In situ functional cell phenotyping reveals microdomain networks in colorectal cancer recurrence

Functionally and Metabolically Divergent Melanoma-Associated Macrophages Originate from Common Bone-Marrow Precursors

Tumor-associated macrophages (TAMs) can be widely heterogeneous, based on their ontogeny and function, and driven by the tissue-specific niche. TAMs are highly abundant in the melanoma tumor microenvironment (TME), usually correlating with worse prognoses. However, the understanding of their diversity may be harnessed for therapeutic purposes. Here, we used the clinically relevant YUMM1.7 model to study melanoma TAM origin and dynamics during tumor progression. In i.d. YUMM1.7 tumors, we identified distinct TAM subsets based on F4/80 expression, with the F4/80^high fraction increasing over time and displaying a tissue-resident-like phenotype. While skin-resident macrophages showed mixed ontogeny, F4/80⁺ TAM subsets in the melanoma TME originated almost exclusively from bone-marrow precursors. A multiparametric analysis of the macrophage phenotype showed a temporal divergence of the F4/80⁺ TAM subpopulations, which also differed from the skin-resident subsets and their monocytic precursors. Overall, the F4/80⁺ TAMs displayed co-expressions of M1- and M2-like canonical markers, while RNA sequencing showed differential immunosuppressive and metabolic profiles. Gene-set enrichment analysis (GSEA) revealed F4/80^high TAMs to rely on oxidative phosphorylation, with increased proliferation and protein secretion, while F4/80^low cells had high pro-inflammatory and intracellular signaling pathways, with lipid and polyamine metabolism. Overall, we provide an in-depth characterization of and compelling evidence for the BM-dependency of melanoma TAMs. Interestingly, the transcriptomic analysis of these BM-derived TAMs matched macrophage subsets with mixed ontogeny, which have been observed in other tumor models. Our findings may serve as a guide for identifying potential ways of targeting specific immunosuppressive TAMs in melanoma.

In Search of a Unifying Concept in Human Diseases

Throughout the history of biological/medicine sciences, there has been opposing strategies to find solutions to complex human disease problems. Both empirical and deductive approaches have led to major insights and concepts that have led to practical preventive and therapeutic benefits for the human population. The classic definitions of "science" (to know) has been paired with the classic definition of technology (to do). One knew more as the technology developed, and that development was often based on science. In other words, one could do more if science could improve the technology. In turn, this made possible to know more science with improved technology. However, with the development of new technologies of today in biology and medicine, major advances have been made, such as the information from the Human Genome Project, genetic engineering techniques and the use of bioinformatic uses of sophisticated computer analyses. This has led to the renewed idea that Precision Medicine, while raising some serious ethical concerns, also raises the expectation of improved potential of risk predictions for prevention and treatment of various genetically and environmentally influenced human diseases. This new field Artificial Intelligence, as a major handmaiden to Precision Medicine, is significantly altering the fundamental means of biological discovery. However, can today's fundamental premise of "Artificial Intelligence", based on identifying DNA, as the primary nexus of human health and disease, provide the practical solutions to complex human diseases that involve the interaction of those genes with the broad spectrum of "environmental factors"? Will it be "precise" enough to provide practical solutions for prevention and treatments of diseases? In this "Commentary", with the example of human carcinogenesis, it will be challenged that, without the integration of mechanistic and hypothesis-driven approaches with the "unbiased" empirical analyses of large numbers of data, the Artificial Intelligence approach with fall short.