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Stefano Iacobelli Medical Oncology University G. D’Annunzio, Chieti-Pescara Prognostic and predictive markers and the role of genomics & proteomics.

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Presentazione sul tema: "Stefano Iacobelli Medical Oncology University G. D’Annunzio, Chieti-Pescara Prognostic and predictive markers and the role of genomics & proteomics."— Transcript della presentazione:

1 Stefano Iacobelli Medical Oncology University G. D’Annunzio, Chieti-Pescara Prognostic and predictive markers and the role of genomics & proteomics

2 1. Prognostic & predictive biomarkers: General concepts 1. Novel technologies Genomics (Proteomics) 3. Application in breast cancer

3 Human cancer : a series of genetic and epigenetic alterations that can be classified into 6 main classes and are responsable of the characteristics of the neoplastic phenotype Self-sufficiency in growth signals Limitless replicative potential Tissue invasion & metastasis Sustained angiogenesis Evasion of apoptosis Insensitivity to anti-growth signals

4 These genetic and/or functional alterations may play an important role as tumor “ biomarkers” Monitoring patients with established disease for - Recurrence/Prognosis assessment - Prediction of response to drugs Biomarkers are important tools for cancer management Early detection of asymptomatic patients - Aiding in the diagnosis - Surveillance of individuals known to be at risk of cancer - Surrogate endpoint markers for primary prevention strategies (i.e. chemoprevention)

5 The identification of pts : at minimal risk of disease recurrence; at elevated risk, who may benefit from systemic treatment; more likely to respond to specific treatment The ideal tumor biomarker It must give clinically useful information for: The recognition of subgroups of pts who differ in disease outcome

6 BIOMARKER Predictive NOT prognostic (bax and chemotherapy) Prognostic NOT predictive (lymphnode status) pos Survival (%) neg treatment control neg Survival (%) pos treatment control Adapted from Hayes et al., BCRT 52, 1998 Separates poor from favorable groups independent of therapy Outcome in the absence of therapy is the same regardless the marker is + or -

7 Prognostic AND Predictive neg pos Survival (%) treatment control ER and hormone therapy lowhigh Survival (%) treatment control TLI and CMF-based CT Adapted from Hayes et al., BCRT 52, 1998 Separates groups to some extent but much more in the presence of specific treatment

8 Three categories of prognostic factors strongmoderateweak Disease recurrence (%) High risk Low risk medium risk adapted from Hayes et al., BCRT 52, 1998

9 IPrognostic relevance Clinical stage (T,N,M) & proven clinical usefulness histologic grade mitotic index, histotype, steroid hormone receptorsu (uPA & PAI-1) IIMany studiesProliferation indices biological & clinical, Peritumoral invasion but need ofHER-2 /neu, p53 statistical evaluation IIIPrognostic relevance ploidy, neo-angiogenesis but not proven clinical apoptosis (bcl-2), usefulness based onGF and their Rec, available information pS2, Cathepsin D PROGNOSTIC factors in breast cancer College of American Pathologists Consensus Statement 1999, Arch Pathol Lab Med, 2000

10 Follow-up (years) Silvestrini et al., JCO 1995; CCR 1997 Estrogen ReceptorsProgesterone Receptors 1800 patients with N - breast cancer undergoing loco-regional treatment only Incidence of distant metastases

11 Cell Proliferation (TLI) Follow-up (years) Silvestrini et al., J Clin Oncol 1995; CCR 1997 Incidence of distant metastases (1800 N-) Response to treatment (281 N- TLI >3%) CMF None 1 DISEASE FREE SURVIVAL YEARS HR=0.59 p=0.028 Amadori et al., J Clin Oncol 18, 2000

12 Coradini et al., Br J Cancer 2001 Disease recurrence (226 N- pts, surgery only) Angiogenesis (Intratumoral VEGF) Response to treatment (212 N+/ER+ pts treated with Tamoxifen) VEGF+ VEGF- HR=2.46 ( ), P= VEGF- VEGF+ Coradini et al., Br J Cancer 2003

13 Prognostic relevance of uPA and PAI patients - Incidence of distant metastases Look et al. JNCI, 2002

14 The score: A comprehensive view that helps: To identify patients: at low risk of disease recurrence who do not need adjuvant treatment at high risk of disease recurrence who may benefit from adjuvant systemic treatment

15 NOTTINGHAM PROGNOSTIC INDEX (NPI) Tumor Size (cm) x 0.2 = points Tumor Grade*: from 1 (better) to 3 (worse) = points Axillary Lymph Nodes: negative nodes = 1 point; positive nodes, 1 to 3 positive = 2 points; positive nodes, >4 = 3 points Size + grade + lymph-node = Total NPI points 80% 15 yrs if NPI <3.4 sum 42% 15 yrs if NPI sum 13% 15 yrs if NPI >5.4 sum Conclusions: as to need for adjuvant CT need is doubtful chemo needed MAY BENEFIT with CT Groups *by whatever system

16 Six-year recurrence rate as a function of bio-pathological score

17 Novel analytical tools Microarray technology Proteomics ……. From a “ reductionistic ” approach (gene by gene) to an “ olistic ” approach (global genomic analysis) Molecular signature of cancer The innovation

18 Proteomica Genomica Ricerca Clinica Diagnostica Farmacogenomica Prevenzione

19 Genomics Gene Sequencing Conventional Karyotyping FISH (Fluorescent in Situ Hybridization) CISH (Chromogenic in Situ Hybridization) CGH (Comparative Genomic Hybridization) SKY (Spectral Karyotyping) Real Time RT-PCR cDNA Microarrays Novel analytical tools

20 2D-PAGE MS (Mass Spectrometry) HPLC (High Performance Liquid Chromatography) CA (Capillary Array) MALDI (Matrix Associated Laser Desorption/Ionisation) MALDI-TOF – MS (Time of Flight) MALDI – ION TRAP- TOF – MS ESI (Electron Spray Ionisation) Tandem – MS Quadrupole Functional proteomics TWO YEAST– HYBRID SYSTEM PROTEIN MICROARRAY FRET (Fluorescence Resonance) SELDI (Surface-Enhanced Laser Desorption/Ionisation) TISSUE MICROARRAY Proteomics

21 Multispot Arrays Sonde (DNA, oligonucleotidi, proteine, anticorpi) Spots sulla superficie di un substrato solido Deposito o sintesi Gene chip, DNA chip, DNA array, Protein chip….. Sonde: antigeni anticorpi cDNA oligonucleotidi prodotti di PCR plasmidi BACs (Bacterial Artificial Chromos.) YACs (Yeast Artificial Chromos.) Substrato: vetro nitrocellulosa nylon vetro rivestito di poliacrilammide polipropilene silicone polistirene Deposito blotting printing elettrodipendente Sintesi in situ meccanica fotolitografica elettrodi printing di precisione deposito sulla superficie tensione- dipendente

22 MICROARRAYS a cDNA o OLIGONUCLEOTIDI Sistemi utilizzati per confrontare i livelli di espressione genica in due campioni diversi. Estrazione RNA cellulare Trasformazione in cDNA Marcatura del cDNA Ibridazione (DNA/nucleotidi) Lettura laser Analisi dati De Risi et al Science 278:680 (1997) Heller et al PNAS 94:2150 (1997)

23 Un microarray è costituito da una superficie sulla quale sono depositate migliaia di sequenze specifiche di nucleotidi, ciascuna delle quali identifica un particolare gene. Le diverse migliaia di cDNA sono poste in spot separati. Ciascuno spot rappresenta un gene, in quanto contiene numerose copie di un cDNA corrispondente a tale gene spot GeneChip array Milioni di catene di DNA in ciascuno spot 25 basi in ogni catena 1.28 cm

24 Ibridando tale superficie con cDNA ottenuti dalla retro- trascrizione dell’RNA estratto da due campioni diversi è possibile determinare il livello di espressione dei singoli geni per confronto diretto tra l’abbondanza relativa di RNA prodotto. RNA del tumore Plot multidimensionale RNA normale cDNA del tumore cDNA normale Analisi statistica Ibridazione

25 Per effettuare tale confronto, i cDNA corrispondenti ai due differenti campioni vengono marcati con sostanze fluorescenti diverse e, ad ibridazione avvenuta, il microarray viene esposto ad una sorgente di luce laser. Gli spettri di emissione vengono quindi raccolti da uno scanner e le immagini monocromatiche indicanti i livelli diversi di espressione genica vengono pseudocolorate da un software di acquisizione d’immagine. De Risi J.L. et al Science 1997; 278: Heller R.A. et al PNAS 1997; 94:

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27 Utilizzo dei microarrays Fattori Prognostici e predittivi Markers Diagnostici Targets per farmaci Attività farmaci Per lo studio di: Tumori Patologie su base genetica Malattie infettive ……

28 I cDNA MICROARRAYS nel 2004 Agilent's microarray, con geni e transcritti su un singolo vetrino 1 x 3". I probes sono sintetizzati in situ usando la tecnologia ink-jet Tre ditte stanno lanciando dei chip per l’intero genoma umano: Applied Biosystems, geni tecnologia chemiluminescenza NimbleGenSystems, probes con una media di 5 pobes per gene tecnologia fotolitografica Agilent Technologies, probes per geni e trascritti tecnologia injk-jet

29 Limiti dei Microarrays Disponibilità di tessuto “fresco” Esame dell’espressione genica limitato alla valutazione della presenza di mRNA Riproducibilità dei dati Eterogeneità delle cellule presenti nel campione

30 Campione eterogeneo Campione A Campione B Microdissezione Popolazione eterogenea Laser capture microdissection (LCM) Popolazione omogenea Popolazione omogenea

31 ANALISI PROTEOMICA Possibilità di individuare markers molecolari di tumori (o altre patologie) Siero Proteine Frazionamento Digestione con enzimi proteolitici Peptidi Cromatografia o 2D-PAGE Spettrometria di massa Analisi con algoritmi specifici Sidransky D. Emerging molecular markers of cancer. Nature Cancer Rev 2002; 2:210-9.

32 PROTEIN MICROARRAYS (ProteinChip) Sono utilizzati per esaminare: i livelli di espressione delle proteine le interazioni proteina-proteina le interazioni proteina-piccole molecole (farmaci, etc) le attività enzimatiche Page, M. J. et al. Proteomic definition of normal human luminal and myoepithelial breast cells purified from reduction mammoplasties. PNAS 1999; 96:12589–12594.

33 PROTEIN MICROARRAYS Esistono due tipi principali di chip: antibody arrays Ab Microarray 500™ - BD Biosciences' Clontech division, Palo Alto, CA > 500 anticorpi per quantificare proteine in lisati cellulari o altri campioni biologici TranSignal Human Cytokine Antibody Array 2.0 (Redwood City, CA) > 21 anticorpi per misurare citochine general protein arrays Yeast ProtoArray™ from Protometrix, Branford, CT, con circa polipeptidi da Saccharomyces cervisiae per monitorare le interazioni proteina-proteina e proteina-piccole molecole (farmaci….) Yeast ProtoArray™

34 PROTEIN MICROARRAYS Conrads TM et al. Cancer diagnosis using proteomic patterns. Expert Rev Mol Diagn 3: (2003) Individuazione nuovi biomarkers Siero Proteine Bio-chip SELDI-TOF MS m/z Pattern proteicoRiconoscimento del pattern

35 Nuovi Biomarkers individuati con il ProteinChip e tecnologia SELDI-TOF-MS TumorekDaNomeAutore Pancreas16.57HIP/PAP-1Rosty et al. Cancer Res 2002; 62: Vescica3.4Alpha-DefensinaVlahou et al. Am J. Pathol 2002; 158: Nasofaringe Serum Amyloid A (SAA) isoform Yip et al - AACR 2002 Prostata100PSMAWang et al. Int J. Cancer 2002; 92: Ovaio Frammento di Aptoglobina Transferrina Catena pesante Ig Ye et al. Poster AACR 2002 Rai et al. Arch. Pathol. Lab. Med 2002; 126: “ “ “

36 Gene profiling of breast cancer

37 Sorlie et al., PNAS 98, , 2001

38 Hierarchical clustering of 78 primary breast cancers and 4 normal breast tissue Dendrogramma “Alberi di unione tra i vari casi che si assomigliano” (i.e: intensità di colore relativo ad un gene o a gruppi di geni) 5 differenti fenotipi

39

40 ER-ER+ Cluster analysis Sorlie et al., PNAS, 98, 2001

41 Van ‘t Veer et al. Nature 415, 530, January 2002

42 ~5000 genes significantly regulated ( in > 3 tumors) 231 genes correlated w disease outcome 70 genes = Poor/Good prognostic signature correctly predicted disease outcome in 65/78 sporadic tumors Unsupervised hierarchical clustering 78 sporadic BC (T<5cm, N-) + 20 BRCA1/2+ BC 34 pts w metastases <5 y44 pts NED >5 y 25,000 genes of microarray Supervised hierarchical classification Rank-ordered based on p-value

43 Tumori clinici: studio pilota Tumori clinici: serie di validazione (N=19) Van’t Veer et al., Nature 415, 2002 Good prognosis signature Poor prognosis signature

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46 Van de Vijver et al., NEJM, 347, 25, carcinomi mammari sporadici T<5 cm, N - /N + < 53 anni: Decorso clinico in base al profilo di espressione genica (70-gene prognosis signature)

47 Van de Vijver et al., N Engl J Med, sporadic breast cancers: Clinical course according to gene expression profile

48 Van de Vijver et al., N Engl J Med, N - patients

49 144 N + patients Van de Vijver et al., N Engl J Med, 2002

50 151 N - patients: Clinical course according to molecular signature (A) or clinico- patological classification (B, C)

51 Therapeutic benefit According to usual selection criteria (EBCTCG) over 100 pts N- pre-menopausal pts receiving adjuvant chemotherapy, 83.5 are alive even without chemotherapy and 13.5 die despite chemotherapy at 5 years FU. Using gene expression profile, only 22.5% of pts will be over-treated

52 Clin Cancer Res 10: , 2004

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56 Clin Cancer Res 9: , 2003

57 BM, Ab to CK PE, Ab to CK & HER-2 FISH BM, Ab to CK & nuclear Counterstaining w d-p-indole Same as in C Ab to uPAI-R

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59 AdnaGen CancerSelect Genzyme Virotech GmbH Test system for the early detection of disseminated cells in blood for a better diagnostic and monitoring of colon and breast cancer patients

60 Will the new molecular knowledge be applied to “bedside”?

61 The first large-scale independent trial to prospectively validate the 70-gene expression signature (MammaPrint ® ) in breast cancer. EORTC/TRANSBIG MINDACT TRIAL MammaPrint ® has reached level 3 in Evidence Based Medicine Adequate Processed Core Biopsy Prognostic Risk Evaluation Randomize Clinico-pathological Microarray Low Risk Average/High Risk Chemotherapy Possible further randomization Endocrine therapy Possible further randomization

62 Other ongoing trials incorporating translational research in BC Evaluating predictive factors for response: BIG p53(EORTC 10994): pts with LABC Tax vs Non-Tax CT (Neoadjuvant) Evaluating prognostic factors (uPA/PAI-1) EORTC-RBG: High-Risk, Node-negative (NNBC-3) FEC vs FEC Docetaxel ADEBAR:  4+ lymph nodes Adjuvant Epirubicin Docetaxel ( Wilex's uPA inhibitor WX-UK1 )

63 Affymetrix Launches ENCODE Array to Uncover Hidden Function of Human Genome October 22, 2004 BioTrove Announces OpenArray™ Transcription Analysis System Date: September 20, 2004 Expression Analysis Launches Affymetrix Microarray-Based Genotyping Services Date: September 14, 2004 Agilent Partners with TGen to Develop CGH Arrays for Cancer Research June 8 Gene Logic Provides Data from GeneExpress System to FDA May 13, 2004 Toray Develops Ultra Sensitive, Quick DNA Chip Date : September 20, 2004 SIRS-Lab releases new biochip Date: September 22nd, 2004 Agilent Acquire Silicon Genetics August 29, 2004 Velcura to use custom Affymetrix technology August 03, 2004 Toshiba to Develop DNA Chip with Osaka University July 20, 2004 Affymetrix and Immusol to Collaborate on Cancer Drug Discovery June 22, 2004 Predicting cancer patient survival with gene- expression data Date: May 06, 2004 Setting The Gene Expression Base-Line For Breast Cancer Research Date: May 05, 2004 Illumina Announces 100,000 SNPs on a Single BeadChip Date: April 21, 2004 NCI awards grant for gene expression research DATE: Thursday, April 15, 2004

64 Can the novel technologies be used to predict the therapeutic response?

65 Nature Clin Pract Oncol 1; 44-50, 2004

66 New Engl J. Med. 351: (2004) Early BC

67 Panel of the 21 genes and the Recurrence Score Algorithm Oncotype DX  from Genomic Health

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70 J. Clin. Oncol. 23: (2005) Advanced BC

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74 AnthracyclinesTopoisomerase II , MDR, MRP, ErbB-2 5-FU, CapecitabineCyclin D1, Thymidilate synthase, Thymidine phosphorylase, NFkB, p53, Bax/Bcl2 GemcitabineRibonucleotide reductase, 5-nucleotidase,  -tubilin III deoxycytidine-kinase Vinca AlkaloidsMAP4, Topoisomerase I Taxol  -tubulin III, MDR, MAP4, survivin Platin compoundsERCC1, MDR1,  -tubulin III, XPA, XPD, cJun XPG, p53, cyclinD1, GSTpi, MLH1, MSH6 Irinotecan, TopotecanTopoisomerase I, p14ARF, carboxylesterase, MDR Small TRK inhibitorsAkt, MAPK, ecc GENES & GENE PRODUCTS INVOLVED IN DRUG RESISTANCE/SENSITIVITY (Cancer literature)

75 Breast cancer 1. Anthracyclines 2. Fluoropyrimidines Topo II MDR1 1. TS Cyclin D1 2. p14ARFTopo I Taxanes 1. TS Cyclin D1 2. ERCC1p FU, capecitabine 3. Platin compounds cJun “Smart Chip” (antibody array) A CINBO’s Project Colorectal cancer 1.Irinotecan ErbB-2 Bcl-2/bax Topo Ip14ARF Bcl-2/bax

76 + FFIA (Fluorescent Immuno Assay) 1. Biopsia del paziente (Biomarkers) 2. Rh-Fusion-GFP proteins Biotin-Antibody coated chip Fluorescence intensity Amount of Biomarker “Smart Chip” (antibody array) A CINBO’s Project

77 Conclusion The emerging fields of genomics (and proteomics) offer the ability to precisely analyze the molecular portrait of a particular patient’ tumor; These approaches appear extremely useful for defining individual patient’s prognosis and assessing responiveness to anti-cancer therapy; A new era will come soon, wherein we will treat each patient with a “prescription” based on the molecular profile of its tumor resulting in more rationale use of the therapy

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79 Good prognostic signature Poor prognostic signature White = ED pts Black = NED pts


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