Francesco Iorio
- Research Group Leader, Iorio Group
Francesco is a computer scientist by training. He completed his PhD studies at the University of Salerno and the TeleThon Institute of Genetics and Medicine (TIGEM, Naples – Italy), where he focused on computational methods for drug discovery and repositioning.
Subsequently, he has been awarded a joint EMBL – European Bioinformatics Institute (EBI) and Wellcome Sanger Institute (WSI) post-doctoral (ESPOD) fellowship to work on integrative computational frameworks for predicting and dissecting drug sensitivity in cancer, analysing data from large-scale in vitro drug screens.
Following this, as a senior bioinformatician at EBI, Francesco has been the leading the analysis of data from a large-scale genome-wide CRISPR-Cas9 pooled screen across hundreds of cancer cell lines, with the aim of identifying synthetic lethalities in cancer and identifying new therapeutic targets.
From 2018 to 2020 he has been leading the WSI’s Cancer Dependency Map Analytics team, providing computational support to the Cancer Dependency Map partnership: an international endeavour involving the WSI and Broad Institute of MIT and Harvard aiming at identifying all the genetic dependencies and vulnerabilities existing in cancer cells. In this role, he has been leading the development of new algorithms and computational tools for the analysis and integration of large-scale cancer pharmacogenomics and functional genomics datasets (from chemical and genome editing screens).
Since late 2020 Francesco is a Research Group Leader in Computational Biology at the Human Technopole (Milan, Italy) where he is establishing a research program in Computational cancer Pharmacogenomics and Therapeutic Target Discovery.
Since November 2019 he is a Scientific Advisor for the joint Cancer Research Horizon – AstraZeneca Functional Genomics Centre (Cambridge, UK).
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Publications
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11/2024 - Nature Communications
Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer patient-derived xenografts
Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models – such as cancer cell lines (CCLs) and organoids – in terms of structural complexity, heterogeneity, and stromal interactions. Here, we characterise 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic levels, […]
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08/2024 - Blood
An unbiased lncRNAs dropout CRISPR-Cas9 screen reveals RP11-350G8.5 as a novel therapeutic target for Multiple Myeloma
Key Points We unveiled 8 lncRNAs essential for Multiple Myeloma (MM) cell fitness and associated with poor prognosis and high expression in MM patients We identified lncRNA RP11-350G8.5 as a therapeutic target for MM and characterised its oncogenic role, molecular and structural features Multiple Myeloma (MM) is an incurable malignancy characterised by altered expression of […]
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07/2024 - Genome Biology
A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data
Background CRISPR-Cas9 dropout screens are formidable tools for investigating biology with unprecedented precision and scale. However, biases in data lead to potential confounding effects on interpretation and compromise overall quality. The activity of Cas9 is influenced by structural features of the target site, including copy number amplifications (CN bias). More worryingly, proximal targeted loci tend […]
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07/2024 - Nature Communications
Distinct genetic liability profiles define clinically relevant patient strata across common diseases
Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility […]
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01/2024 - Cancer Cell
A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization
Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer […]
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