Evidence map›Paper›PMID 24742783›Full record

ArticleDisease models & mechanisms2014

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

Peter J Belmont, Eva Budinska, Ping Jiang, Mark J Sinnamon, Erin Coffee, Jatin Roper, Tao Xie, Paul A Rejto, Sahra Derkits, Owen J Sansom and 4 more

Abstract read
In one paragraph

Article in Disease models & mechanisms, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Colorectal cancer models for novel drug discovery.Expert opinion on drug discovery · 2015
    Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Peter J BelmontOncology Research Unit, Pfizer Global Research and Development, San Diego, CA 92121, USA.
Eva BudinskaInstitute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic. Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
Ping JiangOncology Research Unit, Pfizer Global Research and Development, San Diego, CA 92121, USA.
Mark J SinnamonDivision of Gastroenterology, Tufts Medical Center, Boston, MA 02111, USA.
Erin CoffeeDivision of Gastroenterology, Tufts Medical Center, Boston, MA 02111, USA.
Jatin RoperDivision of Gastroenterology, Tufts Medical Center, Boston, MA 02111, USA.
Tao XieOncology Research Unit, Pfizer Global Research and Development, San Diego, CA 92121, USA.
Paul A RejtoOncology Research Unit, Pfizer Global Research and Development, San Diego, CA 92121, USA.
Sahra DerkitsThe Beatson Institute for Cancer Research, Garscube Estate, Glasgow, G61 1BD, UK.
Owen J SansomThe Beatson Institute for Cancer Research, Garscube Estate, Glasgow, G61 1BD, UK.
Mauro DelorenziBioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
Sabine TejparUniversity Hospital Gasthuisberg, Katholieke Universiteit Leuven, 3000 Leuven, Belgium.
Kenneth E HungPfizer Biotherapeutics Clinical Research, Cambridge, 02140 MA, USA.
Eric S MartinOncology Research Unit, Pfizer Global Research and Development, San Diego, CA 92121, USA. esmartin.phd@gmail.com.

Funding

Cancer Research UK 12481
6 · The paper itself

Abstract

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.

Indexed as

AllelesAnimalsColorectal NeoplasmsDisease Models, AnimalGenes, rasHumansMiceMitogen-Activated Protein KinasesProto-Oncogene Proteins B-rafSpecies SpecificityBRAF protein, humanMitogen-Activated Protein KinasesProto-Oncogene Proteins B-rafBRAFColorectal cancerGEMMGenomic signaturesKRASMAPK

Identifiers

PMID24742783
PMCPMC4036469

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.