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
The New Cancer Diagnostics

               mRNA Protein
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
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?)
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
Technological Advances
 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.
Science 2004; 306: 630-631
Microscope slide                 microarray

                        16           17        18

                       Actin       CyclinD    DHFR
                   7   DNA          DNA       DNA

                         RB         E2F1      tubulin
                   8    DNA         DNA        DNA

                       control      Myc        Src1
                   9    DNA         DNA        DNA
                          RNA extraction and labeling
                         to determine expression level
   sample 1
                         RNA                       RNA           sample 2
                               cDNA        cDNA                 (reference)
                               cRNA        cRNA

           Cy5-dUTP                                  green fluorescent
      red fluorescent

                                                         sample of interest
reverse transcriptase,
                                                         compared to
T7 RNA polymerase
                                                         standard reference
Tumor tissue        Reference tissue
cRNA (red)          cRNA (green)
















                                          Human genes
                                       on a microarray slide




Microarray Milestone: June 2003
  Following their papers in Nature and NEJM

    Nature 2002; 415: 530-536

    NEJM 2002; 347: 1999-2009

   Van’t 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
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
Microarrays, such as
Affymetrix’s GeneChip, now
include all 50,000 known
human genes.

Science, 302: 211, 10 October, 2003
Comparative Genomic Hybridization

 A method of comparing differences in DNA copy
  number between tests (e.g. tumor) and reference

 Can use paraffin-embedded tissues

 Good method for identifying gene amplifications
  or deletions by scanning the whole genome.
Comparative Genomic Hybridization

                                   Cot1DNA blocks repeats)

                 Label with Cy-3                             Label with Cy-5

Nature Reviews Cancer 2001;1:151-157
Arrayed CGH
 Same as previous slide but use arrays of BAC
  clones instead of chromosomes
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.
Tumor Heterogeneity (Prostate Cancer)

 Tumor Cells: Red
 Benign Glands: Blue   Rubin MA J Pathol 2001;195;80-86
 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 2001;195:80-86
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)
Gene Expression Analysis of Tumors

cDNA Microarray

     Lakhani and Ashworth Nature Reviews Cancer 2001;1:151-157
                      From Jacquemier1 et al
Tissue Microarray   Cancer Res 2005;65:767-779
Molecular Profiling of
 Prostate Cancer

           Rubin MA,
    J Pathol 2001;195:80-86
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
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?
 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.
Why Are SNPs Useful?
Diagnostic Application

   Determine somebody’s haplotype (sets of SNPs) and
    assess disease risk.

   Be careful:
    These disease-related haplotypes are not as yet
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
Commercial Microarray for Clinical Use

                           Roche Product

                           CYP 450 Genotyping
                           (drug metabolizing

                           First FDA approved
                           diagnostic test; 2004
Proteomics & Protein Microarrays
High-throughput proteomic analysis

      Label all Proteins in Mixture

   Protein array now commercially
available from BD Biosciences (2002)   Haab et al. Genome Biology 2000;1:1-22
Applications of Protein Microarrays
Screening for:
 Small molecule
   Post-translational
   Protein-protein
   Protein-DNA
   Enzyme assays
   Epitope mapping
Cytokine Specific Microarray ELISA

      IL-1               IL-6             IL-10      VEGF   MIX

                                     marker protein

                  Detection system

               BIOTINYLATED MAB
               CAPTURE MAB
Recently Published Examples
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.
   The phenotypic diversity of cancer might be
    accompanied by a corresponding diversity in
   gene expression patterns that can be captured
              by using cDNA microarray
   Systematic investigation of gene expression
  patterns in human tumors might provide the basis
         of an improved taxonomy of cancer
             Molecular portraits of cancer
                 Molecular signatures
Molecular Portraits of Cancer
                     Breast Cancer
                     Perou et al. Nature 2000;406:747-752

                      Green: Underexpression
                      Black: Equal expression
                      Red: Overexpression

                     Left Panel: Cell Lines
                     Right Panel: Breast Tumors

                    Figure Represents 1753 Genes
 Differential Diagnosis of
 Childhood Malignancies

         Ewing Sarcoma: Yellow

         Rhabdomyosarcoma: Red

         Burkitt Lymphoma: Blue

         Neuroblastoma: Green

Khan et al. Nature Medicine 2001;7:673-679
Applications (continued)
Vant’t Veer L. et al. Nature 2002:415-586

 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.
Validation of prognosis signature
 performance on unselected consecutive series at 10
  years (n=295)
 Lymph node negative patients (n=151)
 Lymph node positive patients (n=144)
  <53 yrs, tumor <5cm, no prior malignancy

 predictive value compared to classical clinical

 relevance for treatment tailoring

                     Van’t Veer et al New Engl J Med 2002;347:1999-2009
               Cohort of 295 tumors
      patients < 53 yrs, lymph node negative or positive

                                            14 metastases / 115


                                            74 metastases / 180

         70 prognosis genes
Unselected consecutive patient series, mean follow-up ~ 7 yrs
Kaplan-Meier survival curves
     for all 295 patients
          Treatment tailoring by profiling

           premenopausal, lymph node negative

                  Gene Expression Profiling
                      60%           40%
       Poor signature                    Good signature
~ 56 % metastases at 10 yrs        ~ 13 % metastases at 10 yrs
   ~ 50 % death at 10 yrs             ~ 4 % death at 10 yrs

   Adjuvant chemo- and                No adjuvant therapy
    hormonal therapy                or hormonal therapy only
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
 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 >
   $500 million!)
 Test has no value for predicting response to treatment

Am J Pathol 2004;164:35-42
 Commercial Products
   Mammaprint marketed by Agendia, Amsterdam,
     The Netherlands

   Based on L.Van’t 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 2004;303:1754-5
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”
ProteinChip® Arrays:
SELDI affinity chip surfaces (Ciphergen)

   Reverse Phase Anionic              Cationic          IMAC             Normal Phase
                                       NR        NR
                 SO4         SO 4           NR         Me(II)   Me(II)
                       SO4                                  Me(II)

    Receptor Ligand                 Enzyme        Antibody Protein A/G         DNA
 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)

                                                                 Molecular Weight

   Sample                                                                               100 m2
                                                                                         1 mm2

                                   ProteinChip® Array
                              The Future of Biomarkers
         Mass Spectrometry-Based Proteomics and Bioinformatics
                                          Flight Tube

         Relative Intensity

 Results: Ovarian Cancer
                    Classification by Proteomic Pattern
                                      Cancer       Unaffected   New Cluster
Unaffected Women
No evidence of ovarian cysts            2/24         22/24         0/24
Benign ovarian cysts <2.5cm             1/19         18/19         0/19
Benign ovarian cysts >2.5cm              0/6          6/6           0/6
Benign gynecological inflammatory
                                         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 2002;359:572-577
Reviews / Opinions / Commentaries
 Diamandis, EP Clin Chem 2003; 49:

 Diamandis, EP J Natl Cancer Inst 2004; 96: 353-

 Diamandis, EP Mol Cell Proteomics 2004;
Microarray discrepancies (185 genes)

Science 2004; 306: 630-631
Prediction of cancer outcome with microarrays: a
multiple random validation strategy

      Michiels et al. Lancet, 2005; 365: 488-492.
   Description of eligible studies
   Cancer type         Chip type            No. of genes    Journal           Authors
                    Lymphochip      240        6693          NEJM         Rosenwald et al.
Acute lymphocytic
                    Affymetrix      233        12236       Cancer cell       Yeoh et al.

Breast cancer       Agilent          97        4948          Nature       van’t Veer et al.

                    Affymetrix       86        6532         Nat Med          Beer et al.

                                                             PNAS        Bhattacharjee et al.
                    Affymetrix       62        5403
                                                           Nat Genet     Ramaswamy et al.

Medulloblastoma     Affymetrix       60        6778          Nature        Pomeroy et al.

                    Affymetrix       60        4861          Lancet          Iizuka et al.
        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
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.
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.
The Future??
   General Population

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

  Screen-positive patients

  Prevention; Effective Therapy
The Future??

Asymptomatic individuals

              Whole genome SNP analysis

Predisposition to certain disease

Prevention (drugs; lifestyle)
The Future??
           Cancer patient
                     Surgery / Biopsy

          Cancerous tissue

                     Array analysis

         Tumour fingerprint

       Individualized treatment