The Immunological Landscape of M1 and M2 Macrophages and Their Spatial Distribution in Patients with Malignant Pleural Mesothelioma

BACKGROUND

Several tumor-associated macrophages (TAMs) have shown promise as prognosticators in cancer. Our aim was to validate the importance of TAMs in malignant pleural mesothelioma (MPM) using a two-stage design.

METHODS

We explored The Cancer Genome Atlas (TCGA-MESO) to select immune-relevant macrophage genes in MPM, including M1/M2 markers, as a discovery cohort. This computational cohort was used to create a multiplex immunofluorescence panel. Moreover, a cohort of 68 samples of MPM in paraffin blocks was used to validate the macrophage phenotypes and the co-localization and spatial distribution of these immune cells within the TME and the stromal or tumor compartments.

RESULTS

The discovery cohort revealed six immune-relevant macrophage genes (CD68, CD86, CD163, CD206, ARG1, CD274), and complementary genes were differentially expressed by M1 and M2 phenotypes with distinct roles in the tumor microenvironment and were associated with the prognosis. In addition, immune-suppressed MPMs with increased enrichment of CD68, CD86, and CD163 genes and high densities of M2 macrophages expressing CD163 and CD206 proteins were associated with worse overall survival (OS). Interestingly, below-median distances from malignant cells to specific M2a and M2c macrophages were associated with worse OS, suggesting an M2 macrophage-driven suppressive component in these tumors.

CONCLUSIONS

The interactions between TAMs in situ and, particularly, CD206+ macrophages are highly relevant to patient outcomes. High-resolution technology is important for identifying the roles of macrophage populations in tissue specimens and identifying potential therapeutic candidates in MPM.

[An "a la carte" treatment for patients with pancreatic adenocarcinoma]

Pancreatic adenocarcinoma is a heterogeneous disease. Undeniably, the appearance and accumulation of genetic mutations promote the development of pancreatic adenocarcinoma. However, counterintuitively, genetic analyzes, no matter how precise and in-depth they may be, do not allow stratification of patients to predict their clinical evolution or to select the most effective treatment in each case. This is due to the fact that the clinical evolution and sensitivity to treatments are associated with the tumoral phenotype, which, in turn, is determined by the global expression of genes that is regulated at the transcriptomic level. Therefore, the stratification of these patients must be done by analysis at the transcriptomic level and not by genetic analysis. The data obtained from large cohorts of patients indicate that studying the transcription of a selected set of genes could predict the clinical outcome and can help to decide about the most appropriate treatment. We are moving very rapidly towards a personalized medicine for this disease, which in itself has a poor prognosis, even worse if the therapeutic decision is not the most adapted to each patient. We are convinced that in the near future the treatment of cancers will be preceded by an extensive transcriptomic characterization in order to select the most suitable "a la carte" treatments.