Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures

View ORCID ProfileDaniel Osorio, Xavier Tekpli, Vessela N. Kristensen, View ORCID ProfileMarieke L. Kuijjer
doi: https://doi.org/10.1101/2022.03.31.486602
Daniel Osorio
1Centre for Molecular Medicine Norway (NCMM), University of Oslo, Oslo, Norway
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Daniel Osorio
  • For correspondence: daniecos@uio.no marieke.kuijjer@ncmm.uio.no
Xavier Tekpli
2Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vessela N. Kristensen
2Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
3Department of Clinical Molecular Biology and Laboratory Science (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Marieke L. Kuijjer
1Centre for Molecular Medicine Norway (NCMM), University of Oslo, Oslo, Norway
4Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
5Leiden Center for Computational Oncology, Leiden University Medical Center (LUMC), Leiden, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Marieke L. Kuijjer
  • For correspondence: daniecos@uio.no marieke.kuijjer@ncmm.uio.no
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

Developing novel cancer treatments is a challenging task that can benefit from computational techniques matching transcriptional signatures to large-scale drug response data. Here, we present ‘retriever,’ a tool that extracts robust disease-specific transcriptional drug response profiles based on cellular response profiles to hundreds of compounds from the LINCS-L1000 project. We used retriever to extract transcriptional drug response signatures of triple-negative breast cancer (TNBC) cell lines and combined these with a single-cell RNA-seq breast cancer atlas to predict drug combinations that antagonize TNBC-specific disease signatures. After systematically testing 152 drug response profiles and 11,476 drug combinations, we identified the combination of kinase inhibitors QL-XII-47 and GSK-690693 as the topmost promising candidate for TNBC treatment. Our new computational approach allows the identification of drugs and drug combinations targeting specific tumor cell types and subpopulations in individual patients. It is, therefore, highly suitable for the development of new personalized cancer treatment strategies.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/dosorio/L1000-TNBC

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted April 01, 2022.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures
Daniel Osorio, Xavier Tekpli, Vessela N. Kristensen, Marieke L. Kuijjer
bioRxiv 2022.03.31.486602; doi: https://doi.org/10.1101/2022.03.31.486602
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures
Daniel Osorio, Xavier Tekpli, Vessela N. Kristensen, Marieke L. Kuijjer
bioRxiv 2022.03.31.486602; doi: https://doi.org/10.1101/2022.03.31.486602

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4397)
  • Biochemistry (9624)
  • Bioengineering (7120)
  • Bioinformatics (24937)
  • Biophysics (12665)
  • Cancer Biology (9991)
  • Cell Biology (14395)
  • Clinical Trials (138)
  • Developmental Biology (7988)
  • Ecology (12146)
  • Epidemiology (2067)
  • Evolutionary Biology (16022)
  • Genetics (10950)
  • Genomics (14778)
  • Immunology (9899)
  • Microbiology (23732)
  • Molecular Biology (9503)
  • Neuroscience (51045)
  • Paleontology (370)
  • Pathology (1544)
  • Pharmacology and Toxicology (2692)
  • Physiology (4037)
  • Plant Biology (8693)
  • Scientific Communication and Education (1512)
  • Synthetic Biology (2404)
  • Systems Biology (6456)
  • Zoology (1349)