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Baek B, Jang E, Park S, Park SH, Williams DR, Jung DW, Lee H. Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma. PLoS One 2024; 19:e0295629. [PMID: 38277404 PMCID: PMC10817174 DOI: 10.1371/journal.pone.0295629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/24/2023] [Indexed: 01/28/2024] Open
Abstract
Targeted therapies for inhibiting the growth of cancer cells or inducing apoptosis are urgently needed for effective rhabdomyosarcoma (RMS) treatment. However, identifying cancer-targeting compounds with few side effects, among the many potential compounds, is expensive and time-consuming. A computational approach to reduce the number of potential candidate drugs can facilitate the discovery of attractive lead compounds. To address this and obtain reliable predictions of novel cell-line-specific drugs, we apply prediction models that have the potential to improve drug discovery approaches for RMS treatment. The results of two prediction models were ensemble and validated via in vitro experiments. The computational models were trained using data extracted from the Genomics of Drug Sensitivity in Cancer database and tested on two RMS cell lines to select potential RMS drug candidates. Among 235 candidate drugs, 22 were selected following the result of the computational approach, and three candidate drugs were identified (NSC207895, vorinostat, and belinostat) that showed selective effectiveness in RMS cell lines in vitro via the induction of apoptosis. Our in vitro experiments have demonstrated that our proposed methods can effectively identify and repurpose drugs for treating RMS.
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Affiliation(s)
- Bin Baek
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Eunmi Jang
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Sejin Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Sung-Hye Park
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Neuroscience, Seoul National University Hospital, Seoul, Republic of Korea
| | - Darren Reece Williams
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Da-Woon Jung
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
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2
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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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3
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Hu ZT, Yu Y, Chen R, Yeh SJ, Chen B, Huang H. Large-Scale Information Retrieval and Correction of Noisy Pharmacogenomic Datasets through Residual Thresholded Deep Matrix Factorization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570723. [PMID: 38106027 PMCID: PMC10723412 DOI: 10.1101/2023.12.07.570723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Pharmacogenomics studies are attracting an increasing amount of interest from researchers in precision medicine. The advances in high-throughput experiments and multiplexed approaches allow the large-scale quantification of drug sensitivities in molecularly characterized cancer cell lines (CCLs), resulting in a number of open drug sensitivity datasets for drug biomarker discovery. However, a significant inconsistency in drug sensitivity values among these datasets has been noted. Such inconsistency indicates the presence of substantial noise, subsequently hindering downstream analyses. To address the noise in drug sensitivity data, we introduce a robust and scalable deep learning framework, Residual Thresholded Deep Matrix Factorization (RT-DMF). This method takes a single drug sensitivity data matrix as its sole input and outputs a corrected and imputed matrix. Deep Matrix Factorization (DMF) excels at uncovering subtle patterns, due to its minimal reliance on data structure assumptions. This attribute significantly boosts DMF's ability to identify complex hidden patterns among nuisance effects in the data, thereby facilitating the detection of signals that are therapeutically relevant. Furthermore, RT-DMF incorporates an iterative residual thresholding (RT) procedure, which plays a crucial role in retaining signals more likely to hold therapeutic importance. Validation using simulated datasets and real pharmacogenomics datasets demonstrates the effectiveness of our approach in correcting noise and imputing missing data in drug sensitivity datasets (open source package available at https://github.com/tomwhoooo/rtdmf).
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Affiliation(s)
- Zhiyue Tom Hu
- Division of Biostatistics, University of California Berkeley, Berkeley, 94720, U.S.A
| | - Yaodong Yu
- Department of Electrical Engineer and Computer Science, University of California Berkeley, Berkeley, 94720, U.S.A
| | - Ruoqiao Chen
- Department of Pharmacology and Toxicology, Michigan State University, 48824, U.S.A
| | - Shan-Ju Yeh
- School of Medicine, National Tsing Hua University, Hsinchu, 300044, Taiwan R.O.C
| | - Bin Chen
- Department of Pharmacology and Toxicology, Michigan State University, 48824, U.S.A
- Department of Pediatrics and Human Development, Michigan State University, 48824, U.S.A
| | - Haiyan Huang
- Department of Statistics, University of California Berkeley, Berkeley, 94720, U.S.A
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4
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Ploenzke M, Irizarry R. Reassessing pharmacogenomic cell sensitivity with multilevel statistical models. Biostatistics 2023; 24:901-921. [PMID: 35277956 PMCID: PMC10583722 DOI: 10.1093/biostatistics/kxac010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 02/04/2022] [Accepted: 02/14/2022] [Indexed: 10/19/2023] Open
Abstract
Pharmacogenomic experiments allow for the systematic testing of drugs, at varying dosage concentrations, to study how genomic markers correlate with cell sensitivity to treatment. The first step in the analysis is to quantify the response of cell lines to variable dosage concentrations of the drugs being tested. The signal to noise in these measurements can be low due to biological and experimental variability. However, the increasing availability of pharmacogenomic studies provides replicated data sets that can be leveraged to gain power. To do this, we formulate a hierarchical mixture model to estimate the drug-specific mixture distributions for estimating cell sensitivity and for assessing drug effect type as either broad or targeted effect. We use this formulation to propose a unified approach that can yield posterior probability of a cell being susceptible to a drug conditional on being a targeted effect or relative effect sizes conditioned on the cell being broad. We demonstrate the usefulness of our approach via case studies. First, we assess pairwise agreements for cell lines/drugs within the intersection of two data sets and confirm the moderate pairwise agreement between many publicly available pharmacogenomic data sets. We then present an analysis that identifies sensitivity to the drug crizotinib for cells harboring EML4-ALK or NPM1-ALK gene fusions, as well as significantly down-regulated cell-matrix pathways associated with crizotinib sensitivity.
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Affiliation(s)
- Matt Ploenzke
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Building 2, 4th Floor, Boston, MA 02115
| | - Rafael Irizarry
- Department of Data Science, Dana Farber Cancer Institute, 450 Brookline Ave, CLSB 11007, Boston, MA 02215
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5
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Kaushik AC, Zhao Z. Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment. Front Mol Biosci 2023; 10:1215204. [PMID: 37602329 PMCID: PMC10436744 DOI: 10.3389/fmolb.2023.1215204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/21/2023] [Indexed: 08/22/2023] Open
Abstract
Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.
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Affiliation(s)
- Aman Chandra Kaushik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States
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6
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Mehmood A, Nawab S, Jin Y, Hassan H, Kaushik AC, Wei DQ. Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics. ACS Pharmacol Transl Sci 2023; 6:399-409. [PMID: 36926455 PMCID: PMC10012252 DOI: 10.1021/acsptsci.2c00212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Indexed: 02/26/2023]
Abstract
Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.
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Affiliation(s)
- Aamir Mehmood
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Sadia Nawab
- State
Key Laboratory of Microbial Metabolism and School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P.R. China
| | - Yifan Jin
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Hesham Hassan
- Department
of Pathology, College of Medicine, King
Khalid University, Abha 61421, Saudi Arabia
- Department
of Pathology, Faculty of Medicine, Assiut
University, Assiut 71515, Egypt
| | - Aman Chandra Kaushik
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Dong-Qing Wei
- State
Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade
Joint Innovation Center on Antibacterial Resistances, Joint International
Research Laboratory of Metabolic & Developmental Sciences and
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
- Zhongjing
Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan 473006, P.R. China
- Peng
Cheng National Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong 518055, P.R. China
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7
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Abbas-Aghababazadeh F, Xu W, Haibe-Kains B. The impact of violating the independence assumption in meta-analysis on biomarker discovery. Front Genet 2023; 13:1027345. [PMID: 36726714 PMCID: PMC9885264 DOI: 10.3389/fgene.2022.1027345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/25/2022] [Indexed: 01/06/2023] Open
Abstract
With rapid advancements in high-throughput sequencing technologies, massive amounts of "-omics" data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored via simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology.
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Affiliation(s)
| | - Wei Xu
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada,Ontario Institute for Cancer Research, Toronto, ON, Canada,Department of Computer Science, University of Toronto, Toronto, ON, Canada,*Correspondence: Benjamin Haibe-Kains,
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8
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Krushkal J, Vural S, Jensen TL, Wright G, Zhao Y. Increased copy number of imprinted genes in the chromosomal region 20q11-q13.32 is associated with resistance to antitumor agents in cancer cell lines. Clin Epigenetics 2022; 14:161. [PMID: 36461044 PMCID: PMC9716673 DOI: 10.1186/s13148-022-01368-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 10/31/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Parent of origin-specific allelic expression of imprinted genes is epigenetically controlled. In cancer, imprinted genes undergo both genomic and epigenomic alterations, including frequent copy number changes. We investigated whether copy number loss or gain of imprinted genes in cancer cell lines is associated with response to chemotherapy treatment. RESULTS We analyzed 198 human imprinted genes including protein-coding genes and noncoding RNA genes using data from tumor cell lines from the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer datasets. We examined whether copy number of the imprinted genes in 35 different genome locations was associated with response to cancer drug treatment. We also analyzed associations of pretreatment expression and DNA methylation of imprinted genes with drug response. Higher copy number of BLCAP, GNAS, NNAT, GNAS-AS1, HM13, MIR296, MIR298, and PSIMCT-1 in the chromosomal region 20q11-q13.32 was associated with resistance to multiple antitumor agents. Increased expression of BLCAP and HM13 was also associated with drug resistance, whereas higher methylation of gene regions of BLCAP, NNAT, SGK2, and GNAS was associated with drug sensitivity. While expression and methylation of imprinted genes in several other chromosomal regions was also associated with drug response and many imprinted genes in different chromosomal locations showed a considerable copy number variation, only imprinted genes at 20q11-q13.32 had a consistent association of their copy number with drug response. Copy number values among the imprinted genes in the 20q11-q13.32 region were strongly correlated. They were also correlated with the copy number of cancer-related non-imprinted genes MYBL2, AURKA, and ZNF217 in that chromosomal region. Expression of genes at 20q11-q13.32 was associated with ex vivo drug response in primary tumor samples from the Beat AML 1.0 acute myeloid leukemia patient cohort. Association of the increased copy number of the 20q11-q13.32 region with drug resistance may be complex and could involve multiple genes. CONCLUSIONS Copy number of imprinted and non-imprinted genes in the chromosomal region 20q11-q13.32 was associated with cancer drug resistance. The genes in this chromosomal region may have a modulating effect on tumor response to chemotherapy.
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Affiliation(s)
- Julia Krushkal
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr, Rockville, MD, 20850, USA.
| | - Suleyman Vural
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr, Rockville, MD, 20850, USA.,Marie-Josee and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | | | - George Wright
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr, Rockville, MD, 20850, USA
| | - Yingdong Zhao
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr, Rockville, MD, 20850, USA
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9
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Yingtaweesittikul H, Wu J, Mongia A, Peres R, Ko K, Nagarajan N, Suphavilai C. CREAMMIST: an integrative probabilistic database for cancer drug response prediction. Nucleic Acids Res 2022; 51:D1242-D1248. [PMID: 36259664 PMCID: PMC9825458 DOI: 10.1093/nar/gkac911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/18/2022] [Accepted: 10/11/2022] [Indexed: 01/30/2023] Open
Abstract
Extensive in vitro cancer drug screening datasets have enabled scientists to identify biomarkers and develop machine learning models for predicting drug sensitivity. While most advancements have focused on omics profiles, cancer drug sensitivity scores precalculated by the original sources are often used as-is, without consideration for variabilities between studies. It is well-known that significant inconsistencies exist between the drug sensitivity scores across datasets due to differences in experimental setups and preprocessing methods used to obtain the sensitivity scores. As a result, many studies opt to focus only on a single dataset, leading to underutilization of available data and a limited interpretation of cancer pharmacogenomics analysis. To overcome these caveats, we have developed CREAMMIST (https://creammist.mtms.dev), an integrative database that enables users to obtain an integrative dose-response curve, to capture uncertainty (or high certainty when multiple datasets well align) across five widely used cancer cell-line drug-response datasets. We utilized the Bayesian framework to systematically integrate all available dose-response values across datasets (>14 millions dose-response data points). CREAMMIST provides easy-to-use statistics derived from the integrative dose-response curves for various downstream analyses such as identifying biomarkers, selecting drug concentrations for experiments, and training robust machine learning models.
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Affiliation(s)
| | - Jiaxi Wu
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Aanchal Mongia
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Rafael Peres
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Karrie Ko
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | | | - Chayaporn Suphavilai
- To whom correspondence should be addressed. Tel: +65 86213683; Fax: +65 68088292;
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10
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Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer’s disease drug repurposing. PLoS Comput Biol 2022; 18:e1010367. [PMID: 35951653 PMCID: PMC9398009 DOI: 10.1371/journal.pcbi.1010367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 08/23/2022] [Accepted: 07/08/2022] [Indexed: 11/19/2022] Open
Abstract
Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art machine learning methods use a small number of fixed cell lines as a surrogate for predicting actual expressions in a new cell type or tissue, although it is well known that drug responses depend on a cellular context. Thus, the existing approach has limitations when applied to personalized medicine, especially for many understudied diseases whose molecular profiles are dramatically different from those characterized in the training data. Besides the gene expression, dose-dependent cell viability is another important phenotype readout and is more informative than conventional summary statistics (e.g., IC50) for characterizing clinical drug efficacy and toxicity. However, few computational methods can reliably predict the dose-dependent cell viability. To address the challenges mentioned above, we designed a new deep learning model, MultiDCP, to predict cellular context-dependent gene expressions and cell viability on a specific dosage. The novelties of MultiDCP include a knowledge-driven gene expression profile transformer that enables context-specific phenotypic response predictions of novel cells or tissues, integration of multiple diverse labeled and unlabeled omics data, the joint training of the multiple prediction tasks, and a teacher-student training procedure that allows us to utilize unreliable data effectively. Comprehensive benchmark studies suggest that MultiDCP outperforms state-of-the-art methods with unseen cell lines that are dissimilar from the cell lines in the supervised training in terms of gene expressions. The predicted drug-induced gene expressions demonstrate a stronger predictive power than noisy experimental data for downstream tasks. Thus, MultiDCP is a useful tool for transcriptomics-based drug repurposing and compound screening that currently rely on noisy high-throughput experimental data. We applied MultiDCP to repurpose individualized drugs for Alzheimer’s disease in terms of efficacy and toxicity, suggesting that MultiDCP is a potentially powerful tool for personalized drug discovery. Conventional target-based compound screening that follows the one-drug-one-gene drug discovery paradigm has a low success rate in tackling multi-genic systemic diseases such as Alzheimer’s disease. A systems pharmacology strategy is needed to target gene regulatory networks. To enable systems pharmacology-oriented phenotypic screening, it is critical to utilize a mechanistic phenotype readout to link drug responses in a model system to drug toxicity and efficacy in an individual. Chemical-induced dose-dependent gene expression profiles provide critical information on drug mode of action and off-target effects and can identify drug candidates that reverse disease phenotypes. However, state-of-the-art machine learning methods for predicting chemical-induced gene expressions are all trained using data from a limited number of cancer cell lines and can only achieve suboptimal performance when applied to new cell types or patient samples. Here, we have developed a new deep learning framework to address this challenge and demonstrated its potential in personalized drug repurposing using Alzheimer’s disease as a case study.
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11
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Wang XS, Lee S, Zhang H, Tang G, Wang Y. An integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data. Nat Commun 2022; 13:2936. [PMID: 35618721 PMCID: PMC9135729 DOI: 10.1038/s41467-022-30449-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/29/2022] [Indexed: 11/19/2022] Open
Abstract
Low-cost multi-omics sequencing is expected to become clinical routine and transform precision oncology. Viable computational methods that can facilitate tailored intervention while tolerating sequencing biases are in high demand. Here we propose a class of transparent and interpretable computational methods called integral genomic signature (iGenSig) analyses, that address the challenges of cross-dataset modeling through leveraging information redundancies within high-dimensional genomic features, averaging feature weights to prevent overweighing, and extracting unbiased genomic information from large tumor cohorts. Using genomic dataset of chemical perturbations, we develop a battery of iGenSig models for predicting cancer drug responses, and validate the models using independent cell-line and clinical datasets. The iGenSig models for five drugs demonstrate predictive values in six clinical studies, among which the Erlotinib and 5-FU models significantly predict therapeutic responses in three studies, offering clinically relevant insights into their inverse predictive signature pathways. Together, iGenSig provides a computational framework to facilitate tailored cancer therapy based on multi-omics data.
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Affiliation(s)
- Xiao-Song Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
| | - Sanghoon Lee
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15206, USA
| | - Han Zhang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15206, USA
| | - Gong Tang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Yue Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
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12
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Ba-Alawi W, Kadambat Nair S, Li B, Mammoliti A, Smirnov P, Mer AS, Penn LZ, Haibe-Kains B. Bimodal gene expression in cancer patients provides interpretable biomarkers for drug sensitivity. Cancer Res 2022; 82:2378-2387. [PMID: 35536872 DOI: 10.1158/0008-5472.can-21-2395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 02/24/2022] [Accepted: 05/06/2022] [Indexed: 11/16/2022]
Abstract
Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation, support a better translation of gene expression biomarkers between model systems using bimodal gene expression.
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Affiliation(s)
| | | | - Bo Li
- University of Toronto, Toronto, Canada
| | | | | | | | - Linda Z Penn
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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13
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Cai L, Xiao G, Gerber D, D Minna J, Xie Y. Lung Cancer Computational Biology and Resources. Cold Spring Harb Perspect Med 2022; 12:a038273. [PMID: 34751162 PMCID: PMC8805643 DOI: 10.1101/cshperspect.a038273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Comprehensive clinical, pathological, and molecular data, when appropriately integrated with advanced computational approaches, are transforming the way we characterize and study lung cancer. Clinically, cancer registry and publicly available historical clinical trial data enable retrospective analyses to examine how socioeconomic factors, patient demographics, and cancer characteristics affect treatment and outcome. Pathologically, digital pathology and artificial intelligence are revolutionizing histopathological image analyses, not only with improved efficiency and accuracy, but also by extracting additional information for prognostication and tumor microenvironment characterization. Genetically and molecularly, individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities. The resulting multi-omics data sets and their interrogation facilitate both basic research mechanistic studies and translation of the findings into the clinic. In this review, we provide a list of resources and tools potentially valuable for lung cancer basic and translational research. Importantly, we point out pitfalls and caveats when performing computational analyses of these data sets and provide a vision of future computational biology developments that will aid lung cancer translational research.
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Affiliation(s)
- Ling Cai
- Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Children's Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - David Gerber
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - John D Minna
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas 75390, USA
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14
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Xia F, Allen J, Balaprakash P, Brettin T, Garcia-Cardona C, Clyde A, Cohn J, Doroshow J, Duan X, Dubinkina V, Evrard Y, Fan YJ, Gans J, He S, Lu P, Maslov S, Partin A, Shukla M, Stahlberg E, Wozniak JM, Yoo H, Zaki G, Zhu Y, Stevens R. A cross-study analysis of drug response prediction in cancer cell lines. Brief Bioinform 2022; 23:bbab356. [PMID: 34524425 PMCID: PMC8769697 DOI: 10.1093/bib/bbab356] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/26/2021] [Accepted: 08/11/2021] [Indexed: 11/28/2022] Open
Abstract
To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.
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Affiliation(s)
| | | | | | | | | | - Austin Clyde
- Argonne National Laboratory
- University of Chicago
| | | | | | | | | | | | - Ya Ju Fan
- Lawrence Livermore National Laboratory
| | | | | | - Pinyi Lu
- Frederick National Laboratory for Cancer Research
| | | | | | | | | | | | | | - George Zaki
- Frederick National Laboratory for Cancer Research
| | | | - Rick Stevens
- Argonne National Laboratory
- University of Chicago
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15
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Firoozbakht F, Yousefi B, Schwikowski B. An overview of machine learning methods for monotherapy drug response prediction. Brief Bioinform 2022; 23:bbab408. [PMID: 34619752 PMCID: PMC8769705 DOI: 10.1093/bib/bbab408] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/25/2021] [Accepted: 09/06/2021] [Indexed: 12/11/2022] Open
Abstract
For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classification that comprises more than 70 ML methods in 13 subclasses, their input and output data types, modes of evaluation, and code and software availability. ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. Biologists and biomedical researchers are introduced to the basic principles of applicable ML methods, and their application to the problem of drug response prediction. We also provide systematic overviews of commonly used data sources used for training and evaluation methods.
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Affiliation(s)
- Farzaneh Firoozbakht
- Systems Biology Group, Department of Computational Biology, Institut Pasteur, Paris, France
| | - Behnam Yousefi
- Systems Biology Group, Department of Computational Biology, Institut Pasteur, Paris, France
- Sorbonne Université, École Doctorale Complexite du Vivant, Paris, France
| | - Benno Schwikowski
- Systems Biology Group, Department of Computational Biology, Institut Pasteur, Paris, France
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16
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Suphavilai C, Chia S, Sharma A, Tu L, Da Silva RP, Mongia A, DasGupta R, Nagarajan N. Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures. Genome Med 2021; 13:189. [PMID: 34915921 PMCID: PMC8680165 DOI: 10.1186/s13073-021-01000-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 11/02/2021] [Indexed: 12/22/2022] Open
Abstract
While understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc's monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc .
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Affiliation(s)
| | - Shumei Chia
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Ankur Sharma
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Lorna Tu
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Rafael Peres Da Silva
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
- School of Computing, National University of Singapore, Singapore, Singapore
| | - Aanchal Mongia
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi, India
| | | | - Niranjan Nagarajan
- Genome Institute of Singapore, A*STAR, Singapore, Singapore.
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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17
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Orchestrating and sharing large multimodal data for transparent and reproducible research. Nat Commun 2021; 12:5797. [PMID: 34608132 PMCID: PMC8490371 DOI: 10.1038/s41467-021-25974-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 09/08/2021] [Indexed: 11/08/2022] Open
Abstract
Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.
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18
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Rydzewski NR, Peterson E, Lang JM, Yu M, Laura Chang S, Sjöström M, Bakhtiar H, Song G, Helzer KT, Bootsma ML, Chen WS, Shrestha RM, Zhang M, Quigley DA, Aggarwal R, Small EJ, Wahl DR, Feng FY, Zhao SG. Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures. NPJ Genom Med 2021; 6:76. [PMID: 34548481 PMCID: PMC8455625 DOI: 10.1038/s41525-021-00239-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/23/2021] [Indexed: 12/14/2022] Open
Abstract
We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.
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Affiliation(s)
| | - Erik Peterson
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Joshua M Lang
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Menggang Yu
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
| | - S Laura Chang
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
| | - Martin Sjöström
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
| | - Hamza Bakhtiar
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Gefei Song
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Kyle T Helzer
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Matthew L Bootsma
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - William S Chen
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
| | | | - Meng Zhang
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
| | - David A Quigley
- Helen Diller Family Comprehensive Cancer Center, UCSF, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA
| | - Rahul Aggarwal
- Helen Diller Family Comprehensive Cancer Center, UCSF, San Francisco, CA, USA
- Division of Hematology and Oncology, Department of Medicine, UCSF, San Francisco, CA, USA
| | - Eric J Small
- Helen Diller Family Comprehensive Cancer Center, UCSF, San Francisco, CA, USA
- Division of Hematology and Oncology, Department of Medicine, UCSF, San Francisco, CA, USA
| | - Daniel R Wahl
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Felix Y Feng
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, UCSF, San Francisco, CA, USA
- Division of Hematology and Oncology, Department of Medicine, UCSF, San Francisco, CA, USA
- Department of Urology, UCSF, San Francisco, CA, USA
| | - Shuang G Zhao
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA.
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA.
- William S. Middleton Memorial Veterans Hospital, Madison, WI, USA.
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19
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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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20
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Liu N, Wu Y, Cheng W, Wu Y, Wang L, Zhuang L. Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer. BMC Cancer 2021; 21:460. [PMID: 33902514 PMCID: PMC8073914 DOI: 10.1186/s12885-021-08210-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/13/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Gastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics. METHODS We integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation. RESULTS We comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration of WASHC5 (also known as KIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value. CONCLUSIONS These findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC.
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Affiliation(s)
- Nannan Liu
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Yun Wu
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Weipeng Cheng
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Yuxuan Wu
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Liguo Wang
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China.
| | - Liwei Zhuang
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China.
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21
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Vural S, Palmisano A, Reinhold WC, Pommier Y, Teicher BA, Krushkal J. Association of expression of epigenetic molecular factors with DNA methylation and sensitivity to chemotherapeutic agents in cancer cell lines. Clin Epigenetics 2021; 13:49. [PMID: 33676569 PMCID: PMC7936435 DOI: 10.1186/s13148-021-01026-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 02/10/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Altered DNA methylation patterns play important roles in cancer development and progression. We examined whether expression levels of genes directly or indirectly involved in DNA methylation and demethylation may be associated with response of cancer cell lines to chemotherapy treatment with a variety of antitumor agents. RESULTS We analyzed 72 genes encoding epigenetic factors directly or indirectly involved in DNA methylation and demethylation processes. We examined association of their pretreatment expression levels with methylation beta-values of individual DNA methylation probes, DNA methylation averaged within gene regions, and average epigenome-wide methylation levels. We analyzed data from 645 cancer cell lines and 23 cancer types from the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer datasets. We observed numerous correlations between expression of genes encoding epigenetic factors and response to chemotherapeutic agents. Expression of genes encoding a variety of epigenetic factors, including KDM2B, DNMT1, EHMT2, SETDB1, EZH2, APOBEC3G, and other genes, was correlated with response to multiple agents. DNA methylation of numerous target probes and gene regions was associated with expression of multiple genes encoding epigenetic factors, underscoring complex regulation of epigenome methylation by multiple intersecting molecular pathways. The genes whose expression was associated with methylation of multiple epigenome targets encode DNA methyltransferases, TET DNA methylcytosine dioxygenases, the methylated DNA-binding protein ZBTB38, KDM2B, SETDB1, and other molecular factors which are involved in diverse epigenetic processes affecting DNA methylation. While baseline DNA methylation of numerous epigenome targets was correlated with cell line response to antitumor agents, the complex relationships between the overlapping effects of each epigenetic factor on methylation of specific targets and the importance of such influences in tumor response to individual agents require further investigation. CONCLUSIONS Expression of multiple genes encoding epigenetic factors is associated with drug response and with DNA methylation of numerous epigenome targets that may affect response to therapeutic agents. Our findings suggest complex and interconnected pathways regulating DNA methylation in the epigenome, which may both directly and indirectly affect response to chemotherapy.
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Affiliation(s)
- Suleyman Vural
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr., Rockville, MD, 20850, USA
| | - Alida Palmisano
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr., Rockville, MD, 20850, USA
- General Dynamics Information Technology (GDIT), 3150 Fairview Park Drive, Falls Church, VA, 22042, USA
| | - William C Reinhold
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Yves Pommier
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Beverly A Teicher
- Molecular Pharmacology Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Julia Krushkal
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr., Rockville, MD, 20850, USA.
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22
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Luna A, Elloumi F, Varma S, Wang Y, Rajapakse VN, Aladjem MI, Robert J, Sander C, Pommier Y, Reinhold WC. CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics. Nucleic Acids Res 2021; 49:D1083-D1093. [PMID: 33196823 DOI: 10.1093/nar/gkaa968] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/25/2020] [Accepted: 10/19/2020] [Indexed: 12/13/2022] Open
Abstract
CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb) allows integration and analysis of molecular and pharmacological data within and across cancer cell line datasets from the National Cancer Institute (NCI), Broad Institute, Sanger/MGH and MD Anderson Cancer Center (MDACC). We present CellMinerCDB 1.2 with updates to datasets from NCI-60, Broad Cancer Cell Line Encyclopedia and Sanger/MGH, and the addition of new datasets, including NCI-ALMANAC drug combination, MDACC Cell Line Project proteomic, NCI-SCLC DNA copy number and methylation data, and Broad methylation, genetic dependency and metabolomic datasets. CellMinerCDB (v1.2) includes several improvements over the previously published version: (i) new and updated datasets; (ii) support for pattern comparisons and multivariate analyses across data sources; (iii) updated annotations with drug mechanism of action information and biologically relevant multigene signatures; (iv) analysis speedups via caching; (v) a new dataset download feature; (vi) improved visualization of subsets of multiple tissue types; (vii) breakdown of univariate associations by tissue type; and (viii) enhanced help information. The curation and common annotations (e.g. tissues of origin and identifiers) provided here across pharmacogenomic datasets increase the utility of the individual datasets to address multiple researcher question types, including data reproducibility, biomarker discovery and multivariate analysis of drug activity.
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Affiliation(s)
- Augustin Luna
- cBio Center, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Fathi Elloumi
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA.,General Dynamics Information Technology Inc., Fairfax, VA 22042, USA
| | - Sudhir Varma
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA.,HiThru Analytics LLC, Princeton, NJ 08540, USA
| | - Yanghsin Wang
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA.,General Dynamics Information Technology Inc., Fairfax, VA 22042, USA
| | - Vinodh N Rajapakse
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Mirit I Aladjem
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Jacques Robert
- Inserm unité 1218, Université de Bordeaux, Bordeaux 33076, France
| | - Chris Sander
- cBio Center, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Yves Pommier
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - William C Reinhold
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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23
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Kimble DC, Dvergsten E, Thomeas-McEwing V, Karovic S, Conrads TP, Maitland ML. Evaluation of publicly available in vitro drug sensitivity models for ovarian and uterine cancer. Gynecol Oncol 2020; 160:295-301. [PMID: 33190933 DOI: 10.1016/j.ygyno.2020.10.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 10/31/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Publicly available data on drug sensitivity for cancer cell lines have been curated into a single, integrated database, PharmacoDB. The contributing datasets report modeled estimates of drug effect from high throughput assays. These databases have been informative for developing new broad insights, but the reliability of these data specifically for drugs used to treat ovarian and uterine cancers in related cell lines has not been reported. METHODS In vitro viability assays were performed on A2780, OVCAR-3, TOV-21G, and RL95-2 cells with nine drugs to produce high resolution exposure-response curves. Lab generated data were compared to publicly available datasets by IC20, IC50, and IC80 values, and the area between the logarithmic logistic regression curves. RESULTS For exposure-response curve comparisons with clinically indicated drugs between lab generated and publicly available data, the majority had area-between-curves less than 20%, indicating similarity. However, 15 out of 40 of these dataset curves were incomplete as indicated by the lack of, or extrapolated, IC50 value. The common ovarian and uterine cancer drug, carboplatin, exemplified this incomplete status as all of the available dataset curves were incomplete and therefore non-informative. CONCLUSIONS For gynecologic malignancy cell line models, experimental drug sensitivity data is comparable to the available data in PharmacoDB when exposure-response curves are complete. Incomplete exposure-response curves due to incomplete concentration ranges tested and related extrapolation of IC values can mislead individual drug/cell line pair data for downstream applications.
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Affiliation(s)
- Danielle C Kimble
- Inova Schar Cancer Institute, Inova Health System, 8081 Innovation Park Drive, Fairfax, VA 22031, USA
| | - Erik Dvergsten
- Inova Schar Cancer Institute, Inova Health System, 8081 Innovation Park Drive, Fairfax, VA 22031, USA
| | - Vasiliki Thomeas-McEwing
- Inova Schar Cancer Institute, Inova Health System, 8081 Innovation Park Drive, Fairfax, VA 22031, USA
| | - Sanja Karovic
- Inova Schar Cancer Institute, Inova Health System, 8081 Innovation Park Drive, Fairfax, VA 22031, USA
| | - Thomas P Conrads
- Women's Health Integrated Research Center, Women's Service Line, Inova Health System, 3300 Gallows Road, Falls Church, VA 22042, USA
| | - Michael L Maitland
- Inova Schar Cancer Institute, Inova Health System, 8081 Innovation Park Drive, Fairfax, VA 22031, USA; Department of Medicine and Cancer Center, University of Virginia, Charlottesville, VA 22903, USA.
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24
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Assessment of Genetic Drift in Large Pharmacogenomic Studies. Cell Syst 2020; 11:393-401.e2. [DOI: 10.1016/j.cels.2020.08.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 07/04/2020] [Accepted: 08/18/2020] [Indexed: 12/16/2022]
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25
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Ahmadi Moughari F, Eslahchi C. ADRML: anticancer drug response prediction using manifold learning. Sci Rep 2020; 10:14245. [PMID: 32859983 PMCID: PMC7456328 DOI: 10.1038/s41598-020-71257-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/13/2020] [Indexed: 12/05/2022] Open
Abstract
One of the prominent challenges in precision medicine is to select the most appropriate treatment strategy for each patient based on the personalized information. The availability of massive data about drugs and cell lines facilitates the possibility of proposing efficient computational models for predicting anticancer drug response. In this study, we propose ADRML, a model for Anticancer Drug Response Prediction using Manifold Learning to systematically integrate the cell line information with the drug information to make accurate predictions about drug therapeutic. The proposed model maps the drug response matrix into the lower-rank spaces that lead to obtaining new perspectives about cell lines and drugs. The drug response for a new cell line-drug pair is computed using the low-rank features. The evaluation of ADRML performance on various types of cell lines and drug information, in addition to the comparisons with previously proposed methods, shows that ADRML provides accurate and robust predictions. Further investigations about the association between drug response and pathway activity scores reveal that the predicted drug responses can shed light on the underlying drug mechanism. Also, the case studies suggest that the predictions of ADRML about novel cell line-drug pairs are validated by reliable pieces of evidence from the literature. Consequently, the evaluations verify that ADRML can be used in accurately predicting and imputing the anticancer drug response.
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Affiliation(s)
- Fatemeh Ahmadi Moughari
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Changiz Eslahchi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran. .,School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
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26
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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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27
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Rampášek L, Hidru D, Smirnov P, Haibe-Kains B, Goldenberg A. Dr.VAE: improving drug response prediction via modeling of drug perturbation effects. Bioinformatics 2020; 35:3743-3751. [PMID: 30850846 PMCID: PMC6761940 DOI: 10.1093/bioinformatics/btz158] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 02/05/2019] [Accepted: 03/06/2019] [Indexed: 01/02/2023] Open
Abstract
Motivation Individualized drug response prediction is a fundamental part of personalized medicine for cancer. Great effort has been made to discover biomarkers or to develop machine learning methods for accurate drug response prediction in cancers. Incorporating prior knowledge of biological systems into these methods is a promising avenue to improve prediction performance. High-throughput cell line assays of drug-induced transcriptomic perturbation effects are a prior knowledge that has not been fully incorporated into a drug response prediction model yet. Results We introduce a unified probabilistic approach, Drug Response Variational Autoencoder (Dr.VAE), that simultaneously models both drug response in terms of viability and transcriptomic perturbations. Dr.VAE is a deep generative model based on variational autoencoders. Our experimental results showed Dr.VAE to do as well or outperform standard classification methods for 23 out of 26 tested Food and Drug Administration-approved drugs. In a series of ablation experiments we showed that the observed improvement of Dr.VAE can be credited to the incorporation of drug-induced perturbation effects with joint modeling of treatment sensitivity. Availability and implementation Processed data and software implementation using PyTorch (Paszke et al., 2017) are available at: https://github.com/rampasek/DrVAE. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ladislav Rampášek
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Genetics & Genome Biology, SickKids Research Institute, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada
| | - Daniel Hidru
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Genetics & Genome Biology, SickKids Research Institute, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada
| | - Petr Smirnov
- Vector Institute, Toronto, ON, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Benjamin Haibe-Kains
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Genetics & Genome Biology, SickKids Research Institute, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada
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28
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Caroli J, Dori M, Bicciato S. Computational Methods for the Integrative Analysis of Genomics and Pharmacological Data. Front Oncol 2020; 10:185. [PMID: 32175273 PMCID: PMC7056894 DOI: 10.3389/fonc.2020.00185] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 02/03/2020] [Indexed: 01/22/2023] Open
Abstract
Since the pioneering NCI-60 panel of the late'80's, several major screenings of genetic profiling and drug testing in cancer cell lines have been conducted to investigate how genetic backgrounds and transcriptional patterns shape cancer's response to therapy and to identify disease-specific genes associated with drug response. Historically, pharmacogenomics screenings have been largely heterogeneous in terms of investigated cell lines, assay technologies, number of compounds, type and quality of genomic data, and methods for their computational analysis. The analysis of this enormous and heterogeneous amount of data required the development of computational methods for the integration of genomic profiles with drug responses across multiple screenings. Here, we will review the computational tools that have been developed to integrate cancer cell lines' genomic profiles and sensitivity to small molecule perturbations obtained from different screenings.
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Affiliation(s)
- Jimmy Caroli
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Martina Dori
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Silvio Bicciato
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
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29
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Kurilov R, Haibe-Kains B, Brors B. Assessment of modelling strategies for drug response prediction in cell lines and xenografts. Sci Rep 2020; 10:2849. [PMID: 32071383 PMCID: PMC7028927 DOI: 10.1038/s41598-020-59656-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 01/23/2020] [Indexed: 12/20/2022] Open
Abstract
Data from several large high-throughput drug response screens have become available to the scientific community recently. Although many efforts have been made to use this information to predict drug sensitivity, our ability to accurately predict drug response based on genetic data remains limited. In order to systematically examine how different aspects of modelling affect the resulting prediction accuracy, we built a range of models for seven drugs (erlotinib, pacliatxel, lapatinib, PLX4720, sorafenib, nutlin-3 and nilotinib) using data from the largest available cell line and xenograft drug sensitivity screens. We found that the drug response metric, the choice of the molecular data type and the number of training samples have a substantial impact on prediction accuracy. We also compared the tasks of drug response prediction with tissue type prediction and found that, unlike for drug response, tissue type can be predicted with high accuracy. Furthermore, we assessed our ability to predict drug response in four xenograft cohorts (treated either with erlotinib, gemcitabine or paclitaxel) using models trained on cell line data. We could predict response in an erlotinib-treated cohort with a moderate accuracy (correlation ≈ 0.5), but were unable to correctly predict responses in cohorts treated with gemcitabine or paclitaxel.
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Affiliation(s)
- Roman Kurilov
- Division of Applied Bioinformatics, German Cancer Research Center, Heidelberg, Germany. .,Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, 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, M5T 3A1, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, M5G 1L7, Canada
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center, Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Heidelberg, Germany.,German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
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30
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Baptista D, Ferreira PG, Rocha M. Deep learning for drug response prediction in cancer. Brief Bioinform 2020; 22:360-379. [PMID: 31950132 DOI: 10.1093/bib/bbz171] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 11/04/2019] [Indexed: 01/15/2023] Open
Abstract
Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines. We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement. Contact: mrocha@di.uminho.pt.
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Affiliation(s)
| | | | - Miguel Rocha
- Department of Informatics and a Senior Researcher of the Centre of Biological Engineering at the University of Minho
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31
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Ong E, Sun P, Berke K, Zheng J, Wu G, He Y. VIO: ontology classification and study of vaccine responses given various experimental and analytical conditions. BMC Bioinformatics 2019; 20:704. [PMID: 31865910 PMCID: PMC6927110 DOI: 10.1186/s12859-019-3194-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Background Different human responses to the same vaccine were frequently observed. For example, independent studies identified overlapping but different transcriptomic gene expression profiles in Yellow Fever vaccine 17D (YF-17D) immunized human subjects. Different experimental and analysis conditions were likely contributed to the observed differences. To investigate this issue, we developed a Vaccine Investigation Ontology (VIO), and applied VIO to classify the different variables and relations among these variables systematically. We then evaluated whether the ontological VIO modeling and VIO-based statistical analysis would contribute to the enhanced vaccine investigation studies and a better understanding of vaccine response mechanisms. Results Our VIO modeling identified many variables related to data processing and analysis such as normalization method, cut-off criteria, software settings including software version. The datasets from two previous studies on human responses to YF-17D vaccine, reported by Gaucher et al. (2008) and Querec et al. (2009), were re-analyzed. We first applied the same LIMMA statistical method to re-analyze the Gaucher data set and identified a big difference in terms of significantly differentiated gene lists compared to the original study. The different results were likely due to the LIMMA version and software package differences. Our second study re-analyzed both Gaucher and Querec data sets but with the same data processing and analysis pipeline. Significant differences in differential gene lists were also identified. In both studies, we found that Gene Ontology (GO) enrichment results had more overlapping than the gene lists and enriched pathway lists. The visualization of the identified GO hierarchical structures among the enriched GO terms and their associated ancestor terms using GOfox allowed us to find more associations among enriched but often different GO terms, demonstrating the usage of GO hierarchical relations enhance data analysis. Conclusions The ontology-based analysis framework supports standardized representation, integration, and analysis of heterogeneous data of host responses to vaccines. Our study also showed that differences in specific variables might explain different results drawn from similar studies.
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Affiliation(s)
- Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Peter Sun
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA
| | - Kimberly Berke
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA.,Central Michigan University College of Medicine, Mount Pleasant, MI, USA
| | - Jie Zheng
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Guanming Wu
- Oregon Health & Science University, Portland, OR, USA
| | - Yongqun He
- Unit of Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA. .,Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA. .,Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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32
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Nonlinear mixed-effects models for modeling in vitro drug response data to determine problematic cancer cell lines. Sci Rep 2019; 9:14421. [PMID: 31594982 PMCID: PMC6783462 DOI: 10.1038/s41598-019-50936-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 09/23/2019] [Indexed: 01/02/2023] Open
Abstract
Cancer cell lines (CCLs) have been widely used to study of cancer. Recent studies have called into question the reliability of data collected on CCLs. Hence, we set out to determine CCLs that tend to be overly sensitive or resistant to a majority of drugs utilizing a nonlinear mixed-effects (NLME) modeling framework. Using drug response data collected in the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), we determined the optimal functional form for each drug. Then, a NLME model was fit to the drug response data, with the estimated random effects used to determine sensitive or resistant CCLs. Out of the roughly 500 CCLs studies from the CCLE, we found 17 cell lines to be overly sensitive or resistant to the studied drugs. In the GDSC, we found 15 out of the 990 CCLs to be excessively sensitive or resistant. These results can inform researchers in the selection of CCLs to include in drug studies. Additionally, this study illustrates the need for assessing the dose-response functional form and the use of NLME models to achieve more stable estimates of drug response parameters.
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33
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Mammoliti A, Smirnov P, Safikhani Z, Ba-Alawi W, Haibe-Kains B. Creating reproducible pharmacogenomic analysis pipelines. Sci Data 2019; 6:166. [PMID: 31481707 PMCID: PMC6722117 DOI: 10.1038/s41597-019-0174-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 08/13/2019] [Indexed: 02/01/2023] Open
Abstract
The field of pharmacogenomics presents great challenges for researchers that are willing to make their studies reproducible and shareable. This is attributed to the generation of large volumes of high-throughput multimodal data, and the lack of standardized workflows that are robust, scalable, and flexible to perform large-scale analyses. To address this issue, we developed pharmacogenomic workflows in the Common Workflow Language to process two breast cancer datasets in a reproducible and transparent manner. Our pipelines combine both pharmacological and molecular profiles into a portable data object that can be used for future analyses in cancer research. Our data objects and workflows are shared on Harvard Dataverse and Code Ocean where they have been assigned a unique Digital Object Identifier, providing a level of data provenance and a persistent location to access and share our data with the community.
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Affiliation(s)
- Anthony Mammoliti
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, 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
- Vector Institute for Artificial Intelligence, 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.
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.
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Smirnov P, Kofia V, Maru A, Freeman M, Ho C, El-Hachem N, Adam GA, Ba-Alawi W, Safikhani Z, Haibe-Kains B. PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies. Nucleic Acids Res 2019; 46:D994-D1002. [PMID: 30053271 PMCID: PMC5753377 DOI: 10.1093/nar/gkx911] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 09/27/2017] [Indexed: 12/18/2022] Open
Abstract
Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.
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Affiliation(s)
- Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Victor Kofia
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Alexander Maru
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Mark Freeman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Chantal Ho
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Nehme El-Hachem
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - George-Alexandru Adam
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, 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
| | - Zhaleh Safikhani
- 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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Tsvetkov P, Detappe A, Cai K, Keys HR, Brune Z, Ying W, Thiru P, Reidy M, Kugener G, Rossen J, Kocak M, Kory N, Tsherniak A, Santagata S, Whitesell L, Ghobrial IM, Markley JL, Lindquist S, Golub TR. Mitochondrial metabolism promotes adaptation to proteotoxic stress. Nat Chem Biol 2019; 15:681-689. [PMID: 31133756 PMCID: PMC8183600 DOI: 10.1038/s41589-019-0291-9] [Citation(s) in RCA: 255] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 04/16/2019] [Indexed: 12/17/2022]
Abstract
The mechanisms by which cells adapt to proteotoxic stress are largely unknown, but key to understanding how tumor cells, particularly in vivo, are largely resistant to proteasome inhibitors. Analysis of cancer cell lines, mouse xenografts and patient-derived tumor samples all showed an association between mitochondrial metabolism and proteasome inhibitor sensitivity. When cells were forced to use oxidative phosphorylation rather than glycolysis, they became proteasome inhibitor-resistant. This mitochondrial state, however, creates a unique vulnerability: sensitivity to the small-molecule compound elesclomol. Genome-wide CRISPR/Cas9 screening showed that a single gene, encoding the mitochondrial reductase FDX1, could rescue elesclomol-induced cell death. Enzymatic function and NMR-based analyses further showed that FDX1 is the direct target of elesclomol, which promotes a unique form of copper-dependent cell death. These studies elucidate a fundamental mechanism by which cells adapt to proteotoxic stress and suggests strategies to mitigate proteasome inhibitor-resistance.
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Affiliation(s)
| | - Alexandre Detappe
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Kai Cai
- Biochemistry Department, University of Wisconsin-Madison, Madison, WI, USA
| | - Heather R Keys
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Zarina Brune
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Weiwen Ying
- OnTarget Pharmaceutical Consulting LLC, Lexington, MA, USA
| | - Prathapan Thiru
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Mairead Reidy
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | | | - Jordan Rossen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Mustafa Kocak
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Nora Kory
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | | | - Sandro Santagata
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Luke Whitesell
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA.,Molecular Genetics Department, University of Toronto, Toronto, ON, Canada
| | - Irene M Ghobrial
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - John L Markley
- Biochemistry Department, University of Wisconsin-Madison, Madison, WI, USA
| | - Susan Lindquist
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA.,Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Todd R Golub
- Broad Institute of Harvard and MIT, Cambridge, MA, USA. .,Howard Hughes Medical Institute, Chevy Chase, MD, USA. .,Harvard Medical School, Boston, MA, USA. .,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
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36
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Yao F, Madani Tonekaboni SA, Safikhani Z, Smirnov P, El-Hachem N, Freeman M, Manem VSK, Haibe-Kains B. Tissue specificity of in vitro drug sensitivity. J Am Med Inform Assoc 2019; 25:158-166. [PMID: 29016819 DOI: 10.1093/jamia/ocx062] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 05/22/2017] [Indexed: 12/11/2022] Open
Abstract
Objectives We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications. Materials and Methods We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values' area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity. Results We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.05). By restricting the drug-tissue interactions to those with AUC > 0.2, we found that in 52% of interactions, the tissue was predictive of drug sensitivity (concordance index > 0.65). When compared with clinical indications, the observed overlap was weak (Matthew correlation coefficient, MCC = 0.0003, P > .10). Discussion While drugs exhibit significant tissue specificity in vitro, there is little overlap with clinical indications. This can be attributed to factors such as underlying biological differences between in vitro models and patient tumors, or the inability of tissue-specific drugs to bring additional benefits beyond gold standard treatments during clinical trials. Conclusion Our meta-analysis of pan-cancer drug screening datasets indicates that most tested drugs exhibit tissue-specific sensitivities in a large panel of cancer cell lines. However, the observed preclinical results do not translate to the clinical setting. Our results suggest that additional research into showing parallels between preclinical and clinical data is required to increase the translational potential of in vitro drug screening.
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Affiliation(s)
- Fupan Yao
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Seyed Ali Madani Tonekaboni
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Zhaleh Safikhani
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Nehme El-Hachem
- Integrative Systems Biology, Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada.,Department of Medicine, University of Montreal, Montréal, Quebec, Canada
| | - Mark Freeman
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Venkata Satya Kumar Manem
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, 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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Wei D, Liu C, Zheng X, Li Y. Comprehensive anticancer drug response prediction based on a simple cell line-drug complex network model. BMC Bioinformatics 2019; 20:44. [PMID: 30670007 PMCID: PMC6341656 DOI: 10.1186/s12859-019-2608-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2018] [Accepted: 01/04/2019] [Indexed: 12/11/2022] Open
Abstract
Background Accurate prediction of anticancer drug responses in cell lines is a crucial step to accomplish the precision medicine in oncology. Although many popular computational models have been proposed towards this non-trivial issue, there is still room for improving the prediction performance by combining multiple types of genome-wide molecular data. Results We first demonstrated an observation on the CCLE and GDSC datasets, i.e., genetically similar cell lines always exhibit higher response correlations to structurally related drugs. Based on this observation we built a cell line-drug complex network model, named CDCN model. It captures different contributions of all available cell line-drug responses through cell line similarities and drug similarities. We executed anticancer drug response prediction on CCLE and GDSC independently. The result is significantly superior to that of some existing studies. More importantly, our model could predict the response of new drug to new cell line with considerable performance. We also divided all possible cell lines into “sensitive” and “resistant” groups by their response values to a given drug, the prediction accuracy, sensitivity, specificity and goodness of fit are also very promising. Conclusion CDCN model is a comprehensive tool to predict anticancer drug responses. Compared with existing methods, it is able to provide more satisfactory prediction results with less computational consumption. Electronic supplementary material The online version of this article (10.1186/s12859-019-2608-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Dong Wei
- School of Science, Yanshan University, Qinhuangdao, 066004, China
| | - Chuanying Liu
- School of Science, Yanshan University, Qinhuangdao, 066004, China
| | - Xiaoqi Zheng
- Department of Mathematics, Shanghai Normal University, Shanghai, 200234, China.
| | - Yushuang Li
- School of Science, Yanshan University, Qinhuangdao, 066004, China.
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Vural S, Simon R, Krushkal J. Correlation of gene expression and associated mutation profiles of APOBEC3A, APOBEC3B, REV1, UNG, and FHIT with chemosensitivity of cancer cell lines to drug treatment. Hum Genomics 2018; 12:20. [PMID: 29642934 PMCID: PMC5896091 DOI: 10.1186/s40246-018-0150-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 03/23/2018] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The APOBEC gene family of cytidine deaminases plays important roles in DNA repair and mRNA editing. In many cancers, APOBEC3B increases the mutation load, generating clusters of closely spaced, single-strand-specific DNA substitutions with a characteristic hypermutation signature. Some studies also suggested a possible involvement of APOBEC3A, REV1, UNG, and FHIT in molecular processes affecting APOBEC mutagenesis. It is important to understand how mutagenic processes linked to the activity of these genes may affect sensitivity of cancer cells to treatment. RESULTS We used information from the Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer resources to examine associations of the prevalence of APOBEC-like motifs and mutational loads with expression of APOBEC3A, APOBEC3B, REV1, UNG, and FHIT and with cell line chemosensitivity to 255 antitumor drugs. Among the five genes, APOBEC3B expression levels were bimodally distributed, whereas expression of APOBEC3A, REV1, UNG, and FHIT was unimodally distributed. The majority of the cell lines had low levels of APOBEC3A expression. The strongest correlations of gene expression levels with mutational loads or with measures of prevalence of APOBEC-like motif counts and kataegis clusters were observed for REV1, UNG, and APOBEC3A. Sensitivity or resistance of cell lines to JQ1, palbociclib, bicalutamide, 17-AAG, TAE684, MEK inhibitors refametinib, PD-0325901, and trametinib and a number of other agents was correlated with candidate gene expression levels or with abundance of APOBEC-like motif clusters in specific cancers or across cancer types. CONCLUSIONS We observed correlations of expression levels of the five candidate genes in cell line models with sensitivity to cancer drug treatment. We also noted suggestive correlations between measures of abundance of APOBEC-like sequence motifs with drug sensitivity in small samples of cell lines from individual cancer categories, which require further validation in larger datasets. Molecular mechanisms underlying the links between the activities of the products of each of the five genes, the resulting mutagenic processes, and sensitivity to each category of antitumor agents require further investigation.
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Affiliation(s)
- Suleyman Vural
- Computational and Systems Biology Branch, Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr, Rockville, MD 20850 USA
| | - Richard Simon
- Computational and Systems Biology Branch, Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr, Rockville, MD 20850 USA
| | - Julia Krushkal
- Computational and Systems Biology Branch, Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr, Rockville, MD 20850 USA
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39
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Analysis of variability in high throughput screening data: applications to melanoma cell lines and drug responses. Oncotarget 2018; 8:27786-27799. [PMID: 28212541 PMCID: PMC5438608 DOI: 10.18632/oncotarget.15347] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 01/27/2017] [Indexed: 12/16/2022] Open
Abstract
High-throughput screening (HTS) strategies and protocols have undergone significant development in the last decade. It is now possible to screen hundreds of thousands of compounds, each exploring multiple biological phenotypes and parameters, against various cell lines or model systems in a single setting. However, given the vast amount of data such studies generate, the fact that they use multiple reagents, and are often technician-intensive, questions have been raised about the variability, reliability and reproducibility of HTS results. Assessments of the impact of the multiple factors in HTS studies could arguably lead to more compelling insights into the robustness of the results of a particular screen, as well as the overall quality of the study. We leveraged classical, yet highly flexible, analysis of variance (ANOVA)-based linear models to explore how different factors contribute to the variation observed in a screening study of four different melanoma cell lines and 120 drugs over nine dosages studied in two independent academic laboratories. We find that factors such as plate effects, appropriate dosing ranges, and to a lesser extent, the laboratory performing the screen, are significant predictors of variation in drug responses across the cell lines. Further, we show that when sources of variation are quantified and controlled for, they contextualize claims of inconsistencies and reveal the overall quality of the HTS studies performed at each participating laboratory. In the context of the broader screening study, we show that our analysis can also elucidate the robust effects of drugs, even those within specific cell lines.
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40
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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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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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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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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:E2-E4. [PMID: 27905430 DOI: 10.1038/nature19839] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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, University College London, 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 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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45
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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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