miR-155 harnesses Phf19 to potentiate cancer immunotherapy through epigenetic reprogramming of CD8+ T cell fate

T cell senescence and exhaustion are major barriers to successful cancer immunotherapy. Here we show that miR-155 increases CD8+ T cell antitumor function by restraining T cell senescence and functional exhaustion through epigenetic silencing of drivers of terminal differentiation. miR-155 enhances Polycomb repressor complex 2 (PRC2) activity indirectly by promoting the expression of the PRC2-associated factor Phf19 through downregulation of the Akt inhibitor, Ship1. Phf19 orchestrates a transcriptional program extensively shared with miR-155 to restrain T cell senescence and sustain CD8+ T cell antitumor responses. These effects rely on Phf19 histone-binding capacity, which is critical for the recruitment of PRC2 to the target chromatin. These findings establish the miR-155–Phf19–PRC2 as a pivotal axis regulating CD8+ T cell differentiation, thereby paving new ways for potentiating cancer immunotherapy through epigenetic reprogramming of CD8+ T cell fate.


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Data
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Life sciences study design
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Sample size
For tumor experiments we employed 5-7 animal/group. For T cell kinetic and functional studies we employed 2-4 animals/group at each time point. These sample sizes allow for statistically valid comparisons based on previous studies conducted by this laboratory. RNA-seq were done in triplicate samples of three animals (KLRG1negCD62Lneg Phf19 WT versus KLRG1negCD62Lneg Phf19KO; KLRG1negCD62Lneg miR-155 vs KLRG1negCD62Lneg Ctrl miR) or on a single sample from 5 pooled animals/group (bulk miR-155 vs bulk Ctrl). Chip-seq was performed on a single sample. Nanostring was done in duplicate on RNA samples from 3 pooled animals/group (miR-155 vs Ctrl miR).
Data exclusions 1 RNA-seq sample in the Phf19 wt condition was excluded as it behaves as an outlier compared to the other two replicates.

Replication
All data except sequencing results were reliably reproduced in at least two independent experiments. Key results from RNA-seq or ChIP-seq were independently validated in qPCR or ChIP-PCR experiments.
Randomization For all animal experiments, mice were randomly assigned to different treatment and control groups.

Blinding
Tumor size were blindly measured. All other data were acquired and analyzed in a non-blind fashion because did not involve subjective measurements.
Reporting for specific materials, systems and methods Authentication B16-F10 cells and B16-hgp100 were validated by morphology, pigmentation and recognition by gp100-specific TCR transgenic CD8+ T cells.
Platinum-E cell line was validated by the vendor, and by the authors' assessment of cell morphology and ability to produce retro-viral particles.

Mycoplasma contamination
Platinum-E cells, B16-F10, and B16-hgp100 were validated as being mycoplasma free via a PCR-based assay

Wild animals
No wild animals were used in the study.

Field-collected samples
No field-collected samples were employed in this study.

Ethics oversight
All mouse experiments were done with the approval of the National Cancer Institute Animal Care and Use Committee.
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ChIP-seq Data deposition
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Methodology
Replicates None Sequencing depth The average total read of the 4 chip-seq samples (i.e., miR155-H3K27me3, miR155-input, Control-H3K27me3, and Controlinput) is 40.5X10^6 and 99.0% of them can be mapped to the reference mouse genome mm9. Of them, about 95.6% are unique.

Data quality
We filtered the alignments and discarded those alignments with low mapping quality (q-score <30). And we picked the stringent criteria (FDR<0.05) to call peaks.

Software
Basecall was conducted by the Illumina Casava (version 1.7) software, and then reads were mapped to the mouse reference nature research | reporting summary

Flow Cytometry
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Methodology Sample preparation
Mice spleen and thymus were homogenized using 40uM strainer and syringe. Tumors were homogenized using a gentleMACS™ Dissociator and processed with Lympholyte-M solution for lymphocyte enrichment. Naive or whole CD8+ T cells were enriched using enrichment kits from Stem Cell technology or Miltenyi.

Instrument
BD FACS Aria II, BD FACS LSRII, BD FACS Fortessa Software BD FACSDivaTM 8.0 was used to acquire the data, Flowjo_v9.9.4 and v10.5 were used to analyze the data Cell population abundance Purity after cell sort was determined by Flow Cytometry. The target population purity was higher than 90% for all experiments.

Gating strategy
Cell populations were gated using the following strategy:FSC/SSC->singlets (FSC-A/FSC-H)->Live cells-> CD8/reporter or congenic marker. For sorting of non-skewed T cell populations for RNA-seq cells were further selected using a KLRG1-CD62Lgate.
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