Evidence map›Paper›PMID 23589546›Full record

ReviewJournal of clinical oncology : official journal of the American Society of Clinical Oncology2013

Existing and emerging technologies for tumor genomic profiling.

Laura E MacConaill

Abstract readReview
In one paragraph

Review in Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

0numbers the graph read from it
0cells of the map it votes in
59citing 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

59 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Article
  8. Cost-Effectiveness of Tumor Genomic Profiling to Guide First-Line Targeted Therapy Selection in Patients With Metastatic Lung Adenocarcinoma.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2022
    Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Cancers · 2021
    Article
  14. Incidence of theCancers · 2021
    Article
  15. Article
  16. Article
  17. Article
  18. Characteristics of Patients WithJCO oncology practice · 2020
    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

1 author.

Laura E MacConaillCenter for Cancer Genome Discovery, Dana-Farber Cancer Institute, 44 Binney St, Dana 1539, Boston, MA 02115, USA. laura_macconaill@dfci.harvard.edu

Funding

High-Throughput Tumor Genomic Profiling by Massively Parallel SequencingR33CA155554 · NCI · DANA-FARBER CANCER INST · PI GARRAWAY, LEVI A., MACCONAILL, LAURA E · 2011 to 2013
$717k
NCI NIH HHS R33 CA155554
6 · The paper itself

Abstract

Ongoing global genome characterization efforts are revolutionizing our knowledge of cancer genomics and tumor biology. In parallel, information gleaned from these studies on driver cancer gene alterations--mutations, copy number alterations, translocations, and/or chromosomal rearrangements--an be leveraged, in principle, to develop a cohesive framework for individualized cancer treatment. These possibilities have been enabled, to a large degree, by revolutionary advances in genomic technologies that facilitate systematic profiling for hallmark cancer genetic alterations at increasingly fine resolutions. Ongoing innovations in existing genomics technologies, as well as the many emerging technologies, will likely continue to advance translational cancer genomics and precision cancer medicine.

Indexed as

Chromosome AberrationsMutationExomeGene Expression ProfilingGenomicsHigh-Throughput Nucleotide SequencingHumansNeoplasmsTranslational Research, Biomedical

Identifiers

PMID23589546
PMCPMC3808234

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.