A major focus of Pelicci's lab is the characterization of the cancer stem cell-specific properties of self-renewal and differentiation, and their underlying molecular mechanisms. It has been recently shown in breast cancer that the number of cancer stem cells (CSCs) correlates with disease prognosis and that the most aggressive tumors have the highest number of CSCs when compared to less aggressive lesions (1). Mechanistically, expansion in the number of CSCs is the consequence of prevalently symmetric divisions, possibly due to the deregulated expression of the proto-oncogene Myc following p53 loss (2,3). Myc is frequently altered in cancer and has emerged as a critical regulator of many cellular processes such as cell growth, proliferation and survival. Accumulating evidence suggests that Myc might employ its normal and oncogenic functions through the transcriptional regulation of critical target genes. However, the relative contributions of this mechanism, as well as downstream effectors remain elusive.

The aim of the project is to identify transcriptional targets of Myc that might be responsible for its ability to induce symmetric divisions and reprogramming of progenitors using next generation sequencing technologies (NGS). 

In close collaboration with the experimental researchers in the lab, the PhD candidate will carry out the project and develop a computational framework to interpret and understand the biological data. Given the genome-wide nature of the project his/her research work will be fundamental to interpret the data, developing innovative algorithms to answer hypothesis-driven questions and formulate original data-driven hypotheses. The Student will assemble the components of the "Myc reprogramming pathway" into a cellular network and use this network to answer fundamental questions about cellular processes and how cancer originates from them. He/she will use the identified network as analytical tools and develop methods to test its activity in various cancers using multi-dimensional molecular and clinical information available in the lab. 

The Student will work in the Pelicci group under the direct supervision of Dr. Laura Riva, Team Leader in computational biology at the Center for Genomic Science of IIT@SEMM.

References

Pece S, Tosoni D, et al. (2010) Cell 140(1):62-73

Cicalese A, Bonizzi G, et al. (2009) Cell 138(6):1083-95.

Pasi CE, Dereli-Oz A, et al. (2011) Cell Death Differ 18(5):745-53.

 

 

2) Computational biology approaches for the study of the effects of oncogenic proteins on the replication profile of mammalian cells

 

Precise duplication of chromosomal DNA during S phase prevents the occurrence of potentially cancer-prone alterations to the genome. At this purpose, thousands of selected replication origins (ORIs) scattered throughout the genome are activated in a precisely regulated manner, in space (along the DNA fiber) and time (throughout the S phase), among the large excess of potential ones. ORI licensing, which occurs during late mitosis and early G1 phase, involves the stable loading of the pre-replicative complex (pre-RC) onto those ORIs that will be progressively activated upon S-phase entry. 

Pre-malignant and malignant cells frequently show altered expression of licensing proteins, which may have a causal role in cancer development. Oncogenes are known to misregulate licensing by either inducing re-licensing of replicated DNA, or allowing cells to enter S phase with an insufficient number of licensed ORIs. We have recently developed a novel methodology, which allows the ChIP-based genome-wide identification of active mammalian ORIs in their natural chromosome context (using anti-Orc1 antibodies; submitted). Using this technology, we preliminarily showed that conditional expression of an oncogenic transcription factor (PML-RAR) alters the replication program of human cells by de novo activation of several ORIs and inactivation of others. This effect of PML-RAR on replication might contribute to its ability to induce DNA damage, thus leading to genomic instability.

We will further characterize the effects of PML-RAR on replication origins (by anti-Orc1 ChIP-Seq) and investigate its effects on genome organization (using Chromosome Conformation Capture assays: ChIA-PET or Hi-C) and DNA damage (using ChIP-seq with antibodies directed against gamma-H2AX and NBS1). Finally, we will extend analysis to human cells expressing other oncogenic proteins (i.e., Myc and Ras).

Biological data will be analysed using computational-biology approaches, and methodologies from a variety of disciplines including statistics, mathematics and computer science. In particular, the PhD candidate will:

  -perform data analysis and management to deciphering the complex networks of replication processes in different cellular contexts and experimental conditions;

  -provide on-demand advanced data analysis and software/data integration;

-develop innovative algorithms and novel tools to accelerate hypothesis-driven research, to help results interpretation and to speed development of new hypotheses.

The Student will work in the Pelicci group under the direct supervision of Dr. Lucilla Luzi, Senior Bioinformatician at IFOM-IEO Campus.