Evidence map›Paper›PMID 34816788›Full record

ArticleStatistical methods in medical research2022

Utilizing patient information to identify subtype heterogeneity of cancer driver genes.

Ho-Hsiang Wu, Xing Hua, Jianxin Shi, Nilanjan Chatterjee, Bin Zhu

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistical methods in medical research, 2022. 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
0.2field-weighted citation impact, top 50% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

5 authors at 4 institutions in 1 country.

Ho-Hsiang WuCenter for Biologics Evaluation and Research, 4137Food and Drug Administration, Silver Spring, MD, USA.
Xing HuaBiostatistics and Biomathematics Program, 7286Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Jianxin ShiDivision of Cancer Epidemiology and Genetics, 2511National Cancer Institute, National Institute of Health, Bethesda, MD, USA.
Nilanjan ChatterjeeDepartment of Biostatistics, Bloomberg School of Public Health, 1466Johns Hopkins University, Baltimore, MD, USA.
Bin ZhuDivision of Cancer Epidemiology and Genetics, 2511National Cancer Institute, National Institute of Health, Bethesda, MD, USA.ORCID 0000-0003-0172-5516
National Cancer Institute · USCenter for Biologics Evaluation and Research · USFred Hutch Cancer Center · USJohns Hopkins University · US

Funding

Methods for Epidemiology StudiesZIACP010181 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI ALBERT, PAUL · 2009 to 2025
$34.8M
Intramural NIH HHS Z99 CA999999
6 · The paper itself

Abstract

Identifying cancer driver genes is essential for understanding the mechanisms of carcinogenesis and designing therapeutic strategies. Although driver genes have been identified for many cancer types, it is still not clear whether the selection pressure of driver genes is homogeneous across cancer subtypes. We propose a statistical framework MutScot to improve the identification of driver genes and to investigate the heterogeneity of driver genes across cancer subtypes. Through simulation studies, we show that MutScot properly controls the type I error in detecting driver genes. In addition, we demonstrate that MutScot can identify subtype heterogeneity of driver genes. Applications to three studies in The Cancer Genome Atlas (TCGA) project showcase that MutScot has a desirable sensitivity for detecting driver genes and that MutScot identifies subtype heterogeneity of driver genes in breast cancer and lung cancer with regards to the status of hormone receptor and to the smoking status, respectively.

Indexed as

Lung NeoplasmsNeoplasmsCarcinogenesisComputer SimulationHumansMutationcancer subtypedriver genespatient heterogeneity.Somatic mutations

Identifiers

PMID34816788
PMCPMC9527771
OpenAlexW3216768694

What OpenQuestion holds

Textmetadata
LicenceTDM
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.