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Start Over Author "Costello, James C." Topic antineoplastic agents
9 results on '"Costello, James C."'

1. Inhibition of the CCL2 receptor, CCR2, enhances tumor response to immune checkpoint therapy.

Author

Tu MM, Abdel-Hafiz HA, Jones RT, Jean A, Hoff KJ, Duex JE, Chauca-Diaz A, Costello JC, Dancik GM, Tamburini BAJ, Czerniak B, Kaye J, and Theodorescu D

Subjects
Animals, Antineoplastic Agents pharmacology, CD4-Positive T-Lymphocytes immunology, CD8-Positive T-Lymphocytes immunology, Combined Modality Therapy, Female, Melanoma, Experimental therapy, Mice, Mice, Inbred C57BL, Neoplasms immunology, RNA-Seq, Urinary Bladder Neoplasms therapy, Antineoplastic Agents therapeutic use, Immune Checkpoint Inhibitors therapeutic use, Neoplasms therapy, Receptors, CCR2 antagonists & inhibitors
Abstract

Immunotherapies targeting the PD-1/PD-L1 axis are now a mainstay in the clinical management of multiple cancer types, however, many tumors still fail to respond. CCL2 is highly expressed in various cancer types and has been shown to be associated with poor prognosis. Inhibition or blockade of the CCL2/CCR2 signaling axis has thus been an area of interest for cancer therapy. Here we show across multiple murine tumor and metastasis models that CCR2 antagonism in combination with anti-PD-1 therapy leads to sensitization and enhanced tumor response over anti-PD-1 monotherapy. We show that enhanced treatment response correlates with enhanced CD8 + T cell recruitment and activation and a concomitant decrease in CD4 + regulatory T cell. These results provide strong preclinical rationale for further clinical exploration of combining CCR2 antagonism with PD-1/PD-L1-directed immunotherapies across multiple tumor types especially given the availability of small molecule CCR2 inhibitors and antibodies.

Published
2020
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2. Identification of a Small-Molecule Inhibitor That Disrupts the SIX1/EYA2 Complex, EMT, and Metastasis.

Author

Zhou H, Blevins MA, Hsu JY, Kong D, Galbraith MD, Goodspeed A, Culp-Hill R, Oliphant MUJ, Ramirez D, Zhang L, Trinidad-Pineiro J, Mathews Griner L, King R, Barnaeva E, Hu X, Southall NT, Ferrer M, Gustafson DL, Regan DP, D'Alessandro A, Costello JC, Patnaik S, Marugan J, Zhao R, and Ford HL

Subjects
Animals, Antineoplastic Agents therapeutic use, BRCA1 Protein metabolism, Breast Neoplasms genetics, Breast Neoplasms pathology, Epithelial-Mesenchymal Transition drug effects, Female, Gene Expression Regulation, Neoplastic drug effects, Homeodomain Proteins metabolism, Humans, Intracellular Signaling Peptides and Proteins metabolism, Kaplan-Meier Estimate, MCF-7 Cells, Mice, Neoplasm Metastasis prevention & control, Nuclear Proteins metabolism, Protein Binding drug effects, Protein Tyrosine Phosphatases metabolism, RNA-Seq, Signal Transduction drug effects, Signal Transduction genetics, Xenograft Model Antitumor Assays, Antineoplastic Agents pharmacology, Breast Neoplasms drug therapy, Homeodomain Proteins antagonists & inhibitors, Intracellular Signaling Peptides and Proteins antagonists & inhibitors, Nuclear Proteins antagonists & inhibitors, Protein Tyrosine Phosphatases antagonists & inhibitors
Abstract

Metastasis is the major cause of mortality for patients with cancer, and dysregulation of developmental signaling pathways can significantly contribute to the metastatic process. The Sine oculis homeobox homolog 1 (SIX1)/eyes absent (EYA) transcriptional complex plays a critical role in the development of multiple organs and is typically downregulated after development is complete. In breast cancer, aberrant expression of SIX1 has been demonstrated to stimulate metastasis through activation of TGFβ signaling and subsequent induction of epithelial-mesenchymal transition (EMT). In addition, SIX1 can induce metastasis via non-cell autonomous means, including activation of GLI-signaling in neighboring tumor cells and activation of VEGFC-induced lymphangiogenesis. Thus, targeting SIX1 would be expected to inhibit metastasis while conferring limited side effects. However, transcription factors are notoriously difficult to target, and thus novel approaches to inhibit their action must be taken. Here we identified a novel small molecule compound, NCGC00378430 (abbreviated as 8430), that reduces the SIX1/EYA2 interaction. 8430 partially reversed transcriptional and metabolic profiles mediated by SIX1 overexpression and reversed SIX1-induced TGFβ signaling and EMT. 8430 was well tolerated when delivered to mice and significantly suppressed breast cancer-associated metastasis in vivo without significantly altering primary tumor growth. Thus, we have demonstrated for the first time that pharmacologic inhibition of the SIX1/EYA2 complex and associated phenotypes is sufficient to suppress breast cancer metastasis. SIGNIFICANCE: These findings identify and characterize a novel inhibitor of the SIX1/EYA2 complex that reverses EMT phenotypes suppressing breast cancer metastasis., (©2020 American Association for Cancer Research.)

Published
2020
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3. A Whole-genome CRISPR Screen Identifies a Role of MSH2 in Cisplatin-mediated Cell Death in Muscle-invasive Bladder Cancer.

Author

Goodspeed A, Jean A, and Costello JC

Subjects
Cell Death drug effects, Cell Line, Tumor, Databases, Genetic, Drug Resistance, Neoplasm genetics, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Neoplasm Invasiveness, Phenotype, Urinary Bladder Neoplasms genetics, Urinary Bladder Neoplasms metabolism, Urinary Bladder Neoplasms pathology, Antineoplastic Agents pharmacology, Biomarkers, Tumor genetics, CRISPR-Cas Systems, Cisplatin pharmacology, MutS Homolog 2 Protein genetics, Urinary Bladder Neoplasms drug therapy, Whole Genome Sequencing methods
Abstract

Background: The response to first-line, platinum-based treatment of muscle-invasive bladder cancer has not improved in 3 decades., Objective: To identify genes that influence cisplatin resistance in bladder cancer., Design, Setting, and Participants: We performed a whole-genome CRISPR screen in a bladder cancer cell line to identify genes that mediate resistance to cisplatin., Outcome Measurements and Statistical Analysis: Targeted validation was performed in two bladder cancer cell lines. The top gene candidate was validated in a publicly available bladder cancer dataset., Results and Limitations: From the CRISPR screen, we identified MSH2 as the most significantly enriched gene and mismatch repair as the most significantly enriched pathway that promoted resistance to cisplatin. Bladder cancer cells with knockdown of MSH2 showed a reduction in cisplatin-mediated apoptosis. MSH2 loss did not impact the sensitivity to other chemotherapies, including the cisplatin analog oxaliplatin. Bladder tumors with low MSH2 protein levels, quantified using reverse-phase protein array, showed poorer survival when treated with cisplatin- or carboplatin-based therapy; these results require future validation using immunohistochemistry. Additionally, results are retrospective from patients with primarily high-grade tumors; thus, validation in a controlled clinical trial is needed., Conclusions: We generated in vitro evidence that bladder cancer cell lines depleted of MSH2 are more resistant to cisplatin. We additionally found an association between low MSH2 in bladder tumors and poorer patient survival when treated with platinum-based chemotherapy. If successfully validated prospectively, MSH2 protein level could assist in the selection of patients for chemotherapy., Patient Summary: We report the first evidence that MSH2 protein level may contribute to chemotherapy resistance observed in muscle-invasive bladder cancer. MSH2 has potential as a biomarker predictive of response to platinum-based therapy., (Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Published
2019
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4. A DREAM Challenge to Build Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration-Resistant Prostate Cancer.

Author

Seyednasrollah F, Koestler DC, Wang T, Piccolo SR, Vega R, Greiner R, Fuchs C, Gofer E, Kumar L, Wolfinger RD, Kanigel Winner K, Bare C, Neto EC, Yu T, Shen L, Abdallah K, Norman T, Stolovitzky G, Soule HR, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Elo LL, Zhou FL, Guinney J, and Costello JC

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Antineoplastic Agents administration & dosage, Antineoplastic Combined Chemotherapy Protocols adverse effects, Antineoplastic Combined Chemotherapy Protocols therapeutic use, Clinical Trials as Topic, Docetaxel administration & dosage, Humans, Male, Meta-Analysis as Topic, Middle Aged, Prednisone, Prognosis, Prostatic Neoplasms, Castration-Resistant mortality, Time Factors, Treatment Outcome, Young Adult, Antineoplastic Agents therapeutic use, Docetaxel therapeutic use, Models, Theoretical, Prostatic Neoplasms, Castration-Resistant drug therapy, Prostatic Neoplasms, Castration-Resistant pathology
Abstract

Purpose: Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge., Patients and Methods: The comparator arms of four phase III clinical trials in first-line mCRPC were collected, annotated, and compiled, with a total of 2,070 patients. Early discontinuation was defined as treatment stoppage within 3 months as a result of adverse treatment effects; 10% of patients discontinued treatment. We designed an open-data, crowd-sourced DREAM Challenge for developing models with which to predict early discontinuation of docetaxel treatment. Clinical features for all four trials and outcomes for three of the four trials were made publicly available, with the outcomes of the fourth trial held back for unbiased model evaluation. Challenge participants from around the world trained models and submitted their predictions. Area under the precision-recall curve was the primary metric used for performance assessment., Results: In total, 34 separate teams submitted predictions. Seven models with statistically similar area under precision-recall curves (Bayes factor ≤ 3) outperformed all other models. A postchallenge analysis of risk prediction using these seven models revealed three patient subgroups: high risk, low risk, or discordant risk. Early discontinuation events were two times higher in the high-risk subgroup compared with the low-risk subgroup. Simulation studies demonstrated that use of patient discontinuation prediction models could reduce patient enrollment in clinical trials without the loss of statistical power., Conclusion: This work represents a successful collaboration between 34 international teams that leveraged open clinical trial data. Our results demonstrate that routinely collected clinical features can be used to identify patients with mCRPC who are likely to discontinue treatment because of adverse events and establishes a robust benchmark with implications for clinical trial design.

Published
2017
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5. Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics.

Author

Goodspeed A, Heiser LM, Gray JW, and Costello JC

Subjects
Antineoplastic Agents therapeutic use, Epigenesis, Genetic drug effects, Genetic Heterogeneity, High-Throughput Screening Assays methods, Humans, Models, Biological, Neoplasms drug therapy, Antineoplastic Agents pharmacology, Cell Line, Tumor drug effects, Neoplasms genetics
Abstract

Compared with normal cells, tumor cells have undergone an array of genetic and epigenetic alterations. Often, these changes underlie cancer development, progression, and drug resistance, so the utility of model systems rests on their ability to recapitulate the genomic aberrations observed in primary tumors. Tumor-derived cell lines have long been used to study the underlying biologic processes in cancer, as well as screening platforms for discovering and evaluating the efficacy of anticancer therapeutics. Multiple -omic measurements across more than a thousand cancer cell lines have been produced following advances in high-throughput technologies and multigroup collaborative projects. These data complement the large, international cancer genomic sequencing efforts to characterize patient tumors, such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Given the scope and scale of data that have been generated, researchers are now in a position to evaluate the similarities and differences that exist in genomic features between cell lines and patient samples. As pharmacogenomics models, cell lines offer the advantages of being easily grown, relatively inexpensive, and amenable to high-throughput testing of therapeutic agents. Data generated from cell lines can then be used to link cellular drug response to genomic features, where the ultimate goal is to build predictive signatures of patient outcome. This review highlights the recent work that has compared -omic profiles of cell lines with primary tumors, and discusses the advantages and disadvantages of cancer cell lines as pharmacogenomic models of anticancer therapies., (©2015 American Association for Cancer Research.)

Published
2016
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6. A community effort to assess and improve drug sensitivity prediction algorithms.

Author

Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ, Bansal M, Ammad-ud-din M, Hintsanen P, Khan SA, Mpindi JP, Kallioniemi O, Honkela A, Aittokallio T, Wennerberg K, Collins JJ, Gallahan D, Singer D, Saez-Rodriguez J, Kaski S, Gray JW, and Stolovitzky G

Subjects
Algorithms, Antineoplastic Agents adverse effects, Epigenomics methods, Gene Expression Regulation, Neoplastic drug effects, Genomics methods, Humans, Neoplasms genetics, Proteomics methods, Antineoplastic Agents therapeutic use, Drug Resistance, Neoplasm genetics, Gene Expression Profiling, Neoplasms drug therapy
Abstract

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

Published
2014
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7. Biologically Informed Neural Networks Predict Drug Responses.

Author

Greene, Casey S. and Costello, James C.

Subjects
*ANTINEOPLASTIC agents, *GENE ontology, *FORECASTING, *DRUG abuse, *DRUGS
Abstract

Deep neural networks often achieve high predictive accuracy on biological problems, but it can be hard to contextualize how and explain why predictions are made. In this issue, Kuenzi et al. model the sensitivity of cancers to drugs using deep neural networks with a hierarchical structure derived from the Gene Ontology. [ABSTRACT FROM AUTHOR]

Published
2020
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8. A community effort to assess and improve drug sensitivity prediction algorithms

Author

Costello, James C, Heiser, Laura M, Georgii, Elisabeth, Gönen, Mehmet, Menden, Michael P, Wang, Nicholas J, Bansal, Mukesh, Ammad-ud-din, Muhammad, Hintsanen, Petteri, Khan, Suleiman A, Mpindi, John-Patrick, Kallioniemi, Olli, Honkela, Antti, Aittokallio, Tero, Wennerberg, Krister, Abbuehl, Jean-Paul, Allen, Jeffrey, Altman, Russ B, Balcome, Shawn, Battle, Alexis, Bender, Andreas, Berger, Bonnie, Bernard, Jonathan, Bhattacharjee, Madhuchhanda, Bhuvaneshwar, Krithika, Bieberich, Andrew A, Boehm, Fred, Califano, Andrea, Chan, Christina, Chen, Beibei, Chen, Ting-Huei, Choi, Jaejoon, Coelho, Luis Pedro, Cokelaer, Thomas, Collins, James C, Creighton, Chad J, Cui, Jike, Dampier, Will, Davisson, V Jo, De Baets, Bernard, Deshpande, Raamesh, DiCamillo, Barbara, Dundar, Murat, Duren, Zhana, Ertel, Adam, Fan, Haoyang, Fang, Hongbin, Gallahan, Dan, Gauba, Robinder, Gottlieb, Assaf, Grau, Michael, Gray, Joe W, Gusev, Yuriy, Ha, Min Jin, Han, Leng, Harris, Michael, Henderson, Nicholas, Hejase, Hussein A, Homicsko, Krisztian, Hou, Jack P, Hwang, Woochang, IJzerman, Adriaan P, Karacali, Bilge, Kaski, Samuel, Keles, Sunduz, Kendziorski, Christina, Kim, Junho, Kim, Min, Kim, Youngchul, Knowles, David A, Koller, Daphne, Lee, Junehawk, Lee, Jae K, Lenselink, Eelke B, Li, Biao, Li, Bin, Li, Jun, Liang, Han, Ma, Jian, Madhavan, Subha, Mooney, Sean, Myers, Chad L, Newton, Michael A, Overington, John P, Pal, Ranadip, Peng, Jian, Pestell, Richard, Prill, Robert J, Qiu, Peng, Rajwa, Bartek, Sadanandam, Anguraj, Saez-Rodriguez, Julio, Sambo, Francesco, Shin, Hyunjin, Singer, Dinah, Song, Jiuzhou, Song, Lei, Sridhar, Arvind, Stock, Michiel, Stolovitzky, Gustavo, Sun, Wei, Ta, Tram, Tadesse, Mahlet, Tan, Ming, Tang, Hao, Theodorescu, Dan, Toffolo, Gianna Maria, Tozeren, Aydin, Trepicchio, William, Varoquaux, Nelle, Vert, Jean-Philippe, Waegeman, Willem, Walter, Thomas, Wan, Qian, Wang, Difei, Wang, Wen, Wang, Yong, Wang, Zhishi, Wegner, Joerg K, Wu, Tongtong, Xia, Tian, Xiao, Guanghua, Xie, Yang, Xu, Yanxun, Yang, Jichen, Yuan, Yuan, Zhang, Shihua, Zhang, Xiang-Sun, Zhao, Junfei, Zuo, Chandler, van Vlijmen, Herman W T, van Westen, Gerard J P, Collins, James J, National Centre for Plasma Science and Technology (NCPST), Dublin City University [Dublin] ( DCU ), Institut Lumière Matière [Villeurbanne] ( ILM ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Mécanique des Contacts et des Structures [Villeurbanne] ( LaMCoS ), Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Helsinki Institute for Information Technology, University of Westminster [London] ( UOW ), Stanford Center for BioMedical Informatics Research ( BMIR ), Stanford University [Stanford], DSTO, Equipe NEMESIS - Centre de Recherches de l'Institut du Cerveau et de la Moelle épinière ( NEMESIS-CRICM ), Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière ( CRICM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland, Brisbane, QLD 4072, Nanjing University of Information Science & Technology, Department of Physics [Stockholm], Stockholm University, Chercheur indépendant, Instituto de Engenharia de Sistemas e Computadores ( INESC ), European Bioinformatics Institute [Hinxton] ( EMBL-EBI ), European Molecular Biology Laboratory [Hinxton], Department of Agronomy, Tianjin Agricultural University ( TJAU ), Medicine Faculty, Erciyes University, Faculty of anima Medicine l, Northeast Agricultural University [Harbin], Oxford e-Research Centre [Oxford], University of Oxford [Oxford], Department of Computer Science [Alabama], University of Alabama [Tuscaloosa] ( UA ), Roberval, Université de Technologie de Compiègne ( UTC ), Institut de Mathématiques de Jussieu ( IMJ ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Centre for Inflammation Research, University of Edinburgh-Queen's Medical Research Institute, Division of Medicinal Chemistry, Universiteit Leiden [Leiden], University of Pittsburgh [Pittsburg], University of Pittsburgh, Department of Chemistry and Nano Science, EWHA Womans University ( EWHA ), Institute of Materials Chemistry, Technical University of Vienna [Vienna] ( TU WIEN ), Leiden Academic Center for Drug Research, Peking Univ, Coll Urban & Environm Sci, Lab Earth Surface Proc, Beijing 100871, Peoples R China, Queensland Research Lab, National ICT Australia [Sydney] ( NICTA ), Institut des Systèmes Intelligents et de Robotique ( ISIR ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Centre for Plant Integrative Biology [Nothingham] ( CPIB ), University of Nottingham, UK ( UON ), Department of Electrical and Computer Engineering, University of Utah, SUN Yatsen University, Sidney Kimmel Cancer Center, Jefferson (Philadelphia University + Thomas Jefferson University), Molecular Carcinogenesis [Sutton], Institute of cancer research, Centre d'études et de recherches appliquées à la gestion (Grenoble), Centre d'études et de recherches appliquées à la gestion ( CERAG ), Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Grenoble Alpes ( UGA ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Grenoble Alpes ( UGA ), China Meteorological Administration, Equipe de Recherche Interdisciplinaire sur le Tourisme ( IUKB ), Institut Universitaire Kurt Bösch, University of Leeds, Dipartimento di Chimica, Università degli Studi di Roma 'La Sapienza' [Rome], Centre d'élaboration de matériaux et d'études structurales ( CEMES ), Institut National des Sciences Appliquées - Toulouse ( INSA Toulouse ), Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Toulouse III - Paul Sabatier ( UPS ), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -INSTITUT CURIE, Centre de Bioinformatique ( CBIO ), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL ), Computer Graphics Group, Department of Computer Science [Hong Kong], City University of Hong Kong [Hong Kong] ( CUHK ) -City University of Hong Kong [Hong Kong] ( CUHK ), Ingénierie Moléculaire et Physiopathologie Articulaire ( IMoPA ), Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Computing [London], Biomedical Image Analysis Group [London] ( BioMedIA ), Imperial College London-Imperial College London, State Key Laboratory of Virology, Wuhan University [China], MoNOS, Huygens Laboratory, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China ( IMP ), University of Science and Technology Beijing [Beijing] ( USTB ), Laboratoire de Chimie Physique - Matière et Rayonnement ( LCPMR ), Dalian University of Technology, Chalmers University of Technology [Göteborg], Advanced Photon Source [ANL] ( APS ), Argonne National Laboratory [Lemont] ( ANL ) -University of Chicago-US Department of Energy, Beth Israel Deaconess Medical Center, Harvard Medical School [Boston] ( HMS ), Dublin City University [Dublin] (DCU), Helsinki Institute for Information Technology (HIIT), Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Aalto University, University of Westminster [London] (UOW), Stanford Center for BioMedical Informatics Research (BMIR), Stanford University, Equipe NEMESIS - Centre de Recherches de l'Institut du Cerveau et de la Moelle épinière (NEMESIS-CRICM), Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institute for Molecular Bioscience, University of Queensland [Brisbane], Nanjing University of Information Science and Technology (NUIST), Instituto de Engenharia de Sistemas e Computadores (INESC), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Tianjin Agricultural University (TJAU), University of Oxford, University of Alabama [Tuscaloosa] (UA), Roberval (Roberval), Université de Technologie de Compiègne (UTC), University of Edinburgh-Queen's Medical Researche Institute, University of Edinburgh, Universiteit Leiden, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), EWHA Womans University (EWHA), Vienna University of Technology (TU Wien), National ICT Australia [Sydney] (NICTA), Centre for Plant Integrative Biology [Nothingham] (CPIB), University of Nottingham, UK (UON), Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Bioinformatique (CBIO), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Computer Graphics Group [Hong Kong], City University of Hong Kong [Hong Kong] (CUHK)-City University of Hong Kong [Hong Kong] (CUHK), Biomedical Image Analysis Group [London] (BioMedIA), Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China (IMP), University of Science and Technology Beijing [Beijing] (USTB), Advanced Photon Source [ANL] (APS), Argonne National Laboratory [Lemont] (ANL)-University of Chicago-US Department of Energy, Harvard Medical School [Boston] (HMS), Aalto University-University of Helsinki, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], MINES ParisTech - École nationale supérieure des mines de Paris, TR11527, Karaçalı, Bilge, and Izmir Institute of Technology. Electronics and Communication Engineering

Subjects
Epigenomics, Proteomics, Biological pathways, Inference, computer.software_genre, Genomic information, Applied Microbiology and Biotechnology, 0302 clinical medicine, Neoplasms, Computational models, Profiling (information science), ta518, [ SDV.BIBS ] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ta515, ComputingMilieux_MISCELLANEOUS, 0303 health sciences, Computational model, ta213, Genomics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Gene Expression Regulation, Neoplastic, 030220 oncology & carcinogenesis, Molecular Medicine, Algorithms, Biotechnology, Data integration, Bayesian probability, Biomedical Engineering, Antineoplastic Agents, Bioengineering, Biology, Machine learning, Article, 03 medical and health sciences, Humans, 030304 developmental biology, ta113, ta112, Proteomic Profiling, business.industry, Gene Expression Profiling, Precision medicine, Drug Resistance, Neoplasm, ta5141, Gene expression, Artificial intelligence, business, computer, Forecasting
Abstract

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods., MaGNeT grant 5U54CA121852-08; National Institutes of Health, National Cancer Institute (U54 CA 112970); Stand Up To Cancer-American Association for Cancer Research Dream Team Translational Cancer Research (SU2C-AACR-DT0409); Prospect Creek Foundation; Howard Hughes Medical Institute (HHMI); Academy of Finland (Finnish Center of Excellence in Computational Inference Research COIN) (251170--140057)

Published
2014

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