Evidence map›Paper›PMID 25708799›Full record

ReviewCurrent oncology reports2015

Precision medicine in breast cancer: genes, genomes, and the future of genomically driven treatments.

Daniel G Stover, Nikhil Wagle

Abstract readReview
In one paragraph

Review in Current oncology reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype.IEEE/ACM transactions on computational biology and bioinformatics
    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

2 authors.

Daniel G StoverDepartment of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.
Nikhil Wagle

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 L30 CA189226NCI NIH HHS T32 CA009172
6 · The paper itself

Abstract

Remarkable progress in sequencing technology over the past 20 years has made it possible to comprehensively profile tumors and identify clinically relevant genomic alterations. In breast cancer, the most common malignancy affecting women, we are now increasingly able to use this technology to help specify the use of therapies that target key molecular and genetic dependencies. Large sequencing studies have confirmed the role of well-known cancer-related genes and have also revealed numerous other genes that are recurrently mutated in breast cancer. This growing understanding of patient-to-patient variability at the genomic level in breast cancer is advancing our ability to direct the appropriate treatment to the appropriate patient at the appropriate time--a hallmark of "precision cancer medicine." This review focuses on the technological advances that have catalyzed these developments, the landscape of mutations in breast cancer, the clinical impact of genomic profiling, and the incorporation of genomic information into clinical care and clinical trials.

Indexed as

Biomarkers, TumorBreast NeoplasmsFemaleGene Expression ProfilingHumansMolecular Targeted TherapyMutationPrecision MedicinePredictive Value of TestsProto-Oncogene Proteins B-rafBiomarkers, TumorBRAF protein, humanProto-Oncogene Proteins B-raf

Identifiers

PMID25708799
PMCPMC5854397

What OpenQuestion holds

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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.