Evidence map›Paper›PMID 32923845›Full record

ReviewJCO precision oncology2019

Phase III Precision Medicine Clinical Trial Designs That Integrate Treatment and Biomarker Evaluation.

Mei-Yin C Polley, Edward L Korn, Boris Freidlin

Open access · bronzeAbstract readReview
In one paragraph

Review in JCO precision oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.7field-weighted citation impact, top 14% 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

9 citing papers in PubMed, 13 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Review
  6. Molecular Profiling in Neuro-Oncology: Where We Are, Where We're Heading, and How We Ensure Everyone Can Come Along.American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting · 2023
    Article
  7. Review
  8. Liquid biopsies and cancer omics.Cell death discovery · 2020
    Review
  9. Cancer predictive studies.Biology direct · 2020
    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

3 authors at 2 institutions in 1 country.

Mei-Yin C PolleyMayo Clinic, Rochester, MN.
Edward L KornNational Cancer Institute, Bethesda, MD.
Boris FreidlinNational Cancer Institute, Bethesda, MD.
National Cancer Institute · USMayo Clinic in Arizona · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in biotechnology and cancer genomics have afforded enormous opportunities for development of more effective anticancer therapies. A key thrust of this modern drug development paradigm is successful identification of predictive biomarkers that can distinguish patients who might be sensitive to new targeted therapies. To respond to this challenge, a number of phase III cancer trial designs integrating biomarker-based objectives have been proposed and implemented in oncology drug development. In this article, we provide an updated review of commonly used biomarker-based randomized clinical trial designs, with a particular focus on design efficiency. When the efficacy of a new therapy may be limited to a biomarker-defined subgroup, the choice of an appropriate randomized clinical trial design should be guided by the strength of the biomarker's credentials. If compelling evidence indicates that a targeted therapy is beneficial only in a particular biomarker-defined subgroup, an enrichment design should be used. If there is strong evidence that the treatment is likely to be more beneficial in the biomarker-positive patients but a meaningful benefit is also possible in the biomarker-negative patients, then a properly powered biomarker-stratified design (eg, a subgroup-specific or Marker Sequential Test strategy) would provide the most rigorous determination of the sensitive populations. If the evidence supporting the predictive value of the biomarker is weak and the treatment is expected to work in the overall population, then a fallback design could be used. Careful selection of an appropriate phase III design strategy that integrates evaluation of a new anticancer therapy and its companion diagnostic is critical to the success of precision medicine in oncology.

Identifiers

PMID32923845
PMCPMC7446320
OpenAlexW2982099082

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.