Dr Francesco iorio | Principal Staff Scientist

iorio, Francesco

Francesco leads the development of new algorithms and computational tools for the analysis of large-scale cancer pharmacogenomics and functional genomics datasets (from chemical and genome editing screens), to identify molecular markers of drug response and new oncology therapeutic targets.

Toward this aim, he is responsible for the design of analytical methods and software, and the management of day-by-day operations to deliver scientific milestones toward the definition of a global Cancer Dependency Map: an atlas of genetic dependencies and vulnerabilities, at an individual cancer cell resolution, which could be exploited for the development of cancer targeted and personalized therapies.

Francesco completed his PhD in the Systems, Synthetic and Computational Biology Laboratory of the TeleThon Institute of Genetics and Medicine (TIGEM, Naples, Italy), focusing on computational methods for drug discovery and repositioning based on the analysis of large compendia of gene expression profiles.

Subsequently he was awarded a joint EMBL-EBI/Sanger postdoctoral fellowship (ESPOD) and he worked on integrative computational frameworks for predicting and dissecting drug susceptibility in cancer based on the analysis of data from large-scale drug screens.

Prior joining CASM at the Wellcome Sanger Institute, he has been the leading computational scientist in a project funded by Open Targets (a public-private initiative spearheaded by EMBL-EBI, GSK, Biogen and the Wellcome Sanger Institute), aiming at identifying new therapeutic targets and synthetic lethalities in cancer, through the analysis of data from a large-scale, genome-wide CRISPR-Cas9 knockout screen across hundreds of cancer cell lines.

Within Open Targets he currently leads the CELLector project to systematically evaluate the disease relevance of cancer in-vitro models, and he has been coleading the DoRothEA project linking trascription factors activities to somatic mutations and cancer response to therapy.

Francesco is also interested in designing computationally efficient methods simulating constrained null models for testing combinatorial properties in cancer genomics datasets and networks; unsupervised machine learning; data visualisation; information theory and theoretical computer science.

Publications

  • Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting.

    Iorio F, Behan FM, Gonçalves E, Bhosle SG, Chen E et al.

    BMC genomics 2018;19;1;604

  • Pathway-based dissection of the genomic heterogeneity of cancer hallmarks' acquisition with SLAPenrich.

    Iorio F, Garcia-Alonso L, Brammeld JS, Martincorena I, Wille DR et al.

    Scientific reports 2018;8;1;6713

  • Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer.

    Garcia-Alonso L, Iorio F, Matchan A, Fonseca N, Jaaks P et al.

    Cancer research 2018;78;3;769-780

  • A Landscape of Pharmacogenomic Interactions in Cancer.

    Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP et al.

    Cell 2016;166;3;740-754

  • Pharmacogenomic agreement between two cancer cell line data sets.

    Cancer Cell Line Encyclopedia Consortium and Genomics of Drug Sensitivity in Cancer Consortium

    Nature 2015;528;7580;84-7

  • Prospective derivation of a living organoid biobank of colorectal cancer patients.

    van de Wetering M, Francies HE, Francis JM, Bounova G, Iorio F et al.

    Cell 2015;161;4;933-45

  • Exploiting combinatorial patterns in cancer genomic data for personalized therapy and new target discovery.

    Schubert M and Iorio F

    Pharmacogenomics 2014;15;16;1943-6

  • Discovery of drug mode of action and drug repositioning from transcriptional responses.

    Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P et al.

    Proceedings of the National Academy of Sciences of the United States of America 2010;107;33;14621-6

  • A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches.

    Cantone I, Marucci L, Iorio F, Ricci MA, Belcastro V et al.

    Cell 2009;137;1;172-81

  • Drug repurposing: progress, challenges and recommendations.

    Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S et al.

    Nature reviews. Drug discovery 2018

  • The germline genetic component of drug sensitivity in cancer cell lines.

    Menden MP, Casale FP, Stephan J, Bignell GR, Iorio F et al.

    Nature communications 2018;9;1;3385

  • Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting.

    Iorio F, Behan FM, Gonçalves E, Bhosle SG, Chen E et al.

    BMC genomics 2018;19;1;604

  • Pathway-based dissection of the genomic heterogeneity of cancer hallmarks' acquisition with SLAPenrich.

    Iorio F, Garcia-Alonso L, Brammeld JS, Martincorena I, Wille DR et al.

    Scientific reports 2018;8;1;6713

  • GDSCTools for mining pharmacogenomic interactions in cancer.

    Cokelaer T, Chen E, Iorio F, Menden MP, Lightfoot H et al.

    Bioinformatics (Oxford, England) 2018;34;7;1226-1228

  • Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer.

    Garcia-Alonso L, Iorio F, Matchan A, Fonseca N, Jaaks P et al.

    Cancer research 2018;78;3;769-780

  • Loss of functional BAP1 augments sensitivity to TRAIL in cancer cells.

    Kolluri KK, Alifrangis C, Kumar N, Ishii Y, Price S et al.

    eLife 2018;7

  • Comprehensive Pharmacogenomic Profiling of Malignant Pleural Mesothelioma Identifies a Subgroup Sensitive to FGFR Inhibition.

    Quispel-Janssen JM, Badhai J, Schunselaar L, Price S, Brammeld J et al.

    Clinical cancer research : an official journal of the American Association for Cancer Research 2018;24;1;84-94

  • Stem cell-like transcriptional reprogramming mediates metastatic resistance to mTOR inhibition.

    Mateo F, Arenas EJ, Aguilar H, Serra-Musach J, de Garibay GR et al.

    Oncogene 2017;36;19;2737-2749

  • Genome-wide chemical mutagenesis screens allow unbiased saturation of the cancer genome and identification of drug resistance mutations.

    Brammeld JS, Petljak M, Martincorena I, Williams SP, Alonso LG et al.

    Genome research 2017;27;4;613-625

  • Hemopoietic-specific Sf3b1-K700E knock-in mice display the splicing defect seen in human MDS but develop anemia without ring sideroblasts.

    Mupo A, Seiler M, Sathiaseelan V, Pance A, Yang Y et al.

    Leukemia 2017;31;3;720-727

  • Efficient randomization of biological networks while preserving functional characterization of individual nodes.

    Iorio F, Bernardo-Faura M, Gobbi A, Cokelaer T, Jurman G and Saez-Rodriguez J

    BMC bioinformatics 2016;17;1;542

  • Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy.

    Knijnenburg TA, Klau GW, Iorio F, Garnett MJ, McDermott U et al.

    Scientific reports 2016;6;36812

  • A CRISPR Dropout Screen Identifies Genetic Vulnerabilities and Therapeutic Targets in Acute Myeloid Leukemia.

    Tzelepis K, Koike-Yusa H, De Braekeleer E, Li Y, Metzakopian E et al.

    Cell reports 2016;17;4;1193-1205

  • A Landscape of Pharmacogenomic Interactions in Cancer.

    Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP et al.

    Cell 2016;166;3;740-754

  • Transcriptional response networks for elucidating mechanisms of action of multitargeted agents.

    Kibble M, Khan SA, Saarinen N, Iorio F, Saez-Rodriguez J et al.

    Drug discovery today 2016;21;7;1063-75

  • Multilevel models improve precision and speed of IC50 estimates.

    Vis DJ, Bombardelli L, Lightfoot H, Iorio F, Garnett MJ and Wessels LF

    Pharmacogenomics 2016;17;7;691-700

  • Integrated transcriptomic and proteomic analysis identifies protein kinase CK2 as a key signaling node in an inflammatory cytokine network in ovarian cancer cells.

    Kulbe H, Iorio F, Chakravarty P, Milagre CS, Moore R et al.

    Oncotarget 2016;7;13;15648-61

  • Unravelling druggable signalling networks that control F508del-CFTR proteostasis.

    Hegde RN, Parashuraman S, Iorio F, Ciciriello F, Capuani F et al.

    eLife 2015;4

  • Pharmacogenomic agreement between two cancer cell line data sets.

    Cancer Cell Line Encyclopedia Consortium and Genomics of Drug Sensitivity in Cancer Consortium

    Nature 2015;528;7580;84-7

  • Blood transcriptomics of drug-naïve sporadic Parkinson's disease patients.

    Calligaris R, Banica M, Roncaglia P, Robotti E, Finaurini S et al.

    BMC genomics 2015;16;876

  • Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury.

    Melas IN, Sakellaropoulos T, Iorio F, Alexopoulos LG, Loh WY et al.

    Integrative biology : quantitative biosciences from nano to macro 2015;7;8;904-20

  • Prospective derivation of a living organoid biobank of colorectal cancer patients.

    van de Wetering M, Francies HE, Francis JM, Bounova G, Iorio F et al.

    Cell 2015;161;4;933-45

  • BRAF inhibitor resistance mediated by the AKT pathway in an oncogenic BRAF mouse melanoma model.

    Perna D, Karreth FA, Rust AG, Perez-Mancera PA, Rashid M et al.

    Proceedings of the National Academy of Sciences of the United States of America 2015;112;6;E536-45

  • A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions.

    Iorio F, Shrestha RL, Levin N, Boilot V, Garnett MJ et al.

    PloS one 2015;10;10;e0139446

  • Exploiting combinatorial patterns in cancer genomic data for personalized therapy and new target discovery.

    Schubert M and Iorio F

    Pharmacogenomics 2014;15;16;1943-6

  • Fast randomization of large genomic datasets while preserving alteration counts.

    Gobbi A, Iorio F, Dawson KJ, Wedge DC, Tamborero D et al.

    Bioinformatics (Oxford, England) 2014;30;17;i617-23

  • Heterogeneity of genomic evolution and mutational profiles in multiple myeloma.

    Bolli N, Avet-Loiseau H, Wedge DC, Van Loo P, Alexandrov LB et al.

    Nature communications 2014;5;2997

  • Network based elucidation of drug response: from modulators to targets.

    Iorio F, Saez-Rodriguez J and di Bernardo D

    BMC systems biology 2013;7;139

  • Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors.

    Casado P, Alcolea MP, Iorio F, Rodríguez-Prados JC, Vanhaesebroeck B et al.

    Genome biology 2013;14;4;R37

  • Transcriptional data: a new gateway to drug repositioning?

    Iorio F, Rittman T, Ge H, Menden M and Saez-Rodriguez J

    Drug discovery today 2013;18;7-8;350-7

  • DvD: An R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data.

    Pacini C, Iorio F, Gonçalves E, Iskar M, Klabunde T et al.

    Bioinformatics (Oxford, England) 2013;29;1;132-4

  • Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties.

    Menden MP, Iorio F, Garnett M, McDermott U, Benes CH et al.

    PloS one 2013;8;4;e61318

  • Cancer develops, progresses and responds to therapies through restricted perturbation of the protein-protein interaction network.

    Serra-Musach J, Aguilar H, Iorio F, Comellas F, Berenguer A et al.

    Integrative biology : quantitative biosciences from nano to macro 2012;4;9;1038-48

  • Systematic identification of genomic markers of drug sensitivity in cancer cells.

    Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A et al.

    Nature 2012;483;7391;570-5

  • Transcriptional gene network inference from a massive dataset elucidates transcriptome organization and gene function.

    Belcastro V, Siciliano V, Gregoretti F, Mithbaokar P, Dharmalingam G et al.

    Nucleic acids research 2011;39;20;8677-88

  • Artificial neural network analysis of circulating tumor cells in metastatic breast cancer patients.

    Giordano A, Giuliano M, De Laurentiis M, Eleuteri A, Iorio F et al.

    Breast cancer research and treatment 2011;129;2;451-8

  • Identification of small molecules enhancing autophagic function from drug network analysis.

    Iorio F, Isacchi A, di Bernardo D and Brunetti-Pierri N

    Autophagy 2010;6;8;1204-5

  • Discovery of drug mode of action and drug repositioning from transcriptional responses.

    Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P et al.

    Proceedings of the National Academy of Sciences of the United States of America 2010;107;33;14621-6

  • A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches.

    Cantone I, Marucci L, Iorio F, Ricci MA, Belcastro V et al.

    Cell 2009;137;1;172-81

  • NIRest: a tool for gene network and mode of action inference.

    Lauria M, Iorio F and di Bernardo D

    Annals of the New York Academy of Sciences 2009;1158;257-64

  • Identifying network of drug mode of action by gene expression profiling.

    Iorio F, Tagliaferri R and di Bernardo D

    Journal of computational biology : a journal of computational molecular cell biology 2009;16;2;241-51

  • Interactive data analysis and clustering of genomic data.

    Ciaramella A, Cocozza S, Iorio F, Miele G, Napolitano F et al.

    Neural networks : the official journal of the International Neural Network Society 2008;21;2-3;368-78

iorio, Francesco
Francesco's Timeline
2018

Became Principal Staff Scientist at WTSI to lead the Cancer DepMap Analytics team

2014

Started as Senior Bioinformatician at EMBL-EBI with Open Targets

2011

Joined Saez-Rodriguez Lab (EMBL-EBI) & McDermott/Garnett Groups (WTSI), as EBI-Sanger Post-Doctoral (ESPOD) fellow

2007

Started PhD at TeleThon Institute of Genetics and Medicine & University of Salerno

Started to work as Research Associate at TeleThon Institute of Genetics and Medicine

2006

Completed Msci in Computer Science at University of Salerno (Italy)