Advances in genome and tissue engineering have spurred significant progress and opportunity for innovation in cancer modeling. Human induced pluripotent stem cells (iPSCs) are an established and powerful tool to study cellular processes in the context of disease-specific genetic backgrounds; however, their application to cancer has been limited by the resistance of many transformed cells to undergo successful reprogramming. Here, we review the status of human iPSC modeling of solid tumors in the context of genetic engineering, including how base and prime editing can be incorporated into "bottom-up"cancer modeling, a term we coined for iPSC-based cancer models using genetic engineering to induce transformation. This approach circumvents the need to reprogram cancer cells while allowing for dissection of the genetic mechanisms underlying transformation, progression, and metastasis with a high degree of precision and control. We also discuss the strengths and limitations of respective engineering approaches and outline experimental considerations for establishing future models.
Bibliographical noteFunding Information:
B.R.W. acknowledges funding from NIH grants R21CA237789, R21AI163731, P01CA254849 Alex's Lemonade Stand Foundation, Children's Cancer Research Fund, and Rein in Sarcoma. Research in the Moriarity Lab is supported by NIH grants P50CA136393, P01CA254849, R01AI161017, and R01AI146009. D.A.L. acknowledges funding from the American Cancer Society Research Professor Award and the National Institutes of Health (R01NS115438). M.H. acknowledges funding from the Wright Trust Foundation, The Saban Research Institute, and National Institutes of Health (R00CA197484). Research in the Weiss lab is supported by NIH grants R01CA255369, R01NS106155, R01CA221969, P01CA217959, P30CA082103, P50CA097257, U01CA217864, U54CA243125, Alex's Lemonade Stand, The Brain Tumour Charity, Cancer Research UK grant A28592, St. Baldrick, and Samuel G. Waxman Foundations; and the Evelyn and Mattie Anderson Chair. Figures were created using BioRender (BioRender.com).
© 2022 Mary Ann Liebert, Inc., publishers.
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't