Understanding the role and significance of genomic and non-genomic alterations that occur in cancer diseases 

Our research goal consists in understanding the role and significance of genomic and non-genomic alterations that occur in cancer diseases (in particular Breast cancer and Lynch Disease) by using systems biology approaches.

Tumours are complex ecosystems characterized by a high degree of genomic, epigenomic and transcriptional variability (i.e., intra-tumour heterogeneity). Thus, therapies targeting early clonal alterations present at all sites of the disease often have only short-term effect and drive the selection of resistant and more aggressive rare sub-populations of cancer cells.

We therefore use system biology approaches to identify novel biomarker genes of drug response useful to effectively translate the genomic disease signature of a patient into the most effective therapeutic intervention to apply. For example, we have shown that machine learning can be used to detect expression-based biomarker genes of drug sensitivity useful to predict from single-cell transcriptomic data which combination of compounds should be used to kill co-existing heterogenous sub-populations of breast cancer cells.

While we continue to characterize novel biomarker genes of drug response, we also use them to (1) elucidate mechanisms of drug resistance; (2) assess the role of tumour heterogeneity on drug response and (3) to develop novel computational method in the framework of precision medicine.

Cereda M, Gambardella G, Benedetti L, Iannelli F, Patel D, Basso G,  Guerra RF, Mourikis TP, Puccio I, Sinha S, Laghi L, Spencer J, Rodriguez-Justo M, Ciccarelli FD. Patients with genetically heterogeneous synchronous colorectal cancer carry rare damaging germline mutations in immune-related genes. Nat Commun. 2016 Jul 5;7:12072. doi: 10.1038/ncomms12072.

Gambardella G, Carissimo A, Chen A, Cutillo L, Nowakowski TJ, di Bernardo D, Blelloch R. The impact of microRNAs on transcriptional heterogeneity and gene co-expression across single embryonic stem cells. Nat Commun. 2017 Jan 19;8:14126. doi: 10.1038/ncomms14126.

Gambardella G, di Bernardo D. A Tool for Visualization and Analysis of Single-Cell RNA-Seq Data Based on Text Mining. Front Genet. 2019 Aug 9;10:734. doi: 10.3389/fgene.2019.00734. eCollection 2019.

Gambardella G, Moretti MN, de Cegli R, Cardone L, Peron A, di Bernardo D. Differential network analysis for the identification of condition-specific pathway activity and regulation. Bioinformatics. 2013 Jul 15;29(14):1776-85. doi: 10.1093/bioinformatics/btt290. Epub 2013 Jun 6.

Gambardella G, Peluso I, Montefusco S, Bansal M, Medina DL, Lawrence N, di Bernardo D. A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data. BMC Bioinformatics. 2015 Sep 3;16:279. doi: 10.1186/s12859-015-0700-3.