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Cancer Diagnostics The Old and the New Eleftherios P. Diamandis, M.D., Ph.D., FRCP(C). Course LMP1506S,Thursday,March 7,2002. UNIVERSITY OF TORONTO.

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cancer diagnostics the old and the new eleftherios p diamandis m d ph d frcp c
Cancer DiagnosticsThe Old and the NewEleftherios P. Diamandis, M.D., Ph.D., FRCP(C)

Course LMP1506S,Thursday,March 7,2002

UNIVERSITY OF TORONTO

slide2

Laboratory Medicine and PathologyCompositional analysis of cells, fluids, tissues (proteins, metabolites, DNA, RNA)Information invaluable for patient diagnosis,monitoring, selection of therapy, prognosis,classification

timeline of molecular pathology
Timeline of Molecular Pathology

Lakhani and Ashworth Nature Reviews Cancer 2001;1:151-157

slide4

Today’s Laboratory Physician / PathologistMisconception: 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).

slide5

The Current Pathologist and Cancer Tumor ClassificationEssential 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

slide6

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  Tamoxifen HER2/NEU expression  Herceptin BCR/ABL translocation  Gleevac

slide9

The Current Laboratory Physician / Scientist / Clinical Pathologist• Tumor marker analysis in serum - screening - diagnosis - prognosis - therapy response - monitoring for relapsePSA,CEA,AFP,hCG,CA125,CA15.3

slide10

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

slide11

The ProblemsTumor 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

slide12

ConclusionsWe 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

slide13

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?).

slide14

The New Laboratory Physician / Scientist / PathologistChanges 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

slide15

Technological

Advances

slide16

MicroarraysWhat 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

principles of dna microarrays printing oligos by photolithography
Principles of DNA Microarrays(Printing oligos by photolithography)

Fodor et al.Science 1991;251:767-773)

microarray technology manufacture or purchase microarray hybridize detect data analysis aacc 2001
Microarray TechnologyManufacture or Purchase MicroarrayHybridizeDetectData AnalysisAACC 2001
slide19

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 interactionsProvides: Massive parallel information AACC 2001

slide20

Microarray Advantages• Small volume deposition (nL)• Minimal wasted reagents• Access many genes / proteins simultaneously• Can be automated• QuantitativeAACC 2001

if microarrays are so good why didn t we use them before
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 as

by-products of the Human Genome Initiative and bioinformatics

slide22

Limitations of Microarrays • New technology• Technical problems (background;reproducibility)• Need to better define human genes (many ESTs)• Manual• ExpensiveAACC 2001

slide23

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 informationMolecular Signatures/Portraits of TumorsAACC 2001

tissue expression of klk6 by microarray
Tissue Expression of KLK6 byMicroarray

Highest Expression

brain,spinal cord,then salivary gland,spleen,kidney

MedianX10

Cell Line or Tissue

slide26

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 tissueLimitations: - RNA data; not protein- great variability in resultsAACC 2001

whole genome biology with microarrays
Whole Genome Biology With Microarrays

Cell cycle in yeast

Study of all yeast genes

simultaneously!

Red;High expression

Blue:Low expression

Lockhart and Winzeler Nature 2000;405:827-836

slide30

Microarray Imaging of Tissue Sections Clinical CareDiagnosis PrognosisPrediction of therapeutic responseMonitoring

Research

Understanding Disease Pathogenesis

slide31

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.

comparative genomic hybridization
Comparative Genomic Hybridization

Cot1DNA blocks repeats)

Label with Cy-5

Label with Cy-3

Nature Reviews Cancer 2001;1:151-157

slide33

Laser Capture MicrodissectionAn inverted microscope with a low intensity laser that allows the precise capture of single or defined cell groups from frozen or paraffin-embedded histological sectionsAllows working with well-defined clinical material.

tumor heterogeneity prostate cancer
Tumor Heterogeneity(Prostate Cancer)

Tumor Cells, Red

Benign Glands,Blue

Rubin MA J Pathol 2001;195;80-86

laser capture microdissection
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

slide36

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

gene expression analysis of tumors
Gene Expression Analysis of Tumors

cDNA Microarray

Lakhani and Ashworth Nature Reviews Cancer 2001;1:151-157

tissue microarray
Tissue Microarray

Alizadeh et al J Pathol 2001;195:41-52

slide39
H&E

HK6

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)

slide40
H&E

HK6

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)

slide41
H&E

HK6

Histochemical staining of a microarray tissue core of ovarian serous adenocarcinoma. -tjc-Note: Cytoplasmic positivity for HK6 in carcinoma, endothelium and stromal cells.

molecular profiling of prostate cancer
Molecular Profiling of Prostate Cancer

Rubin MA,

J Pathol 2001;195:80-86

slide43

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 intronsAACC 2001

slide44

Why Are SNPs Useful?• Human genetic diversity depends on SNPs between individuals (these are our genetic differences!)• Specific combinations of alleles (called “TheHaplotype”) seem to play a major role in our genetic diversity• How does thisgenotype affect thephenotypeDisease predisposition?Continued:…….. AACC 2001

slide45

Why are SNPs useful………………..continued:Diagnostic ApplicationDetermine somebody’s haplotype (sets of SNPs) and assess disease risk.Be careful: These disease-related haplotypes are not as yet known!AACC 2001

genotyping snp microarray

TTAGCTAGTCTGGACATTAGCCATGCGGAT

TTAGCTAGTCTGGACATTAGCCATGCGGAT

GACCTGTAATCG

GACCTATAATCG

Genotyping: SNP Microarray
  • Immobilized allele specific oligo probes
  • Hybridize with labeled PCR product
  • Assay multiple SNPs on a single array
slide47

High- Throughput Proteomic Analysis

By Mass Spectrometry

Haab et al Genome Biology 2000;1:1-22

applications of protein microarrays
Applications of Protein Microarrays
  • Screening for-
  • Small molecule targets
  • Post-translational modifications
  • Protein-protein interactions
  • Protein-DNA interactions
  • Enzyme assays
  • Epitope mapping
cytokine specific microarray elisa

MIX

Detection system

IL-1 

IL-6

IL-10

VEGF

BIOTINYLATED MAB

ANTIGEN

CAPTURE MAB

marker protein

cytokine

Cytokine Specific Microarray ELISA
slide51

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.

slide52

HypothesisThe 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

molecular portraits of cancer
Molecular Portraits of Cancer

Breast Cancer

Perou et al Nature 2000;406:747-752

Green:Gene underexpression

Black:Equal Expression

Red:Overexpression

Left Panel:Cell Lines

Right Panel:Breast Tumors

Figure Represents 1753 Genes

differential diagnosis of childhood malignancies
Differential Diagnosis of Childhood Malignancies

Ewing Sarcoma:Yellow

Rhabdomyosarcoma:Red

Burkitt Lymphoma:Blue

Neuroblastoma:green

Khan et al.Nature Medicine 2001;7:673-679

differential diagnosis of childhood malignancies small round blue cell tumors srbct
Differential Diagnosis of Childhood Malignancies(small round blue-cell tumors,SRBCT)

EWS=Ewing Sarcoma

NB=Neuroblastoma

RMS=Rhabdomyosarcoma

BL=Burkitt Lymphoma

Note the relatively small number of

genes necessary for complete

discrimination

Khan et al.Nature Medicine 2001;7:673-679

aggressive vs non aggressive breast cancer cell lines
Aggressive vs Non-Aggressive Breast Cancer Cell Lines

Can accurately predict

aggressiveness with a

set of only 24 genes

Zajchowski et al

Cancer Res 2001;61:5168-78

slide57

Selected Applications of MicroarraysAlizadeh 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.

novel classification of lymphoma
Novel Classification of Lymphoma

Alizadeh et al

Nature 2000;403:503-511

gi tumors with kit mutations
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-positive

Gastrointestinal Stromal Tumors

Blue Dots:Spindle Cell Carcinomas

Allander et al.Cancer Res 2001;61:8624-8628

gene expression profile of gi stromal tumors with kit mutations
Gene Expression Profile of GI Stromal Tumors with KIT Mutations

KIT(+)

KIT (-)

KIT gene

Allander et al.Cancer Res 2001;61:8624-8628

slide61

Applications (continued)Vant’t Veer L. et al. Nature 2002:415-586Examine lymph node negative breast cancer patients and identified specific signatures for:* Poor prognosis* BRCA carriersThe “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.

prognostic signature of breast cancer
Prognostic Signature of Breast Cancer

Patients above line

No Distant Metastasis

Patients Below Line

Distant metastasis

Van’t Veer et al.Nature 2002;415:530-536

er vs er signatures in breast cancer sporadic vs brca1 signatures in breast cancer
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-positive

Patients below line:BRCA1-negative

Van’t Veer et al.Nature 2002;415:530-536

molecular signatures for selecting treatment options
Molecular Signatures for Selecting Treatment Options

Van’t Veer et al.Nature 2002;415:530-536

slide66

Human Genome

Establish tissue expression of all human genes by microarray technology

Identify “tissue-specific” genes

Compare “normal” vs “cancer”

Select highly overexpressed genes

Evaluate in detail

Many potential pitfalls

slide67

An Example of Genome Mining Approach to Discovery Circulating Markers for Ovarian Carcinoma.Welsh JB, et al., PNAS 2001; 98: 1176 - 1181AACC 2001

slide68

Method49 arrays on a 7x7 Matrix ARRAYS ON ARRAYS Hybridize 49 different samples in one shot (some normal; some malignant) 6,000 genes per array

slide69

A.Tumor Classification

B.Expression of genes

RED:Overexpression

GREEN:Underexpression

genes overexpressed in ovarian cancer
Genes Overexpressed in Ovarian Cancer

From:Welsh et al PNAS 2001;98:1176-1181

genes overexpressed in ovarian cancer rt pcr verification
Genes Overexpressed in Ovarian Cancer(RT-PCR Verification)

CD 24

HE4

LU

rRNA

From:Welsh et al PNAS 2001:98:1176-1181

slide72

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

slide74

Results _________________________________________________ Classification by Proteomic PatternCancerUnaffectedNew 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

slide75
The Future??Cancer PatientSurgery/BiopsyCancerous TissueArray AnalysisTumor FingerprintIndividualized Treatment
slide76

The Future??General Population- Imaging- Multiparametric/ miniature testing of serum on a protein array- Mass spectrometric serum/urine proteomic pattern generationScreen-positive patientsPrevention; Effective Therapy

slide77

The Future?Asymptomatic individuals Whole genome SNP analysisPredisposition to certain diseasePrevention (drugs; lifestyle)Surveillance

slide78

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 ABOVEYoung and Wilson, Clin Cancer Res 2002;8:11-16

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