Hematopoietic aging promotes cancer by fueling IL-1?-driven emergency myelopoiesis

Age is a major risk factor for cancer, but how aging impacts tumor control remains unclear. Here, we establish that aging of the immune system, regardless of the age of the stroma and tumor, impacts lung cancer progression. Hematopoietic aging enhances emergency myelopoiesis, resulting in the local accumulation of myeloid progenitor-like cells in lung tumors. These cells are a major source of IL-1? that drives the enhanced myeloid response. The age-associated decline of DNMT3A enhances IL-1? production, and disrupting IL-1R1 signaling early during tumor development normalized myelopoiesis and slowed the growth of lung, colonic, and pancreatic tumors. In human tumors, we identified an enrichment for IL-1?-expressing monocyte-derived macrophages linked to age, poorer survival, and recurrence, unraveling how aging impacts cancer. Overall design: Sample preparation: Single-cell suspensions from lung tissues were obtained, as described above. Samples were broadly enriched for myeloid and lymphoid cells by fluorescence-activated cell sorting, and these cells were suspended in PBS supplemented with 0.5% BSA. Samples were loaded onto the 10x Genomics Next GEM 5' assay, as per the manufacturer's instructions, for a target cell recovery of 10,000 cells per lane. Libraries were constructed, according to manufacturer's instructions. All libraries were quantified via Agilent 2100 hsDNA Bioanalyzer and KAPA library quantification kit (Roche, Cat. #0797014001). Libraries were sequenced at a targeted depth of 25,000 reads per cell; all libraries were sequenced using the Illumina NovaSeq S2 100 cycle kit. scRNAseq analysis: Gene expression reads were aligned to the mm10 reference transcriptome and count matrices were generated using the default CellRanger 2.1 workflow, using the 'raw' matrix output. Following alignment, barcodes matching cells that contained > 500 unique molecular identifiers (UMIs) were extracted. From these cells, those with transcripts > 25% mitochondrial genes were filtered from downstream analyses. Matrix scaling, logarithmic normalization, and batch correction via data alignment through canonical correlation analysis, and unsupervised clustering using a K-nn graph partitioning approach were performed as previously described. Differentially expressed genes were identified using the FindMarkers function (Seurat). Mean UMI were imputed to determine logarithmic fold changes in expression between cell states to further the analysis of markers of interest.