This study aims to characterize tumor-infiltrating macrophages (TAMs), myeloid-derived suppressor cells (MDSC), and the related molecular milieu regulating anti-tumor immunity in limited-stage neuroendocrine (NE)-high and NE-low small cell lung cancer. Primary tumors and matched lymph node (LN) metastases of 32 resected, early-stage SCLC patients were analyzed by immunohistochemistry (IHC) with antibodies against pan-macrophage marker CD68, M2-macrophage marker CD163, and MDSC marker CD33. Area-adjusted cell counting on TMAs showed that TAMs are the most abundant cell type in the TME, and their number in tumor nests exceeds the number of CD3 + T-cells (64% vs. 38% in NE-low and 71% vs. 18% in NE-high). Furthermore, the ratio of CD163-expressing M2-polarized TAMs in tumor nests was significantly higher in NE-low vs. NE-high tumors (70% vs. 31%). TAM density shows a strong positive correlation with CD45 and CD3 in tumor nests, but not in the stroma. fGSEA analysis on a targeted RNAseq oncological panel of 2560 genes showed that NE-high tumors exhibited increased enrichment in pathways related to cell proliferation, whereas in NE-low tumors, immune response pathways were significantly upregulated. Interestingly, we identified a subset of NE-high tumors representing an immune-oasis phenotype, but with a different gene expression profile compared to NE-low tumors. In contrast, we found that a limited subgroup of NE-low tumors is immune-deserted and express distinct cellular pathways from NE-high tumors. Furthermore, we identified potential molecular targets based on our expression data in NE-low and immune-oasis tumor subsets, including CD70, ANXA1, ITGB6, TP63, IFI27, YBX3 and CXCR2.

Subcellular dynamics of non-muscle myosin 2 (NM2) is crucial for a broad-array of cellular functions. To unveil mechanisms of NM2 pharmacological control, we determined how the dynamics of NM2 diffusion is affected by NM2′s allosteric inhibitors, i.e. blebbistatin derivatives, as compared to Y-27632 inhibiting ROCK, NM2′s upstream regulator. We found that NM2 diffusion is markedly faster in central fibers than in peripheral stress fibers. Y-27632 accelerated NM2 diffusion in both peripheral and central fibers, whereas in peripheral fibers blebbistatin derivatives slightly accelerated NM2 diffusion at low, but markedly slowed it at high inhibitor concentrations. In contrast, rapid NM2 diffusion in central fibers was unaffected by direct NM2 inhibition. Using our optopharmacological tool, Molecular Tattoo, sub-effective concentrations of a photo-crosslinkable blebbistatin derivative were increased to effective levels in a small, irradiated area of peripheral fibers. These findings suggest that direct allosteric inhibition affects the diffusion profile of NM2 in a markedly different manner compared to the disruption of the upstream control of NM2. The pharmacological action of myosin inhibitors is channeled through autonomous molecular processes and might be affected by the load acting on the NM2 proteins.

Background: The function and polarization of macrophages has a significant impact on the outcome of many diseases. Targeting tumor-associated macrophages (TAMs) is among the greatest challenges to solve because of the low in vitro reproducibility of the heterogeneous tumor microenvironment (TME). To create a more comprehensive model and to understand the inner workings of the macrophage and its dependence on extracellular signals driving polarization, we propose an in silico approach. Methods: A Boolean control network was built based on systematic manual curation of the scientific literature to model the early response events of macrophages by connecting extracellular signals (input) with gene transcription (output). The network consists of 106 nodes, classified as 9 input, 75 inner and 22 output nodes, that are connected by 217 edges. The direction and polarity of edges were manually verified and only included in the model if the literature plainly supported these parameters. Single or combinatory inhibitions were simulated mimicking therapeutic interventions, and output patterns were analyzed to interpret changes in polarization and cell function. Results: We show that inhibiting a single target is inadequate to modify an established polarization, and that in combination therapy, inhibiting numerous targets with individually small effects is frequently required. Our findings show the importance of JAK1, JAK3 and STAT6, and to a lesser extent STK4, Sp1 and Tyk2, in establishing an M1-like pro-inflammatory polarization, and NFAT5 in creating an anti-inflammatory M2-like phenotype. Conclusions: Here, we demonstrate a protein–protein interaction (PPI) network modeling the intracellular signalization driving macrophage polarization, offering the possibility of therapeutic repolarization and demonstrating evidence for multi-target methods.