Whole Genome Approaches to Cancer

1. What other tumor is a given rare tumor most like?

2. Is tumor X likely to respond to drug Y?

Oligonucleotide Arrays

• 300,000 25-mer probes• in situ photolithographic synthesis• single color hybridizations• chips available for 40,000 human genes and 25,000 murine genes

1.28cm

1.28 cm

1 2 3

n

106 oligos

24 micronsGenes

1 2 ... ..20

matchmismatch

3’ UTRcodingGene n

Estimating Message Abundance

perfect match (PM)

mismatch (MM)

Message abundance = trimmed mean (PM1-MM1 . . .PM20-MM20)

1 2 3. . . . . .20

Confidence measure:

‘A’ low confidence‘P’ high confidence

Oligonucleotide Arrays: Sample Preparation

AAA TTT-T7TTT-T7AAA-T7

TTTB B

BBSA

computer

10 g total RNA cDNA ds cDNA

cRNA

RT

IVTbio-NTPs

hybSAPEscan

ion argon laser

chip

+2X

-2X

100 1000 10000 100000100

1000

10000

100000

‘P’ calls (2301) ‘A’ calls (4830)

Reproducibility ExperimentsSame Target on 2 Arrays

+2X

-2X

Cancer Classification

Identify previouslyunrecognized classes

Assign new tumors to known classes

Class PredictionClass Discovery

Type 1

Type 2

Type 3Type 1 Type 2 Type 3

Proof of Concept: Acute Leukemia Diagnosis

ALL AML

Molecularly distinct tumors are morphologically similar

ALL

genes

low high

normalizedexpression

AML

Gene Expression Correlates of LeukemiaGenes sorted according to correlation with ALL/AML distinction

Permutation Test

1000 genes more highlycorrelated than expected

Terminal transferase

Myelo-peroxidase

38 pre-treatment marrows (ALL or AML)No leukemia cell purification

3-10 g total RNA per sample

Proof of Principle: ALL vs. AML Distinction

Sort genes by degree of correlation with ALL vs. AML

Randomly withhold one sample

6800 gene arrays

Biotin label RNA

Choose most highly correlated genes

Predict class of withheld sample Error Rate

Removeeach sample in turn

Results

Initial set (n=38) 36 predictions 2 uncertain

100% correct

Independent set (n=34) 29 predictions 5 uncertain

100% correct

AML T-ALLB-ALL

Class DiscoveryWhat if ALL/AML distinction was not previously known?

Could we discover it by expression alone?

38 samples Cluster by SOM

Golub et al., Science, 1999

Can a gene expression-based model ‘learn’how to predict treatment response?

p = 0.0003

Lymphoma Outcome Prediction: All patients (n=58)M. Shipp, J. Aster

predicted ‘good’

predicted ‘bad’

Chemosensitivity Prediction: NCI-60J. Staunton, J. Weinstein

• panel of 60 human cancer cell lines• known sensitivity to 000’s of compounds• we measured expression of 6800 genes in untreated cells

Are gene expression patterns sufficient to predict sensitivity?

Choose pair of sens/resistant within each tissue type

Build best model

Test on remaining samples

0

10

20

30

40

50

10 20 30 40 50 60 70 80 90 100

% accuracy

num

ber

of d

rugs

random prediction gene-based prediction

Kolmogorov-Smirnov p = 10-24

Expression-Based Prediction

50

40

30

20

10

0

Num

ber

of D

rugs

% Accuracy

BR PR LU CR LY M LEU R P

First Generation Global Cancer MapS. Ramaswamy

BR PR CO CNS LY ME LEU RE PA

genes

300 tumors and normals27 tumor classes

13,000 genes/ESTs