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gBlocks™ Gene Fragments used in an antibody discovery workflow to test the efficiency of tumor-reactive antibodies

Mazor, RD, Nathan N, Gilboa, A et al. Tumor-reactive antibodies evolve from non-binding and autoreactive precursors. Cell. 2022. 185. 1208-12011.

Mazor et al. recently published a paper in the scientific journal, Cell where they used antibody discovery research workflow to identify tumor-reactive antibodies. Using gBlocks™ Gene Fragments, Mazor et al. synthesized and assessed the efficacy of potential tumor-reactive antibodies that were identified through immunoglobulin sequencing. Their results identified promising cancer fighting antibodies that were reactive with the MMP14 protein, which promotes the spread of cancer cells [1].


Antibody discovery research is one of the first steps in immune-oncology drug development. This research process includes target assessment, hit generation, and lead optimization, and utilizes various synthetic biology and bioinformatic approaches. This type of research contributes important information towards the utilization of antibodies for treating cancer as it helps to characterize how tumor-reactive antibodies are formed in the body as well as to identify specific antibodies that can be candidates in further pre-clinical studies. Here, Mazor et al. performed an antibody discovery research workflow that allowed them to observe and classify tumor-reactive antibodies that are secreted in the microenvironment of human ovarian carcinomas. Using gBlocks Gene Fragments, they were able to generate patient derived monoclonal antibodies and assess their specificity to the tumor surface protein, MMP14.

Antibody discovery workflow
Antibody discovery workflow.


Mazor et al. used several approaches to investigate antibodies generated from antibody secreting cells (ASCs) within the microenvironment around human ovarian carcinomas. These include tumor section imaging, single-cell immunoglobulin sequencing, antibody expression, and phage display assays. More specifically, after confirming that the tumor samples they had obtained were systematically recognized by antibodies via imaging approaches, Mazor et al. used single cell immunoglobulin sequencing to identify potential tumor-reactive antibodies.

From the sequencing data, Mazor et al. selected antibody candidates and then generated synthetic heavy and light chain gene fragments using gBlocks Gene Fragments. These were then cloned into human expression vectors, which were transferred into bacteria that made copies of the plasmids. These plasmids were then transferred into HEK293t cells (Corning) for expression. After the HEK293t cells had expressed the antibodies, they were harvested and further assessed to determine their reactivity with a major cancer protein, MMP14.


Target assessment of ovarian carcinomas

Imaging ovarian carcinomas and other tumors allowed Mazor et al. to conclude that some types of tumors are consistently recognized by endogenous antibodies, or antibodies that are made by the immune system. Then using single cell immunoglobin (antibody) sequencing, the researchers ascertained important information about the antibodies produced by ASCs in the tumor microenvironment.

Hit generation using gBlocks Gene Fragments

Antibodies that are used in cancer therapeutics are proteins that have undergone extensive optimization and assessment in a research setting. Moreover, these antibodies are monoclonal, meaning that they were made in a laboratory and have a high bind-specificity to target different proteins in or on cancer cells. Mazor et al used blood sample derived polyclonal antibodies to first determine what tumor-associated proteins were being targeted by the immune system. They found that matrix metalloproteinases (MMP) which facilitate important changes during tumor development [2], reproducibly and reliably reacted with the antibody across all the samples that they used. More specifically, they found a strong reactivity with MMP14 which has been characterized to mediate the invasion of tumor cells [1]. RNA-seq data analyzed by Mazor et al. also showed that this protein is highly expressed in tumor samples. To further investigate the antibodies that were binding to MMP14, these researchers used gBlocks Gene Fragments to create gene fragments that were subsequently cloned and expressed to generate monoclonal antibodies. They found that these monoclonal antibodies showed a strong bind-specificity to tumor associated MMP14. This made these antibodies strong candidates for further optimization. One specific candidate, T13, was selected for further investigation as it displayed effective binding to MMP14 in multiple antigen assays. The binding specificity of T13 was tested by using various human-associated proteins (both from tumors and healthy tissue) and Mazor et al found that the T13 antibody was highly specific for MMP14 and did not bind to other cell surface proteins. Finally, the researchers used a phage display assay to determine the binding site of T13 on MMP14. They found that T13 binds to the catalytic site of MMP14, and these results were confirmed with traditional ELISA assays and computational analyses were completed using the protein structure prediction software, AlphaFold [3].


Mazor et al. used an antibody discovery workflow that enabled them to identify antibodies that have the potential to be used further in immuno-oncology drug development. This research workflow included imaging tumor samples for the verification that ovarian carcinomas are recognized by endogenous antibodies; single cell immunoglobulin sequencing and bioinformatic analyses for the identification of potentially tumor-reactive antibodies from blood samples; as well as monoclonal antibody expression and binding assays to determine the binding-specificity of candidate antibodies.

The results of these analyses and others performed in their recent publication, helped them to identify the various tumor-reactive monoclonal antibodies, like T13 which could prove to be useful in cell-mediated therapies in the future.


  1. Sato H, Takino T, Okada Y, et al. A matrix metalloproteinase expressed on the surface of invasive tumour cells. Nature. Jul 7 1994;370(6484):61-5. doi:10.1038/370061a0
  2. Gonzalez-Molina J, Gramolelli S, Liao Z, Carlson JW, Ojala PM, Lehti K. MMP14 in Sarcoma: A Regulator of Tumor Microenvironment Communication in Connective Tissues. Cells. Aug 28 2019;8(9)doi:10.3390/cells8090991
  3. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. Aug 2021;596(7873):583-589. doi:10.1038/s41586-021-03819-2
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Published Jul 4, 2022