ABSTRACT: Purpose: Eliciting effective anti-tumor immune responses in patients who fail checkpoint inhibitor therapy is a critical challenge in cancer immunotherapy, and in such patients, tumor-associated myeloid cells and macrophages (TAMs) are promising therapeutic targets. We demonstrate in an autochthonous, poorly immunogenic mouse model of melanoma that combination therapy with an agonistic anti-CD40 mAb and CSF1R inhibitor potently suppressed tumor growth. Microwell assays to measure multiplex protein secretion by single cells identified that untreated tumors have distinct TAM subpopulations secreting MMP9 or co-secreting CCL17/22, characteristic of an M2-like state. Combination therapy reduced the frequency of these subsets, while simultaneously inducing a separate polyfunctional inflammatory TAM subset co-secreting TNF, IL-6, and IL-12. Tumor suppression by this combined therapy was partially dependent on T cells, TNF and IFN. Together, this study demonstrates the potential for targeting TAMs to convert a “cold” into an “inflamed” tumor microenvironment capable of eliciting protective T cell responses. Methods: Total RNA was purified with the use of QIAzol and RNeasy Mini kit (QIAGEN), in which an on-column DNase treatment was included. Purified RNA was submitted to the Yale Center for Genomic Analysis where it was subjected to mRNA isolation and library preparation. Non-strand specific libraries were generated from 50ng total RNA using the SMARTer Ultra Low Input RNA for Illumina Sequencing kit. Libraries were pooled, six samples per lane, and sequenced on an Illumina HiSeq 2500 (75-bp paired end reads), and aligned using STAR to the GRCm38 (mm10) reference genome. A count-based differential expression protocol was adapted for this analysis(Anders et al., 2013); mappable data were counted using HTSeq, and imported into R for differential expression analysis using the DESeq2.To find differentially regulated sets of genes for signature generation, a 1.5-Log2 fold-change difference between samples and p-adjusted (Holm-Sidak) ≤ 0.01 was used. Results: To begin to understand how these treatments modulated T cells to control tumor growth, and to possibly illuminate additional biomarkers of response, we examined the transcriptomes of CD11b+ Ly6G- cells treated with CD40 or CSF1Ri, alone or in combination, relative to control, using high throughput RNA-sequencing. Principal components analysis (PCA) on the genome-wide dataset demonstrated that treating with CD40 and CSF1Ri individually caused largely non-overlapping changes in transcription, as indicated by their movement along orthogonal principal components (PC) relative to the control. Importantly, combination therapy was visualized as a systems-level combination of each individual treatment in PC space. We then examined the mRNAs most altered by either treatment alone or in combination relative to Controls (Log2FC>1.5, p<.01) by unsupervised hierarchical clustering. Five major gene patterns emerged from the clustering of genes. Cluster #1 comprises genes that are upregulated by CD40 and CSF1Ri+CD40 treatment but are mostly unaffected by CSF1Ri, suggesting that CD40 is the primary driver of this cluster in the combination treatment. Notable genes in this cluster include Tnfa, IfngIl12b and Cxcl9; interestingly, for Tnfa and Il12b, CSF1Ri+CD40 appears to have a synergistic effect on expression. In contrast to Cluster #1, Cluster #5 contains genes substantially downregulated by CSF1Ri and CSF1Ri+CD40 treatments, but are largely unaffected by CD40, suggesting that CSF1Ri is the driver of this cluster in the combination treatment. Cluster #5 genes include Cd36 and Fabp4, suggesting alterations in lipid homeostasis in the TAMs after treatment. Cluster #2 includes genes that are modestly upregulated by CD40 and CSF1Ri individually, leading to a stronger upregulation when combined. Finally, Clusters #3 and #4 include, for the most part, genes that are differentially affected by CD40 versus CSF1Ri and for which the combination treatment yields an intermediate response. In summary, these data show that CSF1Ri and CD40 agonism elicit predominantly distinct changes in gene expression in the CD11b+ cells, indicating they target different biological processes in myeloid cells. The net result of the changes in myeloid gene expression from the combination of CSF1Ri+CD40 treatment reveal additive effects by the individual treatments, but also synergy in the expression of several pro-inflammatory genes (e.g., Tnfa, Ifng, Il6 and Il12b). We further examined our dataset with Gene Set Enrichment Analysis (GSEA). Although CSF1Ri and CD40 treatments did not closely match any immunological signatures in the immunological database of MSigDb, combined CSF1Ri+CD40 had a strikingly similar signature to myeloid cells exposed to a variety of inflammatory stimulants, most closely reflected by BMDMs treated with lipopolysaccharide (LPS). This motivated us to look specifically at categories of NF-κB target genes that are significantly affected by LPS treatment, including transcription factors, cytokines and chemokines. Indeed, most of these NF-κB target genes associated with inflammation were strongly upregulated by CSF1Ri+CD40 treatment. Finally, Ingenuity Pathway Analysis identified TNFR1 and TNFR2 signaling and Acute phase response signaling among the top genetic signatures produced by the CSF1Ri+CD40 treatment combination, matching what we observed with GSEA. Thus, gene expression analysis not only revealed several biomarkers of response that may be relevant for assessing therapeutic activity in ongoing clinical trials using these drugs, but illuminated lead biological factors that may cause tumor regression. Conclusions: myeloid-targeted immunotherapies anti-CD40+CSF1R inhibition synergistically induce a pro-inflammatory microenviroment
