-4 and α-PD-L1 to treat mice with metastatic osteosarcoma; this resulted in 60% of mice achieving disease-free survival and protective immunity against metastatic osteosarcoma. 12 We originally wanted to see if the survival rate could be boosted by pairing the immune blockade therapy with another current, standard of care, radiation. We had found that there were certain, key features to experimental design that had to be maintained and explored further in order to raise survival rates, ultimately with the goal of reestablishing the 60% survival rate seen in mice treated with the immune blockade therapy. Our results show that mice with mature immune systems, which develop by 6-8 weeks, should be used in experiments testing an immune blockade, or other forms of immunotherapy, as they are capable of properly responding to treatment. Treatment as early as one day after should be maintained in future experiments looking at the immune blockade therapy for the treatment of metastatic osteosarcoma in mice. The immune blockade therapy, using α-PD-L1 and α-CTLA-4, seems to work synergistically with radiation, a current standard of care. The combination of these therapies could potentially boost the 60% survival rate, as previously seen in mice treated with α-PD-L1 and α-CTLA-4, to a higher percent by means of reducing tumor burden and prolonging length of life in metastatic osteosarcoma.

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Agent

Created

Date Created
  • 2017-05

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Neoantigen Prediction Pipeline

Description

Cells become cancerous due to changes in their genetic makeup. In cancers, an altered amino acid due to a tumor mutation can result in proteins that are identified as "foreign"

Cells become cancerous due to changes in their genetic makeup. In cancers, an altered amino acid due to a tumor mutation can result in proteins that are identified as "foreign" by the immune system. An MHC molecule will bind to these "foreign" peptide fragments, also called neoantigens. There are 2 classes of MHC molecules. While the MHC I complex is found in all cells with a nucleus, MHC II complexes are mostly found in antigen presenting cells (APCs), such as macrophages, B cells, and dendritic cells. The MHC molecule then presents the neoantigen on the cell's surface. If an immune cell, such as a T-cell, is able to bind to the neoantigen, it can then destroy the tumor cell. However, there are molecules that act as checkpoints on certain immune cells that have to be activated or inactivated to start an immune response. This ensures that healthy cells are not being killed. However, sometimes cancer cells can find ways to use these checkpoints to avoid being attacked. An example of immunotherapy which has had clinical successes is checkpoint blockade inhibition, which means blocking the activity of immune checkpoint proteins in order to release the "brakes" on the immune system to increase its ability to destroy cancer cells. Studies have found that there is a correlation between mutational load and response to immunotherapy. The goal of this project is to create a pipeline that identifies tumor neoantigens. This involved researching various softwares and implementing them to work together. This project involved developing a neoantigen prediction pipeline, which works with TGen's genomics pipeline, to help understand a patient's immune response. The neoantigen prediction pipeline first creates two protein fastas from the high quality non-synonymous mutations, frameshifts, codon insertions, and codon deletions from vcfmerger. One of the protein fastas includes the mutations, while the other one does not representing the wildtype protein. The pipeline then predicts both classes of HLA genotypes of the MHC molecules using DNA or RNA expression in the form of fastqs. The protein fastas and each HLA are fed into IEDB to obtain peptide-MHC binding predictions. Wildtype peptides and neoantigens with low binding affinities are then removed. RNA expression information is then added into the final text file from dseq and sailfish files from TGen's genomics pipeline.

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Agent

Created

Date Created
  • 2017-05

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Prediction of Binding Affinity of T cell Receptor and Antigens using Deep Neural Networks

Description

Immunotherapy is an effective treatment for cancer which enables the patient's immune system to recognize tumor cells as pathogens. In order to design an individualized treatment, the t cell receptors

Immunotherapy is an effective treatment for cancer which enables the patient's immune system to recognize tumor cells as pathogens. In order to design an individualized treatment, the t cell receptors (TCR) which bind to a tumor's unique antigens need to be determined. We created a convolutional neural network to predict the binding affinity between a given TCR and antigen to enable this.

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Agent

Created

Date Created
  • 2020-12

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Construction and Characterization of Recombinant anti-PD-L1 Single Chain Antibodies

Description

Programmed cell death ligand-1 (PD-L1) is an overexpressed protein on many tumor cell types. PD-L1 is involved in normal immune regulation, playing an important role in self-tolerance and controlling autoimmunity.

Programmed cell death ligand-1 (PD-L1) is an overexpressed protein on many tumor cell types. PD-L1 is involved in normal immune regulation, playing an important role in self-tolerance and controlling autoimmunity. However, ligation of PD-L1 to PD-1 on activated T cells leads to tumor-mediated T cell suppression. Inhibiting the PD-1/PD-L1 pathway has emerged as an effective target for anti-tumor immunotherapies. Monoclonal antibodies (mAbs) targeting tumor-associated antigens such as PD-L1 have proven to be effective checkpoint blockades, improving therapeutic outcomes for cancer patients and receiving FDA approval as first line therapies for some cancers. A single chain variable fragment (scFv) is composed of the variable heavy and light chain regions of a mAb, connected by a flexible linker. We hypothesized that scFv proteins based on the published anti-PD-L1 monoclonal antibody sequences of atezolizumab and avelumab would bind to cell surface PD-L1. Four single chain variable fragments (scFvs) were constructed based on the sequences of these mAbs. PCR was used to assemble, construct, and amplify DNA fragments encoding the scFvs which were subsequently ligated into a eukaryotic expression vector. Mammalian cells were transfected with the scFv and scFv-IgG plasmids. The scFvs were tested for binding to PD-L1 on tumor cell lysates by western blot and to whole tumor cells by staining and flow cytometry analysis. DNA sequence analysis demonstrated that the scFv constructs were successfully amplified and cloned into the expression vectors and recombinant scFvs were produced. The binding capabilities of the scFvs constucts to PD-L1 protein were confirmed by western blot and flow cytometry analysis. This lead to the idea of constructing a CAR T cell engineered to target PD-L1, providing a possible adoptive T cell immunotherapy.

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Agent

Created

Date Created
  • 2018-05

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Creating an artificial antigen presenting cell system for HPV16 proteins

Description

Background: High risk types of human papillomavirus (HPV) are known to cause cancer, including cervical (99%) and oropharyngeal cancer (70%). HPV type 16 is the most common subtype. Three antigens

Background: High risk types of human papillomavirus (HPV) are known to cause cancer, including cervical (99%) and oropharyngeal cancer (70%). HPV type 16 is the most common subtype. Three antigens that are critical for integration or tumor progression are E2, E6 and E7. In this study, we developed a systematic approach to identify naturally-processed HPV16-derived HLA class I epitopes for immunotherapy development. Methods: K562 cells, which lack HLA expression, were transduced with each HPV16 antigen using lentivirus and supertransfected with HLA-A2 by nucleofection. Stable cell lines expressing each antigen were selected for and maintained throughout the investigation. In order to establish a Gateway-compatible vector for robust transient gene expression, a Gateway recombination expression cloning cassette was inserted into the commercial Lonza pMAX GFP backbone, which has been experimentally shown to display high transfection expression efficiency. GFP was cloned into the vector and plain K562 cells were transfected with the plasmid by nucleofection. Results: Expression of K562-A2 was tested at various time points by flow cytometry and A2 expression was confirmed. Protein expression was shown for the transduced K562 E7 by Western blot analysis. High transfection efficiency of the pMAX_GFP_Dest vector (up to 97% GFP+ cells) was obtained 48 hours post transfection, comparable to the commercial GFP-plasmid. Conclusion: We have established a rapid system for target viral antigen co-expression with single HLA molecules for analysis of antigen presentation. Using HPV as a model system, our goal is to identify specific antigenic peptide sequences to develop immunotherapeutic treatments for HPV-associated cancers.

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Agent

Created

Date Created
  • 2016-05

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Mathematical Modeling of Novel Cancer Immunotherapies

Description

Immunotherapy has received great attention recently, as it has become a powerful tool in fighting certain types of cancer. Immunotherapeutic drugs strengthen the immune system's natural ability to identify and

Immunotherapy has received great attention recently, as it has become a powerful tool in fighting certain types of cancer. Immunotherapeutic drugs strengthen the immune system's natural ability to identify and eradicate cancer cells. This work focuses on immune checkpoint inhibitor and oncolytic virus therapies. Immune checkpoint inhibitors act as blocking mechanisms against the binding partner proteins, enabling T-cell activation and stimulation of the immune response. Oncolytic virus therapy utilizes genetically engineered viruses that kill cancer cells upon lysing. To elucidate the interactions between a growing tumor and the employed drugs, mathematical modeling has proven instrumental. This dissertation introduces and analyzes three different ordinary differential equation models to investigate tumor immunotherapy dynamics.

The first model considers a monotherapy employing the immune checkpoint inhibitor anti-PD-1. The dynamics both with and without anti-PD-1 are studied, and mathematical analysis is performed in the case when no anti-PD-1 is administrated. Simulations are carried out to explore the effects of continuous treatment versus intermittent treatment. The outcome of the simulations does not demonstrate elimination of the tumor, suggesting the need for a combination type of treatment.

An extension of the aforementioned model is deployed to investigate the pairing of an immune checkpoint inhibitor anti-PD-L1 with an immunostimulant NHS-muIL12. Additionally, a generic drug-free model is developed to explore the dynamics of both exponential and logistic tumor growth functions. Experimental data are used for model fitting and parameter estimation in the monotherapy cases. The model is utilized to predict the outcome of combination therapy, and reveals a synergistic effect: Compared to the monotherapy case, only one-third of the dosage can successfully control the tumor in the combination case.

Finally, the treatment impact of oncolytic virus therapy in a previously developed and fit model is explored. To determine if one can trust the predictive abilities of the model, a practical identifiability analysis is performed. Particularly, the profile likelihood curves demonstrate practical unidentifiability, when all parameters are simultaneously fit. This observation poses concerns about the predictive abilities of the model. Further investigation showed that if half of the model parameters can be measured through biological experimentation, practical identifiability is achieved.

Contributors

Agent

Created

Date Created
  • 2020

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Pathogenic peptides to enhance treatment of glioblastoma: evaluation of RVG-29 from rabies virus and chlorotoxin from scorpion venom

Description

Glioblastoma (GBM) is a highly invasive and deadly late stage tumor that develops from abnormal astrocytes in the brain. With few improvements in treatment over many decades, median patient survival

Glioblastoma (GBM) is a highly invasive and deadly late stage tumor that develops from abnormal astrocytes in the brain. With few improvements in treatment over many decades, median patient survival is only 15 months and the 5-year survival rate hovers at 6%. Numerous challenges are encountered in the development of treatments for GBM. The blood-brain barrier (BBB) serves as a primary obstacle due to its innate ability to prevent unwanted molecules, such as most chemotherapeutics, from entering the brain tissue and reaching malignant cells. The GBM cells themselves serve as a second obstacle, having a high level of genetic and phenotypic heterogeneity. This characteristic improves the probability of a population of cells to have resistance to treatment, which ensures the survival of the tumor. Here, the development and testing of two different modes of therapy for treating GBM is described. These therapeutics were enhanced by pathogenic peptides known to improve entry into brain tissue or to bind GBM cells to overcome the BBB and/or tumor cell heterogeneity. The first therapeutic utilizes a small peptide, RVG-29, derived from the rabies virus glycoprotein to improve brain-specific delivery of nanoparticles encapsulated with a small molecule payload. RVG-29-targeted nanoparticles were observed to reach the brain of healthy mice in higher concentrations 2 hours following intravenous injection compared to control particles. However, targeted camptothecin-loaded nanoparticles were not capable of producing significant treatment benefits compared to non-targeted particles in an orthotopic mouse model of GBM. Peptide degradation following injection was shown to be a likely cause for reduced treatment benefit. The second therapeutic utilizes chlorotoxin, a non-toxic 36-amino acid peptide found in the venom of the deathstalker scorpion, expressed as a fusion to antibody fragments to enhance T cell recognition and killing of GBM. This candidate biologic, known as anti-CD3/chlorotoxin (ACDClx) is expressed as an insoluble protein in Nicotiana benthamiana and Escherichia coli and must be purified in denaturing and reducing conditions prior to being refolded. ACDClx was shown to selectively activate T cells only in the presence of GBM cells, providing evidence that further preclinical development of ACDClx as a GBM immunotherapy is warranted.

Contributors

Agent

Created

Date Created
  • 2019

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Oncolytic viral and immunotherapy models combined with strategies to ameliorate cancer burden

Description

Combination therapy has shown to improve success for cancer treatment. Oncolytic virotherapy is cancer treatment that uses engineered viruses to specifically infect and kill cancer cells, without harming healthy cells.

Combination therapy has shown to improve success for cancer treatment. Oncolytic virotherapy is cancer treatment that uses engineered viruses to specifically infect and kill cancer cells, without harming healthy cells. Immunotherapy boosts the body's natural defenses towards cancer. The combination of oncolytic virotherapy and immunotherapy is explored through deterministic systems of nonlinear differential equations, constructed to match experimental data for murine melanoma. Mathematical analysis was done in order to gain insight on the relationship between cancer, viruses and immune response. One extension of the model focuses on clinical needs, with the underlying goal to seek optimal treatment regimens; for both frequency and dose quantity. The models in this work were first used to estimate parameters from preclinical experimental data, to identify biologically realistic parameter values. Insight gained from the mathematical analysis in the first model, allowed for numerical analysis to explore optimal treatment regimens of combination oncolytic virotherapy and dendritic vaccinations. Permutations accounting for treatment scheduled were done to find regimens that reduce tumor size. Observations from the produced data lead to in silico exploration of immune-viral interactions. Results suggest under optimal settings, combination treatment works better than monotherapy of either type. The most optimal result suggests treatment over a longer period of time, with fractioned doses, while reducing the total dendritic vaccination quantity, and maintaining the maximum virotherapy used in the experimental work.

Contributors

Agent

Created

Date Created
  • 2016