Evidence map›Paper›PMID 39052872›Full record

ArticleJournal of the National Cancer Institute2024

Variation to biology: optimizing functional analysis of cancer risk variants.

Stefanie Nelson, Danielle Carrick, Danielle Daee, Ian Fingerman, Elizabeth Gillanders

Abstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Stefanie NelsonDivision of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.ORCID 0009-0006-4619-4965
Danielle CarrickDivision of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.
Danielle DaeeDivision of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.ORCID 0009-0004-1794-9260
Ian FingermanDivision of Cancer Biology, National Cancer Institute, Rockville, MD, USA.
Elizabeth GillandersDivision of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.

Funding

NCI NIH HHS
6 · The paper itself

Abstract

Research conducted over the past 15+ years has identified hundreds of common germline genetic variants associated with cancer risk, but understanding the biological impact of these primarily non-protein coding variants has been challenging. The National Cancer Institute sought to better understand and address those challenges by requesting input from the scientific community via a survey and a 2-day virtual meeting, which focused on discussions among participants. Here, we discuss challenges identified through the survey as important to advancing functional analysis of common cancer risk variants: 1) When is a variant truly characterized; 2) Developing and standardizing databases and computational tools; 3) Optimization and implementation of high-throughput assays; 4) Use of model organisms for understanding variant function; 5) Diversity in data and assays; and 6) Creating and improving large multidisciplinary collaborations. We define these 6 challenges, describe how success in addressing them may look, propose potential solutions, and note issues that span all the challenges. Implementation of these ideas could help develop a framework for methodically analyzing common cancer risk variants to understand their function and make effective and efficient use of the wealth of existing genomic association data.

Indexed as

Genetic Predisposition to DiseaseNeoplasmsComputational BiologyDatabases, GeneticGenetic VariationGerm-Line MutationHumansNational Cancer Institute (U.S.)Risk FactorsUnited States

Identifiers

PMID39052872
PMCPMC11630534

What OpenQuestion holds

Textmetadata
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.