Evidence map›Paper›PMID 36648331›Full record

ArticleBioinformatics (Oxford, England)2023

A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes.

Licai Huang, James P Long, Ehsan Irajizad, James D Doecke, Kim-Anh Do, Min Jin Ha

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

6 authors.

Licai HuangDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
James P Long
Ehsan IrajizadDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
James D DoeckeCSIRO, Royal Brisbane and Women's Hospital, Brisbane, Australia.
Kim-Anh DoDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Min Jin HaDepartment of Health Informatics and Biostatistics, Graduate School of Public Health, Yonsei University, Seoul, South Korea.ORCID 0000-0002-5165-1642

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
Tropism Enhanced Oncolytic Adenovirus for the Treatment of Brain TumorsP50CA127001 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Juan Fueyo · 2008 to 2026
$41.2M
Targeting Tumor Microenvironment-Induced Therapy Resistance in Prostate Cancer Bone MetastasisP50CA140388 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI THOMPSON, TIMOTHY CHARLES · 2009 to 2022
$23.4M
Center for Clinical and Translational Sciences (CCTS)UL1TR000371 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI MCPHERSON, DAVID D · 2012 to 2016
$13.4M
Bayesian Network-Based Integrative Genomics Methods for Precision MedicineR01CA244845 · NCI · UNIVERSITY OF PENNSYLVANIA · PI BALADANDAYUTHAPANI, VEERABHADRAN, MORRIS, JEFFREY S · 2021 to 2024
$1.8M
NCATS NIH HHS UL1 TR000371NCI NIH HHS 5R21CA220299NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA127001NCI NIH HHS P50 CA140388NCI NIH HHS R01 CA244845
6 · The paper itself

Abstract

motivationMultilevel molecular profiling of tumors and the integrative analysis with clinical outcomes have enabled a deeper characterization of cancer treatment. Mediation analysis has emerged as a promising statistical tool to identify and quantify the intermediate mechanisms by which a gene affects an outcome. However, existing methods lack a unified approach to handle various types of outcome variables, making them unsuitable for high-throughput molecular profiling data with highly interconnected variables.

resultsWe develop a general mediation analysis framework for proteogenomic data that include multiple exposures, multivariate mediators on various scales of effects as appropriate for continuous, binary and survival outcomes. Our estimation method avoids imposing constraints on model parameters such as the rare disease assumption, while accommodating multiple exposures and high-dimensional mediators. We compare our approach to other methods in extensive simulation studies at a range of sample sizes, disease prevalence and number of false mediators. Using kidney renal clear cell carcinoma proteogenomic data, we identify genes that are mediated by proteins and the underlying mechanisms on various survival outcomes that capture short- and long-term disease-specific clinical characteristics. AVAILABILITY AND IMPLEMENTATION: Software is made available in an R package (https://github.com/longjp/mediateR). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

NeoplasmsProteogenomicsComputer SimulationHumansMediation AnalysisSoftware

Identifiers

PMID36648331
PMCPMC9879726

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Registered trials

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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.