Evidence map›Paper›PMID 20212083›Full record

ArticleDisease models & mechanisms

The evolution of cancer modeling: the shadow of stem cells.

Carolina Vicente-Dueñas, César Cobaleda, Jesús Pérez-Losada, Isidro Sánchez-García

Open access · bronzeAbstract read
In one paragraph

Article in Disease models & mechanisms. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.5field-weighted citation impact, top 17% of its field
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

4 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. The cellular architecture of multiple myeloma.Cell cycle (Georgetown, Tex.) · 2012
    Article
  4. MALT lymphoma meets stem cells.Cell cycle (Georgetown, Tex.) · 2012
    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

4 authors at 3 institutions in 1 country.

Carolina Vicente-DueñasExperimental Therapeutics and Translational Oncology Program, Instituto de Biología Molecular y Celular del Cáncer, CSIC/Universidad de Salamanca, Campus M. Unamuno s/n, 37007-Salamanca, Spain.
César Cobaleda
Jesús Pérez-Losada
Isidro Sánchez-García
Centro de Investigación del Cáncer · ESCentro de Biología Molecular Severo Ochoa · ESUniversidad de Salamanca · ES

Funding

Significance and Function of HGAL in LymphomaR01CA109335 · NCI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI LOSSOS, IZIDORE S · 2005 to 2013
$2.1M
NCI NIH HHS 2R01CA109335-04A1NCI NIH HHS R01 CA109335
6 · The paper itself

Abstract

Cancer is a complex and highly dynamic process. Genetically engineered mouse models (GEMs) that develop cancer are essential systems for dissecting the processes that lead to human cancer. These animal models provide a means to determine the causes of malignancy and to develop new treatments, thus representing a resource of immense potential for medical oncology. The sophistication of modeling cancer in mice has increased to the extent that now we can induce, study and manipulate the cancer disease process in a manner that is impossible to perform in human patients. However, all GEMs described so far have diverse shortcomings in mimicking the hierarchical structure of human cancer tissues. In recent years, a more detailed picture of the cellular and molecular mechanisms determining the formation of cancer has emerged. This Commentary addresses new experimental approaches toward a better understanding of carcinogenesis and discusses the impact of new animal models.

Indexed as

Disease Models, AnimalAnimalsEmbryonic Stem CellsGene Knock-In TechniquesHumansMiceMutationNeoplasmsNeoplastic Stem CellsOncogenes

Identifiers

PMID20212083
PMCPMC2869490
OpenAlexW2138093888

What OpenQuestion holds

Textmetadata
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.