Iorio Group
Iorio group’s Apps, Tools and Computable Manuscripts
The Iorio Group works at the interface of biology, machine learning, statistics and information theory with the goal of understanding and predicting how genomic alterations and molecular traits from other omics contribute to pathological processes, biological circuits’ rewiring and have an impact on therapeutic response in human cancers and other diseases.
Our research aims at advancing human health by designing algorithms, computational tools and novel analytical methods for the integration and the analysis of pharmacogenomics and functional-genomics datasets, with the objective of identifying new therapeutic targets, biomarkers and drug repositioning opportunities.
With our experimental collaborators, we are contributing to the creation of a comprehensive map of all the genetic dependencies occurring in human cancers, and to the development of a computational infrastructure for translating this map into guidelines for early-stage drug development and precision medicine.
The Iorio Group designs, implements and maintains bioinformatics methods and original tools for the assessment of cancer pre-clinical models, the pre-processing, analysis and visualisation of genome-editing screening data, for the in-silico correction of new-technology-specific biases in such data, and for the optimization of single guide RNA libraries for pooled CRISPR-Cas9 screens and other experimental settings.
We are also interested in big-data analytics, the development of biomedical predictive models based on non-biomedical data, and computationally efficient constrained randomization strategies for testing combinatorial properties in large-scale genomic datasets and networks.
Group members
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Francesco Iorio
Research Group Leader -
Fateema Hani Bazzi
Undergraduate Intern -
Lorenzo Mathieu Brochier
PhD Student -
Ottavio Croci
Senior Data Scientist -
Alessandro Digilio
Postgraduate Fellow -
Irene Fernández Rebollo
PhD Student -
Raffaele Iannuzzi
PhD Student -
Athanasios Oikonomou
Postdoc -
Flavio Passante
PhD Student -
Ludovica Proietti
Research Fellow -
Nevenka Radic
Postdoc -
Aurora Savino
Postdoc -
Vanessa Spagnolo
Technician -
Yasin Tepeli
Scientific Visitor -
Alessandro Vinceti
Postdoc -
Gianluca Vozza
Postdoc
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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