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Pyclik, A.

Publications and source records attributed to Pyclik, A..

2 recordsLinked to original sources

A Human Genetics Framework for De-risking Gene Editing Targets for Hematopoietic Cell and Gene Therapy

Developing novel therapeutics requires robust early-stage target de-risking to ensure safety and efficacy. We developed a scalable proteogenomic framework integrating population-scale human genetics and plasma proteomics to identify genes tolerant of inactivation (i.e., dispensable) within hematopoietic compartments, thereby enabling safer targeted immunotherapies. Using CD33 as a validated benchmark, we observed that naturally occurring loss-of-function (LoF) variants lead to concordant RNA and protein depletion, supporting functional gene inactivation. Early clinical results from the Trem-Cel trial (NCT05945849) further provide proof of concept that deletion of dispensable antigens can enable safe and effective immunotherapy in humans. We extended this approach genome-wide in the UK Biobank and identified 237 candidate dispensable genes, filtered by plasma proteomic data and hematopoietic expression, highlighting LY75 (CD205) as a novel candidate with strong proteogenomic evidence of LoF tolerance. This work establishes a generalizable, quantitative proteogenomic framework for systematic prioritization of dispensable gene targets for editing, providing a foundation for next-generation cell and gene therapies that minimize on-target, off-tumor toxicities.

genetics↗

A resource and computational approach for quantifying gene editing allelism at single-cell resolution

CRISPR-Cas9-based gene editing is a powerful approach to developing gene and cell therapies for several diseases. Engineering cell therapies requires accurate assessment of gene editing allelism because editing patterns can vary across cells leading to genotypic heterogeneity. This can hinder development of robust cell therapies. Droplet-based targeted single-cell DNA sequencing (scDNAseq) has been used to genotype targeted loci across thousands of cells enabling high-throughput assessment of gene editing efficiency. Here, we constructed a "ground truth" gene editing single-cell DNAseq atlas, along with an artifact-aware computational workflow called GUMM (Genotyping Using Mixture Models) to systematically infer single-cell allelism from these data. This resource was created by expanding CRISPR-Cas9-edited HL-60 clones that harbored distinct insertion-deletion (indel) profiles in CLEC12A and mixing them at pre-defined ratios to create artificial cocktails that mimic the potential editing diversity of a CRISPR-Cas9 experiment. This enabled assessment of technical artifacts that confound interpretation of allelism in the readouts of gene edited cells. GUMM was able to accurately genotype cells and infer the original clonal composition of the artificial cocktails even in the presence of artifacts.

bioinformatics↗