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Genome-Wide Mutational Analyses of Human Cancers: Lessons Learned From Sequencing Cancer Genomes

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

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Genome-Wide Mutational Analyses of Human Cancers: Lessons Learned From Sequencing Cancer Genomes

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

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

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

  4. Cancer is a genetic disease 30 to 40 years

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

  6. What we know about cancer genetics

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

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

  9. Mutational analysis of signaling pathways in colorectal cancer • 138 protein tyrosine kinases • 16 phosphatidylinositol 3-kinases • 87 protein tyrosine phosphatases • 200 chromosomal instability genes • 350 serine / threoninekinases Analyzed in a collection of colorectal and other human tumors 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 Colorectal cancer 74/234 32% Breast cancer 13/53 27% Hepatocellular cancer 26/73 35% Brain cancer 4/15 27% Gastric cancer 3/12 25% Lung cancer 1/24 4% 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)

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

  12. Goals for “Cancer Genomics” • 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 identify new cancer genes for basic research and improvements in diagnosis, prevention, and therapy

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

  14. Driver vs. Passenger mutations 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

  15. Mutation Prioritization • Frequency2. Type3. Predicted effects4. Structural models5. Analogous mutations6. Functional studies

  16. Evaluating Genes based on Mutation Frequency • 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

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

  18. What tumors? Breast and Colon cancers

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

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

  21. Total mutations Mutations per tumor CAN-gene mutations

  22. Lessons learned – 1 Mutations and candidate cancer genes • Many genes are mutated in these solid tumors • 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

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

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

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

  27. Top colon CAN-genes Mutated in <1-5% of cancers

  28. Landscape of colon cancers

  29. Landscape of colon cancers FBXW7 TP53 PIK3CA KRAS APC

  30. Landscape of colon cancers FBXW7 TP53 PIK3CA KRAS APC

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

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

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

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

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

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

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

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

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

  40. Altered genes in GBM

  41. Core genetic pathways in GBMs

  42. IDH1 mutations

  43. Isocitrate dehydrogenases (IDHs) Catalyze the oxidative carboxylation of isocitrate to a-ketoglutarate Isocitrate + NAD(P)+ ----------> a-ketoglutarate + CO2 + NAD(P)H Isocitrate binding site residues: One subunit: Thr77, Ser94, Arg100, Arg109, Arg132, Tyr139, Asp275 Other subunit: Lys212, Thr214, Asp252

  44. Five isocitrate dehydrogenase (IDH) genes reported (e- acceptor) NAD(+) NADP(+) • Form heterotetramer a2bg • Catalyze rate-limiting • step of TCA cycle • Form homodimer • Regeneration of NADPH • for biosynthetic processes • -Defense against oxidative • damage? IDH3A CCDS10297.1 Chr 15 IDH3G CCDS14730.1 Chr X IDH3B CCDS13031.1 CCDS13032.1 Chr 20 IDH1 CCDS2381.1 Chr 2 IDH2 CCDS10359.1 Chr 15 Mitochondria Cytoplasm/peroxisomes

  45. Isocitrate dehydrogenases (IDHs) Catalyze the oxidative carboxylation of isocitrate to a-ketoglutarate Isocitrate + NAD(P)+ ----------> a-ketoglutarate + CO2 + NAD(P)H Isocitrate binding site residues: One subunit: Thr77, Ser94, Arg100, Arg109, Arg132, Tyr139, Asp275 Other subunit: Lys212, Thr214, Asp252

  46. Characteristics of IDH1-mutated GBMs

  47. IDH1 mutation and patient age

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