Cancer Diagnostics The Old and the New Eleftherios P. Diamandis, M.D., Ph.D., FRCP(C) UNIVERSITY OF...
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Transcript of Cancer Diagnostics The Old and the New Eleftherios P. Diamandis, M.D., Ph.D., FRCP(C) UNIVERSITY OF...
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Cancer DiagnosticsThe Old and the New
Eleftherios P. Diamandis, M.D., Ph.D., FRCP(C)
UNIVERSITY OF TORONTO
Course LMP1506S,Thursday,March 7,2002
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Laboratory Medicine and Pathology
Compositional analysis of cells, fluids, tissues (proteins, metabolites, DNA, RNA)
Information invaluable for patient diagnosis,monitoring, selection of therapy, prognosis,classification
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Timeline of Molecular Pathology
Lakhani and Ashworth Nature Reviews Cancer 2001;1:151-157
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Today’s Laboratory Physician / Pathologist
Misconception: Pathologists are those who perform autopsies and work in isolation by looking down a microscope all day.
Reality: Participate in teams with surgeons, oncologists, radiologists; information provided forms basis for diagnosis and management and for performing new clinical trials(by identifying patient groups).
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•
The Current Pathologist and Cancer
Tumor Classification
Essential for cancer prognosis and selection of treatment:
-Carcinoma vs sarcoma vs lymphoma-Primary vs metastatic cancer-Breast carcinoma (ductal vs lobular vs tubular vs mucinous ) -Tumor grade (degree of differentiation)-Tumor stage (size, lymph node involvement plus imaging information)-Surgical margins
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The Current Pathologist
Cancer Prognosis
• Pathologically classified classes of tumors (by stage, grade, histological type) behave differently.
• Different responses to therapy: ER, PR (+) breast cancers TamoxifenHER2/NEU expression HerceptinBCR/ABL translocation Gleevac
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Classical Grading System for Breast Cancer
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Classical Staging System for Cancer
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The Current Laboratory Physician / Scientist / Clinical
Pathologist
• Tumor marker analysis in serum- screening- diagnosis- prognosis- therapy response- monitoring for relapse
PSA,CEA,AFP,hCG,CA125,CA15.3
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The ProblemsMorphology:• Subjective analysis - variation between
observers• The morphology of the tumor does not
always reveal the underlying biology; patients with same tumor type can experience different course of the disease• Immunohistochemistry targets single
molecules; biology depends on many
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The Problems
Tumor Markers:• No true tumor marker exists (with
notable exceptions)• Generally single tumor markers not
good for screening/diagnosis (poor sensitivity and specificity)
• Very limited role for predicting therapeutic response/prognosis
• Useful as aids for monitoring response to therapy
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Conclusions
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 for cancer prevention
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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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The New Laboratory Physician / Scientist / Pathologist
Changes seen are driven by recent biological / technological advances:
- Human Genome Project- Bioinformatics- Array Analysis- Mass Spectrometry
_______________________________________ -Automated DNA Sequencing /PCR:
- DNA Arrays- Protein Arrays- Tissue Arrays- 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.
AACC 2001
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Principles of DNA Microarrays(Printing oligos by photolithography)
Fodor et al.Science 1991;251:767-773)
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Microarray Technology
Manufacture or Purchase Microarray
Hybridize
Detect
Data Analysis AACC 2001
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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
AACC 2001
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Microarray Advantages
• Small volume deposition (nL)
• Minimal wasted reagents
• Access many genes / proteins
simultaneously
• Can be automated
• Quantitative
AACC 2001
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If Microarrays Are So Good Why Didn’t We Use Them Before??
• Not all genes were available
• No SNPs known
• No suitable bioinformatics
• New proteins now becoming available
Microarrays and associated technologies should be regarded asby-products of the Human Genome Initiative and bioinformatics
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Limitations of Microarrays
• New technology
• Technical problems (background;reproducibility)
• Need to better define human genes (many
ESTs)
• Manual
• Expensive
AACC 2001
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International Genomics Consortium (IGC)
• New initiative
• Aims to generate expression data by
microarrays
• Claims to analyze for all genes 10,000
tumor specimens within 1 year!
• All patients will have detailed follow-up
information
Molecular Signatures/Portraits of
Tumors
AACC 2001
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Differential Gene Expression(Budding vs Non-Budding Yeast)
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Tissue Expression of KLK6 by Microarray
Highest Expressionbrain,spinal cord,then salivary gland,spleen,kidney
Cell Line or Tissue
MedianX10
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Tissue Expression Profiles
• Many proprietary databases - created by microarray analysis• Can search as follows:
* which genes are expressed in which tissues (tissue specific expression)
* unique genes expressed only in one tissue * quantitative relationships between levels of expression * expression is normal vs diseased tissue
Limitations:- RNA data; not protein- great variability in results
AACC 2001
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Lung Tumor: Up-Regulated
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Lung Tumor: Down-Regulated
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Whole Genome Biology With Microarrays
Cell cycle in yeastStudy of all yeast genessimultaneously!Red;High expressionBlue:Low expression
Lockhart and Winzeler Nature 2000;405:827-836
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Microarray Imaging of Tissue Sections
Clinical Care Diagnosis PrognosisPrediction of therapeutic responseMonitoring
ResearchUnderstanding Disease Pathogenesis
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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
Label with Cy-3 Label with Cy-5
Cot1DNA blocks repeats)
Nature Reviews Cancer 2001;1:151-157
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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,BlueRubin MA J Pathol 2001;195;80-86
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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 surfaceof 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 directlyonto an eppendorf tube for extraction(B).
Rubin MA,J Pathol 2001;195:80-86
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Tissue Microarrays
• Printing on a slide tiny amounts of tissue
• Array many patients in one slide (e.g. 500)
• Process all at once (e.g. immuno-
histochemistry)
• Works with archival tissue (paraffin
blocks)
AACC 2001
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Gene Expression Analysis of TumorscDNA Microarray
Lakhani and Ashworth Nature Reviews Cancer 2001;1:151-157
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Tissue Microarray
Alizadeh et al J Pathol 2001;195:41-52
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Histochemical staining of microarray tissue cores of ovarian serous adenocarcinoma. -tjc -
Identical microscopic fields showing variable staining intensity of various tissue cores for HK6 (right)
• H&E • HK6
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Histochemical staining of a microarray tissue core of ovarian clear cell adenocarcinoma. -tjc-
Identical microscopic fields showing strong cytoplasmic positivity for HK6 within carcinoma (and endothelium, lower right)
• H&E • HK6
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Histochemical staining of a microarray tissue core of ovarian serous adenocarcinoma. -tjc-
Note: Cytoplasmic positivity for HK6 in carcinoma, endothelium and stromal cells.
• H&E • HK6
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Molecular Profiling of Prostate Cancer
Rubin MA,J Pathol 2001;195:80-86
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Single Nucleotide Polymorphisms (SNP)
• DNA variation at one base pair level; found at a frequency of 1 SNP per 1,000 - 2,000 bases • Currently, a map of 1.42 x 106 SNPs have been described in humans (Nature 2001; 409:928-933)
by the International SNP map working group) • Identification: Mainly a by-product of human genome sequencing at a depth of x10 and overlapping clones
• 60,000 SNPs fall within exons; the rest are in introns
AACC 2001
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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
predisposition?
Continued:……..
AACC 2001
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Why are SNPs useful………………..continued:
Diagnostic Application
Determine somebody’s haplotype (sets of SNPs) and assess disease risk.
Be careful: These disease-related haplotypes are not as yet known!
AACC 2001
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Genotyping: SNP Microarray
Immobilized allele specific oligo probes Hybridize with labeled PCR product Assay multiple SNPs on a single array
TTAGCTAGTCTGGACATTAGCCATGCGGAT
GACCTGTAATCG
TTAGCTAGTCTGGACATTAGCCATGCGGAT
GACCTATAATCG
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High- Throughput Proteomic Analysis By Mass Spectrometry
Haab et al Genome Biology 2000;1:1-22
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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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marker protein
cytokine
VEGFIL-10IL-6IL-1 MIX
BIOTINYLATED MAB
CAPTURE MAB
ANTIGEN
Detection system
Cytokine Specific Microarray ELISA
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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 microarraysThenSystematic 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 CancerPerou et al Nature 2000;406:747-752
Green:Gene underexpressionBlack:Equal ExpressionRed:Overexpression
Left Panel:Cell LinesRight 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 2001;7:673-679
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Differential Diagnosis of Childhood Malignancies(small round blue-cell tumors,SRBCT)
EWS=Ewing SarcomaNB=NeuroblastomaRMS=RhabdomyosarcomaBL=Burkitt Lymphoma
Note the relatively small number ofgenes necessary for completediscrimination
Khan et al.Nature Medicine 2001;7:673-679
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Aggressive vs Non-Aggressive Breast Cancer Cell Lines
Can accurately predictaggressiveness with a set of only 24 genes
Zajchowski et alCancer Res 2001;61:5168-78
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Selected Applications of Microarrays
Alizadeh et al. Nature 2000;403:503-511
• Identified two very distinct forms of large B-cell Lymphoma
• The two forms had different clinical outcomes (overall survival).
ConclusionMolecular classification of tumors on the basis of gene expression can identify previously undetected and clinically significant subtypes of cancer.
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Novel Classification of Lymphoma
Alizadeh et alNature 2000;403:503-511
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GI Tumors with KIT Mutations
A:IHC with KIT antibody(negative)B:IHC with KIT antibody(positive)
C:Multidimensional scaling plot
Orange Dots:KIT mutation-positiveGastrointestinal Stromal Tumors
Blue Dots:Spindle Cell Carcinomas
Allander et al.Cancer Res 2001;61:8624-8628
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Gene Expression Profile of GI Stromal Tumors with KIT Mutations
KIT (-) KIT(+)
KIT gene
Allander et al.Cancer Res 2001;61:8624-8628
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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.
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Prognostic Signature of Breast Cancer
Patients above lineNo Distant Metastasis
Patients Below LineDistant metastasis
Van’t Veer et al.Nature 2002;415:530-536
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ER(+)vs ER(-) Signatures in Breast CancerSporadic vs BRCA1 Signatures in Breast Cancer
Patients above line:ER(+)Patients below line:ER(-)
Patients above line:BRCA1-positivePatients below line:BRCA1-negative
Van’t Veer et al.Nature 2002;415:530-536
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Molecular Signatures for Selecting Treatment Options
Van’t Veer et al.Nature 2002;415:530-536
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Strategiesto Discover New Cancer
Markers
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Many potential pitfallsMany potential pitfalls
Human GenomeHuman Genome
Establish tissue expression of all human genes by Establish tissue expression of all human genes by microarray technologymicroarray technology
Identify “tissue-specific” genesIdentify “tissue-specific” genes
Compare “normal” vs “cancer”Compare “normal” vs “cancer”
Select highly overexpressed genesSelect highly overexpressed genes
Evaluate in detailEvaluate in detail
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An Example of Genome Mining Approach to Discovery Circulating Markers for Ovarian Carcinoma.
Welsh JB, et al., PNAS 2001; 98: 1176 - 1181AACC 2001
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Method
49 arrays on a 7x7 Matrix
ARRAYS ON ARRAYS
Hybridize 49 different samples in one shot (some
normal; some malignant)
6,000 genes per array
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A.Tumor ClassificationB.Expression of genes
RED:Overexpression
GREEN:Underexpression
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Genes Overexpressed in Ovarian Cancer
From:Welsh et al PNAS 2001;98:1176-1181
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Genes Overexpressed in Ovarian Cancer(RT-PCR Verification)
CD 24
HE4
LU
rRNA
From:Welsh et al PNAS 2001:98:1176-1181
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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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Serum Fingerprint by Mass Spectrometry
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Results _________________________________________________
Classification by Proteomic Pattern Cancer Unaffected New Cluster
________________________________________________Unaffected WomenNo evidence of ovarian cysts 2/24 22/24 0/24Benign ovarian cysts <2.5cm 1/19 18/19 0/19Benign ovarian cysts >2.5cm 0/6 6/6 0/6Benign gynecological 0/7 0/7 7/7 inflammatory disorder__________________________________________________________Women with Ovarian CancerStage I 18/18 0/18 0/18Stage II, III, IV 32/32 0/32 0/32__________________________________________________________
Petricoin III EF, et al. Lancet 2002;359:572-577
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The Future??
Cancer Patient
Surgery/Biopsy
Cancerous Tissue
Array Analysis
Tumor Fingerprint
Individualized Treatment
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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
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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?
• Miniature ingestible or intravenous diagnostic devices will provide “images” or “information” related to body function.
• Wristwatch devices for biomonitoring• Telemedicine-Videoconferencing• Electronic medical record• New, highly effective therapies• “Electronic” behavioural modification• Gene therapy.
NONE OF THE ABOVE
Young and Wilson, Clin Cancer Res 2002;8:11-16