Evidence map›Paper›PMID 30968590›Full record

ArticleCancer medicine2019

Systematic evaluation of cancer-specific genetic risk score for 11 types of cancer in The Cancer Genome Atlas and Electronic Medical Records and Genomics cohorts.

Zhuqing Shi, Hongjie Yu, Yishuo Wu, Xiaoling Lin, Quanwa Bao, Haifei Jia, Chelsea Perschon, David Duggan, Brian T Helfand, Siqun L Zheng and 1 more

Abstract read
In one paragraph

Article in Cancer medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
–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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  6. Do Polygenic Risk Scores Add to Clinical Data in Predicting Pancreatic Cancer? A Scoping Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023
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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

11 authors.

Zhuqing ShiProgram for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois.ORCID 0000-0001-6321-950X
Hongjie YuProgram for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois.
Yishuo WuFudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China.
Xiaoling LinState Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China.
Quanwa BaoState Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China.
Haifei JiaFudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China.
Chelsea PerschonProgram for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois.
David DugganTranslational Genomics Research Institute, Phoenix, Arizona.
Brian T HelfandProgram for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois.
Siqun L ZhengProgram for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois.
Jianfeng XuProgram for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenetic risk score (GRS) is an odds ratio (OR)-weighted and population-standardized method for measuring cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). We hypothesize that GRS is a valid tool for risk assessment of most common cancers.

methodsUtilizing genotype and phenotype data from The Cancer Genome Atlas (TCGA) and Electronic Medical Records and Genomics (eMERGE), we tested 11 cancer-specific GRSs (bladder, breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, prostate, renal, and thyroid cancer) for association with the respective cancer type. Cancer-specific GRSs were calculated, for the first time in these cohorts, based on previously published risk-associated SNPs using the Caucasian subjects in these two cohorts.

resultsMean cancer-specific GRS in the population controls of eMERGE approximated the expected value of 1.00 (between 0.98 and 1.02) for all 11 types of cancer. Mean cancer-specific GRS was consistently higher in respective cancer patients than controls for all 11 types of cancer (P < 0.05). When subjects were categorized into low-, average-, and high-risk groups based on cancer-specific GRS (<0.5, 0.5-1.5, and >1.5, respectively), significant dose-response associations of higher cancer-specific GRS with higher OR of respective type of cancer were found for nine types of cancer (P

conclusionValidity of GRS for predicting cancer risk is demonstrated for most types of cancer. If confirmed in larger studies, cancer-specific GRS may have the potential for developing personalized cancer screening strategy.

Indexed as

Electronic Health RecordsGenetic Predisposition to DiseaseGenome, HumanGenomicsAllelesFemaleGenetic Association StudiesGenotypeHumansMaleNeoplasmsOdds RatioPolymorphism, Single NucleotidePublic Health Surveillanceage at diagnosiscancergenetic risk score

Identifiers

PMID30968590
PMCPMC6558466

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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.