ArticleNature communications2021
Accelerating target deconvolution for therapeutic antibody candidates using highly parallelized genome editing.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Discovery of candidate antibodies and antigens for acute myeloid leukemia therapy by combining whole-cell phage display selection and CRISPR-Cas9 library screening.Antibody therapeutics · 2026Article
- Sequence enrichment profiles enable target-agnostic antibody generation for a broad range of antigens.Cell reports methods · 2023Article
- CRISPR in cancer biology and therapy.Nature reviews. Cancer · 2022Review
- Identification of lamprey variable lymphocyte receptors that target the brain vasculature.Scientific reports · 2022Article
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Therapeutic antibodies are transforming the treatment of cancer and autoimmune diseases. Today, a key challenge is finding antibodies against new targets. Phenotypic discovery promises to achieve this by enabling discovery of antibodies with therapeutic potential without specifying the molecular target a priori. Yet, deconvoluting the targets of phenotypically discovered antibodies remains a bottleneck; efficient deconvolution methods are needed for phenotypic discovery to reach its full potential. Here, we report a comprehensive investigation of a target deconvolution approach based on pooled CRISPR/Cas9. Applying this approach within three real-world phenotypic discovery programs, we rapidly deconvolute the targets of 38 of 39 test antibodies (97%), a success rate far higher than with existing approaches. Moreover, the approach scales well, requires much less work, and robustly identifies antibodies against the major histocompatibility complex. Our data establish CRISPR/Cas9 as a highly efficient target deconvolution approach, with immediate implications for the development of antibody-based drugs.
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