Difference between revisions of "Part:BBa K5073010"
 
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<partinfo>BBa_K5073010 short</partinfo>
 
<partinfo>BBa_K5073010 short</partinfo>
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GPC3 scFv (single-chain variable fragment) is a genetically engineered antibody fragment designed to specifically recognize the glypican-3 (GPC3) antigen, which is overexpressed in hepatocellular carcinoma (HCC) and other cancers. This scFv consists of the variable regions of both the heavy and light chains (VH and VL) connected by a flexible linker. It is typically used in CAR-T cell therapy, where it functions as the antigen-binding domain of the chimeric antigen receptor (CAR), allowing T cells to specifically recognize and attack GPC3-positive cancer cells.
 
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To use this part in your project, you can clone the GPC3 scFv gene into a suitable vector for expression in your system, typically in CAR-T cells or other immune cell-based therapies. It's essential to ensure that your expression system is optimized for proper folding and surface display of the scFv. This part can be integrated into various therapeutic or diagnostic platforms targeting GPC3-expressing tumors.
 
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<partinfo>GPC3 scFv proof</partinfo>
 
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Given that GPC3 is a tumor-associated antigen that is overexpressed in HCC but is almost absent in normal liver tissues and most other normal tissues, numerous GPC3 CAR-T projects have already entered the clinical trial stage, demonstrating its feasibility as a target [6]. Therefore, we chose GPC3 as a target for optimizing CAR-T therapy.
 
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We referenced to studies that have entered the clinical stage and drew on their GPC3 scFv sequence design. At the beginning of the project, we performed molecular simulations of the interaction between the GPC3 protein and the GPC3 scFv sequence using AlphaFold3, which showed good binding ability. Subsequently, we further verified this finding through a series of wet experiments, which showed that GPC3 was able to successfully activate the CAR molecule, fully demonstrating the functional feasibility of the design.
 
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We established a TIDE score model to simulate the response of GPC3 high-expressing liver cancer cells to immunosuppressants. The results showed that liver cancer cells with high GPC3 expression had higher TIDE scores, suggesting these cells may possess stronger immune escape ability. As a result, they may respond poorly to immune checkpoint inhibitors, suggesting that a higher intensity of T-cell stimulation may be required to overcome this resistance.
 
  
  
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<partinfo>BBa_K5073010 parameters</partinfo>
 
<partinfo>BBa_K5073010 parameters</partinfo>
 
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<html lang="en">
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<meta charset="UTF-8">
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<body>
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<p>Given that GPC3 is a tumor-associated antigen that is overexpressed in HCC but is almost absent in normal liver
 +
    tissues and most other normal tissues, numerous GPC3 CAR-T projects have already entered the clinical trial stage,
 +
    demonstrating its feasibility as a target <a href="#r1">[1]</a> . Therefore, we chose GPC3 as a target for
 +
    optimizing CAR-T therapy.</p>
 +
<p>We referenced to studies that have entered the clinical stage and drew on their GPC3 scFv sequence design. At the
 +
    beginning of the project, we performed molecular simulations of the interaction between the GPC3 protein and the
 +
    GPC3 scFv sequence using AlphaFold3, which showed good binding ability. Subsequently, we further verified this
 +
    finding through a series of wet experiments, which showed that GPC3 was able to successfully activate the CAR
 +
    molecule, fully demonstrating the functional feasibility of the design.</p>
 +
<p>We established a TIDE score model to simulate the response of GPC3 high-expressing liver cancer cells to
 +
    immunosuppressants. The results showed that liver cancer cells with high GPC3 expression had higher TIDE scores,
 +
    suggesting these cells may possess stronger immune escape ability. As a result, they may respond poorly to immune
 +
    checkpoint inhibitors, suggesting that a higher intensity of T-cell stimulation may be required to overcome this
 +
    resistance.</p>
 +
<p>To assess the expression pattern and prognostic function of GPC3 during liver cancer development, we analyzed based
 +
    on 371 liver cancer samples and 50 Paracancerous tissue or normal liver tissue samples from the TCGA database. The
 +
    results showed that the expression level of GPC3 was significantly up-regulated in liver cancer tissues compared
 +
    with Paracancerous tissues or normal liver tissues (Fig 1A, ****, p < 0.0001). To further assess whether GPC3 is an
 +
    independent prognostic marker for liver cancer, we conducted time-dependent AUC analysis. The results showed that
 +
    the AUC model based on GPC3 expression had a high predictive value for the clinical outcomes of liver cancer
 +
    patients, with GPC3 (AUC = 0.919) outperforming the traditional marker AFP (AUC = 0.723) in predictive efficacy (Fig
 +
    1B).
 +
</p>
 +
<p>In addition, prognostic analyses showed that high expression of GPC3 was closely associated with poor prognosis of
 +
    liver cancer patients. In the prediction of clinical outcomes at 1, 3, and 5 years, the AUC values of the prediction
 +
    model basis on GPC3 expression were 0.568 at 1 year, 0.543 at 3 years, and 0.532 at 5 years (Fig1 D-E). To explore
 +
    the role of GPC3 in immune escape, immunotherapy and chemoradiotherapy tolerance in liver cancer, we divided 371
 +
    liver cancer samples in the TCGA database into high-expression (n = 93) and low-expression groups (n = 93) according
 +
    to the expression level of GPC3, and performed immune checkpoint gene expression analysis. The results showed that
 +
    in the samples from the GPC3 high-expression group, the expression levels of immune escape marker genes, such as
 +
    HAVCR2, were significantly higher than those in the low-expression group. The signaling pathway analysis results
 +
    showed that, compared to the low expression group, the GPC3 high expression group has 345 upregulated genes and 116
 +
    downregulated genes (Fig1 G).</p>
 +
<p>GO analysis indicated that the up-regulated genes are primarily enriched in pathways related to cellular redox
 +
    reactions and intracellular transport, while the down-regulated genes were enriched in RNA metabolism and cell cycle
 +
    regulation-related pathways (Fig1.H, I).KEGG analysis further revealed that up-regulated genes are mainly involved
 +
    in biological processes such as cell adhesion molecule pathways (CAMs) and oxidative phosphorylation, while the
 +
    down-regulated genes were enriched in metabolic pathways (e.g. oxidative phosphorylation and the TCA cycle) and cell
 +
    cycle-related pathways (Fig1.K, L)</p>
 +
 +
<div style="width: 50%;height: 50%;justify-content: center;align-content: center">
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    <img style="height: 100%;width: 100%;" src="https://static.igem.wiki/teams/5073/results-dry/fig3.png">
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</div>
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<div style="font-size: 0.6em">
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    <p>
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        Fig1. A. GPC3 expression levels of tumor (n=371) and normal tissues from TCGA database (****, p<0.0001); B. AUC
 +
        analysis of AFP and GPC3; C-E. Prognostic analysis results of GPC3; F. Expression analysis of immune checkpoint
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        genes in GPC3 high expression (n=93) and low expression (n=93) samples; G. Volcano plots of RNA-seq data for
 +
        GPC3 high expression (n=93) and low expression (n=93) samples (up=345,down=116); H-I. GO analysis of
 +
        up-regulated pathways with KEGG enrichment analysis; J. Heat maps of RNA-seq of GPC3 high expression (n=93) and
 +
        low expression (n=93) samples (up=345, down=116) K-L. GO analysis of downregulated pathways with KEGG enrichment
 +
        analysis
 +
    </p>
 +
</div>
 +
 +
 +
<p>In our study, we used AlphaFold 3 to predict the structures of GPC3 and its single-chain antibody (scFv). Utilizing
 +
    this advanced deep learning model, we can accurately predict the interaction pattern and spatial conformation
 +
    between the two. This provides an important structural basis for understanding the potential role of GPC3 in liver
 +
    cancer therapy.
 +
    Additionally, in AlphaFold 3 studies, we also predicted the binding between the interactions GPC3 mRNA and the
 +
    CD63-L7Ae fusion protein. By simulating the molecular interactions, we gained insights into how the engineered GPC3
 +
    mRNA can be effectively targeted through the exosome delivery system.</p>
 +
 +
<div style="width: 50%;height: 50%;justify-content: center;align-content: center">
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    <img style="height: 100%;width: 100%;" src="https://static.igem.wiki/teams/5073/parts/cgpc-scfa-2.jpg">
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</div>
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<div><p>
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    Fig2. A. Binding results of GPC3 and GPC3 scFv;
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</p></div>
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<p>See more in our wiki's <a href="https://2024.igem.wiki/hbmu-taihe/Results(dry)">Dry Lab</a>, <a
 +
        href="https://2024.igem.wiki/hbmu-taihe/Results(wet)">Wet Lab</a> and <a
 +
        href="https://2024.igem.wiki/hbmu-taihe/software">Software</a>.</p>
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<h5 style="font-weight: bold">References</h5>
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<div style="font-size: 0.7em">
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    <p id="r1">[1]Shi, D., Shi, Y., Kaseb, A. O., Qi, X., Zhang, Y., Chi, J., Lu, Q., Gao, H., Jiang, H., Wang, H.,
 +
        Yuan, D., Ma, H., Wang, H., Li, Z., & Zhai, B. (2020). Chimeric Antigen Receptor-Glypican-3 T-Cell Therapy for
 +
        Advanced Hepatocellular Carcinoma: Results of Phase I Trials. Clinical cancer research : an official journal of
 +
        the American Association for Cancer Research, 26(15), 3979–3989.</p>
 +
</div>
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 +
</body>
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</html>

Latest revision as of 12:52, 2 October 2024


GP3C scFv


Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]


Given that GPC3 is a tumor-associated antigen that is overexpressed in HCC but is almost absent in normal liver tissues and most other normal tissues, numerous GPC3 CAR-T projects have already entered the clinical trial stage, demonstrating its feasibility as a target [1] . Therefore, we chose GPC3 as a target for optimizing CAR-T therapy.

We referenced to studies that have entered the clinical stage and drew on their GPC3 scFv sequence design. At the beginning of the project, we performed molecular simulations of the interaction between the GPC3 protein and the GPC3 scFv sequence using AlphaFold3, which showed good binding ability. Subsequently, we further verified this finding through a series of wet experiments, which showed that GPC3 was able to successfully activate the CAR molecule, fully demonstrating the functional feasibility of the design.

We established a TIDE score model to simulate the response of GPC3 high-expressing liver cancer cells to immunosuppressants. The results showed that liver cancer cells with high GPC3 expression had higher TIDE scores, suggesting these cells may possess stronger immune escape ability. As a result, they may respond poorly to immune checkpoint inhibitors, suggesting that a higher intensity of T-cell stimulation may be required to overcome this resistance.

To assess the expression pattern and prognostic function of GPC3 during liver cancer development, we analyzed based on 371 liver cancer samples and 50 Paracancerous tissue or normal liver tissue samples from the TCGA database. The results showed that the expression level of GPC3 was significantly up-regulated in liver cancer tissues compared with Paracancerous tissues or normal liver tissues (Fig 1A, ****, p < 0.0001). To further assess whether GPC3 is an independent prognostic marker for liver cancer, we conducted time-dependent AUC analysis. The results showed that the AUC model based on GPC3 expression had a high predictive value for the clinical outcomes of liver cancer patients, with GPC3 (AUC = 0.919) outperforming the traditional marker AFP (AUC = 0.723) in predictive efficacy (Fig 1B).

In addition, prognostic analyses showed that high expression of GPC3 was closely associated with poor prognosis of liver cancer patients. In the prediction of clinical outcomes at 1, 3, and 5 years, the AUC values of the prediction model basis on GPC3 expression were 0.568 at 1 year, 0.543 at 3 years, and 0.532 at 5 years (Fig1 D-E). To explore the role of GPC3 in immune escape, immunotherapy and chemoradiotherapy tolerance in liver cancer, we divided 371 liver cancer samples in the TCGA database into high-expression (n = 93) and low-expression groups (n = 93) according to the expression level of GPC3, and performed immune checkpoint gene expression analysis. The results showed that in the samples from the GPC3 high-expression group, the expression levels of immune escape marker genes, such as HAVCR2, were significantly higher than those in the low-expression group. The signaling pathway analysis results showed that, compared to the low expression group, the GPC3 high expression group has 345 upregulated genes and 116 downregulated genes (Fig1 G).

GO analysis indicated that the up-regulated genes are primarily enriched in pathways related to cellular redox reactions and intracellular transport, while the down-regulated genes were enriched in RNA metabolism and cell cycle regulation-related pathways (Fig1.H, I).KEGG analysis further revealed that up-regulated genes are mainly involved in biological processes such as cell adhesion molecule pathways (CAMs) and oxidative phosphorylation, while the down-regulated genes were enriched in metabolic pathways (e.g. oxidative phosphorylation and the TCA cycle) and cell cycle-related pathways (Fig1.K, L)

Fig1. A. GPC3 expression levels of tumor (n=371) and normal tissues from TCGA database (****, p<0.0001); B. AUC analysis of AFP and GPC3; C-E. Prognostic analysis results of GPC3; F. Expression analysis of immune checkpoint genes in GPC3 high expression (n=93) and low expression (n=93) samples; G. Volcano plots of RNA-seq data for GPC3 high expression (n=93) and low expression (n=93) samples (up=345,down=116); H-I. GO analysis of up-regulated pathways with KEGG enrichment analysis; J. Heat maps of RNA-seq of GPC3 high expression (n=93) and low expression (n=93) samples (up=345, down=116) K-L. GO analysis of downregulated pathways with KEGG enrichment analysis

In our study, we used AlphaFold 3 to predict the structures of GPC3 and its single-chain antibody (scFv). Utilizing this advanced deep learning model, we can accurately predict the interaction pattern and spatial conformation between the two. This provides an important structural basis for understanding the potential role of GPC3 in liver cancer therapy. Additionally, in AlphaFold 3 studies, we also predicted the binding between the interactions GPC3 mRNA and the CD63-L7Ae fusion protein. By simulating the molecular interactions, we gained insights into how the engineered GPC3 mRNA can be effectively targeted through the exosome delivery system.

Fig2. A. Binding results of GPC3 and GPC3 scFv;

See more in our wiki's Dry Lab, Wet Lab and Software.

References

[1]Shi, D., Shi, Y., Kaseb, A. O., Qi, X., Zhang, Y., Chi, J., Lu, Q., Gao, H., Jiang, H., Wang, H., Yuan, D., Ma, H., Wang, H., Li, Z., & Zhai, B. (2020). Chimeric Antigen Receptor-Glypican-3 T-Cell Therapy for Advanced Hepatocellular Carcinoma: Results of Phase I Trials. Clinical cancer research : an official journal of the American Association for Cancer Research, 26(15), 3979–3989.