Overview

3D representation of a DNA filamentOur laboratory develops innovative computational and systems-level approaches to uncover the biological mechanisms underlying cancer initiation, evolution, and treatment resistance. We integrate advanced bioinformatics methods, computational modeling, and algorithmic development to analyze next-generation sequencing and high-density genomic data within comprehensive systems biology frameworks.
We also apply artificial intelligence strategies, including protein language models and biologically informed neural networks, to enhance the interpretation of complex, high-dimensional molecular data. By combining data-driven modeling with mechanistic insight, we seek to extract clinically meaningful knowledge from large-scale genomic datasets and translate computational discoveries into advances for cancer diagnosis and treatment.

 

Research Directions

A central focus of our research is to understand how genetic variation shapes cancer risk and tumor behavior. We investigate the functional consequences of genomic alterations in their regulatory and molecular contexts, examining their effects on gene expression, allele-specific activity, protein structure and function, and the perturbation of cellular networks.
We place particular emphasis on the interplay between germline and somatic variants, aiming to elucidate how inherited genetic background influences the emergence, selection, and functional impact of somatic alterations during tumor development and progression.
Through the integration of these complementary research directions, we aim to identify robust prognostic and predictive biomarkers that can inform precision oncology strategies.

Group members

  • Alessandro Romanel, PI
  • Fabio Mazza, PhD student (co-supervised with Gianluca Lattanzi)
  • Filippo Gastaldello, PhD student (co-supervised with COSBI)
  • Eylul Bulut, Master student, QCB

Grants

  • 2026-2030, AIRC IG
  • 2020-2024, Ministero della Salute, Ricerca Finalizzata
  • 2018-2023, AIRC MFAG

Selected publications

M. Marchesin, D. Dalfovo, A. Romanel. CLISGen: A Comprehensive Resource of SNP Genotypes for Human Cell Lines. Journal of Molecular Biology 2026.

F. Mazza*, F. Gastaldello*, D. Dalfovo, G. Lattanzi, A. Romanel. HapScoreDB: a database of protein language model functional scores for haplotype-resolved protein sequences. Nucleic Acids Research, 2025.

F. Mazza, D. Dalfovo, A. Bartocci, G. Lattanzi#, A. Romanel#. An Integrative Computational Analysis of Common EXO5 Haplotypes: Impact on Protein Dynamics, Genome Stability, and Cancer Progression. Journal of Chemical Information and Modeling, 2025.

R. Scandino*, A. Nardone*, N. Casiraghi, F. Galardi, M. Genovese, D. Romagnoli, M. Paoli, C. Biagioni, A. Tonina, I. Migliaccio, M. Pestrin, E. Moretti, L. Malorni, L. Biganzoli, M. Benelli#, A. Romanel# . Enabling sensitive and precise detection of ctDNA through somatic copy number aberrations in breast cancer. npj Breast Cancer, 2025. 

D. Dalfovo, R. Scandino, M. Paoli, S. Valentini, A. Romanel. Germline determinants of aberrant signaling pathways in cancer. npj Precision Oncology 2024. 

D. Dalfovo, A. Romanel. Analysis of Genetic Ancestry from NGS Data Using EthSEQ. Current Protocols, 2023.

R. Scandino, F. Calabrese, A. Romanel. Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data. Bioinformatics, 2023. 

S. Valentini, F. Gandolfi, M. Carolo, D. Dalfovo, L. Pozza, A. Romanel. Polympact: exploring functional relations among common human genetic variants. Nucleic Acids Research, 2022.

S. Valentini*, C. Marchioretti*, A. Bisio*, A. Rossi, S. Zaccara, A. Romanel#, A. Inga#. TranSNPs: a class of functional SNPs affecting mRNA translation potential revealed by fraction-based allelic imbalance. iScience, 2021.

D. Dalfovo, S. Valentini, A. Romanel. Exploring functionally annotated transcriptional consensus regulatory elements with CONREL. Database: The Journal of Biological Databases and Curation, 2020.

N. Casiraghi*, F. Orlando*, Y. Ciani, J. Xiang, A. Sboner, O. Elemento, G. Attard, H. Beltran, F. Demichelis#, A. Romanel#. ABEMUS: platform specific and data informed detection of somatic SNVs in cfDNA. Bioinformatics, 2020.

S. Valentini, T. Fedrizzi, F. Demichelis, A. Romanel. PaCBAM: fast and scalable processing of whole exome and targeted sequencing data. BMC Genomics, 20:1018, 2019.

A. Romanel. Allele-Specific Expression Analysis in Cancer Using Next-Generation Sequencing Data. Cancer Bioinformatics, Methods in Molecular Biology, Springer 1878:125-137, 2019.