Evidence map›Paper›PMID 26420882›Full record

ArticleJournal of the National Cancer Institute2015

Predictive Value Tools as an Aid in Chemopreventive Agent Development.

Barbara K Dunn, Vernon E Steele, Richard M Fagerstrom, Carol F Topp, David Ransohoff, Christopher Cunningham, Ron Lubet, Leslie G Ford, Barnett S Kramer

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the National Cancer Institute, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 24% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Barbara K DunnDivision of Cancer Prevention, National Cancer Institute, Bethesda, Maryland (BKD, VES, RMF, RL, LGF, BSK); CCS Associates, Inc., McLean, VA (CFT); Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC (DR); Information Management Services, Rockville, MD (CC). dunnb@mail.nih.gov.
Vernon E SteeleDivision of Cancer Prevention, National Cancer Institute, Bethesda, Maryland (BKD, VES, RMF, RL, LGF, BSK); CCS Associates, Inc., McLean, VA (CFT); Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC (DR); Information Management Services, Rockville, MD (CC).
Richard M FagerstromDivision of Cancer Prevention, National Cancer Institute, Bethesda, Maryland (BKD, VES, RMF, RL, LGF, BSK); CCS Associates, Inc., McLean, VA (CFT); Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC (DR); Information Management Services, Rockville, MD (CC).
Carol F ToppDivision of Cancer Prevention, National Cancer Institute, Bethesda, Maryland (BKD, VES, RMF, RL, LGF, BSK); CCS Associates, Inc., McLean, VA (CFT); Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC (DR); Information Management Services, Rockville, MD (CC).
David RansohoffDivision of Cancer Prevention, National Cancer Institute, Bethesda, Maryland (BKD, VES, RMF, RL, LGF, BSK); CCS Associates, Inc., McLean, VA (CFT); Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC (DR); Information Management Services, Rockville, MD (CC).
Christopher CunninghamDivision of Cancer Prevention, National Cancer Institute, Bethesda, Maryland (BKD, VES, RMF, RL, LGF, BSK); CCS Associates, Inc., McLean, VA (CFT); Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC (DR); Information Management Services, Rockville, MD (CC).
Ron LubetDivision of Cancer Prevention, National Cancer Institute, Bethesda, Maryland (BKD, VES, RMF, RL, LGF, BSK); CCS Associates, Inc., McLean, VA (CFT); Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC (DR); Information Management Services, Rockville, MD (CC).
Leslie G FordDivision of Cancer Prevention, National Cancer Institute, Bethesda, Maryland (BKD, VES, RMF, RL, LGF, BSK); CCS Associates, Inc., McLean, VA (CFT); Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC (DR); Information Management Services, Rockville, MD (CC).
Barnett S KramerDivision of Cancer Prevention, National Cancer Institute, Bethesda, Maryland (BKD, VES, RMF, RL, LGF, BSK); CCS Associates, Inc., McLean, VA (CFT); Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC (DR); Information Management Services, Rockville, MD (CC).
National Cancer Institute · USCCS Associates (United States) · USUniversity of North Carolina at Chapel Hill · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOver 25 years, the National Cancer Institute's Division of Cancer Prevention has entered some 800 agents into a chemopreventive agent testing program. Two critical steps involve: 1) in vitro/in vivo morphologic assays and 2) animal tumor assays (incidence/multiplicity reduction). We sought to determine how accurately the earlier-stage (morphologic) assays predict efficacy in the later-stage (animal tumor) assays.

methodsFocusing on 210 agents tested in both morphologic and animal tumor assays, we carried out statistical modeling of how well the six most commonly used morphologic assays predicted drug efficacy in animal tumor assays. Using multimodel inference, three statistical models were generated to evaluate the ability of these six morphologic assays to predict tumor outcomes in three different sets of animal tumor assays: 1) all tumor types, 2) mammary cancer only, and 3) colon cancer only. Using this statistical modeling approach, each morphologic assay was assigned a value reflecting how strongly it predicted outcomes in each of the three different sets of animal tumor assays.

resultsWe demonstrated differences in the predictive value of specific morphologic assays for positive animal tumor assay results. Some of the morphologic assays were strongly predictive of meaningful positive efficacy outcomes in animal tumor assays representing specific cancer types, particularly the aberrant crypt focus (ACF) assay for colon cancer. Moreover, less strongly predictive assays can be combined and sequenced, resulting in enhanced composite predictive ability.

conclusionsPredictive models such as these could be used to guide selection of preventive agents as well as morphologic and animal tumor assays, thereby improving the efficiency of our approach to chemopreventive agent development.

Indexed as

Models, StatisticalAnimalsAnticarcinogenic AgentsBreast NeoplasmsCell Line, TumorChemopreventionColonic NeoplasmsFemaleHumansMiceNeoplasmsPredictive Value of TestsPrimary PreventionRatsTreatment OutcomeAnticarcinogenic Agents

Identifiers

PMID26420882
PMCPMC4862414
OpenAlexW2217543148

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.