Computational Biology

Director Prof. Michele Ceccarelli

The Computational Biology Laboratory, coordinated by Prof. Michele Ceccarelli, is a multidisciplinary research group dealing with computational biology and precision oncology. The main research areas of the laboratory are:

  • Cancer genomics and drug resistance. This research area focuses on the in-depth analysis of the molecular mechanisms that drive cancer progression and the development of resistance to oncological treatments. Specific attention is paid to gliomas, a particularly aggressive and complex form of brain tumor. Cancer genomics investigates the genetic and genomic alterations that contribute to the malignancy of tumors, identifying mutations, amplifications and deletions in genes that regulate cell growth and response to drugs. The main goal is to understand how these molecular alterations influence the behavior of tumors and their resistance to treatments. Through the analysis of the genetic and transcriptional profiles of tumor cells, researchers can identify predictive biomarkers of drug response and resistance.
  • Immunological characterization of tumors. Using advanced technologies such as Next-Generation Sequencing (NGS), the laboratory develops computational approaches to analyze the tumor microenvironment. The study of the mechanisms of interaction between the immune system and cancer cells mainly aims at the identification of biomarkers that can predict the response to immunotherapies. The laboratory is actively involved in a research project funded by AIRC (Italian Association for Cancer Research) to study the role of methylation in tumor progression in patients affected by metastatic melanoma and pleural mesothelioma.
  • Development of bioinformatics tools. The laboratory is specialized in the development of open-source bioinformatics software for the analysis and integration of omics data, improving the identification of genetic mutations and the immune characterization of tumors, including: SCEVAN for the identification of the clonal structure of tumors; MOViDA, a deep learning-based approach, used for the prediction of drug sensitivity; scTHI that analyzes ligand-receptor interactions to identify the mechanisms of communication between the tumor and the host cells. The laboratory also developed the CPTACBiolinks and TCGABiolinks software, widely used by the scientific community for downloading and analyzing CPTAC and TCGA data.
  • Early diagnosis of tumors. The laboratory research focuses on the development of advanced tests for the early diagnosis of tumors, based on the DNA methylation analysis from liquid biopsies. DNA methylation techniques are crucial for identifying epigenetic modifications associated with the tumor onset and progression. Through the analysis of specific biomarkers in circulating DNA, it is possible to develop non-invasive tests that make possible to detect early signals of tumor lesions and to constantly monitor the patients and to adapt the treatments according to changes in the epigenetic profile.

Furthermore, the computational biology laboratory actively collaborates with top-level international research institutions, combining expertise in oncology, immunology and bioinformatics, and contributing to the advancement of knowledge in the field of personalized cancer therapy.

Latest publications with first, last, and/or corresponding author

 2023

1.     Simeone I, Ceccarelli M. Pan-cancer onco-signatures reveal a novel mitochondrial subtype of luminal breast cancer with specific regulators. J Transl Med. 2023 Jan 30;21(1):55. doi: 10.1186/s12967-023-03907-z. PMID: 36717859

2.     Cerulo L, Pezzella N, Caruso FP, Parente P, Remo A, Giordano G, Forte N, Busselez J, Boschi F, Galiè M, Franco B, Pancione M. Single-cell proteo-genomic reveals a comprehensive map of centrosome-associated spliceosome components. iScience. 2023 Apr 10;26(5):106602. doi: 10.1016/j.isci.2023.106602. PMID: 37250316

3.     De Falco A, Caruso F, Su XD, Iavarone A, Ceccarelli M. A variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data. Nat Commun. 2023 Feb 25;14(1):1074. doi: 10.1038/s41467-023-36790-9. 

4.     Ferraro L, Scala G, Cerulo L, Carosati E, Ceccarelli M. MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model. Bioinformatics. 2023 Jul 1;39(7):btad432. doi: 10.1093/bioinformatics/btad432.

5.     Noviello TMR, Di Giacomo AM, Caruso FP, Covre A, Mortarini R, Scala G, Costa MC, Coral S, Fridman WH, Sautès-Fridman C, Brich S, Pruneri G, Simonetti E, Lofiego MF, Tufano R, Bedognetti D, Anichini A, Maio M, Ceccarelli M. Guadecitabine plus ipilimumab in unresectable melanoma: five-year follow-up and integrated multi-omic analysis in the phase 1b NIBIT-M4 trial. Nat Commun. 2023 Sep 22;14(1):5914. doi: 10.1038/s41467-023-40994-4.

2024

1.     Costa MC, Angelini C, Franzese M, Iside C, Salvatore M, Laezza L, Napolitano F, Ceccarelli M. Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions. J Transl Med. 2024 Mar 15;22(1):281. doi: 10.1186/s12967-024-05006-z.

2.     Grisolia P, Tufano R, Iannarone C, De Falco A, Carlino F, Graziano C, Addeo R, Scrima M, Caraglia F, Ceccarelli A, Nuzzo PV, Cossu AM, Forte S, Giuffrida R, Orditura M, Caraglia M, Ceccarelli M. Differential methylation of circulating free DNA assessed through cfMeDiP as a new tool for breast cancer diagnosis and detection of BRCA1/2 mutation. J Transl Med. 2024 Oct 15;22(1):938. doi: 10.1186/s12967-024-05734-2. 

3.     Giordano G, Cipolletta G, Mellone A, Puopolo G, Coppola L, De Santis E, Forte N, Napolitano F, Caruso FP, Parente P, Landriscina M, Cerulo L, Costa MC, Pancione M. Altered centriolar cohesion by CEP250 and appendages impact outcome of patients with pancreatic cancer. Pancreatology. 2024 Sep;24(6):899-908. doi: 10.1016/j.pan.2024.06.010.

2025

1.     Anichini A, Caruso FP, Lagano V, Noviello TMR, Tufano R, Nicolini G, Molla A, Bersani I, Sgambelluri F, Covre A, Lofiego MF, Coral S, Di Giacomo AM, Simonetti E, Valeri B, Cossa M, Ugolini F, Simi S, Massi D, Milione M, Maurichi A, Patuzzo R, Santinami M, Maio M, Ceccarelli M, Mortarini R; EPigenetic Immune-oncology Consortium Airc (EPICA) investigators. Integrated multi-omics profiling reveals the role of the DNA methylation landscape in shaping biological heterogeneity and clinical behaviour of metastatic melanoma. J Exp Clin Cancer Res. 2025 Jul 18;44(1):212. doi: 10.1186/s13046-025-03474-9.

2.     Lofiego MF, Tufano R, Bello E, Solmonese L, Marzani F, Piazzini F, Celesti F, Caruso FP, Noviello TMR, Mortarini R, Anichini A, Ceccarelli M, Calabrò L, Maio M, Coral S, Di Giacomo AM, Covre A; EPigenetic Immune-oncology Consortium Airc (EPICA) investigators. DNA methylation status classifies pleural mesothelioma cells according to their immune profile: implication for precision epigenetic therapy. J Exp Clin Cancer Res. 2025 Feb 18;44(1):58. doi: 10.1186/s13046-025-03310-0.

 

 

 

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