bioRxivCancer Biology
The spatial landscape of Cancer Hallmarks reveals patterns of tumor ecology
M. Sibai, S. Cervilla, D. Grases, E. Musulen, R. Lazcano, C.-K. Mo, V. Davalos, A. Fortian, M. Romeo, A. Bernat, C. Tokheim, E. Grande, F. Real, J. Barretina, A. Lazar, L. Ding, M. Esteller, M. Bailey, E. Porta-Pardo
Abstract
Tumors are complex ecosystems with dozens of interacting cell types. The concept of Cancer Hallmarks distills this complexity into a set of underlying principles that govern tumor growth. Here, we exploit this abstraction to explore the physical distribution of Cancer Hallmarks across 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. We show that Hallmark activity is spatially organized-with 7 out of 13 Hallmarks consistently more active in cancer cells than within the non-cancerous tumor microenvironment (TME). The opposite is true for the remaining six Hallmarks. Additionally, we discovered that genomic distance between tumor subclones correlates with differences in Cancer Hallmark activity, even leading to clone-Hallmark specialization in some cases. Finally, we demonstrate interdependent relationships between Cancer Hallmarks at the junctions of TME and cancer compartments. In conclusion, including the spatial dimension, particularly through the lens of Cancer Hallmarks, can improve our understanding of tumor ecology. SignificanceWe explored Cancer Hallmarks in 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. This study unveiled spatial patterns in Hallmark activity, with some being more active in cancer cells and others in the non-cancerous tumor environment. Genomic distance impacted Hallmark activity, and we identified interdependencies at the TME-cancer junctions, improving our understanding of tumor ecology.
- organism: human
- assay: spatial transcriptomics
- research focus: cell-cell communication
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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