Multiparametric phenotyping of compound effects on patient derived organoids

Patient derived organoids (PDOs) closely resemble individual tumor biology and allow testing of small molecules ex vivo. To systematically dissect compound effects on 3D organoids, we developed a high-throughput imaging and quantitative analysis approach. We generated PDOs from colorectal cancer patients, treated them with >500 small molecules and captured >3 million images by confocal microscopy. We developed the software framework SCOPE to measure compound induced re-organization of PDOs. We found diverse, but re-occurring phenotypes that clustered by compound mode-of-action. Complex phenotypes were not congruent with PDO viability and many were specific to subsets of PDO lines or were influenced by recurrent mutations. We further analyzed specific phenotypes induced by compound classes and found GSK3 inhibitors to disassemble PDOs via focal adhesion signaling or that MEK inhibition led to bloating of PDOs by enhancing of stemness. Finally, by viability classification, we show heterogeneous susceptibilities of PDOs to clinical anticancer drugs.

[1]  Gennady Korotkevich,et al.  Fast gene set enrichment analysis , 2019, bioRxiv.

[2]  M. Ebert,et al.  MEK inhibitors activate Wnt signalling and induce stem cell plasticity in colorectal cancer , 2019, Nature Communications.

[3]  Florian Heigwer,et al.  Machine learning and image-based profiling in drug discovery , 2018, Current opinion in systems biology.

[4]  Andrea Sottoriva,et al.  Patient-derived organoids model treatment response of metastatic gastrointestinal cancers , 2018, Science.

[5]  Hans Clevers,et al.  Imaging organoids: a bright future ahead , 2018, Nature Methods.

[6]  Bon-Kyoung Koo,et al.  Human Primary Liver Cancer -derived Organoid Cultures for disease modelling and drug screening , 2017, Nature Medicine.

[7]  Tom Misteli,et al.  High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease. , 2017, Trends in genetics : TIG.

[8]  Lassi Paavolainen,et al.  Data-analysis strategies for image-based cell profiling , 2017, Nature Methods.

[9]  Davide Prandi,et al.  Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine. , 2017, Cancer discovery.

[10]  Catherine L. Worth,et al.  Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors , 2017, Nature Communications.

[11]  Alexey Sergushichev,et al.  An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation , 2016 .

[12]  Toshio Uraoka,et al.  A Colorectal Tumor Organoid Library Demonstrates Progressive Loss of Niche Factor Requirements during Tumorigenesis. , 2016, Cell stem cell.

[13]  Anne E Carpenter,et al.  Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes , 2016, Nature Protocols.

[14]  F. Cunningham,et al.  The Ensembl Variant Effect Predictor , 2016, bioRxiv.

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

[16]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[17]  M. Boutros,et al.  Microscopy-Based High-Content Screening , 2015, Cell.

[18]  Wolfgang Huber,et al.  A chemical–genetic interaction map of small molecules using high‐throughput imaging in cancer cells , 2015, Molecular systems biology.

[19]  James Y. Zou Analysis of protein-coding genetic variation in 60,706 humans , 2015, Nature.

[20]  Hans Clevers,et al.  Preserved genetic diversity in organoids cultured from biopsies of human colorectal cancer metastases , 2015, Proceedings of the National Academy of Sciences.

[21]  Jeffrey S. Morris,et al.  The Consensus Molecular Subtypes of Colorectal Cancer , 2015, Nature Medicine.

[22]  Krister Wennerberg,et al.  Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose–response data , 2015, Bioinform..

[23]  R. Bianco,et al.  Src inhibitors act through different mechanisms in Non-Small Cell Lung Cancer models depending on EGFR and RAS mutational status , 2015, Oncotarget.

[24]  Hayley E. Francies,et al.  Prospective Derivation of a Living Organoid Biobank of Colorectal Cancer Patients , 2015, Cell.

[25]  R. Arceci,et al.  Targeting cell cycle regulators in hematologic malignancies , 2015, Front. Cell Dev. Biol..

[26]  F. Pampaloni,et al.  Light sheet-based fluorescence microscopy (LSFM) for the quantitative imaging of cells and tissues , 2015, Cell and Tissue Research.

[27]  Raphael Gottardo,et al.  Orchestrating high-throughput genomic analysis with Bioconductor , 2015, Nature Methods.

[28]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[29]  M. Spector,et al.  Organoid Models of Human and Mouse Ductal Pancreatic Cancer , 2015, Cell.

[30]  Hans Clevers,et al.  Long-Term Culture of Genome-Stable Bipotent Stem Cells from Adult Human Liver , 2015, Cell.

[31]  Mingming Jia,et al.  COSMIC: exploring the world's knowledge of somatic mutations in human cancer , 2014, Nucleic Acids Res..

[32]  Hans Clevers,et al.  Organoid cultures for the analysis of cancer phenotypes. , 2014, Current opinion in genetics & development.

[33]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[34]  Mauricio O. Carneiro,et al.  From FastQ Data to High‐Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline , 2013, Current protocols in bioinformatics.

[35]  Jeffrey J Meyer,et al.  Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012. (5) , 2013 .

[36]  Ralf Herwig,et al.  The ConsensusPathDB interaction database: 2013 update , 2012, Nucleic Acids Res..

[37]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.

[38]  Jeni P. Mahida,et al.  A High-Content Biosensor-Based Screen Identifies Cell-Permeable Activators and Inhibitors of EGFR Function , 2012, Journal of biomolecular screening.

[39]  Helga Thorvaldsdóttir,et al.  Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration , 2012, Briefings Bioinform..

[40]  Hans Clevers,et al.  Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett's epithelium. , 2011, Gastroenterology.

[41]  Hans Clevers,et al.  The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse. , 2011, Cell stem cell.

[42]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.

[43]  Wolfgang Huber,et al.  EBImage—an R package for image processing with applications to cellular phenotypes , 2010, Bioinform..

[44]  Y. Naomoto,et al.  Progress in researches about focal adhesion kinase in gastrointestinal tract. , 2009, World journal of gastroenterology.

[45]  Zhen Hu,et al.  BMC Bioinformatics BioMed Central Methodology article CLEAN: CLustering Enrichment ANalysis , 2009 .

[46]  H. Clevers,et al.  Single Lgr5 stem cells build crypt–villus structures in vitro without a mesenchymal niche , 2009, Nature.

[47]  T. MacDonald,et al.  Imatinib blocks migration and invasion of medulloblastoma cells by concurrently inhibiting activation of platelet-derived growth factor receptor and transactivation of epidermal growth factor receptor , 2009, Molecular Cancer Therapeutics.

[48]  S. Pastorino,et al.  Glycogen synthase kinase-3 inhibition induces glioma cell death through c-MYC, nuclear factor-kappaB, and glucose regulation. , 2008, Cancer research.

[49]  Anne E Carpenter Image-based chemical screening. , 2007, Nature chemical biology.

[50]  Lani F. Wu,et al.  Image-based multivariate profiling of drug responses from single cells , 2007, Nature Methods.

[51]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[52]  Lani F. Wu,et al.  Multidimensional Drug Profiling By Automated Microscopy , 2004, Science.

[53]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[54]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[55]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..