Ubiquitin ligase STUB1 destabilizes IFNγ-receptor complex to suppress tumor IFNγ signaling

The cytokine IFNγ differentially impacts on tumors upon immune checkpoint blockade (ICB). Despite our understanding of downstream signaling events, less is known about regulation of its receptor (IFNγ-R1). With an unbiased genome-wide CRISPR/Cas9 screen for critical regulators of IFNγ-R1 cell surface abundance, we identify STUB1 as an E3 ubiquitin ligase for IFNγ-R1 in complex with its signal-relaying kinase JAK1. STUB1 mediates ubiquitination-dependent proteasomal degradation of IFNγ-R1/JAK1 complex through IFNγ-R1K285 and JAK1K249. Conversely, STUB1 inactivation amplifies IFNγ signaling, sensitizing tumor cells to cytotoxic T cells in vitro. This is corroborated by an anticorrelation between STUB1 expression and IFNγ response in ICB-treated patients. Consistent with the context-dependent effects of IFNγ in vivo, anti-PD-1 response is increased in heterogenous tumors comprising both wildtype and STUB1-deficient cells, but not full STUB1 knockout tumors. These results uncover STUB1 as a critical regulator of IFNγ-R1, and highlight the context-dependency of STUB1-regulated IFNγ signaling for ICB outcome.


Statistics
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nature research | reporting summary
October 2018

Life sciences study design
All studies must disclose on these points even when the disclosure is negative. Each experiment was performed using at least three independent biological replicates. For the in vivo experiment, an a priori power analysis was performed using G*Power 2 (v3.1.9.2) with the effect size being determined by a preliminary pilot experiment.
No data were excluded.
All findings were successfully replicated in at least three independent experiments.
For the in vivo experiments, randomization occurred at time of treatment by an independently operating technician. Randomization does not applicable to the reported in vitro experiments, since either different treatments or genotypes were compared.
For in vivo experiments researchers were blinded to treatment groups. Additionally, tumor volume measurements were performed by independent mouse technicians and not by primary researchers themselves.
Antibodies for flow cytometry were used at a dilution of 1:100 if not stated otherwise. All antibodies were validated for their applications by the manufacturer or by ourselves using genetic knockouts. Note that full information on the approval of the study protocol must also be provided in the manuscript.

Flow Cytometry
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A numerical value for number of cells or percentage (with statistics) is provided. Cell lines were authenticated using the STR profiling kit by Promega (B9510).

Methodology
All cell lines were tested monthly by PCR to be negative for mycoplasma infection. If cell lines were found to be infected, they were immediately discarded.
This study did not involve wild animals This study did not involve field-collected samples.
All animal studies were approved by the animal ethics committee of the Netherlands Cancer Institute (NKI) and performed in accordance with ethical and procedural guidelines established by the NKI and Dutch legislation.
Cell lines were washed with PBS and single cell suspensions were generated using trypsin digestion. Cells were subsequently washed with 0.1% BSA/PBS and stained with the respective antibodies (diluted in 0.1% BSA/PBS) for 30 minutes on ice. Cells were washed twice with 0.1% BSA/PBS and DAPI was added to each sample to determine the percentage of dead cells. Transplanted tumors were harvested dissociated into small pieces using scissors. To generate a single cell suspension, each tumor was digested using collagenase IV and subsequently forced through 70 µm cell strainers. The resulting single cell suspension was prepared for flow cytometry as stated above.
Samples were analyzed using BD LSR Fortessa Cell analyzer, cells were sorted on the BD FACSAria Illu The FACSDiva (V8) software was used to collect flow cytometry data. FlowJo software (V10) was used to analyze flow cytometry data.
For the sort-based screen, we sorted 10% of cells with the highest and 10% of cells with the lowest expression levels of IFNGR1 from the viable (DAPI-negative) IFNGR1-APC-positive cells.