Humanized mouse tumor models for evaluation of ILT2 and ILT4 antagonist antibodies

Solid tumors are dense three-dimensional (3D) multi-cellular structures that enable efficient receptor–ligand trans interactions via close cell–cell contact. Immunoglobulin-like transcript (ILT)2 and ILT4 are related immune suppressive receptors that play a role in the inhibition of myeloid cells within the tumor microenvironment. The relative contributions of ILT2 and ILT4 to immune inhibition in the context of solid tumor tissue has not been fully explored. We present evidence that both ILT2 and ILT4 contribute to myeloid inhibition. We found that while ILT2 inhibits myeloid cell activation in the context of trans-engagement by MHC-I, ILT4 efficiently inhibits myeloid cells in the presence of either cis- or trans-engagement. In a 3D spheroid tumor model, dual ILT2/ILT4 blockade was required for optimal activation of myeloid cells, including the secretion of CXCL9 and CCL5, upregulation of CD86 on dendritic cells, and downregulation of CD163 on macrophages. Humanized mouse tumor models showed increased immune activation and cytolytic T cell activity with combined ILT2 and ILT4 blockade, including evidence of the generation of immune niches, which have been shown to correlate with clinical response to immune checkpoint blockade. In a human tumor explant histoculture system, dual ILT2/ILT4 blockade increased CXCL9 secretion, downregulated CD163 expression, and increased the expression of M1 macrophage, IFN-?, and cytolytic T cell gene signatures. Thus, we have revealed distinct contributions of ILT2 and ILT4 to myeloid cell biology and provide proof-of-concept data supporting the combined blockade of ILT2 and ILT4 to therapeutically induce optimal myeloid cell reprogramming in the tumor microenvironment. Overall design: Tumors were dissected and dissociated by mincing with a razor blade, followed by grinding through a 70 µm filter cup (Corning) to generate a single cell suspension. Dissociated tumor cells were then washed, counted and human CD45+ cells isolated using the Human CD45 Positive Selection Kit for humanized mice (Stemcell Technologies), according to the manufacturer's instructions. Following four rounds of washing and magnetic bead isolation, the non-dissociated bead/cell mixture was resuspended in 100 µl of RNAlater (Invitrogen) and stored at -80 °C. To isolate RNA, wells were thawed, cells were pelleted and pellets lysed in 100 µl of RLT buffer, followed by centrifugation to removecell debris and magnetic beads. Supernatant was processed to isolate RNA with the RNEasy mini kit following manufacturer's instructions (Qiagen). Total RNA was prepared for sequencing using the Illumina stranded mRNA library prep kit and sequenced on an Illumina NovaSeq S2 (100bp single-end reads and an average of 40 million reads were generated for each sample). For sequencing alignment, RNA-seq data raw reads were filtered using Trim Galore! (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) to remove low quality and adaptor bases. Reads shorter than 20nt were discarded. Filtered reads were mapped to UCSC hg19 and mm9 genome sequences using STAR (v2.7.6a). In the next step, XenofilteR (v1.6) was used on the bam files to exclude mouse RNA sequence reads that originated from host mice. At last, counts of all samples aligned to the human genome were generated using featureCounts (v2.0.1), and edgeR (v3.40.1) was used to get normalized counts and perform differential gene expression analysis.