Evidence map›Paper›PMID 20944664›Full record

ReviewNature reviews. Cancer2010

Engineering the perfect (bacterial) cancer therapy.

Neil S Forbes

Open access · greenAbstract readReview
In one paragraph

Review in Nature reviews. Cancer, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 397 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
397citing papers in PubMed, 1 pooled it
24.3field-weighted citation impact, top 1% 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

397 citing papers in PubMed, 1 synthesis or guideline pooled it, 868 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Review
  4. Article
  5. Article
  6. Deletion of themBio · 2026
    Article
  7. Article
  8. Imaging the hallmarks of cancer.Nature reviews. Cancer · 2026
    Review
  9. Review
  10. Programmable genetic control of tumor-colonizingbioRxiv : the preprint server for biology · 2026
    Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Review
  16. Review
  17. Polysaccharide-Based Encapsulation of Microbes for Enhanced Microbial Therapy.Polymer science & technology (Washington, D.C.) · 2026
    Review
  18. Article
  19. Article
  20. Review

337 more citing papers are in PubMed but not listed here.

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

1 author at 1 institution in 1 country.

Neil S ForbesUniversity of Massachusetts, Amherst, Department of Chemical Engineering, 159 Goessmann Laboratory, Amherst, Massachusetts 01003-9303, USA. forbes@ecs.umass.edu
University of Massachusetts Amherst · US

Funding

Engineering Salmonella to be a targeted intratumoral anti-cancer therapeuticR01CA120825 · NCI · UNIVERSITY OF MASSACHUSETTS AMHERST · PI FORBES, NEIL S. · 2008 to 2011
$1.2M
NCI NIH HHS CA120825NCI NIH HHS R01 CA120825
6 · The paper itself

Abstract

Bacterial therapies possess many unique mechanisms for treating cancer that are unachievable with standard methods. Bacteria can specifically target tumours, actively penetrate tissue, are easily detected and can controllably induce cytotoxicity. Over the past decade, Salmonella, Clostridium and other genera have been shown to control tumour growth and promote survival in animal models. In this Innovation article I propose that synthetic biology techniques can be used to solve many of the key challenges that are associated with bacterial therapies, such as toxicity, stability and efficiency, and can be used to tune their beneficial features, allowing the engineering of 'perfect' cancer therapies.

Indexed as

Genetic EngineeringAnimalsAntineoplastic AgentsClostridiumHumansNeoplasmsSalmonellaAntineoplastic Agents

Identifiers

PMID20944664
PMCPMC3756932
OpenAlexW1979507175

What OpenQuestion holds

Textmetadata
LicenceTDM
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.