Evidence map›Paper›PMID 28145099›Full record

Trial reportMolecular oncology2017

Gene expression panel predicts metastatic-lethal prostate cancer outcomes in men diagnosed with clinically localized prostate cancer.

Rohina Rubicz, Shanshan Zhao, Jonathan L Wright, Ilsa Coleman, Catherine Grasso, Milan S Geybels, Amy Leonardson, Suzanne Kolb, Craig April, Marina Bibikova and 8 more

Open access · hybridAbstract readClinical Trial
In one paragraph

Trial report in Molecular oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

17 citing papers in PubMed, 26 citations in OpenAlex.

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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

18 authors at 8 institutions in 2 countries.

Rohina RubiczDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Shanshan ZhaoBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, USA.
Jonathan L WrightDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Ilsa ColemanDivision of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Catherine GrassoDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Milan S GeybelsDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Amy LeonardsonDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Suzanne KolbDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Craig AprilIllumina, Inc., San Diego, CA, USA.
Marina BibikovaIllumina, Inc., San Diego, CA, USA.
Dean TroyerDepartments of Pathology and Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA, USA.
Raymond LanceDepartment of Urology, Eastern Virginia Medical School, Norfolk, VA, USA.
Daniel W LinDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Elaine A OstranderCancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA.
Peter S NelsonDivision of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Jian-Bing FanIllumina, Inc., San Diego, CA, USA.
Ziding FengDepartment of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Janet L StanfordDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Fred Hutch Cancer Center · USIllumina (United States) · USEastern Virginia Medical School · USMaastricht University · NLNational Human Genome Research Institute · USNational Institute of Environmental Health Sciences · USThe University of Texas MD Anderson Cancer Center · USUniversity of Washington · US

Funding

TRANSCRIPTOME AND PROTEOME STRATIFICATION OF PROSTATE ADENOCARCINOMA PHENOTYPESP50CA097186 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI PETER S NELSON · 2002 to 2026
$58.1M
Finding Genes for Human Prostate CancerZIAHG200325 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI OSTRANDER, ELAINE · 2009 to 2021
$17.7M
NSAIDS and Other Medications in Prostate Cancer EtiologyR01CA092579 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI STANFORD, JANET L · 2001 to 2005
$3.2M
Application of Statistical Methods in Epidemiology StudiesZIAES103308 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI ZHAO, SHANSHAN · 2015 to 2025
$2.2M
Aggressive Prostate Cancer: Linking Epigenomics and Genetics for PreventionK05CA175147 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI STANFORD, JANET L · 2014 to 2018
$445k
PROSTATE CANCER IN RELATION TO VASECTOMYR01CA056678 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI STANFORD, JANET L · 1993 to 1997
–
NCI NIH HHS K05 CA175147NCI NIH HHS P50 CA097186NCI NIH HHS R01 CA056678NCI NIH HHS R01 CA092579
6 · The paper itself

Abstract

Prognostic biomarkers are needed to distinguish patients with clinically localized prostate cancer (PCa) who are at high risk of metastatic progression. The tumor transcriptome may reveal its aggressiveness potential and have utility for predicting adverse patient outcomes. Genomewide gene expression levels were measured in primary tumor samples of 383 patients in a population-based discovery cohort, and from an independent clinical validation dataset of 78 patients. Patients were followed for ≥ 5 years after radical prostatectomy to ascertain outcomes. Area under the receiver-operating characteristic curve (AUC), partial AUC (pAUC, 95% specificity), and P-value criteria were used to detect and validate the differentially expressed transcripts. Twenty-three differentially expressed transcripts in patients with metastatic-lethal compared with nonrecurrent PCa were validated (P < 0.05; false discovery rate < 0.20) in the independent dataset. The addition of each validated transcript to a model with Gleason score showed that 17 transcripts significantly improved the AUC (range: 0.83-0.88; all P-values < 0.05). These differentially expressed mRNAs represent genes with diverse cellular functions related to tumor aggressiveness. This study validated 23 gene transcripts for predicting metastatic-lethal PCa in patients surgically treated for clinically localized disease. Several of these mRNA biomarkers have clinical potential for identifying the subset of PCa patients with more aggressive tumors who would benefit from closer monitoring and adjuvant therapy.

Indexed as

Databases, Nucleic AcidGene Expression Regulation, NeoplasticTranscriptomeAdultBiomarkers, TumorFollow-Up StudiesHumansMaleMiddle AgedNeoplasm MetastasisProstatectomyProstatic NeoplasmsRNA, MessengerRNA, NeoplasmBiomarkers, TumorRNA, MessengerRNA, Neoplasmbiomarkersgene expressionmetastasisprognosisprostate cancervalidation

Identifiers

PMID28145099
PMCPMC5510189
OpenAlexW2529535171

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.