Computational biology

Iorio Group

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

Publications

  • 07/2022 - Cell Reports

    Reduced gene templates for supervised analysis of scale-limited CRISPR-Cas9 fitness screens

    Pooled genome-wide CRISPR-Cas9 screens are furthering our mechanistic understanding of human biology and have allowed us to identify new oncology therapeutic targets. Scale-limited CRISPR-Cas9 screens—typically employing guide RNA libraries targeting subsets of functionally related genes, biological pathways, or portions of the druggable genome—constitute an optimal setting for investigating narrow hypotheses and are easier to execute […]

  • 07/2022 - Molecular Systems Biology

    Computational estimation of quality and clinical relevance of cancer cell lines

    Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision […]

  • 11/2021 - BMC Genomics

    CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens

    Background CRISPR-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. One of the major computational challenges when analysing data derived from such screens is to identify genes that are essential for cell survival invariantly across tissues, conditions, and genomic-contexts (core-fitness genes), and to distinguish them from […]

  • 09/2021 - Clinical Cancer Research

    Functional impact of genomic complexity on the transcriptome of Multiple Myeloma

    Purpose: Multiple Myeloma (MM) is a biologically heterogenous plasma-cell disorder. In this study we aimed at dissecting the functional impact on transcriptome of gene mutations, copy-number abnormalities (CNAs), and chromosomal rearrangements (CRs). Moreover, we applied a geno-transcriptomic approach to identify specific biomarkers for personalized treatments. Methods: We analyzed 514 newly-diagnosed patients from the IA12 release […]

  • 03/2021 - Nature Communications

    Integrated cross-study datasets of genetic dependencies in cancer

    CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated […]