Tumour Markers: Present and Future

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Tumour Markers Present and Future
Eleftherios P. Diamandis, M.D., Ph.D., FRCP(C)
Dept. of Pathology Laboratory Medicine, Mount
Sinai Hospital Dept. of Laboratory Medicine
Pathobiology, University of Toronto, Toronto,
Canada
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The New Cancer Diagnostics
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We Need

• better (more objective) and more
  biologically-relevant tumor classification
  schemes for prognosis, selection of therapy
• better tumor markers for population screening and
  early diagnosis

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Paradigm Shift (2000 and Beyond)

• Traditional Method
  Study one molecule
  at a time.
• New Method
  Multiparametric analysis
  (thousands of molecules at a time).
• Cancer
  Does every cancer have a unique
  fingerprint? (genomic/proteomic?)

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Changes are Coming

• Changes seen are driven by recent biological /
  technological advances
• Human Genome Project
• Bioinformatics
• Array Analysis (DNA proteins tissues)
• Mass Spectrometry
• Automated DNA Sequencing /PCR
• Laser Capture Microdissection
• SNPs
• Comparative Genomic Hybridization

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Technological Advances
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Microarrays

• What is a microarray?A microarray is a compact
  device that contains a large number of
  well-defined immobilized capture molecules (e.g.
  synthetic oligos, PCR products, proteins,
  antibodies) assembled in an addressable
  format.You can expose an unknown (test)
  substance on it and then examine where the
  molecule was captured.You can then derive
  information on identity and amount of captured
  molecule.

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Science 2004 306 630-631
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DNA microarray
Microscope slide
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RNA extraction and labeling to determine
expression level
cRNA
cRNA
Cy3-dUTP green fluorescent
Cy5-dUTP red fluorescent
sample of interest compared to standard reference
reverse transcriptase, T7 RNA polymerase
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Microarray Milestone June 2003

• Following their papers in Nature and NEJM
• Nature 2002 415 530-536NEJM 2002 347
  1999-2009Vant Veer and colleagues (Netherlands
  Cancer Institute) will use microarray profiling
  as a routine tool for breast cancer management
  (administration of adjuvant chemotherapy after
  surgery).
• prospective trials under way EORTC 2005 onwards

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Applications of Microarrays

• Simultaneous study of gene expression patterns of
  genes
• Single nucleotide polymorphism (SNP) detection
• Sequences by hybridization / genotyping /
  mutation detection
• Study protein expression (multianalyte assay)
• Protein-protein interactions
• Provides Massive parallel information

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Microarrays, such as Affymetrixs GeneChip,
now include all 50,000 known human genes.
Science, 302 211, 10 October, 2003
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Comparative Genomic Hybridization

• A method of comparing differences in DNA copy
  number between tests (e.g. tumor) and reference
  samples
• Can use paraffin-embedded tissues
• Good method for identifying gene amplifications
  or deletions by scanning the whole genome.

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Comparative Genomic Hybridization
Cot1DNA blocks repeats)
Label with Cy-5
Label with Cy-3
Nature Reviews Cancer 20011151-157
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Arrayed CGH

• Same as previous slide but use arrays of BAC
  clones instead of chromosomes

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Laser Capture Microdissection

• An inverted microscope with a low intensity
  laser that allows the precise capture of single
  or defined cell groups from frozen or
  paraffin-embedded histological sections
• Allows working with well-defined clinical
  material.

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Tumor Heterogeneity (Prostate Cancer)
Tumor Cells Red Benign Glands Blue
Rubin MA J Pathol 200119580-86
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Laser Capture Microdissection
LCM uses a laser beam and a special
thermoplastic polymer transfer cup (A).The cap is
set on the surface of the tissue and a laser
pulse is sent through the transparent
cap,expanding the thermoplastic polymer. The
selected cells are now adherent to the transfer
cap and can be lifted off the tissue and placed
directly onto an eppendorf tube for extraction
(B).
Rubin MA, J Pathol 200119580-86
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Tissue Microarray

• Printing on a slide tiny amounts of tissue
• Array many patients in one slide (e.g. 500)
• Process all at once (e.g. immunohistochemistry)
• Works with archival tissue (paraffin
  blocks)

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Gene Expression Analysis of Tumors
cDNA Microarray
Lakhani and Ashworth Nature Reviews Cancer
20011151-157
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Tissue Microarray
From Jacquemier1 et al Cancer Res 200565767-779
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Molecular Profiling of Prostate Cancer
Rubin MA, J Pathol 200119580-86
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Single Nucleotide Polymorphisms (SNPs)

• DNA variation at one base pair level found at a
  frequency of 1 SNP per 1,000 - 2,000 bases
• A map of 9 x 106 SNPs have been described in
  humans by the International SNP map working group
  (HapMap)
• 60,000 SNPs fall within exons the rest are in
  introns

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Why Are SNPs Useful?

• Human genetic diversity depends on SNPs between
  individuals (these are our genetic differences!)
• Specific combinations of alleles (called The
  Haplotype) seem to play a major role in our
  genetic diversity
• How does this genotype affect the phenotype
  

Disease Disposition?
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Haplotype Patterns
Person A A T T G A T C G G A T. . . C C A T
C G G A . . . C T A A
Person B A T T G A T A G G A T. . . C C A G
C G G A . . . C T C A
Person C A T T G A T C G G A T. . . C C A T
C G G A . . . C T A A
Person D A T T G A T A G G A T. . . C C A G
C G G A . . . C T C A
Person E A T T G A T C G G A T. . . C C A T
C G G A . . . C T A A
Persons B and D share a haplotype unlike the
other three, characterized by three different
SNPs.
Science, 2002 296 1391-1393.
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Why Are SNPs Useful?

• Diagnostic Application
• Determine somebodys haplotype (sets of SNPs) and
  assess disease risk.
• Be careful
  These disease-related
  haplotypes are not as yet known!
  

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SNP Analysis by Microarray

• GeneChip HuSNPTM Mapping Assay (Affymetrix)
• More than 100,000 single nucleotide polymorphisms
  (SNPs) covering all 22 autosomes and the X
  chromosome in a single experiment
• Coverage 1 SNP per 20 kb of DNA
• Needs 250 ng of genomic DNA-1 PCR reaction

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Commercial Microarray for Clinical Use
(Pharmacogenomics)
Roche Product CYP 450 Genotyping (drug
metabolizing system) First FDA
approved microarray-based diagnostic test 2004
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Proteomics Protein Microarrays
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High-throughput proteomic analysis
Protein array now commercially available from BD
Biosciences (2002)
Haab et al. Genome Biology 200011-22
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Applications of Protein Microarrays

• Screening for
• Small molecule targets
• Post-translational modifications
• Protein-protein interactions
• Protein-DNA interactions
• Enzyme assays
• Epitope mapping

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Cytokine Specific Microarray ELISA
Detection system
BIOTINYLATED MAB
ANTIGEN
CAPTURE MAB
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Recently Published Examples
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Rationale For Improved Subclassification of
Cancer by Microarray Analysis

• Classically classified tumors are clinically very
  heterogeneous some respond very well to
  chemotherapy some do not.

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Hypothesis

• The phenotypic diversity of cancer might be
  accompanied by a corresponding diversity in gene
  expression patterns that can be captured by using
  cDNA microarray
• Then
• Systematic investigation of gene expression
  patterns in human tumors might provide the basis
  of an improved taxonomy of cancer
• ?Molecular portraits of cancerMolecular
  signatures

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Molecular Portraits of Cancer
Breast Cancer Perou et al. Nature 2000406747-752
Green Underexpression Black Equal
expression Red Overexpression
Left Panel Cell Lines Right Panel Breast Tumors
Figure Represents 1753 Genes
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Differential Diagnosis of Childhood Malignancies
Ewing Sarcoma Yellow Rhabdomyosarcoma
Red Burkitt Lymphoma Blue Neuroblastoma Green
Khan et al. Nature Medicine 20017673-679
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Applications (continued)Vantt Veer L. et al.
Nature 2002415-586

• Examine lymph node negative breast cancer
  patients and identified specific signatures for
• Poor prognosis
• BRCA carriers
• The poor prognosis signature consisted of genes
  regulating cell cycle invasion, metastasis and
  angiogenesis.
• Conclusion
• This gene expression profile will outperform all
  currently-used clinical parameters in predicting
  disease outcome
• This may be a good strategy to select
  node-negative patients who would benefit from
  adjuvant therapy.

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Validation of prognosis signature

• performance on unselected consecutive series at
  10 years (n295)
• Lymph node negative patients (n151)
• Lymph node positive patients (n144)
  lt53 yrs, tumor lt5cm,
  no prior malignancy
• predictive value compared to classical clinical
  parameters
• relevance for treatment tailoring

Vant Veer et al New Engl J Med 20023471999-2009
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Cohort of 295 tumors patients lt 53 yrs, lymph
node negative or positive
295 tumors
70 prognosis genes
Unselected consecutive patient series, mean
follow-up 7 yrs
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Kaplan-Meier survival curves
for all 295 patients
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Treatment tailoring by profiling
premenopausal, lymph node negative
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Therapeutic implications

• Who to treat
• Prognosis profile as diagnostic tool
• improvement of accurate selection for adjuvant
  therapy (less under- and overtreatment)
• Prognosis profile implemented in clinical trials
• reduction in number of patients costs (select
  only patients that are at metastases risk)
• How to treat
• Predictive profile for drug response
• selection of patients who benefit

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Commercial Products

• Oncotype DX by Genomic Health Inc, Redwood
  City, CA
• A prognostic test for breast cancer metastasis
  based on profiling 250 genes 16 genes as a group
  have predictive value 3,400 per test
• 215,000 breast cancer cases per year (potential
  market value gt 500 million!)
• Test has no value for predicting response to
  treatment

Am J Pathol 200416435-42
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Commercial Products

• Mammaprint marketed by Agendia, Amsterdam, The
  Netherlands
• Based on L.Vant Veer publications
• Test costs Euro 1650 based on 70 gene signature
• Prospective trials underway
• Celera and Arcturus developing similar tests
  (prognosis/prediction of therapy)

Science 20043031754-5
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Mass Spectrometry for
Proteomic Pattern Generation

• Serum analysis by SELDI-TOF mass spectrometry
  after extraction of lower molecular weight
  proteins
• Data analyzed by a pattern recognition algorithm

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ProteinChip ArraysSELDI affinity chip surfaces
(Ciphergen)
Anionic
Cationic
Reverse Phase
IMAC
Normal Phase
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The SELDI Process and ProteinChip Arrays

• Sample goes directly onto the
  ProteinChip Array

• Proteins are captured, retained and
  purified directly on the chip (affinity
  capture )

• Surface is read by Surface-Enhanced Laser
  Desorption/Ionization (SELDI)

Laser
ProteinChip Array
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The Future of Biomarkers
Mass Spectrometry-Based Proteomics and
Bioinformatics
Laser
Target
Detector
Flight Tube
Relative Intensity
m/z
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Results Ovarian Cancer
Classification by Proteomic Pattern Classification by Proteomic Pattern Classification by Proteomic Pattern Classification by Proteomic Pattern
Cancer Unaffected New Cluster
Unaffected Women
No evidence of ovarian cysts 2/24 22/24 0/24
Benign ovarian cysts lt2.5cm 1/19 18/19 0/19
Benign ovarian cysts gt2.5cm 0/6 6/6 0/6
Benign gynecological inflammatory disorder 0/7 0/7 7/7
Women with Ovarian Cancer
Stage I 18/18 0/18 0/18
Stage II, III, IV 32/32 0/32 0/32
Petricoin III EF, et al. Lancet 2002359572-577
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Reviews / Opinions / Commentaries

• Diamandis, EP Clin Chem 2003 49 1272-1275

• Diamandis, EP J Natl Cancer Inst 2004 96
  353-356

• Diamandis, EP Mol Cell Proteomics 2004
• 3367-78

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Microarray discrepancies (185 genes)
Science 2004 306 630-631
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Prediction of cancer outcome with microarrays a
multiple random validation strategy
Michiels et al. Lancet, 2005 365 488-492.
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Description of eligible studies
Cancer type Chip type Sample size No. of genes Journal Authors
Non-Hodgkin lymphoma Lymphochip 240 6693 NEJM Rosenwald et al.
Acute lymphocytic leukaemia Affymetrix 233 12236 Cancer cell Yeoh et al.
Breast cancer Agilent 97 4948 Nature vant Veer et al.
Lung adenocarcinoma Affymetrix 86 6532 Nat Med Beer et al.
Lung adenocarcinoma Affymetrix 62 5403 PNAS Bhattacharjee et al.
Lung adenocarcinoma Affymetrix 62 5403 Nat Genet Ramaswamy et al.
Medulloblastoma Affymetrix 60 6778 Nature Pomeroy et al.
Hepatocellular carcinoma Affymetrix 60 4861 Lancet Iizuka et al.
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Microarrays molecular research noise
discovery?

• In 5 of the 7 largest studies on cancer
  prognosis, this technology performs no better
  than flipping a coin. The other two studies
  barely beat horoscopes

J.P. Ioannides Lancet 2005 365 454-455
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Prediction of cancer outcome with microarrays a
multiple random validation strategy

• Findings
• The list of genes identified as predictors of
  prognosis was highly unstable molecular
  signatures strongly depended on the selection of
  patients in the training sets

Michiels et al. Lancet, 2005 365 488-492.
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Prediction of cancer outcome with microarrays a
multiple random validation strategy

• Findings
• Because of inadequate validation, our chosen
  studies published overoptimistic results compared
  with those from our own analyses.

Michiels et al. Lancet, 2005 365 488-492.
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The Future??

• Imaging
• Multiparametric/miniature testing of serum on a
  protein array
• Mass spectrometric serum/urine proteomic pattern
  generation

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The Future??
Asymptomatic individuals

• Whole genome SNP analysis

Predisposition to certain disease
Prevention (drugs lifestyle) Surveillance
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The Future??
Cancer patient

• Surgery / Biopsy

Cancerous tissue

• Array analysis

Tumour fingerprint
Individualized treatment
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