Back

PARP and PI3K inhibitor combination therapy eradicates c-MYC-driven murine prostate cancers via cGAS/STING pathway activation within tumor-associated macrophages

Gupta, P. D.; Chaudagar, K.; Sharma-Saha, S.; Bynoe, K. M.; Maillat, L.; Heiss, B.; Stadler, W.; Patnaik, A.

2020-07-18 cancer biology
10.1101/2020.07.17.198598 bioRxiv
Show abstract

The majority of metastatic, castrate-resistant prostate cancer (mCRPC) patients are de novo resistant to immune checkpoint blockade (ICB), so therapeutic strategies to enhance immune-responsiveness are urgently needed. Here we performed a co-clinical trial of PARP inhibitor (PARPi) in combination with PD-1 or PDL-1 antibody in genomically unselected mCRPC patients or homologous-recombination proficient murine models, respectively, which demonstrated lack of efficacy. In contrast, PARPi in combination with PI3K inhibitor (PI3Ki), induced tumor regression via macrophage STING-dependent innate immune activation in vivo, and enhanced T-cell infiltration/activation in c-myc driven murine prostate cancer models, which was augmented by PD-L1 blockade. Ex vivo mechanistic studies revealed that PARPi-induced DNA double strand break-associated microvesicles released from tumor cells, coupled with PI3Ki-mediated c-GAS de-repression, were both required for macrophage cGAS/STING pathway activation. These data demonstrate that PARPi/PI3Ki combination triggers macrophage STING-mediated anti-cancer innate immunity, which is sufficient to induce tumor regression in ICB-refractory c-myc-driven prostate cancer. STATEMENT OF SIGNIFICANCECo-targeting of PARP and PI3K signaling pathways activates c-GAS/STING pathway within tumor-associated macrophages, thereby enhancing T cell recruitment/activation and cancer clearance in c-myc-driven murine prostate cancer models. PARPi/PI3Ki combination therapy could markedly increase the fraction of mCRPC patients responsive to ICB, independent of germline or tumor homologous recombination status.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.