1. 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?

2. 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

3. 123 n 10 6 oligos 24 microns Genes 1 2.....20 match mismatch 3’ UTR codingGene n

4. Estimating Message Abundance perfect match (PM) mismatch (MM) Message abundance = trimmed mean (PM 1 -MM 1...PM 20 -MM 20 ) 1 2 3......20 Confidence measure: ‘A’ low confidence ‘P’ high confidence

5. Oligonucleotide Arrays: Sample Preparation AAATTT-T7 AAA-T7 TTT BB BB SA computer 10  g total RNA cDNAds cDNA cRNA RT IVT bio-NTPs hyb SAPE scan ion argon laser chip

6. +2X -2X 100100010000100000 100 1000 10000 100000 ‘P’ calls (2301) ‘A’ calls (4830) Reproducibility Experiments Same Target on 2 Arrays +2X -2X

7. Cancer Classification Identify previously unrecognized classes Assign new tumors to known classes Class Prediction Class Discovery Type 1 Type 2 Type 3 Type 1Type 2Type 3

8. Proof of Concept: Acute Leukemia Diagnosis ALL AML Molecularly distinct tumors are morphologically similar

9. ALL genes low high normalized expression AML Gene Expression Correlates of Leukemia Genes sorted according to correlation with ALL/AML distinction Permutation Test 1000 genes more highly correlated than expected Terminal transferase Myelo- peroxidase

10. 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 Remove each sample in turn Results Initial set (n=38) 36 predictions 2 uncertain 100% correct Independent set (n=34) 29 predictions 5 uncertain 100% correct

11. AML T-ALL B-ALL Class Discovery What 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

12. 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’

13. Chemosensitivity Prediction: NCI-60 J. 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

14. Kolmogorov-Smirnov p = 10 -24 Expression-Based Prediction 50 40 30 20 10 0 Number of Drugs % Accuracy

15. First Generation Global Cancer Map S. Ramaswamy BRPRCOCNSLYMELEUREPA genes 300 tumors and normals 27 tumor classes 13,000 genes/ESTs