The tumor microenvironment (TME) is an area of keen interest for researchers, but there is still much to learn about its multicellular structure if we are to uncover pathways to new therapeutic targets. Understanding the TME is a lot like understanding any environment – ecosystems are complex, vastly interconnected networks we must examine in granular detail to fully understand how they work.
To this end, a team of researchers extensively mapped TME structures in breast tumors from 693 patients in a bid to learn more about their characteristic heterogeneity (1). The study used imaging mass cytometry and multitiered spatial analysis to identify how the specialized cells in the breast TME organize into various structures. From there, the researchers were able to see how the clusters presented across tumors and, ultimately, how they might impact patient outcomes.
Ten recurring structures were identified in the data, varying by their vascular content, stromal quiescence versus activation, and leukocyte composition. Enrichment patterns were distinctly associated with a range of somatic alterations and differed between genomic breast cancer subtypes. The researchers also found that tissue interfaces and cancer cell-intrinsic factors impact the formation of TME structures, which then exert pressure on cancer cells. Because of the cyclical relationship between structure and function, these multicellular structures may offer valuable insight into the functional state of the TME.
Digging a little deeper, the team also found that large structures, which co-occurred with regulatory and dysfunctional T cells, could predict poor outcomes in estrogen receptor-positive breast cancer – suggesting that TME organization could one day be useful for patient stratification. This type of research will play an important role in identifying new therapeutic targets, facilitated by a more developed understanding of tumoral structural immunity. Now equipped with a map of a previously uncharted world, the researchers hope to develop new ways of identifying treatment-responsive patients.
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References
- E Danenberg et al., Nat Genet, 54, 660 (2022). PMID: 35437329.
