1. Mutations in Cancer Cells Ben Ho Park, M.D., Ph.D. Johns Hopkins University April 11,2007 No Relevant Financial Relationships with Commercial Interests

2. Overview/Objectives  Cancer is a genetic disease  Mutations found in human colorectal and breast cancers  Translation of findings toward clinical diagnosis, prognostic/predictive markers and therapy

3. DNA, Genes and Cancer  DNA is a cellular “blueprint” using four different bases: Adenine, Cytosine, Guanine, Thymine (A,C,G,T)  DNA  RNA  Protein  Two copies (generally) of DNA in each cell

4. DNA, Genes and Cancer  Organized units of DNA form genes  Most cancers involve changes of DNA/genes that are somatic i.e. only in the cancer cell Mutations, “epigenetics”, amplification/loss Mutations, “epigenetics”, amplification/loss  Two general classes of genes involved with cancer formation Tumor suppressor genes (brakes) Tumor suppressor genes (brakes) Oncogenes (accelerator) Oncogenes (accelerator)

5. Tumor suppressor genes “the brakes” Germline Inherited Mutation of One copy Somatic inactivation Of second copy Somatic inactivation Of second copy Park and Vogelstein Cancer Medicine 6 th ed 2006 Somatic mutation Of one copy

6. Oncogenes “the accelerator”  Genes involved with “activating” growth  Generally only one of two copies needs to be mutated  Examples: K-ras, PIK3CA

7. Genetic Model of Colorectal Carcinogenesis Normal Small Adenoma Cancer Large Adenoma Metastasis APC TGF-  RII/ Smad4 p53 K-Ras PRL-3 Chromosomal or Microsatellite Instability

8. EXONs EXONs Primers Primers How is sequencing done?

9. Sequencing “trace”

10. Why look for mutated genes?  Because most human cancers arise from somatic mutations, this makes a physical change in the cancer cell that is different from normal cells, i.e. good target for therapy  Also because mutations leading to cancer are somatic, it can in theory be a marker of cancer and used for detection and possible prognosis  Sequencing involves amplifying via polymerase chain reaction (PCR) a “coding” region of DNA, and then determining the base pairs that are present, e.g. A, C, G, T using a high throughput machine

11. Challenges of finding mutations  Sequencing all known genes (>20,000)  Samples Number of samples for analysis Number of samples for analysis Amount of DNA needed to sequence all genes per a given sample Amount of DNA needed to sequence all genes per a given sample Need for a “normal” matched control from same individual to verify mutations as somatic Need for a “normal” matched control from same individual to verify mutations as somatic

12. Challenges of finding mutations  Sequencing all known genes (>20,000) As an initial first “pass”, we analyzed all known genes in the largest most complete human genome data base: the consensus coding sequences (CCDS) data base containing > 13,000 genes As an initial first “pass”, we analyzed all known genes in the largest most complete human genome data base: the consensus coding sequences (CCDS) data base containing > 13,000 genes Employed automated design of polymerase chain reaction (PCR) amplifying primers Employed automated design of polymerase chain reaction (PCR) amplifying primers high-throughput (robotic) PCR, DNA sequencing, and mutation analysis. This protocol allowed the efficient analysis of more than 135,000 amplicons representing the coding sequences of greater than 13,000 genes, covering more than 500 Mb of tumor sequence. high-throughput (robotic) PCR, DNA sequencing, and mutation analysis. This protocol allowed the efficient analysis of more than 135,000 amplicons representing the coding sequences of greater than 13,000 genes, covering more than 500 Mb of tumor sequence.

13. Challenges of finding mutations  Samples Needed enough samples of breast and colon cancers for statistically sound study, but balanced by small amounts of DNA in clinical specimens and costs of sequencing Needed enough samples of breast and colon cancers for statistically sound study, but balanced by small amounts of DNA in clinical specimens and costs of sequencing Performed a two-tier approach Performed a two-tier approach

14. Sequencing Strategy  First used 11 breast and 11 colon cancer cell lines (unlimited source of DNA) to sequence 13,000 genes: “Discovery Screen”  Any mutations found were confirmed as being somatic, since normal DNA was available for each of these cell lines, and then resequenced for verification  After verification, only mutated genes from this discovery screen using patient samples of breast and colon cancers (24 each) were sequenced for the “Validation Screen”

15. Discovery Screen Sjoblom et al. Science 2006

16. Validation Screen Sjoblom et al. Science 2006

17. What we found  365 somatic mutations in 236 genes  Unlike previously known genes mutated at relatively high frequencies, no such genes were found in this analysis. Rather, many different genes were found to mutated at low frequency hallmarking the heterogeneity of human cancers

18. What we found  Most mutations were heterozygous missense mutations (81%)  Colon cancers have many C  T mutations c/w breast cancers (59% vs. 35%)  Breast cancers have more C  G mutations c/w colon cancers (29% vs. 7%)

19. “Passenger” mutations  Cancers have a high rate of mutation  How to distinguish between functionally important mutations vs. passenger mutations?

20. CaMP scores and CAN genes  Statistical analysis that takes into account that mutations were validated in a two step process  Also factors in frequency of mutation and number of base pairs sequenced along with background rate of mutation  Cancer Mutation Prevalence score (CaMP) equals likelihood gene is mutated at frequency higher than background, i.e. it is a cancer (CAN) gene  CaMP score >1 predicted to have >90% likelihood of mutational frequency above background

21. Number and types of CAN genes  Breast and colon cancers had 122 and 69 CAN genes, respectively  Breast cancers had on average 12 mutated CAN genes (range 4 to 23)  Colon cancers had on average 9 mutated CAN genes (range 3 to 18)  All previous known mutated genes in cancer were found serving as internal control  Most genes were never known to be mutated  Some genes were never previously implicated with human cancers

22. Classes of CAN genes Sjoblom et al. Science 2006

23. Limitations of Study  Technical issues prevented sequencing ~10% of CCDS genes  Sequenced CCDS database (~2/3 of expressed genes); likely that there are more mutations in other genes not yet sequenced  Cannot detect other genetic alterations e.g. amplifications, large deletions, epigenetic silencing,  Starting samples for primary samples homogenous for single breast subtype, i.e. selection bias

24. Summary of study  Feasibility/proof of principle: Study was completed in less than 1 year with largely philanthropic and foundation support  High complexity and heterogeneity of the genes involved with cancer; no cancer sample had more than 6 CAN genes mutated in common with any other sample  Many new genes and pathways have been discovered to be mutated/altered in breast and colon cancers

25. Clinical Implications  Prevention/early screening/diagnosis May be possible in the future to sequence genes that are mutated “early” in the neoplastic process May be possible in the future to sequence genes that are mutated “early” in the neoplastic process Could be useful for detecting early lesions and/or predicting likelihood of progressing to more malignant phenotype, e.g. in situ disease to invasive cancer Could be useful for detecting early lesions and/or predicting likelihood of progressing to more malignant phenotype, e.g. in situ disease to invasive cancer May be helpful in categorizing subtypes of cancers based on mutations found May be helpful in categorizing subtypes of cancers based on mutations found Not likely to happen until “early” genes are sorted out and costs of sequencing declines Not likely to happen until “early” genes are sorted out and costs of sequencing declines

26. Clinical Implications  Predictor/Prognosis Depending on gene/families mutated, may be predictive of response to current and future therapies, e.g. Her2/neu amplification and Herceptin Depending on gene/families mutated, may be predictive of response to current and future therapies, e.g. Her2/neu amplification and Herceptin May yield prognostic information e.g. likelihood of progressing/recurrence/death May yield prognostic information e.g. likelihood of progressing/recurrence/death Would again take time to validate Would again take time to validate

27. Clinical Implications  Predictor/Prognosis Could potentially be used for detecting distant sites of disease with single molecule DNA detection of a mutation found in the primary sample Could potentially be used for detecting distant sites of disease with single molecule DNA detection of a mutation found in the primary sample For example, if a breast cancer was found to have a certain mutation, we could look for that mutation in the lymph nodes or blood of the patient to determine spread of the disease For example, if a breast cancer was found to have a certain mutation, we could look for that mutation in the lymph nodes or blood of the patient to determine spread of the disease

28. Single Molecule Detection of Mutant DNA Normal + Mutant Genes Digital-PCR All1 12 2 Normal Mutant

29. BEAMing Dressman et al., PNAS, 2003

30. Clinical Implications-Therapy  Previous methods of finding drugs to treat cancers involved screening compounds that killed cancer cells but had minimal effect on normal cells  Often was very toxic to other normal cell types  Unknown mechanism of anti-tumor effect  No way to control for differences between various cancer and normal cells

31. High throughput drug screening using paired cell lines

32. p21 knock out cell lines

33. High throughput drug screening using paired cell lines

34. ‘Selectivity’ ‘Normal genotype’ + drug ‘Normal genotype’ no drug’ ‘Cancer genotype’ no drug’ ‘Cancer genotype’ + drug’

35. MW 356.46 A A novel drug selective for p21-/- (ERIK) cells (patent pending)

36. Conclusions  Cancer is a genetic disease, largely of somatic mutations  Large scale genomic sequencing is feasible, though costs of scale need to be reduced for individual cancer genomes to be sequenced efficiently  Most cancers have a number of somatic mutations (9-12) but have striking mutational heterogeneity even within the same organ type

37. Conclusions  This leads to the conclusion that targeted therapies against a given mutated gene will not be pragmatic, and that targeting pathways may be a more reasonable approach  As costs continue to decline, the ability to sequence a patient’s cancer genome becomes a reality and will lead to new and better means of detection, prognosis and treatment

38. Acknowledgments

39. Acknowledgments  Avon Foundation  FAMRI  V Foundation  Department of Defense  NIH/NCI  The Maryland Cigarette Restitution Fund Supported by The Virginia and D.K. Ludwig Fund for Cancer Research, NIH grants CA 121113, CA 43460, CA 57345, CA 62924, GM 07309, RR 017698, P30-CA43703, and CA109274, The Pew Charitable Trusts, The Palmetto Health Foundation, The State of Ohio Biomedical Research and Technology Transfer Commission, The Clayton Fund, The Blaustein Foundation, The National Colorectal Cancer Research Alliance, Strang Cancer Prevention Center.

40. References  B.H. Park and B. Vogelstein, “Tumor Suppressor Genes”, in Cancer Medicine, 7th ed. Holland and Frei editors. BC Decker, Hamilton Ontario, 2006.  Sjoblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber T, Mandelker D, Leary RJ, Ptak J, Silliman N, Szabo S, Buckhaults P, Farrell C, Meeh P, Markowitz SD, Willis J, Dawson D, Willson JK, Gazdar AF, Hartigan J, Wu L, Liu C, Parmigiani G, Park BH, Bachman KE, Papadopoulos N, Vogelstein B, Kinzler KW, Velculescu VE. The Consensus Coding Sequences of Human Breast and Colorectal Cancers, Science, Oct 13;314(5797):268-74, 2006.  Bachman KE, Argani P, Samuels Y, Silliman N, Ptak J, Szabo S, Konishi H, Karakas B, Blair BG, Lin C, Peters BA, Velculescu VE and Park BH. The PIK3CA gene is mutated with high frequency in human breast cancers, Cancer Biology and Therapy 3:772-775, 2004.  Dressman, D., Yan, H., Traverso, G., Kinzler, K. W., and Vogelstein, B. Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proc Natl Acad Sci U S A, 100: 8817-8822, 2003.