Tumori rari come modello di studio per la medicina traslazionale: il caso del carcinoma renale

Tumori rari come modello di studio per la medicina traslazionale: il caso del carcinoma renale

Università degli Studi di Foggia

il caso del carcinoma renaleil caso del carcinoma renale

Elena RanieriElena Ranieri

Cattedra di Patologia Clinica

Dipartimento di Scienze Biomediche

Torino, 21.1.2010

The concept of rarity

‘Rare diseases are life threatening or chronically

debilitating diseases with a low prevalence (less than

1/2000 people) and a high level of complexity.

Most of them are genetic diseases, the others being

rarerare cancerscancers, auto immune diseases, congenital

malformations, toxic and infectious diseases among

other categories '

Characteristics of rare diseases � The European Organization for Rare Diseases (EURORDIS)

estimates that there exist between 5,000 and 7,000 distinct

rare diseases.

� The onset of the disease occurs in childhood for 50% of rare

diseases.

� Rare diseases are veryvery difficultdifficult toto managemanage: families

encounter enormous difficulties in finding adequate

treatment.

Patient and

parent

organization

Fight for recognition

Etiology Etiology

Scientific and

biomedical

research

Pharmaceutical

research and

development

Molecular Pathogenesis

TreatmentTreatment

Public health

authorities

Renal Cell Carcinoma : a Model of StudyRenal Cell Carcinoma : a Model of Study

� Renal cell carcinoma accounts for approximately

3% of adult malignancies and is the sixth leading

cause of cancer death

� Renal cell carcinoma may remain clinically occult

for most of its course. The classic triad of flank

pain, hematuria, and flank mass is uncommon

(10%) and is indicative of advanced disease.(10%) and is indicative of advanced disease.

� 25-30% of patients are asymptomatic, and their

renal cell carcinomas are found on incidental

radiologic study

� It is characterized by, resistance to radiation and

chemotherapy, and infrequent but reproducible

responses to immunotherapy agents such as

interferon alpha and interleukin-2.

Renal Cell Carcinoma Renal Cell Carcinoma

�RCC belongs to a small group of cancers where immune-

mediated mechanisms play important roles in limiting tumor

growth.

�Patients with active, disseminated disease are typically�Patients with active, disseminated disease are typically

characterized by predominant Th2- or T regulatory-type

immunity

�RCC lesions contain tumor-infiltrating lymphocytes reported to

be “functionally inappropriate”, dysfunctional or pro-apoptotic

Tatsumi, T., Kierstead, L.S., Ranieri, E., Gesualdo, et al, J Exp Med, 2003

Renal Cell Carcinoma (RCC)Renal Cell Carcinoma (RCC)

In RCC spontaneous tumor regression can be observed,

although in rare cases, in metastatic disease.

This suggests that RCC expresses tumor antigens

specifically recognized by T cellsspecifically recognized by T cells

ImmuneImmune mechanismsmechanisms playplay aa rolerole inin limitinglimiting tumortumor growthgrowth

Finke J., L.S. Kierstead, E. Ranieri, W.J. Storkus, Humana Press, 2000

Mechanisms of tumor evasionMechanisms of tumor evasion

Mapara MY et al J Clin Oncol, 2004

Mediators of Immune DefenseMediators of Immune Defense

adaptive

Antibodies T-Lymphocytes Macrophages

NK cellsCytokines

innate

Dendritic CellDendritic Cell

B

Antibodies T-LymphocytesNK cells

Cytokines

NK

Th

CTL Treg

Tumor

Study Design

� DC specific T cell immune Response

� Identification of immunogenic RCC cell lines

� Immunotherapy

� Biomarkers identification

� Diagnosis/monitoring of disease

Dendritic cells and immune responsesDendritic cells and immune responses

DENDRITIC CELL BIOLOGYDENDRITIC CELL BIOLOGY

DCs are the most potent antigen-

presenting cells that play a central

role in coordinating human

immunity

They comprise different subsets atThey comprise different subsets at

different stage of maturation

They have potential advantages as

cellular adjuvants for

immunotherapy strategies

Lym

ph

ocy

te m

igra

tio

n

CD8

TAAs

Tumor cellsTumor Antigens

Immune attack against

the tumor

Antigen cross-presentation and T cell immune response

Lym

ph

ocy

te m

igra

tio

n

DC migration

Dendritic cell

Activated/Mature DCCD8

CD4

Lymph node

CD4

RCC :RCC : from where we started….from where we started….

� RCC Cell Lines Characterization to obtain the most immunogenic RCC line:

60 RCC lines: 10% (5-6 RCC lines).

250

300

350

Sp

ot

nu

mb

ers

/10

0.0

00

T c

ell

s

IFNγγγγ Elispot Assay

0

50

100

150

200

NA T0

NA T0 + DC LYS

NA T0 + DC LYS + Ab I

NA T0 + DC LYS + Ab II

NA T0 + TU

NA T0 + TU + Ab I

NA T0 + TU + Ab II

NA T21

NA T21 + DC LYS

NA T21 + DC LYS + Ab II

NA T21 + DC LYS + Ab I

NA T21 + TU

NA T21 + TU + Ab II

NA T21 + TU + Ab I TU

DC + LYS

Sp

ot

nu

mb

ers

/10

0.0

00

T c

ell

s

ELTHEM cell line

(A) Real Time-PCR of tumor markers (OFA, CE,

hTERT, Ruas) and interleukin-6

(B) Microsatellite instability (MSI) and loss of

heterozygosity (LOH)

(C) Immunocytochemistry (CAM 5.2, the

mitochondrial markers, the vimentin,

cytokeratin AE1/AE3, cytokeratin 19, EGF-R, the

EMA, CD10 and Ki 67)

A

B

C

(A) Mixed Lymphocytes Tumor

cell Cultures Cytotoxicity test of

CD8+ T lymphocytes against

autologous ELTHEM clone (60%

lysis, E / T = 60:1).

ELTHEM cell line

A

B (B) ELISPOT test for IFNγγγγrelease. The block of the

response of class I test shows

the 91% specificity.

MW150 KDa

A B

ELTHEM proteomic profile

(A) Representative 2-DE map of proteins extracted from ELTHEM clone

(B) Biological function and (C) localization of identified protein.

10

3 10 pH

C

Therapeutic vaccines based on the use of autologous

dendritic cells as natural cellular adjuvants

Peripheral Blood

Cytokines

Dendritic Dendritic

Re-infusion

RCC AntigenRCC Antigen

Dendritic Dendritic

CellCell

Patent : Linea cellulare di carcinoma renale e suo uso". Brevetto Industriale N. MI2005 A002018 (21.10.05).

Inventors: EE.. RanieriRanieri, L. Gesualdo, W. Herr, M. Battaglia.

International Patent : PCT/EP2006/06763, 20.10.06

DC MONITORINGDC MONITORING

DC phenotyping In RCC tissue and lymph nodes

Immunity induction

cancer, infectivedisease

Tolerogenicity

Induction

Transplant, autoimmunity,

allergy

RCC PATIENT

DC Phenotyping in Peripheral Blood

DC subsets analysis by IHC

Human DendriticHuman Dendritic cellscells

DC comprise two subsets functionally and phenotypically different:

Myeloid DCsMyeloid DCs• BDCABDCA--11++/BDCA/BDCA--33++

•• potent stimulators of T lymphocytepotent stimulators of T lymphocyte

•• IL-6, IL-12p70, IL-10, TNF-α production

Plasmacytoid DCsPlasmacytoid DCs

•• BDCABDCA--22++/BDCA/BDCA--44++

•• generate a Th2 responsegenerate a Th2 response

•• impressive producers of IFNimpressive producers of IFN--α α in viral in viral

infectioninfection

Dendritic cells analysis in RCC peripheral bloodDendritic cells analysis in RCC peripheral blood

15000

20000

25000

30000

35000N

° cel

ls/m

L of

per

iphe

ral b

lood

BDCA1

BDCA2

P<0,01

A significant decrease (p<0,01) in the percentage and in the absolute number

of myeloid DC and plasmacytoid DC subsets are observed in RCC pts compared

with healthy controls

0

5000

10000

15000

Normal RCC

N° c

ells

/mL

of p

erip

hera

l blo

od

BDCA3

Gigante M. et al, Mol Immunol. 2009Gigante M. et al, Mol Immunol. 2009

RCC

Plasmacytoid DCMyeloid DC

Dendritic cells analysis in RCC tissueDendritic cells analysis in RCC tissue

Healthy

A significant increase of myeloid DC and plasmacytoid DC infiltrate in RCC biopsies

compared to normal kidneys (p<0.001)Gigante M., Ranieri E et al, Mol Immunol. 2009Gigante M., Ranieri E et al, Mol Immunol. 2009

Dendritic Cells distribution in RCC lymph nodesDendritic Cells distribution in RCC lymph nodes

A significant decrease of mature DC (CD11c+, CD83+) infiltrate in RCC lymph

nodes compared to normal (p< 0.001)

RCC Normal

Green:: CD11c-FITC

Red: CD83-TRITC

02468

10121416

PATIENTS CONTROLS

CD

11c+

/CD

83+

cells

/hp

f

*

Gigante M. Ranieri E et al, Mol Immunol. 2009Gigante M. Ranieri E et al, Mol Immunol. 2009

Mechanism of Action

Immature DC

Lymphoid tissue

DC subsets

Peripheral blood

Immature DC

Peripheral Lymphoid tissuePeripheral blood Peripheral

tissues/Cancer

IFNIFN--αααααααα--conditioned DCconditioned DC

� IFN-αααα is an important adjuvant for the development of

DC-based vaccines with high clinical efficacy.

� IFN-αααα potently enhances both T cell and antibody

responses and promote immunological memory by direct

action on DC.

IFNIFN--αααααααα--conditioned DC preferentially stimulate Typeconditioned DC preferentially stimulate Type--1 and 1 and limit Treglimit Treg--type type in vitroin vitro T cell responses in RCC patientsT cell responses in RCC patients

IFNIFNαααααααα DCDC ::

� promoted significantly stronger Tc1

effector T cell responses than cytokine

cocktail-matured DC as revealed by

IFN-γγγγ ELISPOT assay

� were more efficient in inducing the40

50

60

70

80

∗

∗

p = 0.022

IFN-DCmDC� were more efficient in inducing the

generation of suppressor CD8+ T cells

than mDC

� induced a lower expansion of Treg

cells than mDC

� IFN-αααα-conditioned DC may be candidates for use in novel

therapeutic vaccines in the setting of RCC

Gigante M., Ranieri E. J. Immunother 2008

0

10

20

30 ∗

MAGE-6 EphA2 SEB

mDCααααDC1

Look Into the FutureLook Into the Future

DC-based vaccine is still a promising emerging treatment option

for patients with RCC

RCCCD8CD8++

T cellT cell

IL-2R

CYTOKINESCell Cycle

CD8CD8++ T cell T cell –– RCC InteractionRCC Interaction

CYTOKINES

INTRACELLULAR SIGNALING

APOPTOSIS

Cell Cycle

In vitroIn vitro CD8CD8++ T cells Response analysisT cells Response analysis

Donor PBL

Tumor cellsResting T cellResting T cell

Patient PBLMatched

HLA

AUTOLOGOUS SYSTEM ALLOGENEIC SYSTEM

Tumor cells

CD8+

T responders

Phenotypical study

Gene expression profilingGene expression profiling

Mutational screening of target genes

Annexin-V

80

% A

popt

otic

Cel

ls

28%12%

CD8+ T cells at Day 0CD8+ T cells at Day 0 CD8+ T cells at Day 35CD8+ T cells at Day 35

TC

RC

C

61% 33%

APOPTOSISAPOPTOSIS

01020304050607080

TC CD8+

T ce

lls

Donor-2

CD8+

T c

ells

Donor-3

CD8+

T ce

lls

% A

popt

otic

Cel

ls

T0

T35* *

**

6%

8%

17%

7%

Don

or -

2D

onor

-3

7%

5% 14%

28%

Gene expression profiling and data analysisGene expression profiling and data analysis

Comparison of gene expression in normal subjects activated T cells versus RCC

CD8+ T cells demonstrated numerous differential expressed genes involved in

proliferation, cell cycle and apoptosis associated to phenotypic and functional

changes in responder T cells

JAK3 dysregulation MCL-1 dysregulation

p18 ink

MCL1E2F4

CdK6

JAK3

PD-1 CdK4 p27kip1

-5

-4

-3

-2

-1

0

1

2

3

4

Fold

chan

ge

in g

ene

expre

ssio

n

�RCC�Donor-2�Donor-3

MCL-1 dysregulation

RCC Anti-Apoptosis Network

Ingenuity Pathways Analysis (IPA) (Ingenuity® Syste ms, www.ingenuity.com)

miRNA analysis in CD8miRNA analysis in CD8 ++ T cellsT cells

2.0

3.0

4.0

B

RQ

miR

-198

* *

RQ

miR

-29b

A

1.5

2.0

2.5

*p< 0.003 p< 0.036

microRNAs (miRNAs) are short non-coding RNA molecules playing regulatory roles by

repressing translation or cleaving RNA transcripts. Abnormalities in miRNA activity, that has

critical functions during normal development and cellular homeostasis, contribute to human

disease pathogenesis such as cancer

E. Ranieri et al.

1.0

JAK3JAK3 MCLMCL--11

LYMPHOCYTELYMPHOCYTETCR TCR

IL-2

RCC and Immunity: Mutational Screening of JAK3 gene RCC and Immunity: Mutational Screening of JAK3 gene in RCC Patientsin RCC Patients

The Janus family kinases (Jaks), Jak1,

Jak2, Jak3, and Tyk2, form one

subgroup of the non-receptor protein

tyrosine kinases.

They are involved in cell growth,

survival, development, and

differentiation of a variety of cells3 3

differentiation of a variety of cells

but are critically important for

immune cells and hematopoietic

cells.

Jaks exert their effect is through the

activation of a relatively small

number of latent, cytosolic DNA-

binding proteins term the Signal

Transducers and Activators of

Transcription (STATs).

Ghoreschi K et al. Janus kinases in immune cell signaling. Immunol Rev. 2009

�� 5050 ItalianItalian patientspatients RCCRCC

werewere enrolledenrolled andand aa

mmutationalutational analysisanalysis ofof

JAKJAK33 genegene waswas

performedperformed..

�� pJAKpJAK33/pSTA/pSTATT55

interactioninteraction waswas

studiedstudied onon TT--cellscells byby

ConfocalConfocal MicroscopyMicroscopy..

RCC and Immunity: Mutational Screening of JAK3 gene

in RCC Patients

ConfocalConfocal MicroscopyMicroscopy..

RESULTS: 4 missense mutations were identified in the coding region of JAK3 gene; three

of these have not been previously described and are completely absent in control

population. All mutations were present in heterozygous status in 7 different RCC patients.

p.Ala677Thrp.Ala677Thr

p.Arg925Serp.Arg925Serp.Gln13Lysp.Gln13Lys

RCC and Immunity: Mutational Screening of JAK3 gene RCC and Immunity: Mutational Screening of JAK3 gene

in RCC Patientsin RCC Patients

•Green : JAK3 –FITC

•Red : STAT5-TRITC

Schematic JAK3 structure, previously described variants and novel mutationsSchematic JAK3 structure, previously described variants and novel mutations

Confocal microscopyM. Gigante, E. Ranieri

SUMMARY I

Immune system :

� DC are less effective and sequestered in diverse districts

� CD8+ T cells are prone to apoptosis

� CD8+ T cells gene expression is alterated

� Jak3 and MCL-1 are downregulated

� Jak3 shows mutations� Jak3 shows mutations

Answers :

� IFNα α α α DC induce robust T cell response

� MIR regulate CD8+ T cells functions

� Immune therapy

� MIR therapy

Mediators of Immune DefenseMediators of Immune Defense

adaptive

Antibodies T-Lymphocytes Macrophages

NK cellsCytokines

innate

Dendritic CellDendritic Cell

B

Antibodies T-LymphocytesNK cells

Cytokines

NK

Th

CTL Treg

Tumor

Study Design

� Identification of immunogenic RCC cell lines

� Immunotherapy

� Biomarkers identification

� Diagnosis/monitoring of disease

� Clinical proteomics is a promising new analytic discipline with the

following main aims:

� discovery of biomarkers allowing early and appropriate detection,

therapeutic monitoring of diseases

Why proteomics…

� development of new non-invasive diagnostic tests and procedures

� identification of protein targets for the development of new

mechanistic intervention therapies with the promise of an

improved clinical outcome

5000 7500 10000 12500

0

5

10

15

20

20

30

SELDI- TOF PLATFORM

Control

CM10, Q10, IMAC30 e H50

5000 7500 10000 12500

0

10

20

0

5

10

••TheThe basicbasic principleprinciple ofof thisthis approachapproach isis thethe selectiveselective bindingbinding ofof

proteinsproteins andand peptidespeptides toto specificspecific chromatographicchromatographic surfacessurfaces thatthat

allowsallows toto visualizevisualize themthem asas massmass peakspeaks

•• TheThe overalloverall numbernumber ofof massmass peakspeaks ofof aa biologicalbiological samplesample

definesdefines itsits massmass spectraspectra

Pathological

Pathological

Profiling RCC tissue Vs controls Urine Profiling RCC Pre Vs

Post nephrectomy

Experimental Design

Differential Peaks List

Common differential Peaks(putative RCC biomarkers)

Differential Peaks List

Urine Profiling RCC Pre Vs

Post nephrectomy

Profiling RCC tissue Vs controls

Experimental Design

Differential Peaks List

Common differential Peaks(putative RCC biomarkers)

Differential Peaks List

METHODSSamples: 71 urines

• 13 urine samples pre- (group 1) e post- surgery of RCC patients

• 24 urine samples pre-surgery of RCC patients (group 2)

• 21 urine samples of controls

Sampling Morning spot harvested in

sterile device

Statistical AnalysisStatistical analysis by software

ProteinChip DataManager® 3.5

Identification of mass peaks

differentially expressed (p-value < 0.05

by Mann-Whitney test )

Protein Profile

Urine profiling (10 μg) by

ProteinChip (chromatographic

cationic exchange surface,

CM10).

Sample preparationFiltration, centrifugation (3000

x g per 5 min) and protein

assay (Bradford method).

VALIDATION OF POTENTIAL BIOMARKERS IN PREVALIDATION OF POTENTIAL BIOMARKERS IN PRE--SURGERY RCC URINES SURGERY RCC URINES versus SECOND GROUP OF PREversus SECOND GROUP OF PRE--SURGERY RCC PATIENTSSURGERY RCC PATIENTS

5000 10000 15000 20000

0

25

50

75

uA RCC pre

Group 10

0

25

50

75

uA

0

25

50

75

uA

5000 10000 15000 20000

RCC pre

Group 2

RCC post

Mass Peaks differentially expressed by comparing

pre G1 vs pre G2 vs post G1

Mass p-valuePre-surgery Vs post-surgery Trend

fold change

12598 0.002307863 Ridotto 2.5

9746 6.72E-04 Ridotto 1.1

22562 0.001210767 Ridotto 1.6

5695 0.005926072 Ridotto 1.9

5383* 0.006141185 Ridotto 2.1

11947 0.006261136 Ridotto 1.6

8301 0.1611726746 Perde significatività -

10964 0.031735343 Ridotto 1.4

3032 0.2882668265 Perde significatività -

11778 0.0599209846 Perde significatività -

11071 0.0991819341 Perde significatività -

8841 0.02818751 Ridotto 1.4

14519 0.012876043 Aumentato 0.6

4123* 0.036011334 Aumentato 2.5

8050 0.009579068 Aumentato 0.5

4180 0.026023491 Aumentato 0.7

4014 0.048340941 Perde significatività -

7657 0.008265548 Perde significatività -

14029 0.02001241 Aumentato 0,7

7539 3.65E-04 Aumentato 0,4

7912 0.011900212 Aumentato 0.2

5000 10000 15000

0

100

200

uA

200

RCC pre-

surgery

RCC post-

Evaluation of Urinary RCC Biomarkers versus Control s

0

100

uA

0

100

200

uA

5000 10000 15000

CTRL

RCC post-

surgery

Mass Peaks differentially expressed by comparing

urine Pre-Surgery (G1+ G2) vs urine Post-Surgery vs CTRL

Mass p-value Pre-surgery Vs post-surgery TendPre-surgery vs CTRL Trend fold change

12598 0.002307863 Ridotto Aumentato 0.6

9746 6.72E-04 Ridotto Perde significatività 1.3

22562 0.001210767 Ridotto Ridotto 2.3

5695 0.005926072 Ridotto Aumentato 0.3

5383* 0.006141185 Ridotto Aumentato 0.6

11947 0.006261136 Ridotto Perde significatività -

8301 0.1611726746 Perde significatività Perde significatività -

10964 0.031735343 Ridotto Ridotto 2.110964 0.031735343 Ridotto Ridotto 2.1

3032 0.2882668265 Perde significatività Perde significatività -

11778 0.0599209846 Perde significatività Perde significatività -

11071 0.0991819341 Perde significatività Perde significatività -

8841 0.02818751 Ridotto Perde significatività -

14519 0.012876043 Aumentato Ridotto 2.1

4123* 0.036011334 Aumentato Aumentato 2.5

8050 0.009579068 Aumentato Perde significatività -

4180 0.026023491 Aumentato Perde significatività -

4014 0.048340941 Perde significatività Perde significatività -

7657 0.008265548 Perde significatività Perde significatività -

14029 0.02001241 Aumentato Perde significatività -

7539 3.65E-04 Aumentato Comparabili 0,1

7912 0.011900212 Aumentato Perde significatività -

0

5

10

15

20

25

30

35

40

22562 10964 4123 5695 5383 12598

CTRL

BeforeAfter

Inte

nsità

med

ia (

nJ)

*

*

** *

*

*

*

*

BIOMARKERS IDENTIFICATION OF PUTATIVE CLINICAL INTEREST

17500 20000 22500 25000

17500 20000 22500 25000

10900 11000 11100

10900 11000 11100

4050 4100 4150

4050 4100 4150

RCC pre-intervento

RCC post-intervento

CTRLs

22562 10964 4123 5695 5383 12598

Molecular Mass (m/z)

11000 12000 13000 14000 15000

11000 12000 13000 14000 15000

5375 5400 5425 5450

5375 5400 5425 5450

5700 5800

5700 5800

RCC post-intervento

RCC pre-intervento

CTRLs

5695 m/z 5383 m/z 12598 m/z

22562 m/z 10964 m/z 4123 m/z

Inflammatory and

remodelingProteins?

Biomarkers?

Profiling RCC tissue Vs controls Urine Profiling RCC Pre Vs

Post nephrectomy

Experimental Design

Differential Peaks List

Common differential Peaks(putative RCC biomarkers)

Differential Peaks List

Inte

nsità

med

ia (

nJ)

*

*

** *

*

*

*

*

EVALUTATION OF PUTATIVE RCC URINARY BIOMARKERS IN TISSUES

URINE TREND

0

50

100

150

200

250

300

22565 10964 4123 5695 5383 12598

Mass peaks(m/z)

Inte

nsity

(nJ

)

TEX CTRL

TEX RCC

Mass peaks (m/z)

Loss of Significance in normal tissue vs tumor tissue

Identified urinary Biomarkers resulted

indirectly indirectly associated to RCC

Do RCC specific biomarkers (in parallel reduced or increased in

tissues and urine) exist?

TREND

Mass (m/z) RCC Tissue Urine pre-Surgery

12494.65 Reduced Reduced

10444.72 Increased Increased

The Magnificent “7”

13463 Reduced Reduced

9635.855 Reduced Reduced

12302.01 Reduced Reduced

7622.687 Increased Increased

7760.241 Increased Increased

p-value < 0.05

Urine Tissue

Peak Trend 12494 m/z

Pre-surgery Post-surgery Control Tumor

p-value < 0.05

Urine Tissue

Peak Trend 10.444 m/z

Control TumorPre-surgery Post-surgery

p-value < 0.05

Coming soon applications

Direct sequencing of differently expressed mass peaks through

Lucid™ ID Identification kit

Direct sequencing of differently expressed mass peaks through

MALDI-TOF/MS/MS interfacing

Summary II

� SELDI profiling allowed rapid screening of 71 urinary samples of

pre- and post-surgery RCC patients and of 26 proteic extracts from

controls and tumor tissues.

� A set of 7 mass peaks differentially expressed in RCC tissues has� A set of 7 mass peaks differentially expressed in RCC tissues has

been identified in pre- and post-surgery urines of RCC patients.

� Biomarkes identification and set up of specific multi-parametric

diagnostic kits might allow rapid and early, and of note, non

invasive diagnosis of RCC

� Etiology

� Alterations :

• Immune system

– DC and T cells

» Antigens

» Biomarkers

Renal Cell Carcinoma as Model of Study

» Biomarkers

» Diagnosis

» Adjunctive Therapy

� Perspectives:

• Application of the model to other pathologies

• Clinical intervention

Research is the Spring of Future…..Research is the Spring of Future…..

Van Gogh

University of FoggiaUniversity of FoggiaProf. L. GesualdoProf. L. Gesualdo

Margherita GiganteMargherita Gigante

Massimo PapaleMassimo PapaleMaria Teresa RocchettiMaria Teresa RocchettiPaola PontrelliPaola PontrelliMilena GiganteMilena GiganteAnnalisa SchirinziAnnalisa SchirinziGiuseppina CerulloGiuseppina Cerullo

University of BariUniversity of Bari

Prof. F.P. SchenaProf. F.P. Schena

Prof. M. BattagliaProf. M. Battaglia

Prof. G. GrandalianoProf. G. Grandaliano

Gianluigi ZazaGianluigi Zaza

Giuseppe CastellanoGiuseppe Castellano

Acknowledgments

University of Pittsburgh, University of Pittsburgh,

School of Medicine, PA, USASchool of Medicine, PA, USA

Prof. W.J. StorkusProf. W.J. StorkusGiuseppina CerulloGiuseppina CerulloClelia PrattichizzoClelia PrattichizzoLucio MontemurnoLucio MontemurnoLea RocaLea RocaStefano NettiStefano Netti

Hematology and Oncology, Johannes Gutenberg, Hematology and Oncology, Johannes Gutenberg,

University of Mainz, GermanyUniversity of Mainz, Germany

Prof. W. Herr