Nucleotide metabolism in cancer cells fuels a UDP-driven macrophage cross-talk promoting immunosuppression and immunotherapy resistance [RNAseq_meta_analysis]

The project aims to identify metabolic genes and pathways associated with immunotherapy resistance. Methods: 4x10e6 Panc02, 1x10e6 MC38 or 2x10e6 CT26 cells were injected subcutaneously (s.c.) in the right flank of the mouse in a final suspension of 200μl PBS. Tumor volumes were measured at least three times per week. At the indicated time points, mice were randomized and treated intraperitoneally (i.p.) with 10 mg/kg α-PD-1 (BioXcell) or α-CTLA4 or control IgG from rat serum (Sigma-Aldrich). At end-stage, tumor-bearing mice were sacrificed by cervical dislocation and RNA was isolated from tumor tissue using TRIzol (Life Technologies). Starting from total RNA, poly-adenylated fragments were isolated, reverse transcribed and converted into indexed sequencing libraries using the Illumina TruSeq RNA sample preparation kit V2 according to the manufacturer’s instructions. The first 50 bases of these libraries were sequenced on a HiSeq 2500 system (Illumina, San Diego, CA). Raw sequencing reads were mapped to the transcriptome and the mouse reference genome (GRCm38/mm10) using TopHat 2.0 and Bowtie2.0 (Langmead and Salzberg, 2012). Mapped reads were assigned to ensemble gene IDs with the HTSeq software package. Transcriptome profiling was performed in three different murine cancer models (MC38, CT26 and panc02) treated with PD-1, CTLA-4 or vehicle Our bulk tumor transcriptomic profiles of responsive (MC38), low responsive (CT26) and non-responsive (PancO2) murine tumor models were integrated with publicly available pretreatment transcriptomic datasets of patients responding and resistant to immune checkpoint inhibitors, such as α-CTLA-4 and α-PD-1 (Van Allen- et al., Science 2015; Hugo et al., Cell 2016; Ascierto et al., Cancer Imm Research 2016). All these datasets were interpreted by using our in-house BIOMEX platform (Goveia et al., EMBO Mol Med 2016), which focuses on metabolic profiles. Metabolic genes were ranked, prioritizing the most consistently upregulated genes associated with resistance to immuno checkpoint inhibitors (ICIs).