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Abstract
Background PD-L1 is a validated biomarker for anti-PD-1/PD-L1 therapies and its expression can be regulated by IDO1, and vice verse IDO1 can regulate PD-L1 expression indirectly through various signaling pathways. Concurrent inhibition of IDO1 and PD-1/PD-L1 may have enhanced anti-tumor effects.
Methods In this study, computational models were established to identify factors involved in interactions between these two therapeutic targets. Abstracts published on IDO1, PD-1, PD-L1, anti-PD1/PD-L1 were downloaded from PubMed, and analyzed by natural language processing and text mining. The information on interactions among gene, compound/therapy, cell/animal model, pathway and disease was extracted. Two gene networks, IDO1->PD-L1 and PD-L1->IDO1, were constructed (figures 1 and 2, respectively).
Results The PD-L1/IDO1 network is primarily mediated through IFN-gamma and Tregs. PD-L1 inhibits IFN-gamma production through down-regulation of NK cells, IL-2 and CD40 and activation of PD-1. In turn, diminished production of IFN-gamma inactivates AhR, IRF1, STAT1, COX2, NF-kappaB and M1 macrophages, leading to down-regulation of IDO1. On the other hand, PD-L1 could induce IDO1 expression through up-regulation of Tregs and PI3K/AKT pathway (figure 1). The key factors involved in the IDO1/PD-L1 network comprise MYC, EMT and IFN-gamma. MYC and EMT contribute to the positive feedback from IDO1 to PD-L1. IDO1 up-regulates IL-6, iNOS and beta-catenin, leading to activation of MYC and subsequent induction of PD-L1. IDO1 could also up-regulate PD-L1 through activation of MDSC, AhR, JAK/STAT, HIF1-alpha and NF-kappaB. However, IDO1 down-regulates IFN-gamma, which is a leading factor inducing PD-L1 expression (figure 2).
The PD-L1/IDO1 networkOrgan curve with an arrow indicates a positive interaction between nodes. Green curve with a bar indicates a negative interaction between nodes
The IDO1/PD-L1 networkOrgan curve with an arrow indicates a positive interaction between nodes. Green curve with a bar indicates a negative interaction between nodes
Conclusions As the network analyses revealed, IDO1 and PD-L1 are involved in complex mutual feedback regulations. Inhibition of IDO1 could either up- or down-regulate PD-L1, and enhance or reduce efficacy of anti-PD-1/PD-L1. The factors involved in the mutual feedback regulations could serve as biomarkers to determine and monitor the efficacy of combining IDO1 and PD-1/PD-L1 inhibitors, as well as additional therapeutic targets. The literature-based modeling approach facilitates the development of combination strategies especially when the experimental evident is lacking.
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