The accumulation of adenosine in the tumor microenvironment mediates immunosuppression and promotes tumor growth and proliferation. Intervention of the adenosine pathway is an important direction of antitumor immunity research. CD39 is an important ecto-nucleotidases for adenosine generation, therefore targeting the CD39-adenosine pathway is an emerging immune checkpoint for anticancer treatment. However, currently no CD39 inhibitor has been approved by the U.S. Food and Drug Administration. The development of CD39 drugs is urgent for clinical application. In this study, we combined homology modeling, virtual screening, and in vitro enzymatic activity to characterize the structural features of the CD39 protein and identify a triazinoindole-based compound as a CD39 inhibitor. The identified inhibitor and one of its analogues could effectively prevent the enzymatic activity of CD39 with IC50 values of 27.42 ± 5.52 and 79.24 ± 12.21 μM, respectively. At the same time, the inhibitor significantly inhibited the adenosine monophosphate production in colorectal cancer cell lines (HT29 and MC38) and thereafter prevented cell proliferation. Molecular docking studies, mutagenesis, and microscale thermophoresis indicated that residues such as R85 could be the main contributor in binding triazinoindole compounds. The binding mode can potentially be utilized for hit-to-lead optimization, and the identified inhibitor can be further tested for its anticancer activity in vivo or may serve as a chemical agent to study CD39-related functions.

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Cancer is a large group of diseases that exert heterogeneity individually, which resulted in the various lifetime individually and the specific response to anti-cancer drugs. Classification of patients as the prognostics is thereafter the key point for drug selection and therapeutic effect. Adenosine pathway which hydrolyzes extracellular ATP into adenosine has been recognized as new immune checkpoint pathway that can significantly impair anti-tumor immunity of multiple types of cancer. Therefore, a prognostic model based on adenosine pathway would benefit for cancer classification and treatment. In this study, 25 adenosine pathway related genes were screened from literature. An adenosine pathway prediction model (AP) based on 9769 patients from TCGA were constructed for cancer classification, and the AP model was sufficient to distinguish the patients with long overall survivals (OS) from short OS. Cancers could be categorized into two types (ADO unfavored and ADO favored) based on AP model. Next, the tumor microenvironment of two subtypes were characterized and they exhibit different cell components and immune patterns. Based on GEO dataset, AP model was capable to screen proper anti-cancer drugs for patients. To identify key genes that cause drug tolerance, the AP related genes and their drug sensitivity network were constructed and a series of key genes and the corresponding drugs were identified. Finally, AP model and drug sensitivity network identified that the expression of NT5E is correlated to the poor survival of patients with APHigh subtype, and the application of NT5E related drugs may be beneficial for the treatment of patients with APHigh subtype.