Background Failure to respond to conventional immunotherapies arises from heterogeneous mechanisms present in the tumor microenvironments (TME) that drive resistance in non-responding patients. Candel’s enLIGHTEN™ Discovery Platform applies advanced analytics to generate in silico prediction of multi-gene payload combinations with potential therapeutic benefit for specific solid tumor indications. Selected payloads, screened through ex vivo and in vivo multiplex assays, are then integrated into a viral chassis selected from Candel’s modified herpes simplex virus-1 (HSV-1) vector suite resulting in a multimodal, programmable treatment combined in a single therapeutic. We applied enLIGHTEN™ to immune checkpoint inhibitor (ICI)-treated patient datasets1–5 to identify potential gene payload combinations that may help overcome mechanisms underlying lack of response to ICI.
Methods Replication-defective HSV-1 vectors (Alpha-201 series) encoding payloads such as IFNγ and IL-12, selected in silico using enLIGHTEN™ advanced analytics, were tested in vitro in Hs578T cells (10 PFU/cell, 24–72 h) followed by RNASeq and gene set enrichment analysis. The ability of multiplexed vector combinations to induce immune activation and subsequent cancer cell killing was tested in an ex vivo cancer/peripheral blood mononuclear cell (PBMC) coculture system using flow cytometry-based readouts. Values are expressed as mean±SEM.
Results From a suite of modified HSV-1 vectors, we selected the Alpha-201 viral chassis for delivery of therapeutic payloads based on its ability to induce MYC and E2F targets, G2M cell cycle checkpoint, and allograft rejection responses (normalized enrichment scores = 2.4, 3.2, 3.0, 1.81, and 1.43 p<0.05), pathways associated with effective anti-tumor immune responses to ICI. Payload expression for Alpha-201-IFNγ was confirmed for up to 6 days post-infection (peak ~1 ng/mL/day; 2 days post-infection) and we observed a strong IFNγ response gene signature including upregulation of ISG15, CCL2, and CXCL10. Alpha-201 infection of cancer cells induced PBMC-mediated cell killing and infection- and payload-dependent alterations in lymphoid and myeloid cell populations. Alpha-201-IFNγ infection increased the number of CD11c+CD16+CD14-Ki67+MHCIIhigh dendritic cells (13.2-fold±3.58, p=0.019) and granzyme B+ NK cells (1.46-fold±0.14, p=0.013) compared to uninfected cocultures. Further, t-sne analysis demonstrated robust phenotypic changes in CD14+ monocyte populations upon infection with Alpha-201 vectors. Immune activation and PBMC-mediated cancer cell killing was further enhanced by selected payload combinations identified by in silico predictions (>60% compared to vector alone).
Conclusions These data validate enLIGHTEN™ in silico predictions of gene payload combinations and highlight the utility of the enLIGHTEN™ Discovery Platform to guide development of novel viral immunotherapeutics to modify the TME by design.
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