Francesco Iorio

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).

Follow on

  • Twitter


  • 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 […]

  • 01/2024 - The CRISPR Journal

    Benchmark Software and Data for Evaluating CRISPR-Cas9 Experimental Pipelines Through the Assessment of a Calibration Screen

    Genome-wide genetic screens using CRISPR-guide RNA libraries are widely performed in mammalian cells to functionally characterize individual genes and for the discovery of new anticancer therapeutic targets. As the effectiveness of such powerful and precise tools for cancer pharmacogenomics is emerging, tools and methods for their quality assessment are becoming increasingly necessary. Here, we provide […]

  • 11/2023 - Cell Reports Medicine

    RAGE engagement by SARS-CoV-2 enables monocyte infection and underlies COVID-19 severity

    The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fueled the COVID-19 pandemic with its enduring medical and socioeconomic challenges because of subsequent waves and long-term consequences of great concern. Here, we chart the molecular basis of COVID-19 pathogenesis by analyzing patients’ immune responses at single-cell resolution across disease course and severity. This […]

  • 07/2023 - Febs Letters

    Highlights from the 1st European cancer dependency map symposium and workshop

    The systematic identification of tumour vulnerabilities through perturbational experiments on cancer models, including genome editing and drug screens, is playing a crucial role in combating cancer. This collective effort is known as the Cancer Dependency Map (DepMap). The 1st European Cancer Dependency Map Symposium (EuroDepMap), held in Milan last May, featured talks, a roundtable discussion, and a poster […]

  • 01/2023 - Bioinformatics

    A heuristic algorithm solving the mutual-exclusivity sorting problem

    Motivation Binary (or boolean) matrices provide a common effective data representation adopted in several domains of computational biology, especially for investigating cancer and other human diseases. For instance, they are used to summarise genetic aberrations—copy number alterations or mutations—observed in cancer patient cohorts, effectively highlighting combinatorial relations among them. One of these is the tendency […]