151
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Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018; 19:506-523. [PMID: 28069634 PMCID: PMC5952941 DOI: 10.1093/bib/bbw112] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Laleh Soltan Ghoraie
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry, Northern Ireland, UK
| | - Galina Glazko
- University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York, USA
| | - Olli Yli-Harja
- Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, Hall in Tyrol, Austria
| | - Benjamin Haibe-Kains
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Frank Emmert-Streib
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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152
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Lavertu A, McInnes G, Daneshjou R, Whirl-Carrillo M, Klein TE, Altman RB. Pharmacogenomics and big genomic data: from lab to clinic and back again. Hum Mol Genet 2018; 27:R72-R78. [PMID: 29635477 PMCID: PMC5946941 DOI: 10.1093/hmg/ddy116] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 03/27/2018] [Accepted: 03/28/2018] [Indexed: 02/06/2023] Open
Abstract
The field of pharmacogenomics is an area of great potential for near-term human health impacts from the big genomic data revolution. Pharmacogenomics research momentum is building with numerous hypotheses currently being investigated through the integration of molecular profiles of different cell lines and large genomic data sets containing information on cellular and human responses to therapies. Additionally, the results of previous pharmacogenetic research efforts have been formulated into clinical guidelines that are beginning to impact how healthcare is conducted on the level of the individual patient. This trend will only continue with the recent release of new datasets containing linked genotype and electronic medical record data. This review discusses key resources available for pharmacogenomics and pharmacogenetics research and highlights recent work within the field.
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Affiliation(s)
- Adam Lavertu
- Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305, USA
| | - Greg McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305, USA
| | - Roxana Daneshjou
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | | | - Teri E Klein
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Russ B Altman
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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153
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Haibe-Kains B, Cescon DW. Gene Expression Analyses in Breast Cancer: Sample Matters. JNCI Cancer Spectr 2018; 2:pky019. [PMID: 31360851 PMCID: PMC6649719 DOI: 10.1093/jncics/pky019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 04/12/2018] [Indexed: 11/23/2022] Open
Affiliation(s)
- Benjamin Haibe-Kains
- Campbell Family Institute for Breast Cancer Research, Department of Research, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, Department of Computer Science, and Division of Medical Oncology, Department of Medicine, University of Toronto, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - David W Cescon
- Campbell Family Institute for Breast Cancer Research, Department of Research, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, Department of Computer Science, and Division of Medical Oncology, Department of Medicine, University of Toronto, Toronto, ON, Canada
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154
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Koudijs KKM, Terwisscha van Scheltinga AGT, Böhringer S, Schimmel KJM, Guchelaar HJ. Personalised drug repositioning for Clear Cell Renal Cell Carcinoma using gene expression. Sci Rep 2018; 8:5250. [PMID: 29588458 PMCID: PMC5869697 DOI: 10.1038/s41598-018-23195-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 03/07/2018] [Indexed: 02/02/2023] Open
Abstract
Reversal of cancer gene expression is predictive of therapeutic potential and can be used to find new indications for existing drugs (drug repositioning). Gene expression reversal potential is currently calculated, in almost all studies, by pre-aggregating all tumour samples into a single group signature or a limited number of molecular subtype signatures. Here, we investigate whether drug repositioning based on individual tumour sample gene expression signatures outperforms the use of tumour group and subtype signatures. The tumour signatures were created using 534 tumour samples and 72 matched normal samples from 530 clear cell renal cell carcinoma (ccRCC) patients. More than 20,000 drug signatures were extracted from the CMAP and LINCS databases. We show that negative enrichment of individual tumour samples correlated (Spearman's rho = 0.15) much better with the amount of differentially expressed genes in drug signatures than with the tumour group signature (Rho = 0.08) and the 4 tumour subtype signatures (Rho 0.036-0.11). Targeted drugs used against ccRCC, such as sirolimus and temsirolimus, which could not be identified with the pre-aggregated tumour signatures could be recovered using individual sample analysis. Thus, drug repositioning can be personalized by taking into account the gene expression profile of the individual's tumour sample.
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Affiliation(s)
- Karel K M Koudijs
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Centre, Leiden, The Netherlands
| | | | - Stefan Böhringer
- Department of Medical Statistics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Kirsten J M Schimmel
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Centre, Leiden, The Netherlands.
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155
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Disruption of the anaphase-promoting complex confers resistance to TTK inhibitors in triple-negative breast cancer. Proc Natl Acad Sci U S A 2018; 115:E1570-E1577. [PMID: 29378962 DOI: 10.1073/pnas.1719577115] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
TTK protein kinase (TTK), also known as Monopolar spindle 1 (MPS1), is a key regulator of the spindle assembly checkpoint (SAC), which functions to maintain genomic integrity. TTK has emerged as a promising therapeutic target in human cancers, including triple-negative breast cancer (TNBC). Several TTK inhibitors (TTKis) are being evaluated in clinical trials, and an understanding of the mechanisms mediating TTKi sensitivity and resistance could inform the successful development of this class of agents. We evaluated the cellular effects of the potent clinical TTKi CFI-402257 in TNBC models. CFI-402257 induced apoptosis and potentiated aneuploidy in TNBC lines by accelerating progression through mitosis and inducing mitotic segregation errors. We used genome-wide CRISPR/Cas9 screens in multiple TNBC cell lines to identify mechanisms of resistance to CFI-402257. Our functional genomic screens identified members of the anaphase-promoting complex/cyclosome (APC/C) complex, which promotes mitotic progression following inactivation of the SAC. Several screen candidates were validated to confer resistance to CFI-402257 and other TTKis using CRISPR/Cas9 and siRNA methods. These findings extend the observation that impairment of the APC/C enables cells to tolerate genomic instability caused by SAC inactivation, and support the notion that a measure of APC/C function could predict the response to TTK inhibition. Indeed, an APC/C gene expression signature is significantly associated with CFI-402257 response in breast and lung adenocarcinoma cell line panels. This expression signature, along with somatic alterations in genes involved in mitotic progression, represent potential biomarkers that could be evaluated in ongoing clinical trials of CFI-402257 or other TTKis.
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156
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Yu R, Longo J, van Leeuwen JE, Mullen PJ, Ba-Alawi W, Haibe-Kains B, Penn LZ. Statin-Induced Cancer Cell Death Can Be Mechanistically Uncoupled from Prenylation of RAS Family Proteins. Cancer Res 2017; 78:1347-1357. [PMID: 29229608 DOI: 10.1158/0008-5472.can-17-1231] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 10/04/2017] [Accepted: 11/30/2017] [Indexed: 11/16/2022]
Abstract
The statin family of drugs preferentially triggers tumor cell apoptosis by depleting mevalonate pathway metabolites farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP), which are used for protein prenylation, including the oncoproteins of the RAS superfamily. However, accumulating data indicate that activation of the RAS superfamily are poor biomarkers of statin sensitivity, and the mechanism of statin-induced tumor-specific apoptosis remains unclear. Here we demonstrate that cancer cell death triggered by statins can be uncoupled from prenylation of the RAS superfamily of oncoproteins. Ectopic expression of different members of the RAS superfamily did not uniformly sensitize cells to fluvastatin, indicating that increased cellular demand for protein prenylation cannot explain increased statin sensitivity. Although ectopic expression of HRAS increased statin sensitivity, expression of myristoylated HRAS did not rescue this effect. HRAS-induced epithelial-to-mesenchymal transition (EMT) through activation of zinc finger E-box binding homeobox 1 (ZEB1) sensitized tumor cells to the antiproliferative activity of statins, and induction of EMT by ZEB1 was sufficient to phenocopy the increase in fluvastatin sensitivity; knocking out ZEB1 reversed this effect. Publicly available gene expression and statin sensitivity data indicated that enrichment of EMT features was associated with increased sensitivity to statins in a large panel of cancer cell lines across multiple cancer types. These results indicate that the anticancer effect of statins is independent from prenylation of RAS family proteins and is associated with a cancer cell EMT phenotype.Significance: The use of statins to target cancer cell EMT may be useful as a therapy to block cancer progression. Cancer Res; 78(5); 1347-57. ©2017 AACR.
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Affiliation(s)
- Rosemary Yu
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Joseph Longo
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Jenna E van Leeuwen
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Peter J Mullen
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Wail Ba-Alawi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Ontario Institute of Cancer Research, Toronto, Ontario, Canada
| | - Linda Z Penn
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
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157
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Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 2017; 18:220. [PMID: 29141660 PMCID: PMC5688663 DOI: 10.1186/s13059-017-1349-1] [Citation(s) in RCA: 2411] [Impact Index Per Article: 344.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 10/10/2017] [Indexed: 12/12/2022] Open
Abstract
Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/.
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Affiliation(s)
- Dvir Aran
- Institute for Computational Health Sciences, University of California, San Francisco, California, 94158, USA.
| | - Zicheng Hu
- Institute for Computational Health Sciences, University of California, San Francisco, California, 94158, USA
| | - Atul J Butte
- Institute for Computational Health Sciences, University of California, San Francisco, California, 94158, USA.
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158
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Safikhani Z, Smirnov P, Thu KL, Silvester J, El-Hachem N, Quevedo R, Lupien M, Mak TW, Cescon D, Haibe-Kains B. Gene isoforms as expression-based biomarkers predictive of drug response in vitro. Nat Commun 2017; 8:1126. [PMID: 29066719 PMCID: PMC5655668 DOI: 10.1038/s41467-017-01153-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 08/23/2017] [Indexed: 01/09/2023] Open
Abstract
Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high frequency of mRNA splicing in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery. To identify robust transcriptomic biomarkers for drug response across studies, we develop a meta-analytical framework combining the pharmacological data from two large-scale drug screening datasets. We use an independent pan-cancer pharmacogenomic dataset to test the robustness of our candidate biomarkers across multiple cancer types. We further analyze two independent breast cancer datasets and find that specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 are significantly associated with AZD6244, lapatinib, erlotinib, and paclitaxel, respectively. Our results support isoform expressions as a rich resource for biomarkers predictive of drug response.
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Affiliation(s)
- Zhaleh Safikhani
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Kelsie L Thu
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Institut de Recherches Cliniques de Montréal, 110 Pine Avenue West, Montreal, QC, Canada, H2W 1R7
| | - Jennifer Silvester
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Institut de Recherches Cliniques de Montréal, 110 Pine Avenue West, Montreal, QC, Canada, H2W 1R7
| | - Nehme El-Hachem
- Institut de Recherches Cliniques de Montréal, 110 Pine Avenue West, Montreal, QC, Canada, H2W 1R7
| | - Rene Quevedo
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Mathieu Lupien
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Tak W Mak
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
- Campbell Family Institute for Breast Cancer Research, 620 University Avenue, Toronto, ON, Canada, M5G2C1
| | - David Cescon
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Campbell Family Institute for Breast Cancer Research, 620 University Avenue, Toronto, ON, Canada, M5G2C1
- Division of Medical Oncology and Hematology, Department of Medicine, University of Toronto, 27 King's College Circle, Toronto, ON, Canada, M5S 1A1
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7.
- Department of Computer Science, University of Toronto, 10 King's College Road, Toronto, ON, Canada, M5S 3G4.
- Ontario Institute of Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, Canada, M5G 0A3.
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159
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Clark NA, Hafner M, Kouril M, Williams EH, Muhlich JL, Pilarczyk M, Niepel M, Sorger PK, Medvedovic M. GRcalculator: an online tool for calculating and mining dose-response data. BMC Cancer 2017; 17:698. [PMID: 29065900 PMCID: PMC5655815 DOI: 10.1186/s12885-017-3689-3] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Accepted: 10/16/2017] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose-response curves such as IC 50 , AUC, and E max , are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521-627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose-response assays and the reliability of pharmacogenomic associations (Hafner et al. 500-502, 2017). RESULTS We describe here an interactive website ( www.grcalculator.org ) for calculation, analysis, and visualization of dose-response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics . CONCLUSIONS GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose-response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose-response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program ( http://www.lincsproject.org /) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose-response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry.
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Affiliation(s)
- Nicholas A. Clark
- LINCS-BD2K DCIC, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45221 USA
| | - Marc Hafner
- HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Michal Kouril
- Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229 USA
| | - Elizabeth H. Williams
- HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Jeremy L. Muhlich
- HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Marcin Pilarczyk
- LINCS-BD2K DCIC, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45221 USA
| | - Mario Niepel
- HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Peter K. Sorger
- HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Mario Medvedovic
- LINCS-BD2K DCIC, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45221 USA
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160
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El-Hachem N, Ba-Alawi W, Smith I, Mer AS, Haibe-Kains B. Integrative cancer pharmacogenomics to establish drug mechanism of action: drug repurposing. Pharmacogenomics 2017; 18:1469-1472. [PMID: 29057710 DOI: 10.2217/pgs-2017-0132] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Nehme El-Hachem
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Wail Ba-Alawi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Ian Smith
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute of Cancer Research, Toronto, Ontario, Canada
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161
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El-Hachem N, Gendoo DMA, Ghoraie LS, Safikhani Z, Smirnov P, Chung C, Deng K, Fang A, Birkwood E, Ho C, Isserlin R, Bader GD, Goldenberg A, Haibe-Kains B. Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy. Cancer Res 2017; 77:3057-3069. [PMID: 28314784 DOI: 10.1158/0008-5472.can-17-0096] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 02/27/2017] [Accepted: 03/13/2017] [Indexed: 11/16/2022]
Abstract
Identification of drug targets and mechanism of action (MoA) for new and uncharacterized anticancer drugs is important for optimization of treatment efficacy. Current MoA prediction largely relies on prior information including side effects, therapeutic indication, and chemoinformatics. Such information is not transferable or applicable for newly identified, previously uncharacterized small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary toward development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose here a new integrative computational pharmacogenomic approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that rely only on basic drug characteristics toward elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation, and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. DNF taxonomy succeeded in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. The scalability of DNF facilitated identification of key drug relationships across different drug categories, providing a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on compound MoA and potential insights for drug repurposing. Cancer Res; 77(11); 3057-69. ©2017 AACR.
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Affiliation(s)
- Nehme El-Hachem
- Integrative Computational Systems Biology, Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada.,Department of Biomedical Sciences. Université de Montréal, Montreal, Quebec, Canada
| | - Deena M A Gendoo
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Laleh Soltan Ghoraie
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Zhaleh Safikhani
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Christina Chung
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Kenan Deng
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Ailsa Fang
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Erin Birkwood
- School of Computer Science, McGill University, Montreal, Quebec, Canada
| | - Chantal Ho
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Ruth Isserlin
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Gary D Bader
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,The Donnelly Centre, Toronto, Ontario, Canada.,The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Hospital for Sick Children, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute of Cancer Research, Toronto, Ontario, Canada
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162
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163
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Safikhani Z, El-Hachem N, Smirnov P, Freeman M, Goldenberg A, Birkbak NJ, Beck AH, Aerts HJWL, Quackenbush J, Haibe-Kains B. Safikhani et al. reply. Nature 2016; 540:E6-E8. [PMID: 27905416 DOI: 10.1038/nature20172] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Zhaleh Safikhani
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Nehme El-Hachem
- Institut de recherches cliniques de Montréal, Montreal, Quebec H2W 1R7, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9, Canada
| | - Mark Freeman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9, Canada
| | - Anna Goldenberg
- Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario M5S 2E4, Canada
| | - Nicolai J Birkbak
- The Francis Crick Institute, London NW1 1AT, UK.,University College London Cancer Institute, London WC1E 6BT, UK
| | - Andrew H Beck
- Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Hugo J W L Aerts
- Harvard Medical School, Boston, Massachusetts 02115, USA.,Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.,Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
| | - John Quackenbush
- Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.,Harvard TH Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario M5S 2E4, Canada.,Ontario Institute of Cancer Research, Toronto, Ontario M5G 1L7, Canada
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164
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Cescon DW, Haibe-Kains B. DNA replication stress: a source of APOBEC3B expression in breast cancer. Genome Biol 2016; 17:202. [PMID: 27716362 PMCID: PMC5045630 DOI: 10.1186/s13059-016-1069-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
APOBEC cytidine deaminases have been implicated as major contributors to the mutation burden in many cancers on the basis of their mutational signature. A new experimental study sheds light on the inciting factors, linking APOBEC3B expression to oncogene- and drug-induced replication stress.
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Affiliation(s)
- David W Cescon
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada. .,Campbell Family Institute for Breast Cancer Research, Toronto, Canada. .,Division of Medical Oncology and Hematology, Department of Medicine, University of Toronto, Toronto, Canada.
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada. .,Department of Computer Science, University of Toronto, Toronto, ON, Canada. .,Ontario Institute of Cancer Research, Toronto, ON, Canada.
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165
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Safikhani Z, Smirnov P, Freeman M, El-Hachem N, She A, Rene Q, Goldenberg A, Birkbak NJ, Hatzis C, Shi L, Beck AH, Aerts HJ, Quackenbush J, Haibe-Kains B. Revisiting inconsistency in large pharmacogenomic studies. F1000Res 2016; 5:2333. [PMID: 28928933 PMCID: PMC5580432 DOI: 10.12688/f1000research.9611.3] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/11/2017] [Indexed: 01/30/2023] Open
Abstract
In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.
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Affiliation(s)
- Zhaleh Safikhani
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Mark Freeman
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Nehme El-Hachem
- Institut de Recherches Cliniques de Montréal, Montréal, H2W 1R7, Canada
| | - Adrian She
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Quevedo Rene
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, M5S 2E4, Canada
- Hospital for Sick Children, Toronto, M5G 1X8, Canada
| | | | - Christos Hatzis
- Yale Cancer Center, Yale University, New Haven, CT, 06510, USA
- Section of Medical Oncology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Leming Shi
- University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
- Fudan University, Shanghai City, 200135, China
| | - Andrew H. Beck
- Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Hugo J.W.L. Aerts
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Boston, MA, 02215, USA
- Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA
| | - John Quackenbush
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
- Department of Computer Science, University of Toronto, Toronto, M5S 2E4, Canada
- Ontario Institute of Cancer Research, Toronto, M5G 1L7, Canada
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166
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Safikhani Z, Smirnov P, Freeman M, El-Hachem N, She A, Rene Q, Goldenberg A, Birkbak NJ, Hatzis C, Shi L, Beck AH, Aerts HJ, Quackenbush J, Haibe-Kains B. Revisiting inconsistency in large pharmacogenomic studies. F1000Res 2016; 5:2333. [PMID: 28928933 PMCID: PMC5580432 DOI: 10.12688/f1000research.9611.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/21/2017] [Indexed: 11/13/2023] Open
Abstract
In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.
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Affiliation(s)
- Zhaleh Safikhani
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Mark Freeman
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Nehme El-Hachem
- Institut de Recherches Cliniques de Montréal, Montréal, H2W 1R7, Canada
| | - Adrian She
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Quevedo Rene
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, M5S 2E4, Canada
- Hospital for Sick Children, Toronto, M5G 1X8, Canada
| | | | - Christos Hatzis
- Yale Cancer Center, Yale University, New Haven, CT, 06510, USA
- Section of Medical Oncology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Leming Shi
- University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
- Fudan University, Shanghai City, 200135, China
| | - Andrew H. Beck
- Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Hugo J.W.L. Aerts
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Boston, MA, 02215, USA
- Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA
| | - John Quackenbush
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
- Department of Computer Science, University of Toronto, Toronto, M5S 2E4, Canada
- Ontario Institute of Cancer Research, Toronto, M5G 1L7, Canada
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167
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Safikhani Z, Smirnov P, Freeman M, El-Hachem N, She A, Rene Q, Goldenberg A, Birkbak NJ, Hatzis C, Shi L, Beck AH, Aerts HJ, Quackenbush J, Haibe-Kains B. Revisiting inconsistency in large pharmacogenomic studies. F1000Res 2016; 5:2333. [PMID: 28928933 PMCID: PMC5580432 DOI: 10.12688/f1000research.9611.1] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/15/2016] [Indexed: 01/22/2023] Open
Abstract
In 2013, we published a comparative analysis mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines, and that the software analysis tools we provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.
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Affiliation(s)
- Zhaleh Safikhani
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Mark Freeman
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Nehme El-Hachem
- Institut de Recherches Cliniques de Montréal, Montréal, H2W 1R7, Canada
| | - Adrian She
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Quevedo Rene
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, M5S 2E4, Canada
- Hospital for Sick Children, Toronto, M5G 1X8, Canada
| | | | - Christos Hatzis
- Yale Cancer Center, Yale University, New Haven, CT, 06510, USA
- Section of Medical Oncology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Leming Shi
- University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
- Fudan University, Shanghai City, 200135, China
| | - Andrew H. Beck
- Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Hugo J.W.L. Aerts
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Boston, MA, 02215, USA
- Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA
| | - John Quackenbush
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada
- Department of Computer Science, University of Toronto, Toronto, M5S 2E4, Canada
- Ontario Institute of Cancer Research, Toronto, M5G 1L7, Canada
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168
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Pozdeyev N, Yoo M, Mackie R, Schweppe RE, Tan AC, Haugen BR. Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies. Oncotarget 2016; 7:51619-51625. [PMID: 27322211 PMCID: PMC5239501 DOI: 10.18632/oncotarget.10010] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 05/29/2016] [Indexed: 01/22/2023] Open
Abstract
The consistency of in vitro drug sensitivity data is of key importance for cancer pharmacogenomics. Previous attempts to correlate drug sensitivities from the large pharmacogenomics databases, such as the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), have produced discordant results. We developed a new drug sensitivity metric, the area under the dose response curve adjusted for the range of tested drug concentrations, which allows integration of heterogeneous drug sensitivity data from the CCLE, the GDSC, and the Cancer Therapeutics Response Portal (CTRP). We show that there is moderate to good agreement of drug sensitivity data for many targeted therapies, particularly kinase inhibitors. The results of this largest cancer cell line drug sensitivity data analysis to date are accessible through the online portal, which serves as a platform for high power pharmacogenomics analysis.
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Affiliation(s)
- Nikita Pozdeyev
- Department of Medicine, University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, CO, USA
| | - Minjae Yoo
- Department of Medicine, University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ryan Mackie
- Department of Medicine, University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, CO, USA
| | - Rebecca E. Schweppe
- Department of Medicine, University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, CO, USA
| | - Aik Choon Tan
- Department of Medicine, University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, CO, USA
| | - Bryan R. Haugen
- Department of Medicine, University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, CO, USA
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169
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Safikhani Z, El-Hachem N, Quevedo R, Smirnov P, Goldenberg A, Juul Birkbak N, Mason C, Hatzis C, Shi L, Aerts HJWL, Quackenbush J, Haibe-Kains B. Assessment of pharmacogenomic agreement. F1000Res 2016; 5:825. [PMID: 27408686 PMCID: PMC4926729 DOI: 10.12688/f1000research.8705.1] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/03/2016] [Indexed: 11/20/2022] Open
Abstract
In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were inconsistent. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response, seemingly contradicting our previous findings. Reanalyzing the authors' published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.
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Affiliation(s)
- Zhaleh Safikhani
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, M5G 1L7, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
| | - Nehme El-Hachem
- Institut de recherches cliniques de Montréal, Montreal, Quebec, H2W 1R7, Canada
| | - Rene Quevedo
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, M5G 1L7, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, M5G 1L7, Canada
| | - Anna Goldenberg
- Hospital for Sick Children, Toronto, Ontario, M5G 1X8, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, M5S 2E4, Canada
| | | | - Christopher Mason
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
- The Feil Family Brain and Mind Research Institute (BMRI), New York, NY, 10065, USA
| | - Christos Hatzis
- Section of Medical Oncology, Yale School of Medicine, New Haven, CT, 06520, USA
- Yale Cancer Center, Yale University, New Haven, CT, 06510, USA
| | - Leming Shi
- Fudan University, Shanghai City, 200135, China
- University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
| | - Hugo JWL Aerts
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA
| | - John Quackenbush
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, M5G 1L7, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, M5S 2E4, Canada
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