About me

Research Interests

Curriculum Vitae

Outside the Lab

My research in simple words
My research is focused on the study of basic processes driving bladder cancer progression. The accumulation of errors in the DNA (genetic instability) and the abnormal expression of certain cancer-related genes are distinctive features of cancer cells. The tumor suppressor factor DDB2 is, for example, aberrantly under-expressed in colon cancer as compared to normal colon cells. With my work, I am investigating which are the molecular consequences of the reduced DDB2 expression in colon cancer and I am trying to understand whether this drives cancer progression.
To address these points, I integrate cell-based approaches and in silico tools. In particular, I use in vitro cell systems to analyze how the global gene expression profile of colon cancer cells is perturbed when DDB2 is artificially under- or over-expressed. Experiments of this kind may produce very rich datasets, as expression measurements for each expressed gene of the human genome are collected. To extract meaningful information from these large datasets, I employ computational approaches, mainly based on R and Bioconductor packages. For example, in silico tools allow me to predict which biological processes are likely to be affected upon DDB2 down-regulation (gene set enrichment analysis) or to compare datasets we generated in our lab with patient-derived gene-expression datasets publicly available online (at repositories like Oncomine and GEO).
Based on my results, I concluded that DDB2 plays a key role in the prevention of colon cancer progression.

Some more details
In order to understand whether the under-expression of tumor suppressor DDB2 drives tumor progression in colorectal cancer (CRC), I first performed a RNAseq analysis in a colon cancer cell line where DDB2 levels were knocked-down. In these cells, we identified 662 differentially expressed (DE) genes (out of a list of 12,556 expressed genes). In order to check whether these DDB2-regulated genes may be important in the pathogenesis of colon cancer, I checked whether this list included genes commonly dysregulated in colon cancer vs normal. I integrated data from 11 different datasets available at an online repository (Oncomine): altogether, the datasets I analyzed included measurements of up to 20,000 features (genes) for a total of more than 400 patients. By comparing the datasets and performing permutations tests, I defined a CRC gene signature, i.e. a list of 596 genes that are commonly dysregulated in colon cancer. I then compared this list with the list of DDB2–regulated genes. A hypergeometric statistical test confirmed that CRC signature genes are enriched among the DDB2-regulated genes: interestingly, most of the genes belonging to this group are known to play a major role in cancer.
Validation experiments in different cell systems (currently still undergoing) seem to confirm my in silico results and support a key role of DDB2 in preventing CRC progression via its transcriptional regulatory function.

Past Research
During my PhD and my first postdoc I mainly studied the Base Excision DNA Repair (BER) pathway, which is the major cellular process responsible for the repair of oxidative DNA lesions. BER deals with a large set of subtle, non-bulky base lesions. Two key enzymes of this pathway are OGG1 and APE1. During my PhD in Gianluca Tell lab at the University of Udine (Italy), I mainly studied molecular mechanisms regulating APE1 activities. My results were published in 3 research papers (2 first-author) and a review article. During my first postdoc in Pablo Radicella Lab at the CEA (France), I mainly worked on the DNA glycosylase OGG1. I carried on a project investigating the mechanism of inactivation, ubiquitination and proteasome-mediated degradation of OGG1 upon mild heat shock. I also contributed to a project addressing the biological significance of the interaction between APE1 and the cellular prion protein Prp^C during neuronal cells response to genotoxic stress. My results are included in 2 research articles (1 first-author).

Future Research
High-throughput NGS techniques are changing the way Research in Life Science is performed. The availability of very rich genomics datasets provides new exciting opportunities for better understanding how biological systems work (for example, see Nature 498, 255–260). At the same time, genomics Big Data comes with a brand new set of challenges: in order to make sense of these kind of data, Biology and Computer Science have to meet halfway.

During my next postdoc, I hope to continue learning and employing computational methods to analyze genomics Big Data and integrate in silico and molecular approaches to address biologically relevant questions.