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Andersen AN, Brodersen AM, Ayuda-Durán P, Piechaczyk L, Tadele DS, Baken L, Fredriksen J, Stoksflod M, Lenartova A, Fløisand Y, Skånland SS, Enserink JM. Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling. CELL REPORTS METHODS 2023; 3:100654. [PMID: 38065095 PMCID: PMC10753296 DOI: 10.1016/j.crmeth.2023.100654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 09/16/2023] [Accepted: 11/09/2023] [Indexed: 12/21/2023]
Abstract
Current treatment selection for acute myeloid leukemia (AML) patients depends on risk stratification based on cytogenetic and genomic markers. However, the forecasting accuracy of treatment response remains modest, with most patients receiving intensive chemotherapy. Recently, ex vivo drug screening has gained traction in personalized treatment selection and as a tool for mapping patient groups based on relevant cancer dependencies. Here, we systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that ex vivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, ex vivo drug profiling can assess risk for individual AML patients and may guide clinical decision-making.
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Affiliation(s)
- Aram N Andersen
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Blindern, 0318 Oslo, Norway; Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, Blindernveien 31, 0371 Oslo, Norway.
| | - Andrea M Brodersen
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Blindern, 0318 Oslo, Norway; Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, Blindernveien 31, 0371 Oslo, Norway
| | - Pilar Ayuda-Durán
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Blindern, 0318 Oslo, Norway
| | - Laure Piechaczyk
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Blindern, 0318 Oslo, Norway
| | - Dagim Shiferaw Tadele
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Blindern, 0318 Oslo, Norway
| | - Lizet Baken
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Blindern, 0318 Oslo, Norway
| | - Julia Fredriksen
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Blindern, 0318 Oslo, Norway; Department of Haematology, Oslo University Hospital, 0372 Oslo, Norway
| | - Mia Stoksflod
- Department of Haematology, Oslo University Hospital, 0372 Oslo, Norway
| | - Andrea Lenartova
- Department of Haematology, Oslo University Hospital, 0372 Oslo, Norway
| | - Yngvar Fløisand
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Blindern, 0318 Oslo, Norway
| | - Sigrid S Skånland
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Montebello, 0379 Oslo, Norway; K.G. Jebsen Centre for B Cell Malignancies, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
| | - Jorrit M Enserink
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Blindern, 0318 Oslo, Norway; Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, Blindernveien 31, 0371 Oslo, Norway.
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2
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White BS, Khan SA, Mason MJ, Ammad-Ud-Din M, Potdar S, Malani D, Kuusanmäki H, Druker BJ, Heckman C, Kallioniemi O, Kurtz SE, Porkka K, Tognon CE, Tyner JW, Aittokallio T, Wennerberg K, Guinney J. Bayesian multi-source regression and monocyte-associated gene expression predict BCL-2 inhibitor resistance in acute myeloid leukemia. NPJ Precis Oncol 2021; 5:71. [PMID: 34302041 PMCID: PMC8302655 DOI: 10.1038/s41698-021-00209-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 06/22/2021] [Indexed: 11/09/2022] Open
Abstract
The FDA recently approved eight targeted therapies for acute myeloid leukemia (AML), including the BCL-2 inhibitor venetoclax. Maximizing efficacy of these treatments requires refining patient selection. To this end, we analyzed two recent AML studies profiling the gene expression and ex vivo drug response of primary patient samples. We find that ex vivo samples often exhibit a general sensitivity to (any) drug exposure, independent of drug target. We observe that this "general response across drugs" (GRD) is associated with FLT3-ITD mutations, clinical response to standard induction chemotherapy, and overall survival. Further, incorporating GRD into expression-based regression models trained on one of the studies improved their performance in predicting ex vivo response in the second study, thus signifying its relevance to precision oncology efforts. We find that venetoclax response is independent of GRD but instead show that it is linked to expression of monocyte-associated genes by developing and applying a multi-source Bayesian regression approach. The method shares information across studies to robustly identify biomarkers of drug response and is broadly applicable in integrative analyses.
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Affiliation(s)
- Brian S White
- Computational Oncology, Sage Bionetworks, Seattle, WA, USA.
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
| | - Suleiman A Khan
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Mike J Mason
- Computational Oncology, Sage Bionetworks, Seattle, WA, USA
| | - Muhammad Ammad-Ud-Din
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Swapnil Potdar
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Disha Malani
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Heikki Kuusanmäki
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Biotech Research & Innovation Centre (BRIC) and Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), University of Copenhagen, Copenhagen, Denmark
| | - Brian J Druker
- Howard Hughes Medical Institute, Portland, OR, USA
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Caroline Heckman
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Olli Kallioniemi
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Scilifelab, Karolinska Institute, Solna, Sweden
| | - Stephen E Kurtz
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Kimmo Porkka
- HUS Comprehensive Cancer Center, Hematology Research Unit Helsinki and iCAN Digital Precision Cancer Center Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Cristina E Tognon
- Howard Hughes Medical Institute, Portland, OR, USA
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Jeffrey W Tyner
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), University of Oslo, Oslo, Norway
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Biotech Research & Innovation Centre (BRIC) and Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), University of Copenhagen, Copenhagen, Denmark
| | - Justin Guinney
- Computational Oncology, Sage Bionetworks, Seattle, WA, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
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Rahman R, Dhruba SR, Matlock K, De-Niz C, Ghosh S, Pal R. Evaluating the consistency of large-scale pharmacogenomic studies. Brief Bioinform 2020; 20:1734-1753. [PMID: 31846027 DOI: 10.1093/bib/bby046] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 05/04/2018] [Indexed: 12/21/2022] Open
Abstract
Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) that provide genomic and functional characterization information for multiple cell lines. Studies have alluded to the fact that specific characterizations may be inconsistent between different databases. Analysis of the potential discrepancies in the different databases is highly significant, as these sources are frequently used to analyze and validate methodologies for personalized cancer therapies. In this article, we review the recent developments in investigating the correspondence between different pharmacogenomics databases and discuss the potential factors that require attention when incorporating these sources in any modeling analysis. Furthermore, we explored the consistency among these databases using copulas that can capture nonlinear dependencies between two sets of data.
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Affiliation(s)
- Raziur Rahman
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Saugato Rahman Dhruba
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Kevin Matlock
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Carlos De-Niz
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Souparno Ghosh
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA
| | - Ranadip Pal
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.,Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA
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Shockley KR, Gupta S, Harris SF, Lahiri SN, Peddada SD. Quality Control of Quantitative High Throughput Screening Data. Front Genet 2019; 10:387. [PMID: 31143201 PMCID: PMC6520559 DOI: 10.3389/fgene.2019.00387] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 04/10/2019] [Indexed: 01/08/2023] Open
Abstract
Quantitative high throughput screening (qHTS) experiments can generate 1000s of concentration-response profiles to screen compounds for potentially adverse effects. However, potency estimates for a single compound can vary considerably in study designs incorporating multiple concentration-response profiles for each compound. We introduce an automated quality control procedure based on analysis of variance (ANOVA) to identify and filter out compounds with multiple cluster response patterns and improve potency estimation in qHTS assays. Our approach, called Cluster Analysis by Subgroups using ANOVA (CASANOVA), clusters compound-specific response patterns into statistically supported subgroups. Applying CASANOVA to 43 publicly available qHTS data sets, we found that only about 20% of compounds with response values outside of the noise band have single cluster responses. The error rates for incorrectly separating true clusters and incorrectly clumping disparate clusters were both less than 5% in extensive simulation studies. Simulation studies also showed that the bias and variance of concentration at half-maximal response (AC50 ) estimates were usually within 10-fold when using a weighted average approach for potency estimation. In short, CASANOVA effectively sorts out compounds with "inconsistent" response patterns and produces trustworthy AC50 values.
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Affiliation(s)
- Keith R. Shockley
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC, United States
| | - Shuva Gupta
- Statistics Department, University of Pennsylvania, Philadelphia, PA, United States
| | | | - Soumendra N. Lahiri
- Department of Statistics, North Carolina State University, Raleigh, NC, United States
| | - Shyamal D. Peddada
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
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Wass MN, Ray L, Michaelis M. Understanding of researcher behavior is required to improve data reliability. Gigascience 2019; 8:giz017. [PMID: 30715291 PMCID: PMC6528747 DOI: 10.1093/gigascience/giz017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 01/20/2019] [Accepted: 01/25/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND A lack of data reproducibility ("reproducibility crisis") has been extensively debated across many academic disciplines. RESULTS Although a reproducibility crisis is widely perceived, conclusive data on the scale of the problem and the underlying reasons are largely lacking. The debate is primarily focused on methodological issues. However, examples such as the use of misidentified cell lines illustrate that the availability of reliable methods does not guarantee good practice. Moreover, research is often characterized by a lack of established methods. Despite the crucial importance of researcher conduct, research and conclusive data on the determinants of researcher behavior are widely missing. CONCLUSION Meta-research that establishes an understanding of the factors that determine researcher behavior is urgently needed. This knowledge can then be used to implement and iteratively improve measures that incentivize researchers to apply the highest standards, resulting in high-quality data.
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Affiliation(s)
- Mark N Wass
- Industrial Biotechnology Centre and School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK
| | - Larry Ray
- School of Social Policy, Sociology and Social Research, University of Kent, Canterbury, CT2 7NJ, UK
| | - Martin Michaelis
- Industrial Biotechnology Centre and School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK
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PATRI, a Genomics Data Integration Tool for Biomarker Discovery. BIOMED RESEARCH INTERNATIONAL 2018; 2018:2012078. [PMID: 30065933 PMCID: PMC6051285 DOI: 10.1155/2018/2012078] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 05/29/2018] [Indexed: 12/31/2022]
Abstract
The availability of genomic datasets in association with clinical, phenotypic, and drug sensitivity information represents an invaluable source for potential therapeutic applications, supporting the identification of new drug sensitivity biomarkers and pharmacological targets. Drug discovery and precision oncology can largely benefit from the integration of treatment molecular discriminants obtained from cell line models and clinical tumor samples; however this task demands comprehensive analysis approaches for the discovery of underlying data connections. Here we introduce PATRI (Platform for the Analysis of TRanslational Integrated data), a standalone tool accessible through a user-friendly graphical interface, conceived for the identification of treatment sensitivity biomarkers from user-provided genomics data, associated with information on sample characteristics. PATRI streamlines a translational analysis workflow: first, baseline genomics signatures are statistically identified, differentiating treatment sensitive from resistant preclinical models; then, these signatures are used for the prediction of treatment sensitivity in clinical samples, via random forest categorization of clinical genomics datasets and statistical evaluation of the relative phenotypic features. The same workflow can also be applied across distinct clinical datasets. The ease of use of the PATRI tool is illustrated with validation analysis examples, performed with sensitivity data for drug treatments with known molecular discriminants.
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