Cross-species analysis of genetically engineered mouse models of MAPK-driven colorectal cancer identifies hallmarks of the human disease.

Published

Journal Article

Effective treatment options for advanced colorectal cancer (CRC) are limited, survival rates are poor and this disease continues to be a leading cause of cancer-related deaths worldwide. Despite being a highly heterogeneous disease, a large subset of individuals with sporadic CRC typically harbor relatively few established 'driver' lesions. Here, we describe a collection of genetically engineered mouse models (GEMMs) of sporadic CRC that combine lesions frequently altered in human patients, including well-characterized tumor suppressors and activators of MAPK signaling. Primary tumors from these models were profiled, and individual GEMM tumors segregated into groups based on their genotypes. Unique allelic and genotypic expression signatures were generated from these GEMMs and applied to clinically annotated human CRC patient samples. We provide evidence that a Kras signature derived from these GEMMs is capable of distinguishing human tumors harboring KRAS mutation, and tracks with poor prognosis in two independent human patient cohorts. Furthermore, the analysis of a panel of human CRC cell lines suggests that high expression of the GEMM Kras signature correlates with sensitivity to targeted pathway inhibitors. Together, these findings implicate GEMMs as powerful preclinical tools with the capacity to recapitulate relevant human disease biology, and support the use of genetic signatures generated in these models to facilitate future drug discovery and validation efforts.

Full Text

Duke Authors

Cited Authors

  • Belmont, PJ; Budinska, E; Jiang, P; Sinnamon, MJ; Coffee, E; Roper, J; Xie, T; Rejto, PA; Derkits, S; Sansom, OJ; Delorenzi, M; Tejpar, S; Hung, KE; Martin, ES

Published Date

  • June 2014

Published In

Volume / Issue

  • 7 / 6

Start / End Page

  • 613 - 623

PubMed ID

  • 24742783

Pubmed Central ID

  • 24742783

Electronic International Standard Serial Number (EISSN)

  • 1754-8411

Digital Object Identifier (DOI)

  • 10.1242/dmm.013904

Language

  • eng

Conference Location

  • England