Evidence map›Paper›PMID 38168324›Full record

ArticleResearch square2023

Gene signatures derived from transcriptomic-causal networks stratified colorectal cancer patients for effective targeted therapy.

Akram Yazdani, Heinz-Josef Lenz, Gianluigi Pillonetto, Raul Mendez-Giraldez, Azam Yazdani, Hanna Sanof, Reza Hadi, Esmat Samiei, Alan P Venook, Mark J Ratain and 13 more

Open access · greenAbstract readPreprint
In one paragraph

Article in Research square, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors at 1 institution in 1 country.

Akram YazdaniUniversity of Texas Health Science center at Houston.
Heinz-Josef Lenz
Gianluigi Pillonetto
Raul Mendez-Giraldez
Azam Yazdani
Hanna Sanof
Reza Hadi
Esmat Samiei
Alan P Venook
Mark J Ratain
Naim Rashid
Benjamin G Vincent
Xueping Qu
Yujia Wen
Michael Kosorok
William F Symmans
John Paul Y C Shen
Michael S Lee
Scott Kopetz
Andrew B Nixon
Monica M Bertagnolli
Charles M Perou
Federico Innocenti
The University of Texas Health Science Center at Houston · US

Funding

Member Site CoreU10CA180821 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI Evanthia Galanis · 2014 to 2026
$177.3M
Statistics CoreU10CA180882 · NCI · MAYO CLINIC ROCHESTER · PI Sumithra Jay Mandrekar · 2014 to 2026
$115.0M
THE ALLIANCE NCTN BIOREPOSITORY AND BIOSPECIMEN RESOURCEU24CA196171 · NCI · WASHINGTON UNIVERSITY · PI Mine Cicek, Wendy Frankel · 2015 to 2026
$35.0M
NCI NIH HHS U10 CA180821NCI NIH HHS U10 CA180882NCI NIH HHS U24 CA196171
6 · The paper itself

Abstract

Predictive and prognostic gene signatures derived from interconnectivity among genes can tailor clinical care to patients in cancer treatment. We identified gene interconnectivity as the transcriptomic-causal network by integrating germline genotyping and tumor RNA-seq data from 1,165 patients with metastatic colorectal cancer (CRC). The patients were enrolled in a clinical trial with randomized treatment, either cetuximab or bevacizumab in combination with chemotherapy. We linked the network to overall survival (OS) and detected novel biomarkers by controlling for confounding genes. Our data-driven approach discerned sets of genes, each set collectively stratify patients based on OS. Two signatures under the cetuximab treatment were related to wound healing and macrophages. The signature under the bevacizumab treatment was related to cytotoxicity and we replicated its effect on OS using an external cohort. We also showed that the genes influencing OS within the signatures are downregulated in CRC tumor vs. normal tissue using another external cohort. Furthermore, the corresponding proteins encoded by the genes within the signatures interact each other and are functionally related. In conclusion, this study identified a group of genes that collectively stratified patients based on OS and uncovered promising novel prognostic biomarkers for personalized treatment of CRC using transcriptomic causal networks.

Indexed as

bevacizumabcetuximabcolorectal cancerexpression quantitative trait locigene signatureMendelian randomizationPatient stratificationtranscriptomic-causal network

Identifiers

PMID38168324
PMCPMC10760223
OpenAlexW4389774775

What OpenQuestion holds

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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.