Evidence map›Paper›PMID 37846218›Full record

ArticleCancer informatics2023

Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer.

Xiaojia Ji, Kevin P Williams, Weifan Zheng

Open access · goldAbstract read
In one paragraph

Article in Cancer informatics, 2023. 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 43% 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, 1 citations in OpenAlex.

  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

3 authors at 1 institution in 1 country.

Xiaojia JiBRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA.
Kevin P WilliamsBRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA.
Weifan ZhengBRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA.
North Carolina Central University · US

Funding

Understanding Perceptions of HIV Risk, PrEP, and PrEP use among African American Women Attending an HBCUU54MD012392 · NIMHD · NORTH CAROLINA CENTRAL UNIVERSITY · PI Cherise Baldwin Harrington · 2017 to 2026
$38.1M
Targeting the MEKK2-ERK5 Signaling Node in Triple Negative Breast CancerU54CA156735 · NCI · NORTH CAROLINA CENTRAL UNIVERSITY · PI RICHARDSON, MICHELER RICARDO · 2010 to 2021
$13.0M
Addressing the Biology of Health Disparities by Targeting Geographical Ancestry-driven Variants of ImmunityR01MD017405 · NIMHD · NORTH CAROLINA CENTRAL UNIVERSITY · PI KIMBRO, KEVIN SEAN, NAVARRO, HERNAN A · 2021 to 2025
$3.4M
Drug Repurposing for Cancer Therapy: From Man to Molecules to ManU01CA207160 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI TROPSHA, ALEXANDER · 2016 to 2018
$1.2M
Project 2: Identifying and modulating genetic modifiers of GLI1 activation in inflammatory breast cancer.P20CA202924 · NCI · NORTH CAROLINA CENTRAL UNIVERSITY · PI PATIERNO, STEVEN R, WILLIAMS, KEVIN PETER · 2016 to 2019
$870k
Engineering novel methods to study metastases and reduce systemic treatment toxicity in breast cancer patientsR15CA223994 · NCI · NORTH CAROLINA CENTRAL UNIVERSITY · PI TAYLOR, DARLENE KIMBER, WILLIAMS, KEVIN PETER · 2019 to 2019
$444k
NCI NIH HHS P20 CA202924NCI NIH HHS R15 CA223994NCI NIH HHS U01 CA207160NCI NIH HHS U54 CA156735NIMHD NIH HHS R01 MD017405NIMHD NIH HHS U54 MD012392
6 · The paper itself

Abstract

The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.

Indexed as

binary drug perturbation combinationdrug repurposingGene reversal rategene signaturesinflammatory breast cancer

Identifiers

PMID37846218
PMCPMC10576937
OpenAlexW4387639898

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.