Evidence map›Paper›PMID 23339383›Full record

ReviewBreast cancer research : BCR2013

Patient-derived breast tumor xenografts facilitating personalized cancer therapy.

Melissa D Landis, Brian D Lehmann, Jennifer A Pietenpol, Jenny C Chang

Open access · goldAbstract readReview
In one paragraph

Review in Breast cancer research : BCR, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 1 pooled it
8.0field-weighted citation impact, top 2% 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

51 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Patient-Derived Xenograft Models in Breast Cancer Research.Advances in experimental medicine and biology · 2021
    Review
  11. Article
  12. Breast Cancer Metastasis.Advances in experimental medicine and biology · 2021
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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 2 institutions in 1 country.

Melissa D Landis
Brian D Lehmann
Jennifer A Pietenpol
Jenny C Chang
Vanderbilt University · USCornell University · US

Funding

The Core of the Computational BiologyU54CA149196 · NCI · METHODIST HOSPITAL RESEARCH INSTITUTE · PI WONG, STEPHEN TC · 2010 to 2015
$10.6M
Breast cancer response markers to a dual HER1/2 blockerR01CA112305 · NCI · BAYLOR COLLEGE OF MEDICINE · PI CHANG, JENNY C-N · 2005 to 2009
$1.3M
NCI NIH HHS R01 CA112305NCI NIH HHS U54 CA149196
6 · The paper itself

Abstract

Despite improved detection and reduction of breast cancer-related deaths over the recent decade, breast cancer remains the second leading cause of cancer death for women in the US, with 39,510 women expected to succumb to metastatic disease in 2012 alone (American Cancer Society, Cancer Facts &Figures 2012. Atlanta: American Cancer Society; 2012). Continued efforts in classification of breast cancers based on gene expression profiling and genomic sequencing have revealed an underlying complexity and molecular heterogeneity within the disease that continues to challenge therapeutic interventions. To successfully identify and translate new treatment regimens to the clinic, it is imperative that our preclinical models recapitulate this complexity and heterogeneity. In this review article, we discuss the recent advances in development and classification of patient-derived human breast tumor xenograft models that have the potential to facilitate the next phase of drug discovery for personalized cancer therapy based on the unique driver signaling pathways in breast tumor subtypes.

Indexed as

Precision MedicineXenograft Model Antitumor AssaysBreast NeoplasmsFemaleHumansPathology, MolecularPatientsUnited States

Identifiers

PMID23339383
PMCPMC3672825
OpenAlexW2168839110

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.