Evidence map›Paper›PMID 36434556›Full record

ArticleBMC cancer2022

Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors.

Manali Singha, Limeng Pu, Brent A Stanfield, Ifeanyi K Uche, Paul J F Rider, Konstantin G Kousoulas, J Ramanujam, Michal Brylinski

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

8 authors.

Manali Singha *Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA.
Limeng Pu *Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA.
Brent A StanfieldDepartment of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, 70803, USA.
Ifeanyi K UcheDepartment of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, 70803, USA.
Paul J F RiderDepartment of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, 70803, USA.
Konstantin G KousoulasDepartment of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, 70803, USA.
J RamanujamCenter for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA.
Michal BrylinskiDepartment of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA. michal@brylinski.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching.

methodsCancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion.

resultsSelected CancerOmicsNet predictions obtained for "unseen" data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 being the most potent against the pancreatic cancer cell line Panc 04.03.

conclusionsCancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.

Indexed as

Artificial IntelligencePancreatic NeoplasmsHumansMaleMedical OncologyMolecular Docking SimulationPrecision MedicineArtificial intelligenceCancer growth rateCancer-specific networksDifferential gene expressionGene-disease associationGene signatureGraph neural networkKinase inhibitorsLive-cell time course assayNetwork biologyPrecision oncology

Identifiers

PMID36434556
PMCPMC9694576

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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.