Clinical next-generation sequencing (NGS) is a pillar of diagnosis for inborn errors of immunity (IEI). However, for each pathogenic variant identified, these methods yield hundreds of variants of uncertain significance (VUS), creating ambiguity in clinical management. Here, we present a high-throughput framework to precisely generate genetic variants at endogenous loci and map them with the clinically established functional readout. Loss-of-function (LOF) variants in PIK3CD or PIK3R1 can lead to immune deficiency and/or the multisystem SHORT syndrome, while gain-of-function (GOF) variants lead to lymphoproliferation, autoimmunity, and infection—the activated PI3K-delta syndrome (APDS). In primary human T cells from multiple healthy donors, we performed saturation CRISPR base-editor screening of PIK3CD and PIK3R1 coupled with the clinical APDS-diagnostic flow cytometric assay to measure phosphorylated AKT and S6 after T cell receptor (TCR) stimulation. We successfully detected most known pathogenic variants and found >100 variants which were clearly novel GOF or LOF mutations. We individually validated 30 of these variants and show that many GOF, including those already associated with APDS, are sensitive to leniolisib, an FDA-approved PI3Kδ inhibitor for APDS. Next, we used structural modeling to map variant effects to defined regions in the PI3Kδ protein complex, identifying variant “hotspots” associated with pathogenic AKT/S6 signaling. Finally, we acquired peripheral blood samples from patients harboring germline mutations in PIK3CD or PIK3R1. We find that both exhibit pathogenic AKT/S6 signaling and leniolisib sensitivity at similar effect sizes as our screen-identified novel GOFs, emphasizing the clinical predictive value of our variant classification approach. We leveraged multiple precision genome editing approaches to correct the causative SNVs in these patient samples, improving signaling defects. Thousands of new functional annotations from our screens will be incorporated into public databases and used for variant reclassification, substantially broadening the population of previously undiagnosed patients who can immediately benefit from this precision medicine approach. This proof-of-concept study is part of the Human Immune Variome Project, an effort to functionally classify variants across hundreds of genes implicated in IEIs, to remove ambiguity related to clinical management and treatment.
Meeting Abstract|
CIS Meeting Abstracts 2025|
April 25 2025
Massively Scalable Generation, Discovery, and Clinical (Re)Classification of Inborn Errors of Immunity Using Cutting-Edge Genome Engineering
Zachary Walsh,
Zachary Walsh
1MD/PhD Candidate/Columbia University
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Chris Frangieh,
Chris Frangieh
2Postdoctoral Fellow/Columbia University Irving Medical Center
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Clarissa Heck,
Clarissa Heck
2Postdoctoral Fellow/Columbia University Irving Medical Center
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Sarah Grauman,
Sarah Grauman
3MD/PhD Student/Columbia University Irving Medical Center
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Jared Pollard,
Jared Pollard
3MD/PhD Student/Columbia University Irving Medical Center
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Akul Naik,
Akul Naik
3MD/PhD Student/Columbia University Irving Medical Center
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Lei haley Huang,
Lei haley Huang
4PhD Candidate/Columbia University Irving Medical Center
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Ashley Lee,
Ashley Lee
5Clinical Instructor/Columbia University Irving Medical Center
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Dusan Bogunovic,
Dusan Bogunovic
6Professor of Pediatric Immunology/Columbia University Irving Medical Center
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Joshua Milner,
Joshua Milner
7Professor of Pediatrics/Columbia University Irving Medical Center
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Benjamin Izar
Benjamin Izar
8Assistant Professor of Medicine/Columbia University Irving Medical Center
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Zachary Walsh
1MD/PhD Candidate/Columbia University
Chris Frangieh
2Postdoctoral Fellow/Columbia University Irving Medical Center
Clarissa Heck
2Postdoctoral Fellow/Columbia University Irving Medical Center
Sarah Grauman
3MD/PhD Student/Columbia University Irving Medical Center
Jared Pollard
3MD/PhD Student/Columbia University Irving Medical Center
Akul Naik
3MD/PhD Student/Columbia University Irving Medical Center
Lei haley Huang
4PhD Candidate/Columbia University Irving Medical Center
Ashley Lee
5Clinical Instructor/Columbia University Irving Medical Center
Dusan Bogunovic
6Professor of Pediatric Immunology/Columbia University Irving Medical Center
Joshua Milner
7Professor of Pediatrics/Columbia University Irving Medical Center
Benjamin Izar
8Assistant Professor of Medicine/Columbia University Irving Medical Center
© 2025 Walsh et al.
2025
Walsh et al.
This abstract is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by-nc-nd/4.0/).
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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J Hum Immun (2025) 1 (CIS2025): CIS2025abstract.115.
Citation
Zachary Walsh, Chris Frangieh, Clarissa Heck, Sarah Grauman, Jared Pollard, Akul Naik, Lei haley Huang, Ashley Lee, Dusan Bogunovic, Joshua Milner, Benjamin Izar; Massively Scalable Generation, Discovery, and Clinical (Re)Classification of Inborn Errors of Immunity Using Cutting-Edge Genome Engineering. J Hum Immun 25 April 2025; 1 (CIS2025): CIS2025abstract.115. doi: https://doi.org/10.70962/CIS2025abstract.115
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