The first step is to generate the target-pathway interactions using drug response data on cancer cell lines. Those interaction matrices are generated using codes from this publication:
Yang et al. Linking drug target and pathway activation for effective therapy using multi-task learning.
https://www.nature.com/articles/s41598-018-25947-y
https://github.com/saezlab/Macau_project_1
GDSC_DRUG_COMBO_TOP_HITS.Rmd:
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Identify key pathways for synergy stratification for breast tissue.
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Identify protein target to combine with BRAF for colorectal cancer validation.
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Save target functional similarity values for breast, colon and lung_NSCLC from GDSC dataset.
Use the key pathways to compute the Delta Pathway Activity (predicted synergy) and stratify new cell lines.
check_synergy_AZ.Rmd:
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Synergy tratification analysis for breast/colon/lung cancer cell lines on AstraZeneca dataset.
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Synergy prediction on AstraZeneca dataset. We show here that synergy arises in case of strong similarity or anti-similarity for breast and colorectal tissues.
check_synergy_SANGER.Rmd:
- Synergy stratification on 48 colorectal cancer cell lines (Sanger validation).
check_synergy_ALMANAC.Rmd:
- Synergy enrichment in NCI_ALMANAC dataset.
GDSC data were downloaded from: http://www.cancerrxgene.org/
- Drug IC50 version 17a
- Basal gene expression 12/06/2013 version 2
- Drug target version March 2017
DREAM drug combination challenge data were acquired through an AstraZeneca Open Innovation Proposal.
NCI-ALMANAC data is downloaded from the publication Holbeck et al.
In case you encounter any issue, all packages used in this project are saved in the folder packrat/src
https://rstudio.github.io/packrat/commands.html
Distributed under the GNU GPLv3 License. See accompanying file LICENSE.txt or copy at http://www.gnu.org/licenses/gpl-3.0.html.