1. Micro and nanotechnologies in cancer diagnostics and therapy Alp Nano Bio International School 2January 11-15, 2010, Sterzing (Bolzano, Italy) OMICS profiling and prognostic significance in cancer Gian Paolo Tonini Translational Pediatric Oncology, National Cancer Research Institute, Genoa, Italy

3. Genome copy numbervariation

4. Gene expressionprofiling

5. From single gene to the genome and prognostic value

6. Gene expressionprofileasprognosticmarker

7. Genome-transcriptomeintegration

9. Target preparation Probe preparation Data analysis GENERAL DNA MICROARRAY PROCEDURE

10. SPOT TARGET ARRAY ARRAY SET PROBES ROWS COLUMNS NOMENCLATURE FOR MICROARRAYS

11. Non-Target Nucleic Acid TAAGCGTTGAG Target Nucleic Acid A C G T T C C G A A T GCAAGGCTT A C G T T C C G A A A C G T T C C G A A Spot 1 Spot 2 DNA MICROARRAY: THE PRINCIPLE Double strand complementarity Each spot

14. Probe solution Robotic printing Printed array MECHANICAL PRINTING

15. MECHANICAL PRINTING TECHNOLOGIES (1) CONTACT DISPENSING: direct contact between the printing mechanism and the solid support - Microspotting pins (TeleChem International) - Tweezer pins - Split pins - PIN- and RINGTM technique (Genetic MicroSystems)

16. Reservoir Siringe Solenoid valve Piezoelectric crystal Pressure Glass capillary Piezoelectric printing technology Syringe-solenoid printing technology MECHANICAL PRINTING TECHNOLOGIES (2) NONCONTACTINK-JETDISPENSING: ejection of drops from a dispenser onto the surface

17. HIGH-DENSITY MICROARRAYS > 200.000 probe/slide

18. PROBE IN SITU SYNTHESIS (1) Photolithography technology (Affymetrix technology)

19. mRNA reference sequence 5’3’ …ATGGTGGGAATGGGTCAGAAGGACTCCTATGTGGGTGACG… TTACCCAGTCTTCCTGAGGATACACCCAC TTACCCAGTCTTGCTGAGGATACACCCAC Perfect Match Oligo Mismatch Oligo 25 mer Perfect match probe cells Fluorescence intensity Image Mismatch probe cells - To control the hybridization specificity - To subtract non-specific hybridization GENECHIP® PROBE ARRAY DESIGN

20. Affymetrix chips Total RNA 5’ AAAAA 3’ 3’ TTTTT - AAAAA 3’ TTTTT - 1.FIRST STRAND cDNA SYNTHESIS: SuperScript cDNA Synthesis kit (Gibco BRL Life Technology) using a high quality HPLC-purified T7-(dT)24 5’ 5’ 10. IMAGE ANALYSIS 5’ 2.SECOND STRAND cDNA SYNTHESIS 3’ 3. CLEANUP OF DOUBLE-STRANDED cDNA: phenol/chloroform/isoamyl alcohol extraction, using Phase Lock GelTM (Eppendorf) AAAAA TTTTT - 5’ 9. IMAGE ACQUISITION 5’ 3’ 3’ U Biotinylated ribonucleotides 4.BIOTIN LABELING ANTISENSE COPY RNA (cRNA) BioArrayTM HighYieldTM RNA Transcript labeling kit (Enzo Diagnostics). The in vitro transcription reaction is performed by T7 RNA polymerase with biotin-labeled nucleotides C 3’ UUUUU 5’ 8. PROBE ARRAY STAINING 5. CLEANUP OF BIOTYNILATED cRNA: RNeasy Mini Kit (Qiagen) 7. HYBRIDIZATION 6. FRAGMENTATION Tris-acetate, pH 8.1, KOAc and MgOAc RNA Biotyn T7 primer DNA

21. Resistor Off Resistor On Resistor Off Liquid Vaporizes Fill Reservoir Gas Expands Drop Breaks Off Reservoir Refills < 1 msec A C G G A T G G A T C C G A T T C C A A T T C G PROBE IN SITU SYNTHESIS (2) Phosphoamidite / Ink-Jet process (Agilent Technologies)

22. Procedure for chip analysis (Agilent Technology)

23. Cy3 Cy5 Tumor cells Normal cells TARGET LABELLING (2) Emission spectra of some CyDye fluors 2-COLOR ASSAYS: - Fluorophores

24. Glass slide with sample Replicate slides Image analysis Discard spots with poor quality values Calculate ratios Average data Examine variation of ratio between replicates Visualize data Data report Store data in a database MICROARRAY DATA ANALYSIS

27. Time

30. Temperature

32. METAPHASE COMPARATIVE GENOMIC HYBRIDIZATION (CGH) (in our lab since 1998) by Raffaella Defferrari -Whole-genome screeningcapability; - Theresolutionis no less than ~ 10 Mb for loss and gains; - Analysisrequires experienced cytogenetics to determine regions of imbalances

33. METAPHASE COMPARATIVE GENOMIC HYBRIDIZATION (CGH) (in our lab since 1998) -Whole-genome screeningcapability; - Theresolutionis no less than ~ 10 Mb for loss and gains; - Analysisrequires experienced cytogenetics to determine regions of imbalances

34. METAPHASE CGH ARRAY CGH (in our lab since 2001) ARRAY CGH PLATFORMS Bacterial Artificial Chromosomes (BACs) Yeast Artificial Chromosomes (YACs) Plasmid Artificial Chromosomes (PACs) Cosmids Matrix CGH 1 Mb 300 Kb – 2 Mb cDNA < 200 kb cDNA aCGH 0.5 – 2 Kb -Whole-genome screeningcapability; - Theresolutionis no less than ~ 10 Mb for loss and gains; - Analysisrequires experienced cytogenetics to determine regions of imbalances Oligonucleotides -This system significantlyincreases resolutionfor localizing regions of imbalance; - Just as with expression microarray screening,analysis is straightforward and automatic Oligonucleotide aCGH (in our lab since 2005) < 6 kb 30 – 70 bp Probe length Resolution

35. Gene view Chromosome view Genome view View of the feature of probe data and annotations CGH Analytics software (Agilent Technologies)

36. 11q14.1-qter loss Loss: 2p25.3-pter:SH3YL - MTCBP-1(9.8 Mb) 2p25.1:TAF1B - HPCAL1(0.5 Mb) 2p24.3:NAG - FAM49A(1.8 Mb) 2p23.3-p23.2:UBXD4 - EMILIN1(5.6 Mb) 2p23.1:LYCAT - BIRC6(1.9 Mb) 2p22.2:STRN - CDC42EP3(0.7 Mb) 2p21-p16.3:THADA - ALF(5.3 Mb)

39. Gene expression profiling

40. Test RNA Reference RNA Up-regulated gene expression Down-regulated gene expression GENE EXPRESSION PROFILING Quantitate mRNA levels to compare which genes are turned off and on in specific cell states (e.g. tumor vs. normal cell)

41. The length of the branch is inversely proportional to the degree of similarity Shades of red indicate increased relative expression Shades of green indicate decreased relative expression HIERARCHICAL CLUSTERING ALGORITHM - Provides measures of distance between multiple possible grouping of genes - Sorts all genes, such that similar genes appear near each other. This is shown as a DENDROGRAM

42. Tumor 1 Tumor 2 min max HIERARCHICAL CLUSTERING DENDROGRAM OF EXPRESSION DATA Each color patch represents the expression level of the associated gene (ROWS) in each sample (COLUMNS), with a continuum of expression levels from dark green (lowest) to bright red (highest). The Tumor 1 and Tumor 2 are placed on a different trunk 65 Genes more highly expressed in Tumor 1 samples than in Tumor 2 Cluster 1 56 genes more intensely expressed in Tumor 2 tumors than in Tumor 1 ones Cluster 2 Bioconductor software (www.bioconductor.org) Cluster method: average linkage Distance metric: euclidean

43. From single gene to the genome and prognostic valuean example of pediatric cancer

44. Neuroblastoma: a pediatrictumorofperipheralnervous system 5 MARZO 2009

45. Neuroblastoma onset as a localized or a disseminated tumor Localized tumor: favourable prognosis Metastatic tumor: unvafourable prognosis Primary Tumor

47. C + C - A B Southern blot Double colour FISH analysis on interphase nuclei Detection Of MYCN gene amplification by Katia Mazzocco

49. Age at diagnosis INSS stage MYCN status DNA index Shimada histopathology OUTCOME BASED ON RISK FACTORS Maris JM, CurrOpinPediatr 2005, 17:7-13

50. MOLECULAR PROGNOSTIC FACTORS EVALUATION IN THERAPEUTIC PROTOCOLS 1995 - 1999

52. Chromosome 1p36 deletion

53. Chromosome 17q gain

54. Chromosome 3p, 9p, 11q, 14q deletionGENETIC ABNORMALITIES OF NEUROBLASTOMA SUGGESTED TO PERFORM A PANGENOMIC ANALYSIS BY MICROARRAY

57. Life threatening symptoms NO RANDOMIZATION LINES (Low Intermediate NB European Study) Low Risk Study L2 Patients MYCN single copy L2 < 18 months L2 < 18 months Wide-genome analyses No life threatening symptoms No symptoms Symptoms Genomic type 2 Type 1 Type 2 Genomic type 2 Genomic type 1 Type 2 Type 1 Genomic type 1 structural changes no structural changes structural changes no structural changes structural changes structural changes no structural changes no structural changes RANDOMIZE NO RANDOMIZATION RANDOMIZE NO RANDOMIZATION Observational nterventional VP/Carbo2 - 4 2 - 4 VP/ Carbo to resolve 2 - 4 VP/ Carbo to resolve 2 - 4 VP/ Carbo to resolve 2 - 4 VP/ Carbo to resolve Interventional 2-4 VP/Carbo Observational Co2 - 4, VP/Carbo2 - 4 LTS LTS LTS LTS 2-4 CO, 2-4 VP/Carbo Treatment group a Treatment group b Treatment group c Treatment group c Treatment group c Surgical resection 1 year Surgical resection 1 year Aim for surgical resection Aim for surgical resection Aim for surgical resection Aim for surgical resection Aim for surgical resection Aim for surgical resection Aim for surgical resection Aim for surgical resection from diagnosis if IDRF from diagnosis if IDRF when IDRF negative when IDRF negative when IDRF negative when IDRF negative when IDRF negative when IDRF negative when IDRF negative when IDRF negative negative negative Study A/I Study A/I Study C/III Study C/III Study B/II Study A/I Study A/I Study C/III Study C/III Study B/II

58. Gene expression profile as prognostic marker

60. predictionanalysisofmicroarray (PAM): a statisticsalmethodforclasspredictionfrom gene expression data

61. Unbiased amplification of total RNA by Ribo-SPIA® RNA Amplification 20 ng RNA High quality RNA Quantification of the 59-gene signature by qPCR PROSPECTIVE STUDY WITHIN CLINICAL PROTOCOL Detection of inhibitors by SPUD assay (Nolan et al., 2006)

62. Genome-transcriptome integration

63. Genome analysis of metastatic neuroblastoma By a-CGH using high resolution oligonucleotide Human Genome CGH 44K (Agilent Technologies). Group 1 (49 cases) patients 4S, NMYC normal Group 2(37 cases) patients stage 4; < 18 months; MYCN normal; 3 years EFS Group 3(cases 47) patients stage 4; > 19 months; Any case dead for disease. The trend of average number of numerical changes tends to decrease: from Group 1 to Group 3 On the contrary The trend of average number of segmental changes tends to increase: From Group 1 to Group 3

64. Genome analysis of metastatic neuroblastoma By a-CGH using high resolution oligonucleotide Human Genome CGH 44K (Agilent Technologies). Numerical aberrations Structural aberrations The frequency of significant structural changes tends to increase From Group 1 to Group 3 The frequency of significant numerical changes tends to decrease From Group 1 to Group 3

65. Transcriptome analysis of metastatic neuroblastoma Gene expression analysis was performed 11K oligonucleotide microarray (10,163 probes for 8,155 Unigene transcrits) Group 1 vs. Group 2 Group 2 vs. Group 3 30 9 Group 1 (49 cases) patients 4S, NMYC normal Group 2(37 cases) patients stage 4; < 18 months; MYCN normal; 3 years EFS Group 3(cases 47) patients stage 4; > 19 months; Any case dead for disease. 4 66 2 109 240 1436 Group 1 vs. Group 3

66. Cromosome region 5’ 3’ 5’ 3’ Probe Set Genome and gene expression Intergration Chromosome region Normalized gene Expression data # Probe Set # Probe Set OUTPUT Significant differentially expressed probe sets # Probe Set Significant Analysis Microarray (SAM): a statistical method for finding significant genes in a set of microarray experiments FDR = 0.05

67. chromosome regions Group 1 vs Group 2 Genes up Gain Minimal Common Regions Genes down Loss

68. miRNA signature and miRNA-mRNA integration associated to poor patient survival

70. microRNA Signature Highly Correlated With MYCN Amplification (Schulte JH et al. Int. J. Cancer 2008)

71. microRNAs Are Diagnostic For Pediatric Tumors (Wei JS et al. Clin Cancer Res 2009)

73. Joint expression analysis of microRNAs and of their target sequences at mRNA level“SHORT-SURVIVORS”: dead of disease within 36 months from diagnosis “LONG-SURVIVORS”: alive with an overall survival time > 36 months

74. Short-survivors Long-survivors miRNAs signature Human microRNA Microarray v.2 (Agilent Technologies) containing probes for 723 human microRNAs.

75. mRNA SIGNATURE Short-survivors Long-survivors Gene expression analysis was performed 11K oligonucleotide microarray (10,163 probes for 8,155 Unigene transcrits) Agilent Technology

76. hsa-miR-877* hsa-miR-383 hsa-miR-548d-5p hsa-miR-939 Short-survivors 9 5.5 Long-survivors 6 4.0 8 5.0 4.5 5 3.5 7 4.0 6 3.0 4 3.5 5 2.5 3 3.0 4 2.0 2.5 2 3 0 1 0 1 0 1 0 1 P = 0.028 P = 0.020 P = 0.044 P = 0.028 NTRK1 FYN CHD5 miRNAs target mRNAs &

77. mRNA_1 mRNA_2 mRNA_4 mRNA_3 mRNA_5 mRNAome and miRNAome network miRNA_1 miRNA_5 miRNA_3 miRNA_2 miRNA_6 miRNA_4

78. NOW MLPA Translation of Omics to the Clinic NEXT mRNA/miRNA signature Array CGH Howwillchange the moleculardiagnosisofcancer 1990 Southern blot FISH LOH - PCR

79. Flow chart for OMICs patient risk prediction in cancer

80. DNA copy number Numerical Aberrations Segmental Aberrations RNA expression profiling Trisomies Unfavorable Signature Favorable Signature Very low Low High Intermediate Risk for death USING OMICS TO PREDICT PATIENT OUTCOME A model

82. Correct Storage Of Biological SamplesClinicians SIOPEN-R-NET Central Database Reference National Lab For Biology Studies MYCN, 1p36 FISH MLPA/array CGH ... RT-qPCR

83. Something more… Identification of malignant and stromal cells gene signature

84. Laser CaptureMicrodissectionof stromal and tumorcells Stroma Tumor Tumor cells Stromal cells - Tumor cells mainly express genes associated with cell replication, DNA repair and antiapoptoticpathways; - Stromal cells express genes of cell-cell communication and apoptosis. Albino D. et al., CANCER, 2008, 113/6: 1412-22

85. Isolation of tumor cells by laser microdissection

86. Stromal cells Tumor cells

87. Intratumor homogeneity of cells with similar phenotype

88. PMP2 UTS2 TSPAN8 CDH1 EGR3 AASS MEOX2 SLC22A3 RABGAP1L ASPA TGFBR3 LAMA2 P2RY14 EDNRB GPR126 CYP1B1 FYCO1 GPM6B TRIM38 CXCL14 CFI ASPM ABCA8 CALCA CXCL13 PLP1 TNNC1 ABCA6 CDH19 IL7 MYOT CENTD1 DUSP22 NR4A2 FLJ22184 ANGPTL7 MATN2 TRIP6 GINS1 GPC3 CDC25A CHL1 GDAP1L1 GPM6B TOP2A CALB1 FOXO1A CENPF TNRC9 SST ELF1 MICA AHNAK EPHA2 LOC55565 PBK ST6GALNAC2 CLU CXXC4 CDH10 BIRC5 SDC4 CDC14B CRYL1 SSPN MLF1IP NDRG2 MAL BUB1B ARHGAP15 SPAG5 MCM4 RRM2 SPTLC2 BACE2 GREB1 C4A C4B ITPR3 214046_at HBEGF CFLAR STARD13 IGF2BP3 APOD BACE2 EZH2 ITPR3 VASH2 HBEGF C4B ENDOD1 CFLAR VASH2 ARHGAP15 STARD13 MCM4 APOD IGF2BP3 C4A 214046_at CDC14B SPTLC2 SPEG MMP12 FEV TTK MGC17330 ENDOD1 MYCN PRC1 CCND3 POU4F2 NOL5A GATA3 TMEM132A HIST3H2A KCTD12 DPYSL4 PHOX2A MGC17330 HIST3H2A RGL1 RGL1 PHOX2A DPYSL4 NOL4 MGC39900 TMEM132A PRC1 POU4F2 NOL5A TOP2A CYP1B1 INSM1 INSM1 CD59 CD59 FEV CYP1B1 MYCN GDAP1L1 CCND3 TNRC9 SPEG FOXO1A KCTD12 CDC25A MGC39900 GINS1 TSPAN8 ELF1 UTS2 CXXC4 PMP2 MLF1IP AHNAK CDH1 EGR3 LOC55565 MYOT NDRG2 ABCA6 FLJ22184 CDH19 MATN2 PLP1 BUB1B TNNC1 DUSP22 IL7 BIRC5 CXCL13 TTK ST6GALNAC2 CALCA AASS EZH2 MEOX2 SPAG5 MMP12 SLC22A3 RRM2 CHL1 PBK EYA1 GPM6B NHLH2 TRIM38 RP11-35N6.1 EDNRB SOX4 TRIP6 TMSL8 SST NBLA00301 GPC3 TFAP2B MAB21L2 CALB1 MICA GATA3 MAL SLC17A6 SDC4 LOC157627 NOL4 EPHA2 SSPN CLU CDH10 CRYL1 GREB1 GPM6B GPR126 FYCO1 CXCL14 LAMA2 CFI CENPF CYP1B1 MAL P2RY14 PBK CENTD1 CENPF TGFBR3 GPC3 NR4A2 MICA RABGAP1L GPM6B ASPA CHL1 ABCA8 TRIM38 ANGPTL7 TRIP6 ASPM NBLA00301 SST EPHA2 TFAP2B CALB1 MAB21L2 NHLH2 SSPN SDC4 RP11-35N6.1 EYA1 CLU CDH10 LOC157627 SLC17A6 CRYL1 SOX4 ENDOD1 TMSL8 GREB1 LAMA2 GPR126 CFI CXCL14 FYCO1 GPM6B ABCA8 ASPM RABGAP1L ASPA CALCA CYP1B1 P2RY14 TGFBR3 EDNRB CENTD1 CXCL13 CDH19 10 TNNC1 PLP1 ABCA6 IL7 ANGPTL7 NR4A2 AASS MEOX2 SLC22A3 PMP2 UTS2 EGR3 TSPAN8 CDH1 MYOT PHOX2A MYCN RGL1 HIST3H2A DPYSL4 MGC17330 FEV PRC1 POU4F2 MGC39900 CDC25A CXXC4 GDAP1L1 GINS1 DUSP22 FLJ22184 MATN2 8 NOL5A TOP2A SPEG TNRC9 CD59 2182-LMA-2.CEL INSM1 CCND3 TMEM132A KCTD12 FOXO1A CYP1B1 ELF1 MMP12 TTK C4A 214046_at EZH2 IGF2BP3 STARD13 APOD C4B CDC14B CFLAR SPTLC2 MCM4 VASH2 HBEGF ITPR3 ARHGAP15 BACE2 SPAG5 RRM2 MLF1IP 6 NDRG2 BIRC5 BUB1B AHNAK LOC55565 ST6GALNAC2 EYA1 NHLH2 RP11-35N6.1 MAB21L2 TFAP2B NBLA00301 LOC157627 SLC17A6 NOL4 GATA3 SOX4 TMSL8 4 10 8 6 4 4 6 8 10 4 6 8 10 Laser Microdissectionoftumorcells and dendogramof gene expressionprofiling C A B 2182-LMA-1 2182-LMA-1 2182-LMA-2 2182-LMA-1 2182-LMA-3 F D E 2182-LMA-2 r=0.99 2182-LMA-2 2182-LMA-3 G H I 2182-LMA-3.CEL r=0.99 r=0.99 2182-LMA-3

89. A B PRC1 CXXC4 AASS MCM4 RRM2 ABCA6 SPAG5 CDH10 LOC55565 MICA BIRC5 EDNRB TMEM132A NOL5A SOX4 RP11-35N6.1 MGC39900 CENTD1 SPEG MYOT FLJ22184 FYCO1 TRIM38 NBLA00301 MAB21L2 CALCA POU4F2 EZH2 CALB1 GATA3 VASH2 MEOX2 TTK CDH1 SLC17A6 GDAP1L1 TMSL8 MMP12 NOL4 CXCL13 MLF1IP TOP2A BUB1B GINS1 INSM1 TFAP2B NHLH2 TSPAN8 EYA1 DPYSL4 FEV HIST3H2A CRYL1 SPTLC2 CDC25A EGR3 EPHA2 CXCL14 MYCN PHOX2A LOC157627 CFLAR HBEGF GPC3 TRIP6 RABGAP1L ENDOD1 LAMA2 IGF2BP3 CENPF ASPM UTS2 214046_at TNNC1 IL7 TNRC9 PBK GREB1 SLC22A3 SST ABCA8 PMP2 KCTD12 CYP1B1 GPM6B CDH19 AHNAK PLP1 MAL ST6GALNAC2 APOD RGL1 STARD13 MATN2 ARHGAP15 ASPA CD59 C4A TGFBR3 P2RY14 GPR126 CLU ELF1 CDC14B CFI MGC17330 SDC4 NR4A2 ANGPTL7 C4B SSPN CYP1B1 CCND3 1761-LMA-1 GPM6B DUSP22 FOXO1A NDRG2 ITPR3 CHL1 1761-LMA-1 1761-LMA-2 BACE2 12 10 8 6 1761-LMA-2 r=0.97 D C 4 4 6 8 10 12 Laser Microdissectionof stromal cells and dendogramof gene expressionprofiling

90. THANKS TO..... Paola Scaruffi Simona Coco Sara Stigliani Francesca Valdora Carla De Vecchi Translational Paediatric Oncology IST, Genoa, Italy Stefano Bonassi Clinical and Molecular Epidemiology IRCCS San RaffaelePisana, Roma, Italy JessicaThieben Andrè Oberthuer Matthias Fischer Barbara Hero Frank Berthold Paediatric Oncology Centre, Children’s Hospital, Cologne, Germany Stefano Moretti Fabio Gallo Molecular Epidemiology IST, Genoa, Italy FINANCIAL SUPPORT Italian Neuroblastoma Foundation Associazione Italiana per la Ricerca sul Cancro Ministero dell’Università, Ricerca Scientifica e Tecnologica