A community challenge for a pancancer drug mechanism of action inference from perturbational profile data
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Abstract
The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.
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Compounds eliciting similar transcriptional signatures tend to share mechanisms of action.
“It has been shown that compounds eliciting similar transcriptional signatures tend to share mechanisms of action.170,207−209”
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- Authors
- Douglass, E. F., Allaway, R. J., Szalai, B., Wang, W., Tian, T., Fernández-Torras, A., Realubit, R., Karan, C., Zheng, S., Pessia, A., Tanoli, Z., Jafari, M., Wan, F., Li, S., Xiong, Y., Duran-Frigola, M., Bertoni, M., Badia-i-Mompel, P., Mateo, L., Guitart-Pla, O., Chung, V., Tang, J., Zeng, J., Aloy, P., Saez-Rodriguez, J., Guinney, J., Gerhard, D. S., & Califano, A.
- Affiliations
- University of Georgia (US)
- Sage Bionetworks (US)
- Semmelweis University (HU)
- University of Helsinki (FI)
- Tsinghua University (CN)
- Columbia University Irving Medical Center (US)
- Institute for Research in Biomedicine (ES)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- National Institutes of Health (US)
- Corresponding author
- Califano, Andrea
- ORCID
- Douglass, Eugene F.
- Allaway, Robert J.
- Szalai, Bence
- Wang, Wenyu
- Tian, Tingzhong
- Karan, Charles
- Zheng, Shuyu
- Pessia, Alberto
- Tanoli, Ziaurrehman
- Jafari, Mohieddin
- Wan, Fangping
- Li, Shuya
- Xiong, Yuanpeng
- Duran-Frigola, Miquel
- Bertoni, Martino
- Badia-i-Mompel, Pau
- Mateo, Lídia
- Guitart-Pla, Oriol
- Chung, Verena
- Tang, Jing
- Zeng, Jianyang
- Aloy, Patrick
- Saez-Rodriguez, Julio
- Guinney, Justin
- Califano, Andrea
- Venue
- Cell Reports Medicine
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 3
- Issue
- 1
- Pages
- 100492
- ISSN
- 2666-3791 (ISSN-L)
- PMC
- PMC8784774
- OpenAlex
- W4206377455
- Cited by
- 68
- References
- 70
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 15 · 2023: 14 · 2022: 14 · 2021: 6
- FWCI
- 7.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports Medicine (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Työväentutkimus Vuosikirja (submittedVersion)
- Funders
- European Commission (101003633)
- European Research Council (614944)
- K. Albin Johanssons Stiftelse (332454)
- National Cancer Institute
- GlaxoSmithKline
- Engineering Research Centers
- Magyar Tudományos Akadémia (460044)
- China Scholarship Council
- National Institutes of Health (716063, S10-OD012351, S10-OD021764, U01-CA217862)
- Academy of Finland (317680)
- Generalitat de Catalunya (001-P-001647, 001-P-001682)
- Ministerio de Economía y Competitividad (BIO2016-77038-R)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- mechanism of action, polypharmacology, kinase inhibitors, drug target prediction, dose-response relationship, RNA-seq, DREAM challenge, panacea, gene expression similarity, deep learning, pharmacogenomics, drug repurposing
- MeSH terms
- Algorithms, Humans, Neoplasms, Protein Kinases, RNA, Messenger, Transcription, Genetic, Gene Expression Regulation, Neoplastic, Neural Networks, Computer, Gene Expression Profiling, Polypharmacology
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