PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis

Author:

Feizi Nikta1,Nair Sisira Kadambat1ORCID,Smirnov Petr12ORCID,Beri Gangesh1,Eeles Christopher1,Esfahani Parinaz Nasr1,Nakano Minoru1,Tkachuk Denis1,Mammoliti Anthony12,Gorobets Evgeniya3,Mer Arvind Singh12,Lin Eva4,Yu Yihong4,Martin Scott4,Hafner Marc5ORCID,Haibe-Kains Benjamin12678ORCID

Affiliation:

1. Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada

2. Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada

3. Department of Cell and Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada

4. Department of Discovery Oncology, Genentech Inc, South San Francisco, CA 94080, USA

5. Department of Oncology Bioinformatics, Genentech Inc, South San Francisco, CA 94080, USA

6. Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada

7. Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada

8. Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada

Abstract

Abstract Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose–response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug–response analysis such as tissue distribution of dose–response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug–response phenotypes of cancer models.

Funder

Genome Canada

Princess Margaret Cancer Foundation

Princess Margaret Data Science Program

Ontario Institute for Cancer Research

Government of Ontario

Publisher

Oxford University Press (OUP)

Subject

Genetics

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