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

Defining and targeting adaptations to oncogenic KRASG12C inhibition using quantitative temporal proteomics

View ORCID ProfileNaiara Santana-Codina, Amrita Singh Chandhoke, View ORCID ProfileQijia Yu, Beata Małachowska, Miljan Kuljanin, Ajami Gikandi, Marcin Stańczak, Sebastian Gableske, Mark P. Jedrychowski, David A. Scott, Andrew J. Aguirre, Wojciech Fendler, Nathanael S. Gray, View ORCID ProfileJoseph D. Mancias
doi: https://doi.org/10.1101/769703
Naiara Santana-Codina
Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Naiara Santana-Codina
Amrita Singh Chandhoke
Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Qijia Yu
Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Qijia Yu
Beata Małachowska
Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, PolandDepartment of Cell Biology, Harvard Medical School, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Miljan Kuljanin
Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ajami Gikandi
Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Marcin Stańczak
Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sebastian Gableske
Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mark P. Jedrychowski
Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USADepartment of Cell Biology, Harvard Medical School, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
David A. Scott
Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew J. Aguirre
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Wojciech Fendler
Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USADepartment of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nathanael S. Gray
Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joseph D. Mancias
Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Joseph D. Mancias
  • For correspondence: Joseph_Mancias@dfci.harvard.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

ABSTRACT

Covalent inhibitors of the KRASG12C oncoprotein have recently been developed and are being evaluated in clinical trials. Resistance to targeted therapies is common and likely to limit long-term efficacy of KRAS inhibitors (KRASi). To identify pathways of adaptation to KRASi and to predict drug combinations that circumvent resistance, we used a mass spectrometry-based quantitative temporal proteomics and bioinformatics workflow to profile the temporal proteomic response to KRASG12C inhibition in pancreatic and lung cancer 2D and 3D cellular models. We quantified 10,805 proteins across our datasets, representing the most comprehensive KRASi proteomics effort to date. Our data reveal common mechanisms of acute and long-term response between KRASG12C-driven tumors. To facilitate discovery in the cancer biology community, we generated an interactive ‘KRASi proteome’ website (https://manciaslab.shinyapps.io/KRASi/). Based on these proteomic data, we identified potent combinations of KRASi with PI3K, HSP90, CDK4/6, and SHP2 inhibitors, in some instances converting a cytostatic response to KRASi monotherapy to a cytotoxic response to combination treatment. Overall, using our quantitative temporal proteomics-bioinformatics platform, we have comprehensively characterized the proteomic adaptations to KRASi and identified combinatorial regimens to induce cytotoxicity with potential therapeutic utility.

Footnotes

  • https://manciaslab.shinyapps.io/KRASi/

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-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted September 14, 2019.
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.
Defining and targeting adaptations to oncogenic KRASG12C inhibition using quantitative temporal proteomics
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
Defining and targeting adaptations to oncogenic KRASG12C inhibition using quantitative temporal proteomics
Naiara Santana-Codina, Amrita Singh Chandhoke, Qijia Yu, Beata Małachowska, Miljan Kuljanin, Ajami Gikandi, Marcin Stańczak, Sebastian Gableske, Mark P. Jedrychowski, David A. Scott, Andrew J. Aguirre, Wojciech Fendler, Nathanael S. Gray, Joseph D. Mancias
bioRxiv 769703; doi: https://doi.org/10.1101/769703
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Defining and targeting adaptations to oncogenic KRASG12C inhibition using quantitative temporal proteomics
Naiara Santana-Codina, Amrita Singh Chandhoke, Qijia Yu, Beata Małachowska, Miljan Kuljanin, Ajami Gikandi, Marcin Stańczak, Sebastian Gableske, Mark P. Jedrychowski, David A. Scott, Andrew J. Aguirre, Wojciech Fendler, Nathanael S. Gray, Joseph D. Mancias
bioRxiv 769703; doi: https://doi.org/10.1101/769703

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

  • Cancer Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (1522)
  • Biochemistry (2475)
  • Bioengineering (1731)
  • Bioinformatics (9655)
  • Biophysics (3892)
  • Cancer Biology (2964)
  • Cell Biology (4185)
  • Clinical Trials (135)
  • Developmental Biology (2622)
  • Ecology (4092)
  • Epidemiology (2031)
  • Evolutionary Biology (6884)
  • Genetics (5202)
  • Genomics (6490)
  • Immunology (2181)
  • Microbiology (6928)
  • Molecular Biology (2750)
  • Neuroscience (17245)
  • Paleontology (126)
  • Pathology (425)
  • Pharmacology and Toxicology (705)
  • Physiology (1055)
  • Plant Biology (2484)
  • Scientific Communication and Education (642)
  • Synthetic Biology (828)
  • Systems Biology (2684)
  • Zoology (429)