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Stefano Iacobelli
Medical OncologyUniversity G. D’Annunzio, Chieti-Pescara
Prognostic and predictive markers and the role of genomics & proteomics
1. Prognostic & predictive biomarkers: General concepts
1. Novel technologies
Genomics(Proteomics)
3. Application in breast cancer
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 ingrowth signals
Limitless replicative potential
Tissue invasion &metastasis
Sustained angiogenesis
Evasion of apoptosisInsensitivity to
anti-growthsignals
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)
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 idealtumor biomarker
It must give clinically useful information for:
The recognition of subgroups of pts who differ in disease outcome
BIOMARKER
Predictive NOT prognostic (bax and chemotherapy)
Prognostic NOT predictive (lymphnode status)
0
20
40
60
80
100
pos
Su
rviv
al (%
)
neg
treatment
control0
20
40
60
80
100
neg S
urv
ival (%
)pos
treatment
control
Adapted from Hayes et al., BCRT 52, 1998
Separates poor from favorablegroups independent of therapy
Outcome in the absence of therapy is the same regardless the marker is + or -
Prognostic AND Predictive
0
20
40
60
80
100
neg pos
Su
rviv
al (%
) treatment
control
ER and hormone therapy
low highS
urv
ival (%
)
treatment
control
0
20
40
60
80
100
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
0
10
20
30
40
50
60
70
80
90
100
Three categories of prognostic factors
strong
moderate weakD
isease r
ecu
rren
ce
(%)High risk
Low risk
medium risk
adapted from Hayes et al., BCRT 52, 1998
I Prognostic relevance Clinical stage (T,N,M)& proven clinical usefulness histologic grade
mitotic index, histotype, steroid hormone receptorsu
(uPA & PAI-1) II Many studies Proliferation indices
biological & clinical, Peritumoral invasionbut need of HER-2 /neu, p53statistical evaluation
III Prognostic relevance ploidy, neo-angiogenesis but not proven clinical
apoptosis (bcl-2), usefulness based on GF 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
Follow-up (years)
Silvestrini et al., JCO 1995; CCR 1997
Estrogen Receptors Progesterone Receptors
1800 patients with N- breast cancer undergoing loco-regional treatment only
Incidence of distant metastases
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
DIS
EA
SE F
REE S
UR
VIV
AL
0.9
0.8
0.7
0.6
0.5
0.4
0
0 1 2 3 4 5 6 7 8
YEARS
HR=0.59 p=0.028
Amadori et al., J Clin Oncol 18, 2000
Coradini et al., Br J Cancer 2001
Disease recurrence(226 N- pts, surgery only)
0 12 24 36 48 60 720
20
40
60
80
100
rela
pse
-fre
e su
rviv
al (
%)
months
Angiogenesis (Intratumoral VEGF)
Response to treatment(212 N+/ER+ pts treated with Tamoxifen)
VEGF+
VEGF-
HR=2.46 (1.29-4.65), P=0.0059
0 12 24 36 48 60 720
25
50
75
100
log-rank p= 0.06
Relaps
e-fr
ee S
urviva
l (%
)
months
VEGF-
VEGF+
Coradini et al., Br J Cancer 2003
Prognostic relevance of uPA and PAI-14676 patients - Incidence of distant metastases
Look et al. JNCI, 2002
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
NOTTINGHAM PROGNOSTIC INDEX (NPI) Tumor Size (cm) x 0.2 = pointsTumor 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% OS @ 15 yrsif NPI
<3.4 sum
42% OS @ 15 yrsif NPI
3.4-5.4 sum
13% OS @ 15 yrsif NPI
>5.4 sum
Conclusions:as to need foradjuvant CT
need is doubtful chemo neededMAY BENEFITwith CT
Groups
*by whatever system
Combined score Patho-biologic features % in scoregroup
Relapse probability
0 T<1 cm/N-, ER+/PgR+ 1 0%
1 T<1 cm/1-3 N+ or T1-2 cm/N-, ER+/PgR+
T<1 cm/N-, ER- or PgR-8 9%
2 T<1 cm/N-, ER-/PgR-
T<1 cm/>3 N+ or T>2 cm /N-, ER+/PgR+
T1-2 cm/N- or T<1 cm/1-3 N+, ER-or PgR-
T<1 cm/>3N+ or T1-2 cm/1-3N+, ER+/PgR+
23 15%
3 T<1 cm/1-3N+ or T1-2 cm/N-, ER-/PgR-
T1-2 cm/>3N+ or T>2 cm/1-3N+, ER+/PgR+
T<1 cm/>3N+ or T1-2 cm/1-3N+ orT>2 cm/N-, ER- or PgR-
T>2 cm/>3N+, ER+/PgR+
29 26%
4 T1-2 cm/>3N+ or T>2 cm/1-3N+, ER-or PgR-
T<1 cm/>3N+ or T1-2 cm/1-3N+ orT>2 cm/N-, ER-/PgR-
28 42%
5 T>2 cm/>3N+, ER-or PgR-
T<1 cm/>3N+ or T1-2 cm/1-3N+ orT>2 cm/N-, ER-/PgR-
9 61%
6 T>2 cm/>3N+, ER-/PgR- 2 83%
Six-year recurrence rate as a function of bio-pathological score
Novel analytical toolsMicroarray technologyProteomics…….…….
From a “reductionistic” approach (gene by gene) to an “olistic” approach (global genomic analysis)
Molecular signature of cancer
The innovation
Proteomica
Genomica
Ricerca Clinica
Diagnostica
Farmacogenomica
Prevenzione
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
• 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 proteomicsFunctional proteomics
• TWO YEAST– HYBRID SYSTEM
• PROTEIN MICROARRAY
• FRET (Fluorescence Resonance)
• SELDI (Surface-Enhanced Laser Desorption/Ionisation)
• TISSUE MICROARRAY
Proteomics
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
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 datiDe Risi et al Science 278:680 (1997)Heller et al PNAS 94:2150 (1997)
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.
500.000 spot
GeneChip array
Milioni di catene di DNA in ciascuno spot
25 basi in ogni catena
1.28 cm
1.28 cm
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 tumorecDNA normale
Analisi statistica
Ibridazione
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; 278:680-686.Heller R.A. et al PNAS 1997; 94:2150-2155.
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
……
I cDNA MICROARRAYS nel 2004
Agilent's microarray, con 36.000 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, 30.000 geni tecnologia chemiluminescenza
•NimbleGenSystems, 190.540 probes con una media di 5 pobes per gene tecnologia fotolitografica
•Agilent Technologies, 44000 probes per 36.000 geni e trascritti tecnologia injk-jet
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
Campione eterogeneo
Campione A
Campione BMicrodissezione
Microdissezione
Popolazione eterogenea
Popolazione eterogenea
Laser capture
microdissection
(LCM)
Popolazione omogeneaPopolazione
omogenea
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.
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.
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 5.000 polipeptidi da Saccharomyces cervisiae
per monitorare le interazioni proteina-proteina e proteina-piccole molecole (farmaci….)
Yeast ProtoArray™
PROTEIN MICROARRAYS
Conrads TM et al. Cancer diagnosis using proteomic patterns.Expert Rev Mol Diagn 3:411-20 (2003)
IndividuazioneIndividuazionenuovi nuovi
biomarkersbiomarkers
SieroProteine
Bio-chip
SELDI-TOF MS
m/zPattern proteico Riconoscimento
del pattern
Nuovi Biomarkers individuati con il ProteinChip e tecnologia SELDI-TOF-MS
Tumore kDa Nome Autore
Pancreas 16.57 HIP/PAP-1 Rosty et al.
Cancer Res 2002; 62:1868-1875
Vescica 3.4 Alpha-Defensina Vlahou et al. Am J. Pathol 2002; 158:1491-1501
Nasofaringe 11.6
11.8
Serum Amyloid A (SAA) isoform
Yip et al - AACR 2002
Prostata 100 PSMA Wang et al. Int J. Cancer 2002; 92:871-876
Ovaio
9.2
79
54
Frammento di Aptoglobina
Transferrina
Catena pesante Ig
Ye et al. Poster 3687 - AACR 2002
Rai et al. Arch. Pathol. Lab. Med 2002; 126:1518-1526
“ “ “
“ “ “
Gene profiling of breast cancer
Sorlie et al., PNAS 98, 10869-10874, 2001
Hierarchical clustering of 78 primary breast cancers and 4 normal breast tissue
Dendrogramma
“Alberi di unionetra i vari casi chesi assomigliano” (i.e: intensità di colore relativo ad un gene o a gruppi di geni)
5 differenti fenotipi
ER- ER+
Cluster analysis
Sorlie et al., PNAS, 98, 2001
Van ‘t Veer et al.Nature 415, 530, January 2002
~~5000 genes5000 genessignificantly regulated significantly regulated ((in > 3 tumors)in > 3 tumors)
231 genes correlated w disease outcome231 genes correlated w disease outcome
70 genes70 genes= = Poor/Good prognostic signaturePoor/Good prognostic signature
correctly predicted disease outcome in 65/78 correctly predicted disease outcome in 65/78 sporadic tumorssporadic tumors
Unsupervised hierarchical clustering
78 sporadic BC (T<5cm, N-) + 20 BRCA1/2+ BC
34 pts w metastases <5 y 44 pts NED >5 y
25,000 genes of microarray
Supervised hierarchical classification
Rank-ordered based on p-value
Tumori clinici: studio pilota
Tumori clinici: serie di validazione (N=19)
Van’t Veer et al., Nature 415, 2002
Good prognosissignature
Poor prognosissignature
Van de Vijver et al., NEJM, 347, 25, 2002
295 carcinomi mammari sporadici T<5 cm, N-/N+ < 53 anni:
Decorso clinico in base al profilo di espressione genica(70-gene prognosis signature)
Van de Vijver et al., N Engl J Med, 2002
295 sporadic breast cancers:
Clinical course according to gene expression profile
Van de Vijver et al., N Engl J Med, 2002
151 N- patients
144 N+ patients
Van de Vijver et al., N Engl J Med, 2002
Van de Vijver et al., N Engl J Med, 2002
151 N- patients:
Clinical course according to molecular signature (A) or clinico-patological classification (B, C)
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
Clin Cancer Res 10: 2272-83, 2004
Clin Cancer Res 9: 6326-34, 2003
BM, Ab to CKPE, Ab to CK &HER-2 FISH
BM, Ab to CK & nuclear Counterstaining w d-p-indole
Same as in C Ab to uPAI-R
AdnaGen CancerSelectGenzyme 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
Will the new molecular knowledge be applied to “bedside”?
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 BiopsyPrognostic Risk Evaluation
Randomize
Clinico-pathological Microarray
Low Risk Low Risk
Average/High Risk
ChemotherapyPossible further randomization
Endocrine therapyPossible further randomization
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)
Affymetrix Launches ENCODE Array to Uncover Hidden Function of Human GenomeOctober 22, 2004
BioTrove Announces OpenArray™ Transcription Analysis SystemDate: September 20, 2004
Expression Analysis Launches Affymetrix Microarray-Based Genotyping ServicesDate: September 14, 2004
Agilent Partners with TGen to Develop CGH Arrays for Cancer ResearchJune 8
Gene Logic Provides Data from GeneExpress System to FDAMay 13, 2004
Toray Develops Ultra Sensitive, Quick DNA ChipDate : September 20, 2004
SIRS-Lab releases new biochipDate: September 22nd, 2004
Agilent Acquire Silicon GeneticsAugust 29, 2004
Velcura to use custom Affymetrix technologyAugust 03, 2004
Toshiba to Develop DNA Chip with Osaka University July 20, 2004
Affymetrix and Immusol to Collaborate on Cancer Drug DiscoveryJune 22, 2004
Predicting cancer patient survival with gene- expression dataDate: May 06, 2004
Setting The Gene Expression Base-Line For Breast Cancer ResearchDate: May 05, 2004
Illumina Announces 100,000 SNPs on a Single BeadChipDate: April 21, 2004
NCI awards grant for gene expression researchDATE: Thursday, April 15, 2004
Can the novel technologies be used topredict the therapeutic response?
Nature Clin Pract Oncol 1; 44-50, 2004
New Engl J. Med. 351: 2817-2826 (2004)
Early BC
Panel of the 21 genes and the Recurrence Score Algorithm
Oncotype DX from Genomic Health
J. Clin. Oncol. 23: 732-740 (2005)
Advanced BC
Anthracyclines Topoisomerase II, MDR, MRP, ErbB-2
5-FU, Capecitabine Cyclin D1, Thymidilate synthase, Thymidine phosphorylase, NFkB, p53, Bax/Bcl2
Gemcitabine Ribonucleotide reductase, 5-nucleotidase, -tubilin III deoxycytidine-kinase
Vinca Alkaloids MAP4, Topoisomerase I
Taxol -tubulin III, MDR, MAP4, survivin
Platin compounds ERCC1, MDR1, -tubulin III, XPA, XPD, cJunXPG, p53, cyclinD1, GSTpi, MLH1, MSH6
Irinotecan, Topotecan Topoisomerase I, p14ARF, carboxylesterase, MDR
Small TRK inhibitors Akt, MAPK, ecc
GENES & GENE PRODUCTS INVOLVED IN DRUG RESISTANCE/SENSITIVITY(Cancer literature)
Breast cancer
1. Anthracyclines
2. Fluoropyrimidines
Topo II MDR11.
TS Cyclin D12.
p14ARFTopo I3. 3. Taxanes
1.
TS Cyclin D12.
ERCC1 p533.
2. 5-FU, capecitabine
3. Platin compoundscJun
“Smart Chip” (antibody array)A CINBO’s Project
Colorectal cancer
1. Irinotecan
ErbB-2
Bcl-2/bax
Topo I p14ARF
Bcl-2/bax
++
FFIA (Fluorescent Immuno FFIA (Fluorescent Immuno Assay)Assay)
1. Biopsia del 1. Biopsia del pazientepaziente(Biomarkers)(Biomarkers)
2. Rh-Fusion-GFP proteins
Biotin-Antibody coated chipFl
uore
scence
inte
nsi
ty
Amount of Biomarker
“Smart Chip” (antibody array)
A CINBO’s Project
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 responivenessto 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 tumorresulting in more rationale use of the therapy
Good prognostic signature
Poor prognostic
signature
White = ED ptsBlack = NED pts