Deep learning model construction and hit prediction
We used bioactivity data measured from 2,268 unique small compounds against IDO to construct our initial convolution neural network (CNN) classification model. The data were aggregated from four different sources, including patent documents (579 compounds), peer-reviewed literature (62 compounds), PubChem database (442 compounds), and the proprietary screening data (1,185 compounds).
We used RDKit (www.rdkit.org) to extract a 1,024-bit molecular descriptor from individual compounds in the datasets, where bioactivity data was available in varied forms, ranging from binary classification (i.e., active or inactive) to IC50, according to the data source. We thus encoded bioactivity data as a binary feature with 0 and 1 indicating inactive and active enzyme activity, respectively, instead of continuous data values.
We used 5-fold cross-validation to train and evaluate the prediction model. We split the training dataset into five equal-sized, non-overlapping subgroups. Of the five subgroups, one randomly selected subgroup was set aside as a test dataset, and the remaining four subgroups were combined and used to train the CNN-based as a training dataset. The same process was repeated four additional times with a different test dataset being used each time. We optimized the parameters until the trained model achieved a receiver operating characteristic curve-area under the curve of greater than 0.9 for the test data (Supplementary Fig. 1).
The TDO model was trained with the same method except that the initial training data consisted of bioactivity data of 1,710 unique compounds.
We aimed to enhance the generalization of the prediction model, and thus performed four iterative cycles of predictions followed by in vitro validation. IDO and TDO models were employed to screen a chemical library of 270,000 commercially available small compounds. A panel of 189 compounds were curated from the top-ranked potential IDO or TDO inhibitors, followed by experimental validation. The newly produced assay results were then combined with the initial training data to retrain each model. This process was repeated a total of four times.
Compounds that exhibited bioactivity against both IDO and TDO were consolidated from four rounds of iterative training and validation. Of these, three unique structural scaffolds were derived by clustering compounds by structural similarity and selecting a representative set. Derivatives of the selected scaffolds were designed, and subsequent in vitro assays led to one lead compound.
Mice and cell lines
Male BALB/c mice were purchased from Orient Bio Inc. (Seongnam, Korea). Mice were housed in a specific pathogen-free facility (Seongnam, Korea). All animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC, #2000174) of CHA University and carried out following the approved protocols. The CT26 murine colon cancer cell line was obtained from the Korean Cell Line Bank (KCLB, Seoul, Korea, #80009). Human cancer cell lines, HeLa and A172 were also originally obtained from ATCC (#CCL-2 and #CRL-1620). These cells were maintained in DMEM or RPMI 1640 medium, each supplemented with 10% FBS and 1% penicillin/streptomycin, and incubated at 37°C, 5% CO2.
In vitro enzyme- and cell-based assay for IDO and TDO activity
For the determination of enzymatic and cellular IDO and TDO activity, an in vitro assay (BPS Bioscience) was performed according to the manufacturer’s instructions. For the enzyme-based assay, STB-C017 was dissolved in dimethylsulfoxide (DMSO) and added at a variable concentration to a 384-well plate containing the reaction solution, followed by the addition of IDO or TDO. After incubation at room temperature for 1 h, a fluorescence-activating solution was added and continuously incubated for 4 h. For the cell-based assay, HeLa and A172 cells were seeded in a 96-well plate at a density of 3 × 104 cells per well and grown overnight. The next day, 50 ng/ml IFNγ and serial dilutions of STB-C017 were added into cells and incubated at 37°C in a 5% CO2 incubator. After an additional 24 h of incubation, supernatants were mixed with 6.1 N trichloroacetic acid (Sigma) at 50°C for 30 min. Then, the mixture was centrifuged at 3,500 rpm for 5 min, and supernatants were transferred and mixed with the detection solution for 10 min. After the final incubation of enzyme- and cell-based assay, absorbance was read at 400 and 480 nm using a Synergy Neo microplate reader (BioTek).
In vivo lipopolysaccharide (LPS) administration
BALB/c mice were intraperitoneally injected with 0.8 mg/kg LPS and/or orally administrated 100 mg/kg STB-C017. After treatment for 24 h, mouse blood was obtained via retro-orbital puncture.
Measurement of Trp/Kyn levels using ELISA
The biological activity of IDO and TDO was evaluated via measuring Trp and Kyn levels in the plasma and tumors. Mouse blood was centrifuged at 13,000 rpm for 10 min and the plasma was aspirated. Tumor samples were homogenized in PBS for 5 min and debris were removed by centrifugation at 13,000 rpm. Trp and Kyn concentrations were measured using the ELISA kit (ImmuSmol, Pessac, France), according to the manufacturer’s instructions.
Tumor models and treatment regimens
Tumors were implanted via subcutaneous injection of 2 × 105 CT26 cells into the right flank of wild type BALB/c mice. When tumor volume reached >50 mm3, mice were orally administrated STB-C017 twice daily. Mice in the control group were orally treated with the same volume of PBS. For combination therapy, we also administered epacadostat orally (100 mg/kg, EPA, LEAPChem) twice daily. For the cell depletion study, the mice received an intraperitoneal injection of 200 μg of anti-CD8 (clone 53-6.72, BioXCell) antibody every 3 days. For immune checkpoint blockade, each mouse received an intraperitoneal injection of anti-PD-1 (8 mg/kg, clone J43, BioXCell) or anti-CTLA-4 (4 mg/kg, clone 9D9, BioXCell) antibody at the given time points. The surviving mice with complete tumor regression were rechallenged with 2 × 105 CT26 or Renca cells in the left flank, and the tumor growth was monitored. The tumors were measured with a digital caliper, and tumor volumes were calculated using the following modified ellipsoid formula: 1/2 × (length × width2). For survival analysis, the mice were euthanized when the tumor volume exceeded 2000 mm3 or when the mice became moribund.
RNA isolation and NanoString gene expression analysis
For NanoString gene expression analysis, we extracted total RNA from whole tumor tissues using TRIzol (Invitrogen) and purified it with ethanol. RNA concentration and quality were confirmed using a Fragment Analyzer (Advanced Analytical Technologies, IA, USA). Immune profiling was performed with a digital multiplexed NanoString nCounter PanCancer Immune Profiling mouse panel (NanoString Technologies) using 100 ng of total RNA isolated from tumor samples, as per our previously established protocol [8, 36].
Flow cytometry analysis
For flow cytometry analysis, the harvested tumors from each group were minced and incubated for 1 h at 37°C in a digestion buffer comprising 2 mg/ml collagenase D (Roche) and 40 μg/ml DNase I (Roche). Cell suspensions were filtered through a 70 μm cell strainer (Corning) and incubated for 3 min at room temperature in ACK lysis buffer (Gibco) to remove the cell clumps and red blood cells. After washing with FACS buffer (1% FBS in PBS), the cells were filtered through a nylon mesh. Next, the cells were incubated on ice for 30 min in Fixable Viability Dye eFluorTM 450 (Invitrogen) to exclude the dead cells before antibody staining. Then, the cells were washed with FACS buffer and incubated on ice for 30 min in FACS buffer with surface antibodies targeting CD45 (30-F11, Invitrogen), CD3 (17A2 or 145-2C11, Invitrogen), CD8a (53-6.7, Invitrogen), CD4 (RM4-5, Invitrogen), PD-1 (J43, Invitrogen), CD25 (PC61.5, Invitrogen), ICOS (7E.17G9, Invitrogen), CD11b (M1/70, Invitrogen), Ly-6G (RB6-8C5, Invitrogen), F4/80 (BM8, Invitrogen), or Ly-6C (HK1.4, Invitrogen). Cells were further permeabilized using a Foxp3 Staining Buffer kit (Invitrogen) and stained for Foxp3 (FJK-16s, Invitrogen), iNOS (CXNFT, Invitrogen), or Arginase 1 (A1exF5, Invitrogen). The stained cells were analyzed using a CytoFLEX flow cytometer (Beckman Coulter), and the data were analyzed with the FlowJo software (Tree Star Inc.).
Cytometric bead array (CBA)
To measure the levels of cytokines, including IL-2, IL-4, IL-6, IFNγ, TNF, IL-17A, and IL-10 in the plasma, the CBA Mouse Th1/Th2/Th17 Cytokine Kit (BD Biosciences) was performed according to the manufacturer’s instructions. In brief, the prepared capture beads and detection reagents were incubated with the standards or the plasma samples for 2 h at room temperature. After a wash, these complexes were detected using flow cytometry to identify particles with fluorescence characteristics.
Histological analyses via immunofluorescence
For immunofluorescence staining, the tumor samples were fixed in 1% PFA, dehydrated overnight in 20% sucrose solution, and frozen (Leica). The frozen blocks were sectioned into 50 μm-thick slices, which were permeabilized with 0.3% PBS-T (Triton X-100 in PBS), and blocked with 5% normal goat serum in 0.1% PBS-T for 30 min at room temperature. Next, the samples were incubated overnight with the following primary antibodies: Anti-PD-L1 (rabbit, clone 28-8, Abcam), anti-CD8 (rat, clone 53-6.7, BD Pharmingen), anti-CD31 (hamster, clone 2H8, Millipore; rabbit, Abcam), anti-L-Kyn (mouse, clone 3D4-F2, ImmuSmol), anti-Granzyme B (rat, clone NGZB, Invitrogen), anti-Ki67 (rabbit, Abcam), or anti-Caspase3 (rabbit, R&D Systems). After several washes, the samples were incubated for 2 h at room temperature with the following secondary antibodies: FITC- or Cy3-conjugated anti-rabbit IgG (Jackson ImmunoResearch), FITC- or Cy3-conjugated anti-rat IgG (Jackson ImmunoResearch), Cy3-conjugated anti-hamster IgG (Jackson ImmunoResearch), or FITC-conjugated anti-mouse IgG (Jackson ImmunoResearch). Cell nuclei were counterstained with 4’,6-diamidino-2-phenylindole (DAPI, Invitrogen). Finally, samples were mounted with fluorescent mounting medium (DAKO), and images were acquired using a Zeiss LSM 880 microscope (Carl Zeiss).
Density measurements of blood vessels, T lymphocytes, Ki67+ proliferating cells, apoptotic cells, Kyn+ cell area, GzB+ cell area, and PD-L1+ cell area, were performed using ImageJ software (http://rsb.info.nih.gov/ij). Blood vessel density was determined by calculating the CD31+ area per random 0.49 mm2 field on the tumor sections. The degree of cytotoxic T lymphocyte infiltration was calculated as the percentage of CD8+ area per random 0.49 mm2 field. The density of proliferating cells was measured by calculating the percentage Ki67+ area in random 0.49 mm2 fields. The extent of apoptosis was shown as the percentage Caspase3+ area per random 0.49 mm2 fields. To determine the level of Kyn expression, the Kyn+ area per random 0.49 mm2 field was calculated in tumor sections. To define the activation of T lymphocyte, GzB+ area per random 0.49 mm2 field was calculated in intratumoral regions. The density of PD-L1+ cells was quantified as the percentage of PD-L1+ area per random 0.49 mm2 fields. All analyses were performed on at least five fields per mouse.
Statistical analyses were performed using GraphPad Prism 7.0 software (GraphPad Software, La Jolla, California, USA) and PASW statistics 18 (SPSS). Values are presented as the mean ± SD unless otherwise indicated. The statistical differences were assessed using an unpaired 1-tailed Student’s t-test. Pearson’s correlation analysis was performed to investigate the relationship between CD8+ T cell expression and intratumoral Kyn expression. Survival curves were generated using the Kaplan-Meier method, and statistical differences between curves were analyzed using the log-rank test. The level of statistical significance was set at P < 0.05.