Evidence map›Paper›PMID 32652468›Full record

ArticleTranslational oncology2020

Candidate biomarker assessment for pharmacological response.

William C Reinhold, Fathi Elloumi, Sudhir Varma, Jacques Robert, Gordon B Mills, Yves Pommier

Open access · goldAbstract read
In one paragraph

Article in Translational oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 31% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 5 institutions in 2 countries.

William C ReinholdDevelopmental Therapeutic Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America. Electronic address: wcr@mail.nih.gov.
Fathi ElloumiDevelopmental Therapeutic Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America; General Dynamics Information Technology, Falls Church, VA 22042, United States of America.
Sudhir VarmaDevelopmental Therapeutic Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America; HiThru Analytics LLC, Laurel, MD, USA.
Jacques RobertInserm unité 1218, Université de Bordeaux, France.
Gordon B MillsKnight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, United States of America.
Yves PommierDevelopmental Therapeutic Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America.
National Cancer Institute · USCenter for Cancer Research · USNational Institutes of Health · USOregon Health & Science University · USUniversité de Bordeaux · FR

Funding

RNA sequencing (RNA-Seq) of the NCI-60ZICBC011497 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI REINHOLD, WILLIAM · 2013 to 2025
$1.7M
Comparison of molecular factors to drug activities.ZICBC011509 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI REINHOLD, WILLIAM · 2013 to 2025
$1.4M
6 · The paper itself

Abstract

Using the information from our CellMiner (https://discover.nci.nih.gov/cellminer/) and CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/) web-based applications, we identified 3978 molecular events with significant links to pharmacological response for genes that are either targets, biomarkers, or have established causal linkage to drugs. Molecular events included DNA copy number, methylation and mutation; and transcript; and whole or phospho-protein expression for the NCI-60 human cancer cell lines. While all forms of molecular data were informative in some (gene-drug) pairings, the type of significantly linked molecular events was found to vary widely by drug. Some forms of molecular data were found to have more frequent significant correlation than others. Leading were phosphoproteins as measured by antibody (31%), followed by transcript as measured by microarray (16%), and total protein levels as measured by mass spectrometry or antibody (14%). All other measurements ranged between 5 and 11%. Data reliability was underscored by concordant results when using differing drugs with the same targets, as well as different measurements of the same molecular parameter. The significance of correlations of the various molecular parameters to the pharmacological responses provides functional indication of those parameters that are biologically relevant for each gene-drug pairing, as well as comparisons between measurement types.

Identifiers

PMID32652468
PMCPMC7348063
OpenAlexW3041302767

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.