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. 2023 Feb 13;41(2):288-303.e6.
doi: 10.1016/j.ccell.2022.12.009. Epub 2023 Jan 19.

Base editing screens map mutations affecting interferon-γ signaling in cancer

Matthew A Coelho  1 , Sarah Cooper  2 , Magdalena E Strauss  3 , Emre Karakoc  1 , Shriram Bhosle  4 , Emanuel Gonçalves  5 , Gabriele Picco  1 , Thomas Burgold  6 , Chiara M Cattaneo  7 , Vivien Veninga  7 , Sarah Consonni  1 , Cansu Dinçer  4 , Sara F Vieira  1 , Freddy Gibson  4 , Syd Barthorpe  4 , Claire Hardy  8 , Joel Rein  9 , Mark Thomas  9 , John Marioni  3 , Emile E Voest  10 , Andrew Bassett  2 , Mathew J Garnett  11
Affiliations

Base editing screens map mutations affecting interferon-γ signaling in cancer

Matthew A Coelho et al. Cancer Cell. .

Abstract

Interferon-γ (IFN-γ) signaling mediates host responses to infection, inflammation and anti-tumor immunity. Mutations in the IFN-γ signaling pathway cause immunological disorders, hematological malignancies, and resistance to immune checkpoint blockade (ICB) in cancer; however, the function of most clinically observed variants remains unknown. Here, we systematically investigate the genetic determinants of IFN-γ response in colorectal cancer cells using CRISPR-Cas9 screens and base editing mutagenesis. Deep mutagenesis of JAK1 with cytidine and adenine base editors, combined with pathway-wide screens, reveal loss-of-function and gain-of-function mutations, including causal variants in hematological malignancies and mutations detected in patients refractory to ICB. We functionally validate variants of uncertain significance in primary tumor organoids, where engineering missense mutations in JAK1 enhanced or reduced sensitivity to autologous tumor-reactive T cells. We identify more than 300 predicted missense mutations altering IFN-γ pathway activity, generating a valuable resource for interpreting gene variant function.

Keywords: IFN-γ signaling; base editing; cancer genetics; cancer immunotherapy; drug resistance; functional genomics; gene editing; interferon gamma; variants of uncertain significance.

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Conflict of interest statement

Declaration of interests M.J.G. has received research grants from AstraZeneca, GlaxoSmithKline, and Astex Pharmaceuticals, and is a founder and advisor for Mosaic Therapeutics.

Figures

None
Graphical abstract
Figure 1
Figure 1
CRISPR-Cas9 screens identify mediators of IFN-γ sensitivity and resistance (A) Schematic of the integrated CRISPR-Cas9 and base editing screening approaches to identify genetic mediators of sensitivity and resistance to IFN-γ. Cas9 screens identify pathways and genes regulating IFN-γ response in colorectal cancer cell lines and base editing mutagenesis screens assess the functional consequence of VUS in key regulators. (B) Gene-level volcano plots of CRISPR-Cas9 screens comparing IFN-γ-treated and control arms. (C) gRNA-level analysis of top resistance or sensitizing genes, representing essential components of the IFN-γ pathway. (D) Common and private genes conferring sensitivity and resistance to IFN-γ in HT-29 and LS-411N CRC cell lines identified from CRISPR-Cas9 screens. Hits were selected using MAGeCK; p < 0.05 and a false discovery rate of less than 0.05. All data are the average from two independent screens performed on separate days. See also Figure S1 and Table S1.
Figure 2
Figure 2
Base editing mutagenesis screening of JAK1 variants (A) COSMIC data from patient tumor samples show JAK1 cancer mutations are predominantly C->T and G->A missense variants. (B) BE-FLARE assessment of base editing efficiency in HT-29 iBE3 cells treated with doxycycline, based on flow cytometry analysis of a BFP (His66) to GFP (Tyr66) spectral shift. (C) FACS screening assay. After base editing of JAK1 by the addition of doxycycline, HT-29 iBE3 cells that failed to respond to IFN-γ after 48 h were selected by FACS, as determined by the lack of induction of MHC-I and PD-L1 expression. (D) Proliferation screening assay. gRNA depletion or enrichment is indicated by z-score, comparing the control arm with the T0 (time 0) control. Base editing gRNAs designed to introduce stop codons in essential genes in HT-29 iBE3 cells are depleted. (E) Correlation between screening replicates. z-scores for gRNAs targeting JAK1 were compared between replicates and alternative screening assays. The shaded line represents the 95% confidence interval. (F) Correlation between different base editor screening assays for JAK1 variants in HT-29 iBE3 cells. (G) Identification of LOF and GOF alleles in JAK1 protein affecting sensitivity to IFN-γ. z-scores for the base editing screens using FACS vs proliferation were plotted to select potential LOF (blue) and GOF (red) JAK1 variants. Labeling illustrates amino acid positions that were selected for further validation. All data are representative of two independent experiments or screens performed on separate days. See also Figure S2 and Table S2.
Figure 3
Figure 3
Base editing mutagenesis of the IFN-γ pathway (A) Schematic of the key mediators of IFN-γ signaling investigated in base editing screens. Depicted are top hits from our CRISPR-Cas9 screens to determine the modulators of sensitivity to IFN-γ; positive mediators are in blue and negative regulators are in red. The number of predicted LOF and GOF missense variants revealed from all base editing screens are indicated. (B) Base editor mutagenesis of core IFN-γ pathway components using HT-29 iBE3 cells reveals GOF and LOF missense mutations. The average FACS screen score is plotted against the average proliferation screen score for each gene. Positions of validated JAK1 gRNAs and amino acid positions with predicted missense LOF or GOF effect are labeled. (C) Base editing reveals the position of functional domains. Schematics of the domain architecture of proteins in the IFN-γ pathway tiled with base editing gRNAs, with the distribution of GOF and LOF amino acid positions labeled. All data are representative of two independent screens performed on separate days. See also Figure S3 and Table S2.
Figure 4
Figure 4
Base editing reveals JAK1 LOF and GOF variants with clinical precedence (A) Functional variant map of JAK1. z-scores from base editing proliferation screens are plotted for each gRNA across JAK1 protein domains. gRNAs installing candidate LOF and GOF positions referred to in the text are labeled with the predicted edited amino acid positions. Screen z-scores are calculated independently for each base editor and plotted together for comparison. JAK1 screening data from pathway-wide base editing screens from Figure 3 are plotted for comparison. (B) Structural insight into the mechanism of action of JAK1 LOF and GOF mutations. Crystal structure (6C7Y) shows catalytic LOF mutations (blue) proximal to the ATP/adenosine diphosphate (ADP) binding pocket in the kinase domain, and GOF mutations (red) in the binding interface with the negative regulator SOCS1. (C) Western blot of HEK293T cells overexpressing FLAG-tagged WT or Gly590Arg mutant JAK1, with or without IFN-γ stimulation for 1 h. All data are representative of two independent experiments or screens performed on separate days. See also Figures S3 and S4, Tables S2 and S3.
Figure 5
Figure 5
Functional validation of variants conferring altered sensitivity to IFN-γ (A) Functional validation of base editing gRNAs targeting JAK1 in HT-29 iBE3 cells. Proliferation assay: Giemsa stain following growth in the presence or absence of IFN-γ. Base editing screen z-scores for each gRNA are provided for comparison. (B) Western blot analysis of JAK1 expression and JAK-STAT signaling of corresponding JAK1 variants was performed on cells stimulated with IFN-γ for 1 h, after selection in IFN-γ for LOF variants. RNA expression, quantitative PCR analysis of JAK1 RNA expression relative to GAPDH 72 h after base editing. (C) Flow cytometry analysis of MHC-I and PD-L1 expression induction following stimulation with IFN-γ for 48 h. Separation of function (SOF) variants. Bars represent the mean. All data are representative of two independent experiments performed on separate days. See also Figures S5 and S6.
Figure 6
Figure 6
Verification of base editing genotypes with next-generation sequencing (A) Deep sequencing of JAK1 reveals the DNA editing profile of base editor gRNAs. Editing variant allele frequency for predicted LOF and GOF gRNAs within the validation cohort measured by NGS of amplicons in control cells, base edited cells, or base edited cells with selection with IFN-γ for 6 d. Different editing outcomes are grouped by gRNA. Syn, synonymous. Data represent the mean of two independent experiments performed on separate days. (B) Amplicon sequencing assessment of HT-29 iBE3 base editing positions within the gRNA protospacers profiled in Figure 6A in the absence of IFN-γ. (C) Single-cell DNA sequencing of base editing in HT-29 iBE3 cells across 50 gRNAs reveals C->T (or G->A) editing focused in the gRNA activity window. Penetrance (zygosity) 0/1/1 is heterozygous (het), and 1/1/1 is homozygous (homo). The proportion of cells with the same gRNA assignment harboring that edit is indicated. See also Figure S6 and Table S4.
Figure 7
Figure 7
Classified JAK1 missense mutations alter tumor organoid sensitivity to autologous anti-tumor T cells (A) Flow cytometry analysis of PD-L1 and MHC-I expression in response to IFN-γ in cancer cell lines with endogenous, classified LOF or GOF mutations in JAK1. (B) Flow cytometry analysis of PD-L1 and MHC-I expression after correction of an endogenous JAK1 LOF mutation with ABE8e-NGN in K2 cells. The percentage of cells in each gate is indicated. (C) Correlation between iBE3 base editing screens in HT-29 cells and CRC-9 tumor organoids. z-scores from the IFN-γ comparison with the control arm were compared for gRNAs targeting the IFN-γ pathway. (D) Schematic of co-culture experiments to assess T cell-mediated killing of patient-derived, autologous tumor organoids (CRC-9). (E) T cell-mediated killing of autologous human tumor organoids. Flow cytometry analysis of T cells and organoids (expressing iBE3-mApple) after 72 h of co-culture. The percentage of gated organoid cells is indicated. Counting beads were used to quantify the absolute cell counts. (F) Quantification of T cell-mediated killing of autologous tumor organoids from flow cytometry analysis. Data represent the average ± standard deviation of three biological replicates and were compared against parental co-culture controls using an unpaired, two-tailed Student’s t-test (∗∗p < 0.01, ∗p < 0.05). NT, non-targeting gRNA; ø par., parental tumor organoid. All data are representative of two independent experiments performed on separate days. See also Figure S7.
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