Evidence map›Paper›PMID 21228881›Full record

ArticleGene therapy2011

Evaluation of systemic targeting of RET oncogene-based MTC with tumor-selective peptide-tagged Ad vectors in clinical mouse models.

A Schmidt, C Eipel, K Fürst, N Sommer, J Pahnke, B M Pützer

Abstract readEvaluation Study
PubMed Publisher
In one paragraph

Article in Gene therapy, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
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

6 authors.

A SchmidtDepartment of Vectorology and Experimental Gene Therapy, Biomedical Research Center (BMFZ), University of Rostock, Rostock, Germany.
C Eipel
K Fürst
N Sommer
J Pahnke
B M Pützer

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significant advantage of targeted antitumoral treatment consists in the possibility to restrict maximum therapeutic efficacy to the malignant cell population by reducing toxicity in healthy tissues. Using different clinical models for aggressive medullary thyroid carcinoma (MTC), we have recently identified peptide ligands that bind highly selective to tumor cells. By linking the most convincing SRESPHP peptide to an adenoviral (Ad) vector expressing the MTC-related oncogene inhibitor RETΔTK, gene transfer was specifically directed to neoplastic tissue after systemic virus administration. We show that peptide-mediated delivery of RETΔTK significantly enhanced apoptosis, resulting in a strong inhibition of orthotopic and xenograft tumor growth. Conversely, tumors treated with controls expanded their initial size without notable cell death. According to the therapeutic effect, strong virus accumulation was found exclusively in thyroid carcinomas. Strikingly, application of native tropism depleted viral vector linked to tumor-selective peptide was accompanied by a substantial reduction of Ad binding to the liver. Of note, single systemic injection of a low dose (10e8 pfu/mouse) of MTC-specific Ad.RETΔTK induced regression of multiple tumors at different sites in all treated animals. In sum, our results open up the possibility for an efficient cancer cell-specific therapy of primary MTC, their migrating populations and potentially metastases.

Indexed as

Genetic TherapyGenetic VectorsAdenoviridaeAnimalsCarcinoma, MedullaryGene TargetingGene Transfer TechniquesHumansMiceMice, NudeNeoplasm TransplantationPeptidesProto-Oncogene Proteins c-retThyroid NeoplasmsPeptidesProto-Oncogene Proteins c-ret

Identifiers

PMID21228881

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.