Umberto is a human person and also a postdoctoral researcher at Human Technopole in the IorioLab. His current work focuses on combining computational biology, data science, and machine learning methods for pharmacogenomics modelling and biomarker discovery.
PhD in Bioinformatics, 2021
EMBL-EBI / University of Cambridge (UK)
MSc in Molecular Biotechnology, 2016
Università degli Studi di Torino (IT)
BSc in Biotechnology, 2011
Università degli Studi di Torino (IT)
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. We characterised 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic level and measured their response to cetuximab, an EGFR inhibitor in clinical use for metastatic colorectal cancer. After assessing PDXs’ quality, stability, and molecular concordance with publicly available patient cohorts, we trained, interpreted, and validated an integrated ensemble classifier (CeSta) which takes in input the PDXs’ multi-omic characterisation and predicts their sensitivity to cetuximab treatment (AUROC textgreater 0.9). Our study shows that large PDX collections can be used to train accurate, interpretable models of drug sensitivity, which 1) better recapitulate patient-derived therapeutic biomarkers than other models trained on CCL data, 2) can be robustly validated across independent PDX cohorts, and 3) can be used for the development of novel therapeutic biomarkers.