Raffaele Calogero
Raffaele Calogero
affiliation: Università di Torino
research area(s): Cancer Biology, Computational Biology
Course: Complex Systems for Life Sciences
University/Istitution: Università di Torino
1984: Laurea in Biological Sciences summa cum laude from Naples University "Federico II" (Italy).
1984-1985: Post-laurea training at Institute of Genetics, General and Molecular Biology (Naples University "Federico II").
1985-1988: Fellowship at Max Plank Institute fuer Molekulare Genetik in Berlin (Germany).

1989-1992: Researcher at SORIN Biomedica S.p.A. (I).
1992-1998: Associate Professor of Molecular Biology, at Naples University "Federico II".
1998-present: Associate Professor of Molecular Biology at University of Torino.

2004-2008: Consultant for the microarray program (AXXAM S.r.l, Milano, I)
2005-2009: Consultant for the microarray program (Nerviano Medical Sciences S.r.l, Nerviano, MI, I)
2006-today: Consultant for the microarray program (Laboratorio Platanias, Lurie Cancer Center, Northwestern University, Chicago, USA)
2010-today: Consultant for Next generation sequencing application in QC (RBM SERONO, IVREA, I)
Identification of Cancer Biomarkers and Tumor-associated Antigen to device lung/breast cancer broad and efficient immuno-prevention protocols.
Modelling pharmacological response to Cancer Stem Cells.
Definition of control mechanisms of carcinogenesis modulated by miRNAs.
Developing analytical methods for next generation sequencing applied to RNA-seq and ChIP-seq
1: Bolasco G, Calogero R, Carrara M, Banchaabouchi MA, Bilbao D, Mazzoccoli G,
Vinciguerra M. Cardioprotective mIGF-1/SIRT1 signaling induces hypertension,
leukocytosis and fear response in mice. Aging (Albany NY). 2012 Jun 11. [Epub
ahead of print] PubMed PMID: 22691943.

2: Cordero F, Beccuti M, Donatelli S, Calogero RA. Large Disclosing the Nature of
Computational Tools for the Analysis of Next Generation Sequencing Data. Curr Top
Med Chem. 2012 Jun 7. [Epub ahead of print] PubMed PMID: 22690679.

3: Galli GG, Honnens de Lichtenberg K, Carrara M, Hans W, Wuelling M, Mentz B,
Multhaupt HA, Fog CK, Jensen KT, Rappsilber J, Vortkamp A, Coulton L, Fuchs H,
Gailus-Durner V, Hrabě de Angelis M, Calogero RA, Couchman JR, Lund AH. Prdm5
regulates collagen gene transcription by association with RNA polymerase II in
developing bone. PLoS Genet. 2012 May;8(5):e1002711. Epub 2012 May 10. PubMed
PMID: 22589746; PubMed Central PMCID: PMC3349747.

4: Lorenzato A, Martino C, Dani N, Oligschläger Y, Ferrero AM, Biglia N, Calogero
R, Olivero M, Di Renzo MF. The cellular apoptosis susceptibility CAS/CSE1L gene
protects ovarian cancer cells from death by suppressing RASSF1C. FASEB J. 2012
Jun;26(6):2446-56. Epub 2012 Mar 2. PubMed PMID: 22389439.

5: Dani N, Olivero M, Mareschi K, van Duist MM, Miretti S, Cuvertino S, Patanè S,
Calogero R, Ferracini R, Scotlandi K, Fagioli F, Di Renzo MF. The MET oncogene
transforms human primary bone-derived cells into osteosarcomas by targeting
committed osteo-progenitors. J Bone Miner Res. 2012 Feb 24. doi:
10.1002/jbmr.1578. [Epub ahead of print] PubMed PMID: 22367914.

6: Cordero F, Beccuti M, Arigoni M, Donatelli S, Calogero RA. Optimizing a
massive parallel sequencing workflow for quantitative miRNA expression analysis.
PLoS One. 2012;7(2):e31630. Epub 2012 Feb 20. PubMed PMID: 22363693; PubMed
Central PMCID: PMC3282730.

7: Salvatori L, Caporuscio F, Verdina A, Starace G, Crispi S, Nicotra MR, Russo
A, Calogero RA, Morgante E, Natali PG, Russo MA, Petrangeli E. Cell-to-cell
signaling influences the fate of prostate cancer stem cells and their potential
to generate more aggressive tumors. PLoS One. 2012;7(2):e31467. Epub 2012 Feb 6.
PubMed PMID: 22328933; PubMed Central PMCID: PMC3273473.

Project Title:
Definition of circulating miRNAs signature in leukemia
Development of robust methodologies for circulating miRNA quantification in leukemia samples. �Bone marrow samples will be collected using standard medical practices. Plasma will be separated by blood components by centrifugation and stored in aliquots. The supposed TRIzol-LS (Invitrogen) better performance in the extraction of miRNA from liquid samples with respect to other methods [e.g. mirVana PARIS (Ambion), miRNeasy Mini Kit (Qiagen), mirPremier microRNA Isolation Kit (Sigma), High Pure miRNA Isolation Kit (Roche)] will be verified using plasma/bone marrow from donors that will be spiked-in with miRCURY LNATM Array, Spike-in miRNA kit v2 (Exiqon), encompassing 52 synthetic mature miRNA at different concentrations. Comparison between sequence data from miRNAs extracted from sera w/wo spike-in will be used to test yield, reproducibility and sensitivity of the extraction methods and to specifically define: 1) the minimal number of technical replications needed to give reproducibility to miRNA extraction; 2) the sensitivity limits of the extraction methods; 3) the presence of protocol-dependent miRNA specific biases. miRNAs will be also extracted from healthy donor plasma left at room temperature at 0h, 1h, 6h, 12h, 24h, 48h, 72h to evaluate the stability of different miRNAs and the effect of freeze-thaw cycles on it, allowing to define: 1) the minimal requirement for sample collection and serum storage; 2) the presence of miRNA-specific degradation kinetics. Barcoding oligonucleotides will be finally optimized to minimize biases and to adapt the TruSeq Small RNA Sample Preparation protocol (Illumina) to them, in order to obtain robust absolute miRNAs quantification.
An already developed benchmark miRNA set will be used to optimize the analytical pipeline for miRNA quantification allowing to define the minimal number of reads needed for an exhaustive quantification of miRNAs. Implementation in the pipeline pilot software (Accelrys).
Using the above validated protocols RNA samples from at least at least 100 samples per disease entity (MPN/MDS, AML, CLL and ALL) will be sequenced and RNA-seq results will be analyzed with the miRNA pipeline .
Definition of a signature predicting the early onset of the disease and/or the risk of recurrence using miRNAs expression levels, genetic data and clinical outcome�Consolidated digital data statistics (e.g. baySeq, DESeq) will be used to detect miRNAs associated to pathological specimens. Genomic clustering of pathology-associated miRNAs will be evaluated by PREDA Bioconductor tool, detecting regional variations of genomic features from the integrative analysis of high- throughput expression data and genome local structural organization. Logistic regression analysis will be used to select miRNAs correlating to early onset of the disease and/or the risk of recurrence. Furthermore, since it has been demonstrated that miRNA expression patterns could classify translocation-specific subtypes of multiple myeloma, supervised analysis of miRNA data will be performed on the basis of cytogenetic data to evaluate if the combination of these data and miRNA expression could provide a more robust signature.