Machine learning approaches to drug response prediction: challenges and recent progress

[1]  Quan Zou,et al.  Clustering and classification methods for single-cell RNA-sequencing data , 2020, Briefings Bioinform..

[2]  Erratum: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. , 2020, CA: a cancer journal for clinicians.

[3]  Yidong Chen,et al.  Correction to: Predicting drug response of tumors from integrated genomic profiles by deep neural networks , 2019, BMC medical genomics.

[4]  Benjamin Haibe-Kains,et al.  Dr.VAE: improving drug response prediction via modeling of drug perturbation effects , 2019, Bioinform..

[5]  Ameet Talwalkar,et al.  Random Search and Reproducibility for Neural Architecture Search , 2019, UAI.

[6]  Correction: clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets , 2019, PLoS Comput. Biol..

[7]  Rick L. Stevens,et al.  Predicting tumor cell line response to drug pairs with deep learning , 2018, BMC Bioinformatics.

[8]  Joelle Pineau,et al.  Contextual Bandits for Adapting Treatment in a Mouse Model of de Novo Carcinogenesis , 2018, MLHC.

[9]  Petr Smirnov,et al.  Integrative Pharmacogenomics Analysis of Patient Derived Xenografts , 2018, bioRxiv.

[10]  Kyoung-Mee Kim,et al.  Pharmacogenomic landscape of patient-derived tumor cells informs precision oncology therapy , 2018, Nature Genetics.

[11]  Q. Zou,et al.  Deep learning in omics: a survey and guideline , 2018, Briefings in functional genomics.

[12]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

[13]  J. Lee,et al.  Single-cell RNA sequencing technologies and bioinformatics pipelines , 2018, Experimental & Molecular Medicine.

[14]  Kathleen A Cronin,et al.  Annual Report to the Nation on the Status of Cancer, part I: National cancer statistics , 2018, Cancer.

[15]  R. S. Huang,et al.  More than fishing for a cure: The promises and pitfalls of high throughput cancer cell line screens. , 2018, Pharmacology & therapeutics.

[16]  Tae Soon Kim,et al.  Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature , 2018, Scientific Reports.

[17]  Dejan Juric,et al.  Comparison of tissue genotyping (TG) vs circulating tumor DNA (ctDNA) for selection of matched therapy and impact on clinical outcomes among patients with metastatic breast cancer (MBC). , 2018 .

[18]  Yufei Huang,et al.  Predicting drug response of tumors from integrated genomic profiles by deep neural networks , 2018, BMC Medical Genomics.

[19]  Robert Tibshirani,et al.  DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity , 2018, Proceedings of the National Academy of Sciences.

[20]  C. Sander,et al.  Integrative analysis of pharmacogenomics in major cancer cell line databases using CellMinerCDB , 2018, bioRxiv.

[21]  Paul Hoffman,et al.  Integrating single-cell transcriptomic data across different conditions, technologies, and species , 2018, Nature Biotechnology.

[22]  Sandrine Dudoit,et al.  clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets , 2018, bioRxiv.

[23]  Su-In Lee,et al.  DeepProfile: Deep learning of cancer molecular profiles for precision medicine , 2018, bioRxiv.

[24]  Funda Meric-Bernstam,et al.  Efficacy of Larotrectinib in TRK Fusion–Positive Cancers in Adults and Children , 2018, The New England journal of medicine.

[25]  Ao Li,et al.  A novel heterogeneous network-based method for drug response prediction in cancer cell lines , 2018, Scientific Reports.

[26]  Robert M. Vogel,et al.  A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction , 2018 .

[27]  Chris P. Miller,et al.  A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia , 2018, Nature Communications.

[28]  Vinay Prasad,et al.  Cancer Drugs Approved Based on Biomarkers and Not Tumor Type-FDA Approval of Pembrolizumab for Mismatch Repair-Deficient Solid Cancers. , 2017, JAMA oncology.

[29]  Xun Zhu,et al.  Using single-cell multiple omics approaches to resolve tumor heterogeneity , 2017, Clinical and Translational Medicine.

[30]  Kevin R. Moon,et al.  Exploring single-cell data with deep multitasking neural networks , 2017, Nature Methods.

[31]  Andreas Bender,et al.  DeepSynergy: predicting anti-cancer drug synergy with Deep Learning , 2017, Bioinform..

[32]  Michael Q. Ding,et al.  Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics , 2017, Molecular Cancer Research.

[33]  K. Kirschner,et al.  Experimental design for single-cell RNA sequencing , 2017, Briefings in functional genomics.

[34]  Wei Li,et al.  Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles , 2017, Artif. Intell. Medicine.

[35]  Zhaleh Safikhani,et al.  PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies , 2017, bioRxiv.

[36]  Tin Chi Nguyen,et al.  Community assessment of cancer drug combination screens identifies strategies for synergy prediction , 2017, bioRxiv.

[37]  Dongfang Wang,et al.  VASC: dimension reduction and visualization of single cell RNA sequencing data by deep variational autoencoder , 2017, bioRxiv.

[38]  Rationalizing combination therapies , 2017, Nature Medicine.

[39]  A. Condon,et al.  Interpretable dimensionality reduction of single cell transcriptome data with deep generative models , 2017, bioRxiv.

[40]  Mikael Benson,et al.  Single-cell analyses to tailor treatments , 2017, Science Translational Medicine.

[41]  Karsten M. Borgwardt,et al.  Kernelized rank learning for personalized drug recommendation , 2017, Bioinform..

[42]  Casey S. Greene,et al.  Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders , 2017, bioRxiv.

[43]  Lin Wang,et al.  Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization , 2017, BMC Cancer.

[44]  Julio Saez-Rodriguez,et al.  GDSCTools for mining pharmacogenomic interactions in cancer , 2017, bioRxiv.

[45]  Ludmila V. Danilova,et al.  Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade , 2017, Science.

[46]  Krister Wennerberg,et al.  Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression , 2017, Bioinform..

[47]  Benjamin J. Raphael,et al.  Tumor phylogeny inference using tree-constrained importance sampling , 2017, Bioinform..

[48]  Larry Rubinstein,et al.  The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity. , 2017, Cancer research.

[49]  Aaron Goldman,et al.  Preclinical Cancer Models and Biomarkers for Drug Development: New Technologies and Emerging Tools , 2017, Journal of molecular biomarkers & diagnosis.

[50]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[51]  Tao Qing,et al.  Advances in single-cell RNA sequencing and its applications in cancer research , 2017, Oncotarget.

[52]  Leming Shi,et al.  Advances in single-cell RNA sequencing and its applications in cancer research , 2017, Oncotarget.

[53]  B. Haibe-Kains,et al.  Gene isoforms as expression-based biomarkers predictive of drug response in vitro , 2017, bioRxiv.

[54]  Tero Aittokallio,et al.  SynergyFinder: a web application for analyzing drug combination dose–response matrix data , 2017, Bioinform..

[55]  Samuel S. Schoenholz,et al.  Neural Message Passing for Quantum Chemistry , 2017, ICML.

[56]  L. J. K. Wee,et al.  Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors , 2017, Nature Genetics.

[57]  Pilar Nicolás,et al.  Strategies to design clinical studies to identify predictive biomarkers in cancer research. , 2017, Cancer treatment reviews.

[58]  Olivier Elemento,et al.  A Computational Approach for Identifying Synergistic Drug Combinations , 2017, PLoS Comput. Biol..

[59]  Ranadip Pal,et al.  Algorithms for Drug Sensitivity Prediction , 2016, Algorithms.

[60]  Quoc V. Le,et al.  Neural Architecture Search with Reinforcement Learning , 2016, ICLR.

[61]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[62]  Davis J. McCarthy,et al.  A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor , 2016, F1000Research.

[63]  Nancy R. Zhang,et al.  Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing , 2016, Proceedings of the National Academy of Sciences.

[64]  T. Metcalfe,et al.  Precision medicine and oncology: an overview of the opportunities presented by next-generation sequencing and big data and the challenges posed to conventional drug development and regulatory approval pathways. , 2016, Annals of oncology : official journal of the European Society for Medical Oncology.

[65]  Emanuel J. V. Gonçalves,et al.  A Landscape of Pharmacogenomic Interactions in Cancer , 2016, Cell.

[66]  Benjamin J. Raphael,et al.  Inferring the Mutational History of a Tumor Using Multi-state Perfect Phylogeny Mixtures. , 2016, Cell systems.

[67]  Francisco Azuaje,et al.  Computational models for predicting drug responses in cancer research , 2016, Briefings Bioinform..

[68]  Wei Zheng,et al.  Drug combination therapy increases successful drug repositioning. , 2016, Drug discovery today.

[69]  Sergey Plis,et al.  Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. , 2016, Molecular pharmaceutics.

[70]  Scott E. Martin,et al.  Reproducible pharmacogenomic profiling of cancer cell line panels , 2016, Nature.

[71]  Alexandre Bouchard-Côté,et al.  Clonal genotype and population structure inference from single-cell tumor sequencing , 2016, Nature Methods.

[72]  Krister Wennerberg,et al.  Methods for High-Throughput Drug Combination Screening and Synergy Scoring , 2016, bioRxiv.

[73]  Edith M. Ross,et al.  OncoNEM: inferring tumor evolution from single-cell sequencing data , 2016, Genome Biology.

[74]  N. Beerenwinkel,et al.  Tree inference for single-cell data , 2016, bioRxiv.

[75]  Gajendra P. S. Raghava,et al.  Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine , 2016, Scientific Reports.

[76]  Yair Benita,et al.  An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies , 2016, Molecular Cancer Therapeutics.

[77]  Vijay S. Pande,et al.  Molecular graph convolutions: moving beyond fingerprints , 2016, Journal of Computer-Aided Molecular Design.

[78]  Clarke Brian Blackadar,et al.  Historical review of the causes of cancer. , 2016, World journal of clinical oncology.

[79]  Chih-Ming Ho,et al.  Optimization of drug combinations using Feedback System Control , 2016, Nature Protocols.

[80]  Andrew H. Beck,et al.  PharmacoGx: an R package for analysis of large pharmacogenomic datasets , 2015, Bioinform..

[81]  Funda Meric-Bernstam,et al.  The right drugs at the right time for the right patient: the MD Anderson precision oncology decision support platform. , 2015, Drug discovery today.

[82]  Joshua M. Korn,et al.  High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response , 2015, Nature Medicine.

[83]  Xiaoxiao Sun,et al.  Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment , 2015, Acta Pharmacologica Sinica.

[84]  Thomas Martinetz,et al.  Deep convolutional neural networks as generic feature extractors , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[85]  Andrew L. Kung,et al.  Examining the utility of patient-derived xenograft mouse models , 2015, Nature Reviews Cancer.

[86]  S. Sugano,et al.  Single-cell analysis of lung adenocarcinoma cell lines reveals diverse expression patterns of individual cells invoked by a molecular target drug treatment , 2015, Genome Biology.

[87]  Chih-Ming Ho,et al.  Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer , 2015, Angiogenesis.

[88]  Xin Chen,et al.  DCDB 2.0: a major update of the drug combination database , 2014, Database J. Biol. Databases Curation.

[89]  Benjamin J. Raphael,et al.  Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data , 2014, Bioinform..

[90]  Nci Dream Community.  A community effort to assess and improve drug sensitivity prediction algorithms , 2014 .

[91]  Nicola Miller,et al.  The impact of Oncotype DX testing on breast cancer management and chemotherapy prescribing patterns in a tertiary referral centre , 2014, European journal of cancer.

[92]  J. Barnholtz-Sloan,et al.  Computational identification of multi-omic correlates of anticancer therapeutic response , 2014, BMC Genomics.

[93]  Sarah A Heerboth,et al.  Drug Resistance in Cancer: An Overview , 2014, Cancers.

[94]  Mehmet Gönen,et al.  Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning , 2014, Bioinform..

[95]  Robert Clarke,et al.  Enhancing reproducibility in cancer drug screening: how do we move forward? , 2014, Cancer research.

[96]  Obi L. Griffith,et al.  SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution , 2014, PLoS Comput. Biol..

[97]  Shankar Vembu,et al.  PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors , 2015, Genome Biology.

[98]  Lincoln D. Stein,et al.  PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors , 2014, Genome Biology.

[99]  Navdeep Jaitly,et al.  Multi-task Neural Networks for QSAR Predictions , 2014, ArXiv.

[100]  Laura M. Heiser,et al.  A community effort to assess and improve drug sensitivity prediction algorithms , 2014, Nature Biotechnology.

[101]  A. Bouchard-Côté,et al.  PyClone: statistical inference of clonal population structure in cancer , 2014, Nature Methods.

[102]  Justin Guinney,et al.  Systematic Assessment of Analytical Methods for Drug Sensitivity Prediction from Cancer Cell Line Data , 2013, Pacific Symposium on Biocomputing.

[103]  P. Johnston,et al.  Cancer drug resistance: an evolving paradigm , 2013, Nature Reviews Cancer.

[104]  Benjamin Haibe-Kains,et al.  mRMRe: an R package for parallelized mRMR ensemble feature selection , 2013, Bioinform..

[105]  F. Khuri,et al.  Targeting protein-protein interactions as an anticancer strategy. , 2013, Trends in pharmacological sciences.

[106]  Benjamin Haibe-Kains,et al.  Research and applications: Comparison and validation of genomic predictors for anticancer drug sensitivity , 2013, J. Am. Medical Informatics Assoc..

[107]  Levi A Garraway,et al.  Precision oncology: an overview. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[108]  Julio Saez-Rodriguez,et al.  Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties , 2012, PloS one.

[109]  Sridhar Ramaswamy,et al.  Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells , 2012, Nucleic Acids Res..

[110]  Peter Bouwman,et al.  The effects of deregulated DNA damage signalling on cancer chemotherapy response and resistance , 2012, Nature Reviews Cancer.

[111]  S. Ramaswamy,et al.  Systematic identification of genomic markers of drug sensitivity in cancer cells , 2012, Nature.

[112]  Sabine Tejpar,et al.  KRAS, BRAF, PIK3CA, and PTEN mutations: implications for targeted therapies in metastatic colorectal cancer. , 2011, The Lancet. Oncology.

[113]  D. Bojanic,et al.  Impact of high-throughput screening in biomedical research , 2011, Nature Reviews Drug Discovery.

[114]  M. Gilbert,et al.  Clinical Cancer Advances 2013: Annual Report on Progress Against Cancer from the American Society of Clinical Oncology. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[115]  Linda Mol,et al.  Chemotherapy, bevacizumab, and cetuximab in metastatic colorectal cancer. , 2009, The New England journal of medicine.

[116]  J. Ross,et al.  MammaPrint™ 70-gene signature: another milestone in personalized medical care for breast cancer patients , 2009, Expert review of molecular diagnostics.

[117]  Seta Shahin,et al.  A randomized phase IIIB trial of chemotherapy, bevacizumab, and panitumumab compared with chemotherapy and bevacizumab alone for metastatic colorectal cancer. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[118]  F. Couch,et al.  Secondary mutations as a mechanism of cisplatin resistance in BRCA2-mutated cancers , 2008, Nature.

[119]  Jorge S. Reis-Filho,et al.  Resistance to therapy caused by intragenic deletion in BRCA2 , 2008, Nature.

[120]  R. Shoemaker.  The NCI60 human tumour cell line anticancer drug screen , 2006, Nature Reviews Cancer.

[121]  M. Meyerson,et al.  EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. , 2005, The New England journal of medicine.

[122]  M. Perry,et al.  Classical Chemotherapy: Mechanisms, Toxicities and the Therapeutc Window , 2003, Cancer biology & therapy.

[123]  Melvin E Andersen,et al.  Molecular circuits, biological switches, and nonlinear dose-response relationships. , 2002, Environmental health perspectives.

[124]  J. Bertram,et al.  Molecular Biology of Cancer , 1997, Nature Medicine.

[125]  D. Thomas,et al.  Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks. , 1997, Cancer research.

[126]  G. Meersma,et al.  Relationship of cellular glutathione to the cytotoxicity and resistance of seven platinum compounds. , 1992, Cancer research.

[127]  L. Johnson,et al.  Thymidylate synthase overproduction and gene amplification in fluorodeoxyuridine-resistant human cells. , 1985, Molecular pharmacology.

[128]  L. Johnson,et al.  Thymidylate synthase gene amplification in fluorodeoxyuridine-resistant mouse cell lines. , 1985, Molecular pharmacology.

[129]  C. I. Bliss.  THE TOXICITY OF POISONS APPLIED JOINTLY1 , 1939 .

[130]  J. Saiz,et al.  Right‐sided non‐recurrent laryngeal nerve without any vascular anomaly: an anatomical trap , 2021, ANZ journal of surgery.

[131]  J. Gu,et al.  Dimension reduction and visualization of single cell RNA sequencing data by deep variational autoencoder , 2019 .

[132]  Susan M. Chang,et al.  Clinical Cancer Advances 2018: Annual Report on Progress Against Cancer From the American Society of Clinical Oncology. , 2018, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[133]  Baolin Zhang,et al.  Drug-biomarker co-development in oncology - 20 years and counting. , 2017, Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy.

[134]  D. Nam,et al.  Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma , 2016, Genome Biology.

[135]  Andreas Mayr,et al.  Deep Learning as an Opportunity in Virtual Screening , 2015 .

[136]  P. Mahadevan,et al.  An overview , 2007, Journal of Biosciences.

[137]  Manuela Pavan,et al.  DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS , 2006 .