1. Genome-Wide Mutational Analyses of Human Cancers: Lessons Learned From Sequencing Cancer Genomes Ludwig Center for Cancer Genetics and Therapeutics The Sidney Kimmel Cancer Center Johns Hopkins University Sept 5, 2008 Will Parsons, M.D., Ph.D.

2. Overview I. I. Background and overview of cancer genome studies II. II. Lessons from prior analyses of cancer genomes III. III. Results and implications of the current brain cancer study

3. Overview I. I. Background and overview of cancer genome studies II. II. Lessons from prior analyses of cancer genomes III. III. Results and implications of the current brain cancer study

4. 30 to 40 years Cancer is a genetic disease

5. Cancer genotype directed therapies Gleevec (imatinib) Gleevec (imatinib) –CML (BCR-ABL) –Gastrointestinal Stromal Tumors (c-KIT) Herceptin (trastuzumab) Herceptin (trastuzumab) –Breast Cancer (HER-2) Iressa (gefitinib) and (erlotinib) Iressa (gefitinib) and Tarceva (erlotinib) –NSCLC (EGFR)

6. What we know about cancer genetics

7. High throughput sequencing (>10 million bp per day) +=+$$

8. Pre-genomePost-genome Candidate approach High throughput Methods to identify mutations

9. 138 protein tyrosine kinases 138 protein tyrosine kinases 16 phosphatidylinositol 3-kinases 16 phosphatidylinositol 3-kinases 87 protein tyrosine phosphatases 87 protein tyrosine phosphatases 200 chromosomal instability genes 200 chromosomal instability genes 350 serine / threonine kinases 350 serine / threonine kinases Analyzed in a collection of colorectal and other human tumors Mutational analysis of signaling pathways in colorectal cancer Bardelli et al., Science 300:949 (2003) Samuels et al., Science 304, 554 (2004) Wang et al., Science 304 (5674):1164 (2004). Wang et al., Cancer Res 64(9):2998 (2004) Parsons et al., Nature 436(7052):792 (2005)

10. High frequency of mutations of the PI3-kinase PIK3CA in human cancer Samuels et al., Science 304, 554 (2004), Bachman et al., CBT 3 e49 (2004), Broderick et al., Can Res 64, 5048 (2004), Lee et al., Oncogene 24, 1477 (2005) Colorectal cancer 74/234 32% Breast cancer13/53 27% Hepatocellular cancer26/73 35% Brain cancer4/15 27% Gastric cancer3/12 25% Lung cancer1/24 4%

11. Parsons et al. Nature 436: 792 (2005) Mutations of PI3K pathway genes in colorectal cancer

12. Goals for “Cancer Genomics” To develop a strategy for unbiased genome-wide analyses of cancer genes in human tumors To develop a strategy for unbiased genome-wide analyses of cancer genes in human tumors To determine the spectrum and extent of somatic mutations in human tumors of similar and different histologic types To determine the spectrum and extent of somatic mutations in human tumors of similar and different histologic types To identify new cancer genes for basic research and improvements in diagnosis, prevention, and therapy To identify new cancer genes for basic research and improvements in diagnosis, prevention, and therapy

13. Find tumor-specific mutations Dye terminator sequencing PCR amplify coding exons from samples of tumor DNA Design primers Select gene set and tumors Genome-wide mutational analyses n t Discovery Screen Validate mutated genes in larger panel of additional tumors Compare gene mutation frequency to expected background Candidate cancer genes Genes with passenger mutations Validation Screen

14. Driver vs. Passenger mutations Driver mutations – provide a net growth advantage and are positively selected for during tumorigenesis Driver mutations – provide a net growth advantage and are positively selected for during tumorigenesis Passenger mutations – neutral mutations that provide no advantage to the tumor Passenger mutations – neutral mutations that provide no advantage to the tumor

15. Mutation Prioritization 1.Frequency 2. Type 3. Predicted effects 4. Structural models 5. Analogous mutations 6. Functional studies

16. Evaluating Genes based on Mutation Frequency CaMP Score CaMP Score –Metric used to rank genes based on their mutation frequency and type –Takes account of number of mutations, length and nucleotide content of gene, context of mutations Can use statistical methods to determine the likelihood that genes with CaMP scores over a threshold are mutated at a frequency higher than background Can use statistical methods to determine the likelihood that genes with CaMP scores over a threshold are mutated at a frequency higher than background

17. Overview I. I. Background and overview of cancer genome studies II. II. Lessons from prior analyses of cancer genomes III. III. Results and implications of the current brain cancer study

18. What tumors? Breast and Colon cancers

19. What genes? Protein-coding genes in CCDS and RefSeq RefSeq Ensembl Consensus Coding Sequences (CCDS) ~13,000 genes ~18,500 genes ~21,500 genes Canonical start / stop codons Cross-species conservation Identical in RefSeq and Ensembl Consensus splice sites Translatable from reference genome without fs or stop

20. Lessons Lessons learned - 1 Mutations and candidate cancer genes Many genes are mutated in these solid tumors Many genes are mutated in these solid tumors

21. Total mutations Mutations per tumor CAN-gene mutations

22. Lessons Lessons learned – 1 Mutations and candidate cancer genes Many genes are mutated in these solid tumors Many genes are mutated in these solid tumors Vast majority of previously known breast and colon cancer genes were identified Vast majority of previously known breast and colon cancer genes were identified

23. Genes known to be mutated in breast and colorectal cancers are CAN-genes Mutation frequency Breast cancers Colon cancers >10% TP53, PIK3CA TP53, APC, KRAS, PIK3CA, SMAD4, FBXW7 (CDC4) <10% MRE11, BRCA1 EPHA3, NF1, SMAD2, SMAD3, TCF7L2 (TCF4), TGFBRII

24. Lessons Lessons learned – 1 Mutations and candidate cancer genes Many genes are mutated in these solid tumors Many genes are mutated in these solid tumors Vast majority of previously known breast and colon cancer genes were identified Vast majority of previously known breast and colon cancer genes were identified Many new breast and colon CAN-genes were discovered Many new breast and colon CAN-genes were discovered New CAN-genes are likely to exist in other tumor types New CAN-genes are likely to exist in other tumor types

25. The majority of CAN-genes had not previously been implicated in cancer Colon cancers (n=69 genes) Breast cancers (n=122 genes)

26. Lessons Lessons learned – 2 Genomic landscape of cancers More genes involved in cancer than previously anticipated – few “mountains”, many “hills” More genes involved in cancer than previously anticipated – few “mountains”, many “hills”

27. Top colon CAN-genes GeneNameCaMPscore APC adenomatosis polyposis coli >10 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog >10 TP53 tumor protein p53 >10 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide >10 FBXW7 F-box and WD-40 domain protein 7 9.6 NAV3 neuron navigator 3 8.0 EPHA3 EPH receptor A3 7.1 MAP2K7 neuron navigator 3 7.0 SMAD4 SMAD, mothers against DPP homolog 4 6.0 ADAMTSL3 ADAMTS-like 3 5.9 GUCY1A2 guanylate cyclase 1, soluble, alpha 2 5.8 OR51E1 olfactory receptor, family 51, subfamily E, member 1 5.6 TCF7L2 transcription factor 7-like 2 (TCF4) 5.2 ADAMTS18 ADAM metallopeptidase with thrombospondin type 1 motif, 18 5.0 SEC8L1 exocyst complex component 4 4.7 RET ret proto-oncogene 4.6 PTEN phosphatase and tensin homolog 4.5 MMP2 matrix metallopeptidase 2 4.3 GNAS GNAS complex locus 4.3 TGM3 transglutaminase 3 4.0 Mutated in <1-5% of cancers

28. Landscape of colon cancers

29. APC KRAS TP53 PIK3CA FBXW7

30. Landscape of colon cancers APC KRAS TP53 PIK3CA FBXW7

31. Lessons Lessons learned – 2 Genomic landscape of cancers More genes involved in cancer than previously anticipated – few “mountains”, many “hills” More genes involved in cancer than previously anticipated – few “mountains”, many “hills” There is significant heterogeneity between individual tumors (even of the same type) There is significant heterogeneity between individual tumors (even of the same type)

32. Landscape of a single colon cancer APC KRAS TP53 PIK3CA FBXW7

33. Landscape of a single colon cancer APC KRAS TP53 PIK3CA FBXW7

34. Lessons Lessons learned – 2 Genomic landscape of cancers More genes involved in cancer than previously anticipated – few “mountains”, many “hills” More genes involved in cancer than previously anticipated – few “mountains”, many “hills” There is significant heterogeneity between individual tumors (even of the same type) There is significant heterogeneity between individual tumors (even of the same type) Simpler gene groups and pathways emerge when mutation data are considered as a whole Simpler gene groups and pathways emerge when mutation data are considered as a whole

35. PI3K/AKT pathway is mutated in both breast and colorectal cancers, but the specific mutated genes are different.

36. Overview I. I. Background and overview of cancer genome studies II. II. Lessons from prior analyses of cancer genomes III. III. Results and implications of the current brain cancer study

37. Glioblastoma multiforme (GBM) Most common and lethal primary brain tumor Most common and lethal primary brain tumor Occurs in both adults and children Occurs in both adults and children Categorized into two groups Categorized into two groups –Primary (>90%) –Secondary (<10%): have evidence of pre- existing lower-grade lesion

38. What genes? All available protein-coding genes RefSeq Ensembl Consensus Coding Sequences (CCDS) ~13,000 genes ~18,500 genes ~21,500 genes Canonical start / stop codons Cross-species conservation Identical in RefSeq and Ensembl Consensus splice sites Translatable from reference genome without fs or stop

40. Integration of expression analyses Identification of potential target genes in previously-uncharacterized deletions and amplifications Identification of potential target genes in previously-uncharacterized deletions and amplifications Identification of differentially-expressed genes in GBMs relative to normal brain Identification of differentially-expressed genes in GBMs relative to normal brain Analysis of expression changes in pathways implicated by genetic alterations Analysis of expression changes in pathways implicated by genetic alterations

42. Altered genes in GBM

43. Core genetic pathways in GBMs

44. IDH1 mutations

45. Isocitrate dehydrogenases (IDHs) Catalyze the oxidative carboxylation of isocitrate to  -ketoglutarate Isocitrate + NAD(P)+ ---------->  -ketoglutarate + CO 2 + NAD(P)H Isocitrate binding site residues: One subunit: Thr 77, Ser 94, Arg 100, Arg 109, Arg 132, Tyr 139, Asp 275 Other subunit: Lys 212, Thr 214, Asp 252

46. Five isocitrate dehydrogenase (IDH) genes reported NAD(+)NADP(+) (e - acceptor) IDH3A CCDS10297.1 Chr 15 IDH3G CCDS14730.1 Chr X IDH3B CCDS13031.1 CCDS13032.1 Chr 20 -Form heterotetramer    -Catalyze rate-limiting step of TCA cycle IDH1 CCDS2381.1 Chr 2 IDH2 CCDS10359.1 Chr 15 MitochondriaCytoplasm/peroxisomes -Form homodimer -Regeneration of NADPH for biosynthetic processes -Defense against oxidative damage?

47. Isocitrate dehydrogenases (IDHs) Catalyze the oxidative carboxylation of isocitrate to  -ketoglutarate Isocitrate + NAD(P)+ ---------->  -ketoglutarate + CO 2 + NAD(P)H Isocitrate binding site residues: One subunit: Thr 77, Ser 94, Arg 100, Arg 109, Arg 132, Tyr 139, Asp 275 Other subunit: Lys 212, Thr 214, Asp 252

49. Characteristics of IDH1-mutated GBMs

50. IDH1 mutation and patient age

51. IDH1 mutation, age and tumor type Young adult patientsSecondary GBMs

52. IDH1 mutation and patient survival

53. Conclusions Conclusions – 1 Pathway analyses Core set of pathways identified in GBMs using integrated genomic data, including processes specific to the nervous system Core set of pathways identified in GBMs using integrated genomic data, including processes specific to the nervous system

54. Conclusions Conclusions – 1 Pathway analyses Core set of pathways identified in GBMs using integrated genomic data, including processes specific to the nervous system Core set of pathways identified in GBMs using integrated genomic data, including processes specific to the nervous system Necessity for pathway or process-specific view to guide further analyses and therapeutic design Necessity for pathway or process-specific view to guide further analyses and therapeutic design

55. Conclusions Conclusions – 2 Identification of IDH1 IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of patients IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of patients

56. Conclusions Conclusions – 2 Identification of IDH1 IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of patients IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of patients IDH1-mutated GBMs have characteristic clinical and genetic findings IDH1-mutated GBMs have characteristic clinical and genetic findings

57. Conclusions Conclusions – 2 Identification of IDH1 IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of patients IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of patients IDH1-mutated GBMs have characteristic clinical and genetic findings IDH1-mutated GBMs have characteristic clinical and genetic findings Identifies IDH1 as a potentially-useful target for diagnostics and therapeutics Identifies IDH1 as a potentially-useful target for diagnostics and therapeutics

58. Conclusions Conclusions – 2 Identification of IDH1 IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of patients IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of patients IDH1-mutated GBMs have characteristic clinical and genetic findings IDH1-mutated GBMs have characteristic clinical and genetic findings Identifies IDH1 as a potentially-useful target for diagnostics and therapeutics Identifies IDH1 as a potentially-useful target for diagnostics and therapeutics Further functional studies required Further functional studies required

59. Acknowledgements