The Center for Genomic Science of IIT@SEMM applies modern genomic technologies towards a better understanding of complex biological processes and diseases, with particular emphasis on Cancer.

The Center is located within the IFOM-IEO Campus in Milan, one of the largest and most vibrant cancer research communities in Europe. The Center provides state-of-the art technological platforms for functional as well as structural genomics, and benefits from all the infrastructure, technological platforms and didactic activities already present in the IFOM-IEO Campus. Our PhD program is integrated with that of SEMM, the European School of Molecular Medicine.

The mission of our Center is to identify changes in the genome that underlie the development of cancer, as well as its susceptibility to therapeutic intervention. Our general aim is to reduce pathological traits into their molecular components, which might correspond to disease markers or potential targets for pharmacological intervention. We will exploit these molecular markers and targets in order to build up strategic programs for disease prevention, early detection and treatment.

Our scientists work together in teams, using cutting-edge instrumentation and novel conceptual approaches. Groups in the Experimental Genomics program utilize cellular and animal models to classify genetic and epigenetic states in development and cancer. A Computational Biology program is developing in-house methods for the generation and analysis of genomic and epigenomic data from next generation sequencing platforms, operated by the Genomic Unit. Finally, our Screening Unit harnesses the power of siRNA technology to execute discovery biology projects.


Our group has a long-standing interest in the c-myc oncogene and its product, the Myc protein. Under physiological circumstances, Myc is a central regulator of the cellular responses to extracellular stimuli. When its expression become uncontrolled, however, Myc acquires potent oncogenic properties. Myc is a transcription factor: it functions as a heterodimer with a unique partner, Max. The Myc/Max dimer directly or indirectly binds a multitude of target genes, and can either activate or repress transcription.

In general terms, our research aims at explaining the oncogenic activity of Myc, its action on the genome, its effects on cell cycle progression, cell death and differentiation, the tumor suppressor pathways that antagonize it, and their impact on tumor progression and maintenance.

We also use Myc as a paradigm to study the epigenetic organization and regulation of the genome. In particular, we are interested in understanding how specific chromatin environments – or epigenetic states – determine recognition by Myc of its binding sites in the human and mouse genomes, and how Myc further modifies chromatin to regulate gene expression. These studies rely on advanced protocols based on next-generation DNA sequencing technology (ChIP-seq, RNA-seq and others).

Detailed analysis of the Myc-regulated transcriptome in tumor models associated with functional genetic screens is allowing the identification of the key downstream effectors of Myc in tumor progression and maintenance. These gene products and the biological processes/pathways in which they are involved are providing important leads for the pre-clinical development of targeted therapies.

Selected Pubblications

  • Theresia R. Kress, Paola Pellanda, Luca Pellegrinet, Valerio Bianchi, Paola Nicoli, Mirko Doni, Camilla Recordati, Salvatore Bianchi, Luca Rotta, Thelma Capra, Micol Ravà, Alessandro Verrecchia, Enrico Radaelli, Trevor D. Littlewood, Gerard I. Evan, Bruno Amati Identification of MYC-Dependent Transcriptional Programs in Oncogene-Addicted Liver Tumors. Cancer Research Published Online, 15 June 2016. DOI: 10.1158/0008-5472.CAN-16-0316
  • Cheryl M. Koh, Marco Bezzi, Diana H. P. Low, Wei Xia Ang, Shun Xie Teo, Florence P. H. Gay, Muthafar Al-Haddawi, Soo Yong Tan, Motomi Osato, Arianna Sabò, Bruno Amati, Keng Boon Wee & Ernesto Guccione MYC regulates the core pre-mRNA splicing machinery as an essential step in lymphomagenesis. Nature 523, 96–100 (02 July 2015), doi:10.1038/nature14351
  • Theresia R. Kress, Arianna Sabò & Bruno Amati MYC: connecting selective transcriptional control to global RNA production. Nature Reviews Cancer 15, 593–607 (2015), doi:10.1038/nrc3984
  • Tonelli C, Morelli MJ, Bianchi S, Rotta L, Capra T, Sabò A, Campaner S, Amati B Genome-wide analysis of p53 transcriptional programs in B cells upon exposure to genotoxic stress in vivo Oncotarget, 6, 24611-24626 (2015). DOI: 10.18632/oncotarget.5232
  • Sabò A., Kress T. R., Pelizzola M., de Pretis S., Gorski M. M., Tesi A., Morelli M. J., Bora P., Doni M., Verrecchia A., Tonelli C., Fagà G., Bianchi V., Ronchi A., Low D., Müller H., Guccione E., Campaner S. and Amati B. Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis. Nature , vol. 511, pp. 488–492, DOI: 10.1038/nature13537
  • Myc-dependent dynamics of transcriptional and epigenetic regulation
    We are studying how the transcriptional and epigenetic dynamics are altered following Myc overexpression over time (de Pretis S; collaboration with Amati’s Group).
  • Genomic and epigenomic determinants of RNA methylation
    We are characterizing the genomic and epigenomic determinants of RNA methylation using publicly available high-throughput sequencing (HTS) data, including DNA methylation, histone marks and DNA-binding regulatory proteins (Kishore K).
  • Integrative analysis of public (epi)genomic HTS datasets
    We are leveraging on biomedical ontologies and natural language processing tools to associate thousands of (epi)genomic HTS samples to tissue and disease conditions, and to relate different experiments associated to similar (while not identical) tissue and disease states. We are applying these concepts to: (i) dissect Myc binding in various tissue and disease states; (ii) identify distal regulatory elements in various tissue and disease states based on their association to regions of low DNA methylation (Galeota E, Kishore K).
  • Development of pipelines for the analysis of NGS data
    We are finalizing the development of HTS-flow, a web-service and database for the fully- automated analysis of various high-throughput sequencing data types, including ChIP-seq (for both transcription factors and histone marks), RNA-seq (for both total RNA and nascent 4sU- labeled RNA), DNaseI-seq and DNA methylation data (collaboration with Amati’s Group).

The recent discovery of thousands of non-coding RNAs (ncRNAs) with regulatory function is redefining the landscape of transcriptome regulation, highlighting the interplay of epigenetic, transcriptional and post-transcriptional mechanisms in the specification of cell fate and in the regulation of developmental processes. We have witnessed to the identification of an increasing number of either small regulatory RNAs (such as microRNAs, miRNAs) or long non-coding RNAs (lncRNAs) with a critical role in the regulation of molecular circuits associated to numerous biological processes. Essentially all known physiological and pathological processes, including cancer, are regulated by miRNAs and lncRNAs, which can work together to mark differentiation states or alone as authentic oncogenes or tumor suppressors.

1. Dynamics of miRNA regulation in physiology and cancer

Overall, the levels of miRNAs in cells are determined by the sum of two processes: biosynthesis, which generates new miRNA molecules, and decay, which clears old miRNAs. So far, the decay of miRNAs has not been properly elucidated in cancer, due to the lack of proper methodologies. We recently developed new tailored approaches based on in vivo RNA labeling and high-throughput sequencing to study the modes and the mechanisms of miRNA decay (Marzi, Ghini et al., Genome Res 2016). This has allowed us to precisely measure miRNA biosynthesis and decay rates in cells, and has revealed the existence of different pools of miRNAs, distinguished by their decay patterns. Hence, miRNAs are not just stable molecules as previously thought. However, the investigation of such mechanism in cancer is still missing. We propose to apply our approaches to models of human cancer to investigate i) the role of decay in the regulation of miRNAs in cancer and ii) the critical mechanistic steps associated with this process.

2. miRNAs and lncRNAs as ‘modifiers’ of cell fate and development

We are interested in characterizing the activity of either miRNAs or lncRNAs, as part of complex circuits that control cell fate and differentiation, integrated with signaling networks and the transcriptional framework (Tordonato et al., Frontiers Genetics 2015).

We have developed multiple approaches to search for non-coding RNAs able to act as “markers” of stem-cells (SCs) or “modifiers” of stem cell functions, including i) the isolation of RNAs specifically expressed in SCs; ii) unbiased genetic screens to search for non-coding RNAs able to regulate stem cell properties; and iii) mutant mice, to deconvolute the role of endogenous molecules in the regulation of tissue homeostasis and cell. In collaboration with other groups at IIT, we are also exploiting different synthetic platforms (i.e. nanoparticles) as carriers for delivering RNA-based therapeutics into cancer cells. We intend to follow up these studies to

  1. highlight the miRNA- and lncRNA- based circuitries that control the identity and the plasticity of epithelial cells
  2. pinpoint the role of specific RNA molecules as potential therapeutic agents for the treatment of even the most aggressive forms of cancer.

Computational Tools:

isomiRage: a desktop application that counts the number of occurrences of miRNA isoforms from NGS data (in a Bowtie .map alignment file)


  • D. De Petri Tonelli (NBT@IIT Genova) –  RNA profiling in differentiating neural stem cells (NSCs)
  • S. Giordani (D3@IIT Genova) –   Nano-carbon materials for RNA delivery
  • PP. Pompa (D3@IIT Genova) –  Nanomaterials for miRNA delivery and detection
  • PP. Di Fiore (IFOM-Milan, IEO-Milan) – miRNAs in breast cancer stem cells
  • Y. Torrente (UniMi – Milan) – Non-coding RNAs in human mesenchymal stem cells (MSCs)
  • M. Mapelli (IEO-Milan) – Molecular basis of asymmetric cell division

We develop computational approaches and integrative methods to investigate cancer mutations and to unravel the pathways involved in cancer progression.

  • Genomics of cancer. We are characterizing different tumor samples, in terms of their somatic mutations and cancer susceptibility genes, in a number of experimental systems, including clinical samples and patient-derived xenografts. Our aim is to better characterize chemoresistance and metastasis associated mutations.
  • Development of computational tools to prioritize somatic mutations in cancer (i.e. DOTS-Finder).
  • Algorithms for the development of genome-based personalized cancer treatments. We integrate mutational analysis data with clinical phenotype information and develop machine-learning algorithms to identify complex patterns in patient-mutational profiles, which can be used to stratify patients for prognosis and to design treatment strategies.
  • Network biology. The high number and the intrapatient and intratumor heterogeneity of identified somatic mutations make the task of explaining the links between genetic alterations and carcinogenesis challenging. By using a combination of high-throughput experimental approaches to detect genomic, transcriptomic and epigenomic changes and integrative methods, we want to identify candidate driver mutations and reconstruct the pathways that link these genetic alterations to cancer progression.


Access to several shared technological platforms and facilities: next-generation sequencing platforms, computational resources, cell culture facilities and in-house animal facility.


  • Pier Giuseppe Pelicci, European Institute of Oncology
  • Piercesare Secchi, Politecnico di Milano
  • Giovanni Martinelli, Institute of Hematology "Seragnoli", Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna
  • Luisa Lanfrancone, European Institute of Oncology
  • Giuliana Pelicci, European Institute of Oncology

Cancer represents a collection of pathologies characterized by the selective accumulation of alterations in the genome (point mutations, rearrangements, amplifications, deletions) and the epigenome (DNA methylation and histone modifications) leading to aberrant cellular proliferation. In this context, genetically modified mouse models that faithfully recapitulate tumor progression are invaluable for the identification of (epi-) genomic alterations causally involved in cancer etiology.

We are focusing on:

1.The establishment of novel mouse models based on genetic modification of somatic stem cells and progenitors, to allow rapid generation of genetically engineered mice faithfully recapitulating human cancers.

2. Genetic and epigenetic analysis of tumor progression in vivo to map genetic and genomic alterations selected for during tumor progression. With this approach we aim to identify novel tumor suppressors, oncogenes and pathways selectively activated in cancer cells.

3. Functional characterization of the oncogenes, tumor suppressors and potential molecular targets we identify.

4. Reverse genetics in tumor models based on viral shRNA libraries and genome wide transposon-mediated insertional mutagenesis.

5. Development of high-throughput screens based on high-content cellular imaging to identify molecular targets for cancer therapy.


We have access to advanced technological services (all basic requirements for molecular/cellular biology are present). IIT@SEMM focuses on Genomic Science and in particular Cancer Genomics ( We have created an advanced Genomic Unit, which allows routine use of next-generation DNA sequencing (Illumina HiSeq) and NanoString technology. Our Computing Unit ensures data-flow from the sequencing platforms to the servers and advanced computational analysis, in conjunction with a rich community of computational scientists.


Dr. Mark Wade (Screening Unit, Center for Genomic Science of IIT@SEMM)

Dr. Bruno Amati (Oncogenes, Chromatin and Gene Regulation, Center for Genomic Science of IIT@SEMM)

Dr. Mattia Pelizzola (Computational Epigenomics, Center for Genomic Science of IIT@SEMM)

Dr. Francesco Nicassio (MicroRNAs and non-coding RNAs in stemness, proliferation and differentiation, Center for Genomic Science of IIT@SEMM)

Selected Pubblications

1. Rohban S, Campaner S. Myc induced replicative stress response: How to cope with it and exploit it. Biochim Biophys Acta. 2015 May; 1849 (5):517-24. doi: 10.1016/j.bbagrm.2014.04.008.

2. Sabò A, Kress TR, Pelizzola M, de Pretis S, Gorski MM, Tesi A, Morelli MJ, Bora P, Doni M, Verrecchia A, Tonelli C, Fagà G, Bianchi V, Ronchi A, Low D,

Müller H, Guccione E, Campaner S, Amati B. Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis. Nature. 2014 Jul 24;511(7510):488-92. doi: 10.1038/nature13537.

3. Murga M*, Campaner S*, Lopez-Contreras AJ, Toledo LI, Soria R, Montaña MF, D'Artista L, Schleker T, Guerra C, Garcia E, Barbacid M, Hidalgo M, Amati B,

Fernandez-Capetillo O. Exploiting oncogene-induced replicative stress for the selective killing of Myc-driven tumors. Nat Struct Mol Biol. 2011 Nov 27; 18 (12):1331-5. doi: 10.1038/nsmb.2189.

4. Campaner S, Spreafico F, Burgold T, Doni M, Rosato U, Amati B, Testa G. The methyltransferase Set7/9 (Setd7) is dispensable for the p53-mediated DNA damage response in vivo. Mol Cell. 2011 Aug 19;43(4):681-8. Doi: 10.1016/j.molcel.2011.08.007.

5. Campaner S, Doni M, Hydbring P, Verrecchia A, Bianchi L, Sardella D, Schleker T, Perna D, Tronnersjö S, Murga M, Fernandez-Capetillo O, Barbacid M, Larsson LG, Amati B. Cdk2 suppresses cellular senescence induced by the c-myc oncogene. Nat

Cell Biol. 2010 Jan; 12 (1):54-9; sup pp 1-14. doi: 10.1038/ncb2004.

The Unit is equipped to run cell-based functional genomic and small molecule screens with partners within and outside the IIT network. We have in-house genome-wide siRNA libraries for human and mouse genomes, as well as access to several compound collections. We exploit automated liquid handling integrated in a robotic platform to deliver chemical entities to cells, and can handle readouts of phenotypic (image-based) or homogenous (luminescence, fluorescence, etc.). Tailored downstream analyses are performed using a combination of commercial and in-house IT infrastructure and software packages.

In addition to collaboration with several groups across a diverse screening portfolio, the Unit has internal assay development projects, mainly centered on protein-protein interactions. We have a strong interest in mitochondrial regulation of apoptosis, with a particular focus on MARCH5, a mitochondrial E3 ubiquitin ligase with roles in both cancer and neurodegeneration.

In order to manage the flow of sequencing data produced at the Genomic Unit, we manage a Laboratory Information Management System (LIMS) that we have developed and has been used in our institute since 2011 (Venco et al 2013). The latest developments of the LIMS are made toward enabling the usage of metadata with a high-level, declarative GenoMetric Query Language (GMQL) that supports queries over thousands of heterogeneous datasets and samples (Masseroli et al. 2015).

We are also working on the Meta-analysis of somatic mutations in cancer with the goal of identifying driver mutations and specific mutation patterns. For this purpose, we are developing pipelines for the automatic analysis and integration of different genome-wide datasets, statistical and graphical models for the analysis of genomic data (Melloni et al. 2016). Finally, we enable integrated analysis of NGS data in genome visualization tools by developing extensions for the Integrated Genome Browser ( (Céol et al. 2015, Céol et al 2016).


  • CGS@SEMM Genomic Unit: management of the Laboratory Information Management System.
  • High Throughput Screening Unit (M. Wade):  integration of genome visualization with network biology, structural biology and drug discovery, to facilitate the analyses of the effects of drugs and mutations on protein-protein and drug-protein networks
  • Oncogenes, Chromatin and Gene Regulation (B. Amati): Development of HTS flow,  a framework for the management of primary and secondary analysis of NGS data.
  • Politecnico di Milano (M. Masseroli, S. Ceri): definition of the GenoMetric Query Language and application to the LIMS

The computational epigenomics unit studies the epigenetic and regulatory determinants of transcription. Gene transcription is a complex process, and the final abundance of mature mRNA molecules is the result of a set of finely tuned regulatory steps. These include: (i) the synthesis, the processing and the degradation of the transcripts, (ii) the interplay of these processes with epigenetic and epitranscriptomic components, (iii) the action of the polymerase and of transcription factors.

We develop computational tools and we take advantage of large-scale public sequencing datasets to dissect the various steps of transcriptional regulation and to shed light on how these are influenced by the action of epigenetic and epitranscriptomic components.

Specific ongoing projects and recent results from the group include:

  • Inference of the rates of transcriptional regulation (Stefano de Pretis, Mattia Furlan).
    We are developing computational tools for the quantification of the rates of transcriptional regulation, including the synthesis of pre-mRNA, its conversion to mature mRNA, and the subsequent degradation of the mature form (kinetic rates). We leverage on the integrative analysis of nascent and total RNA-seq data (de Pretis et al, Bioinformatics 2015). We are finalizing the development of a computational method that requires total RNA-seq data only.
  • Myc-dependent dynamics of transcriptional and epigenetic regulation (Stefano de Pretis)
    We are studying how the transcriptional and epigenetic dynamics are altered following overexpression of the Myc transcription factor over time. In particular, we focus on the kinetic rates, the dynamics of RNAPII progression through the transcriptional units and the methylation of mRNA.
  • Genomic and epigenomic determinants of RNA methylation.
    We are characterizing the genomic and epigenomic determinants of RNA methylation using publicly available high-throughput sequencing (HTS) data, including DNA methylation, histone marks and DNA-binding regulatory proteins.
  • Development of tools for the integrative analysis of public epigenomic HTS datasets (Eugenia Galeota)
    We are leveraging on biomedical ontologies and text mining tools to perform the semantic annotation of thousands of (epi)genomic HTS datasets  (Galeota E et al. Briefings In Bioinformatics 2016), and we are building a tool to relate different samples associated to similar (while not identical) tissue and disease states. We are applying these concepts to: (i) dissect Myc binding in various tissue and disease states, (ii) identify distal regulatory elements in various tissue and disease states based on their association to regions of low DNA methylation.

Group members:

  • Eugenia Galeota (computer scientist)
  • Mattia Furlan (theoretical physicist)
  • Stefano de Pretis (computational biologist)

Active collaborations:

  • Luisa Di Stefano, Paul Sabatier Toulouse III University, FR (epigenomics)
  • Daniela Palacios, Fondazione Santa Lucia, Roma, IT (cancer epigenomics)
  • Ian Marc Bonapace, University of Insubria, IT (epigenomics)
  • Mitro Nico, University of Milano, IT (epigenomics)
  • Dario Bonanomi, HSR, IT (motor neuron biology)
  • Raffaella Cancello, Istituto Auxologico, IT (epigenomics and obesity)
  • Michele Caselle, University of Torino, IT (theoretical physics and computational biology)
  • Karen Avraham, Tel Aviv University, IL (epigenetics of inner ear)
  • Bruno Amati, IIT@SEMM/IEO, IT (cancer epigenomics)

Selected publications:

  1. Galeota E et al, Briefings in Bioinformatics 2016
  2. Kishore K et al, BMC Bioinformatics 2015
  3. de Pretis S et al, Bioinformatics 2015
  4. Sabo A*, Kress TR*, Pelizzola M* et al, Nature 2014
  5. Lister R*, Pelizzola M* et al, Nature 2011
  6. Lister R*, Pelizzola M* et al, Nature 2009
  7. Koga Y*, Pelizzola M*, et al, Genome Research 2009
  8. Pelizzola M*, Koga Y* et al, Genome Research 2008