Back

CDC42 Inhibitors Alter Patterns of Vessel Arborization in Skin and Tumors in vivo

Vuong, L. M.; Hachey, S. J.; Shiu, J.; Xie, D. F.; Salvador, N.; Brindani, N.; Bertozzi, S. M.; Summa, M.; Bertorelli, R.; Armirotti, A.; Pham, R.; Ku, V. S.; Nguyen, T.; Choi, B.; Hughes, C. C.; De Vivo, M.; Ganesan, A. K.

2024-05-16 cancer biology
10.1101/2024.04.09.588609 bioRxiv
Show abstract

Tumors that arise in the epidermis must develop a vascular supply to grow beyond a millimeter in depth. This process requires CDC42 GTPases such as CDC42, RhoJ and RhoQ. Despite this dependence on angiogenesis for growth, melanoma tumors are minimally responsive to current anti-angiogenesis agents, highlighting the need for more effective drugs in this class. Here we integrate antibody infusion, optical tissue clearing, multiphoton imaging, and three-dimensional semi-automated tracing to develop a quantitative approach to measure changes in vascular architecture in skin and skin tumors. This new approach uncovered differences in vessel arborization in the skin of RhoJ KO mice as compared to wild-type mice. Furthermore, novel small molecules that inhibit CDC42 GTPases inhibited both tumor growth and vessel branching within tumors to a similar degree as Braf inhibitors, which are commonly used to treat melanoma. In contrast to Braf inhibitors, however, which only affected tumor vasculature, CDC42 inhibitors affected vascularization in both tumor and normal skin without apparent toxicity to endothelial or stromal cells. These novel CDC42 inhibitors similarly blocked vessel branching in human cell-based micro-physiological models of normal and tumor vessels. RNA sequencing revealed reduced expression of multiple angiogenesis-related genes in drug-treated skin. Taken together, these studies identify a new class of pharmacologic agents that inhibit vessel branching in both normal skin and tumors with potential utility for treating skin cancer and skin diseases characterized by pathologic angiogenesis.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.