Evidence map›Paper›PMID 27633254›Full record

ArticleBMC cancer2016

Development of prognostic signatures for intermediate-risk papillary thyroid cancer.

Kevin Brennan, Christopher Holsinger, Chrysoula Dosiou, John B Sunwoo, Haruko Akatsu, Robert Haile, Olivier Gevaert

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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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

7 authors.

Kevin BrennanDivision of Biomedical Informatics Research, Department of Medicine, Stanford University, 1265 Welch Road, Stanford, CA, 94305-5479, USA.ORCID 0000-0001-9018-9852
Christopher HolsingerDivision of Head and Neck Surgery, Department of Otolaryngology-Head and Neck Surgery, Stanford University, 801 Welch Road, Stanford, CA, 94304-1611, USA.
Chrysoula DosiouDivision of Endocrinology, Department of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305-5103, USA.
John B SunwooDivision of Head and Neck Surgery, Department of Otolaryngology-Head and Neck Surgery, Stanford University, 801 Welch Road, Stanford, CA, 94304-1611, USA.
Haruko AkatsuDivision of Endocrinology, Department of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305-5103, USA.
Robert HaileDivision of Oncology, Department of Medicine, Stanford University, 265 Campus Drive, Stanford, CA, 94305-5458, USA.
Olivier GevaertStanford Center for Biomedical Informatics Research, Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, USA. olivier.gevaert@stanford.edu.

Funding

Functional Analysis of Oncogenic Networks in Primary OrganoidsU01CA176299 · NCI · STANFORD UNIVERSITY · PI JI, HANLEE P, KUO, CALVIN J · 2013 to 2016
$3.6M
Radiogenomics Framework for Non-Invasive Personalized MedicineR01EB020527 · NIBIB · STANFORD UNIVERSITY · PI GEVAERT, OLIVIER · 2015 to 2018
$2.3M
NCI NIH HHS U01 CA176299NIBIB NIH HHS R01 EB020527
6 · The paper itself

Abstract

backgroundThe incidence of Papillary thyroid carcinoma (PTC), the most common type of thyroid malignancy, has risen rapidly worldwide. PTC usually has an excellent prognosis. However, the rising incidence of PTC, due at least partially to widespread use of neck imaging studies with increased detection of small cancers, has created a clinical issue of overdiagnosis, and consequential overtreatment. We investigated how molecular data can be used to develop a prognostics signature for PTC.

methodsThe Cancer Genome Atlas (TCGA) recently reported on the genomic landscape of a large cohort of PTC cases. In order to decrease unnecessary morbidity associated with over diagnosing PTC patient with good prognosis, we used TCGA data to develop a gene expression signature to distinguish between patients with good and poor prognosis. We selected a set of clinical phenotypes to define an 'extreme poor' prognosis group and an 'extreme good' prognosis group and developed a gene signature that characterized these.

resultsWe discovered a gene expression signature that distinguished the extreme good from extreme poor prognosis patients. Next, we applied this signature to the remaining intermediate risk patients, and show that they can be classified in clinically meaningful risk groups, characterized by established prognostic disease phenotypes. Analysis of the genes in the signature shows many known and novel genes involved in PTC prognosis.

conclusionsThis work demonstrates that using a selection of clinical phenotypes and treatment variables, it is possible to develop a statistically useful and biologically meaningful gene signature of PTC prognosis, which may be developed as a biomarker to help prevent overdiagnosis.

Indexed as

TranscriptomeAdultAgedBiomarkers, TumorCarcinomaCarcinoma, PapillaryCluster AnalysisComputational BiologyFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedMutationNeoplasm MetastasisBiomarkers, TumorGene expressionPapillary thyroid cancerPrognosis

Identifiers

PMID27633254
PMCPMC5025616

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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.