Evidence map›Paper›PMID 35460182›Full record

ReviewBJU international2022

The current state of genetic risk models for the development of kidney cancer: a review and validation.

Hannah Harrison, Nicole Li, Catherine L Saunders, Sabrina H Rossi, Joe Dennis, Simon J Griffin, Grant D Stewart, Juliet A Usher-Smith

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

Review in BJU international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 3 pooled it
1.8field-weighted citation impact, top 14% 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

8 citing papers in PubMed, 3 syntheses or guidelines pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Acceptability of risk stratification within population-based cancer screening from the perspective of the general public: A mixed-methods systematic review.Health expectations : an international journal of public participation in health care and health policy · 2023
    Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Review
  8. Polygenic scores in cancer.Nature reviews. Cancer · 2023
    Review
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

8 authors at 1 institution in 1 country.

Hannah HarrisonDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID 0000-0001-8952-852X
Nicole LiDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Catherine L SaundersDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Sabrina H RossiDepartment of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.ORCID 0000-0001-7048-7158
Joe DennisDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Simon J GriffinDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Grant D StewartDepartment of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.ORCID 0000-0003-3188-9140
Juliet A Usher-SmithDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID 0000-0002-8501-2531
University of Cambridge · GB

Funding

Cancer Research UK 21464Department of HealthMedical Research Council MC_PC_17228Medical Research Council MC_QA137853Medical Research Council MC_UU_00006/6
6 · The paper itself

Abstract

objectiveTo review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models.

methodsRisk models were identified from a recent systematic review and the Cancer-PRS web directory. A narrative synthesis of the models, previous validation studies and related genome-wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort (cases, 452; controls, 487 925).

resultsA total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic-only models (seven of 25) and most of the mixed genetic-phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers.

conclusionsAlthough there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined.

Indexed as

Genome-Wide Association StudyKidney NeoplasmsGenetic Predisposition to DiseaseHumansPolymorphism, Single NucleotideRisk FactorsROC Curvegeneticskidney cancerpolygenic risk scoresRCCrisk modelsrisk stratificationvalidation

Identifiers

PMID35460182
PMCPMC9790357
OpenAlexW4224308125

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.