A recent article, published in Nature Communications, has outlined a new method of deconvolution using CRISPR-Cas9 for phenotypically discovered antibody drug targets.
Phenotypic Discovery of Antibody Drugs
Monoclonal antibodies (mAbs) are one of the fastest-growing class of drug, with several therapeutic antibodies being used to treat cancer. They are effective and often have few side effects. By binding to specific target molecules on cells, the antibodies can either activate the immune system, or cause the cell to self-destruct. However, despite new antibodies entering clinics, they keep targeting the same antigens.
A key problem in the development of antibody drugs is identification of novel antigen targets. To overcome this, researchers are turning to phenotypic discovery (PD) strategies. This technique differs from traditional target-based discovery, as PD enables searches for mAbs with therapeutic potential without specifying a molecular target beforehand. Researchers start by discovering an interaction between an antibody and a cell, then they can work on identifying the molecular target.
Many researchers shy away from the technique since the molecular targets of phenotypically discovered drugs are unknown, which poses the problem of deconvolution or identification of drug targets downstream. Existing deconvolution methods, such as immunoprecipitation and protein library overexpression, are time-consuming, unreliable, and scale poorly with the number of antibodies produced.
Efficient deconvolution methods are needed to facilitate broader use of PD, and thereby the faster development of antibody drugs.
Deconvolution using CRISPR-Cas9
The researchers behind this study propose that CRISPR-Cas9, coupled to cell sorting and parallel single-molecule sequencing, can provide a fast and robust way to deconvolute the targets of cell surface antibodies.
This study transduced cells staining positive with the antibody of interest with a lentiviral sgRNA/Cas9 knockout library. This resulted in a heterogenous cell pool that contained a small population of antigen-negative cells. Using fluorescence-activated cell sorting the cells with gene knockouts that led to lost or diminished antibody binding were isolated. The genomic DNA was then extracted, and the sgRNA-encoding DNA was sequenced. Genes with sgRNAs enriched in the antigen-negative cells were identified, resulting in a proposed antibody target.
Dependency Map Scores
Since deconvolution using CRISPR-Cas9 requires that the target protein encoding gene is not essential for survival of test cells, the researchers assessed whether cell surface proteins are more, or less, essential than genes in general. To determine this, they analysed data from genome-wide sgRNA proliferation screens encompassing 436 cell lines from an existing Dependency Map (DepMap) database. The gene score produced by the DepMap reflects the representation of sgRNAs targeting a gene after three weeks of culture, compared to the representation of the same sgRNAs in the original library. A negative score means the gene is essential.
The researchers observed that genes encoding membrane proteins and cluster designation (CD) antigens contain fewer negative DepMap scores compared to other genes in the genome. Analysis of DepMap data has helped to indicate that failure to deconvolute an antibody target because of gene essentiality is unlikely, since PD antibodies are mainly developed against membrane proteins on cell surfaces.
Based on the success of their DepMap score analysis, the researchers applied their approach within three real-world phenotypic discovery programs, and were able to rapidly deconvolute the targets of 38 of 39 test antibodies. This equals a 97% success rate, which is much higher than existing approaches. Moreover, the study organisers found that the approach scales well, requires less work, and robustly identifies antibodies against the major histocompatibility complex.
The data collected in this study has established CRISPR/Cas9 as a fast and robust way to deconvolute the targets of cell surface antibodies. This finding has the potential to facilitate broader use of PD, thereby accelerating the discovery of antibody-based cancer therapeutics.
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