Evidence map›Paper›PMID 23589549›Full record

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

Clinical analysis and interpretation of cancer genome data.

Eliezer M Van Allen, Nikhil Wagle, Mia A Levy

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 71 papers.

0numbers the graph read from it
0cells of the map it votes in
71citing 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

71 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Artificial Intelligence-Assisted Serial Analysis of Clinical Cancer Genomics Data Identifies Changing Treatment Recommendations and Therapeutic Targets.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Current and future applications of liquid biopsy in nonsmall cell lung cancer from early to advanced stages.European respiratory review : an official journal of the European Respiratory Society · 2020
    Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

11 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

3 authors.

Eliezer M Van AllenDana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Nikhil Wagle
Mia A Levy

Funding

Physician Scientist Training in Cancer ResearchT32CA009172 · NCI · DANA-FARBER CANCER INSTITUTE · PI Jennifer R Brown, James A. DeCaprio · 1985 to 2026
$17.1M
NCI NIH HHS T32 CA009172
6 · The paper itself

Abstract

The scale of tumor genomic profiling is rapidly outpacing human cognitive capacity to make clinical decisions without the aid of tools. New frameworks are needed to help researchers and clinicians process the information emerging from the explosive growth in both the number of tumor genetic variants routinely tested and the respective knowledge to interpret their clinical significance. We review the current state, limitations, and future trends in methods to support the clinical analysis and interpretation of cancer genomes. This includes the processes of genome-scale variant identification, including tools for sequence alignment, tumor-germline comparison, and molecular annotation of variants. The process of clinical interpretation of tumor variants includes classification of the effect of the variant, reporting the results to clinicians, and enabling the clinician to make a clinical decision based on the genomic information integrated with other clinical features. We describe existing knowledge bases, databases, algorithms, and tools for identification and visualization of tumor variants and their actionable subsets. With the decreasing cost of tumor gene mutation testing and the increasing number of actionable therapeutics, we expect the methods for analysis and interpretation of cancer genomes to continue to evolve to meet the needs of patient-centered clinical decision making. The science of computational cancer medicine is still in its infancy; however, there is a clear need to continue the development of knowledge bases, best practices, tools, and validation experiments for successful clinical implementation in oncology.

Indexed as

AlgorithmsComputational BiologyGenetic Predisposition to DiseaseGenome, HumanGenome-Wide Association StudyGenomicsHumansMutationNeoplasms

Identifiers

PMID23589549
PMCPMC4878102

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.