Cancer DiagnosticsThe Old and the New

Eleftherios P. Diamandis, M.D., Ph.D., FRCP(C)

UNIVERSITY OF TORONTO

Course LMP1506S,Thursday,March 7,2002

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

Timeline of Molecular Pathology

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

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).

•

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

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

Classical Grading System for Breast Cancer

Classical Staging System for Cancer

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

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

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

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

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?).

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

Technological Advances

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

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

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

Microarray Advantages

• Small volume deposition (nL)

• Minimal wasted reagents

• Access many genes / proteins

simultaneously

• Can be automated

• Quantitative

AACC 2001

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

Limitations of Microarrays

• New technology

• Technical problems (background;reproducibility)

• Need to better define human genes (many

ESTs)

• Manual

• Expensive

AACC 2001

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

Differential Gene Expression(Budding vs Non-Budding Yeast)

Tissue Expression of KLK6 by Microarray

Highest Expressionbrain,spinal cord,then salivary gland,spleen,kidney

Cell Line or Tissue

MedianX10

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

Lung Tumor: Up-Regulated

Lung Tumor: Down-Regulated

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

Microarray Imaging of Tissue Sections

Clinical Care Diagnosis PrognosisPrediction of therapeutic responseMonitoring

ResearchUnderstanding Disease Pathogenesis

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

Label with Cy-3 Label with Cy-5

Cot1DNA blocks repeats)

Nature Reviews Cancer 2001;1:151-157

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,BlueRubin 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 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

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 TumorscDNA Microarray

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

Tissue Microarray

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

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

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

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

Molecular Profiling of Prostate Cancer

Rubin MA,J Pathol 2001;195:80-86

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

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

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

Genotyping: SNP Microarray

Immobilized allele specific oligo probes Hybridize with labeled PCR product Assay multiple SNPs on a single array

TTAGCTAGTCTGGACATTAGCCATGCGGAT

GACCTGTAATCG

TTAGCTAGTCTGGACATTAGCCATGCGGAT

GACCTATAATCG

High- Throughput Proteomic Analysis By Mass Spectrometry

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

Applications of Protein Microarrays

Screening for- Small molecule targets

Post-translational modifications

Protein-protein interactions

Protein-DNA interactions

Enzyme assays

Epitope mapping

marker protein

cytokine

VEGFIL-10IL-6IL-1 MIX

BIOTINYLATED MAB

CAPTURE MAB

ANTIGEN

Detection system

Cytokine Specific Microarray ELISA

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.

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

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

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)

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

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

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.

Novel Classification of Lymphoma

Alizadeh et alNature 2000;403:503-511

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

Gene Expression Profile of GI Stromal Tumors with KIT Mutations

KIT (-) KIT(+)

KIT gene

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

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.

Prognostic Signature of Breast Cancer

Patients above lineNo Distant Metastasis

Patients Below LineDistant metastasis

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

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

Molecular Signatures for Selecting Treatment Options

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

Strategiesto Discover New Cancer

Markers

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

An Example of Genome Mining Approach to Discovery Circulating Markers for Ovarian Carcinoma.

Welsh JB, et al., PNAS 2001; 98: 1176 - 1181AACC 2001

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

A.Tumor ClassificationB.Expression of genes

RED:Overexpression

GREEN:Underexpression

Genes Overexpressed in Ovarian Cancer

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

Genes Overexpressed in Ovarian Cancer(RT-PCR Verification)

CD 24

HE4

LU

rRNA

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

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

Serum Fingerprint by Mass Spectrometry

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

The Future??

Cancer Patient

Surgery/Biopsy

Cancerous Tissue

Array Analysis

Tumor Fingerprint

Individualized Treatment

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)Surveillance

•

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