Genome-Wide Mutational Analyses Genome-Wide Mutational Analyses of Human Cancers: of Human Cancers: Lessons Learned From Sequencing Cancer Lessons Learned From Sequencing Cancer Genomes Genomes Ludwig Center for Cancer Genetics and Ludwig Center for Cancer Genetics and Therapeutics Therapeutics The Sidney Kimmel Cancer Center The Sidney Kimmel Cancer Center Johns Hopkins University Johns Hopkins University Sept 5, 2008 Sept 5, 2008 Will Parsons, M.D., Ph.D. Will Parsons, M.D., Ph.D.
Transcript
Genome-Wide Mutational Analyses of Genome-Wide Mutational Analyses of Human Cancers: Human Cancers:
Lessons Learned From Sequencing Cancer Lessons Learned From Sequencing Cancer GenomesGenomes
Ludwig Center for Cancer Genetics and TherapeuticsLudwig Center for Cancer Genetics and TherapeuticsThe Sidney Kimmel Cancer CenterThe Sidney Kimmel Cancer Center
Johns Hopkins UniversityJohns Hopkins University
Sept 5, 2008Sept 5, 2008
Will Parsons, M.D., Ph.D.Will Parsons, M.D., Ph.D.
OverviewOverview
I. Background and overview of cancer genome studies
II. Lessons from prior analyses of cancer genomes
III. Results and implications of the current brain cancer study
OverviewOverview
I. Background and overview of cancer genome studies
II. Lessons from prior analyses of cancer genomes
III. Results and implications of the current brain cancer study
NormalEpithelium
Dysplastic ACF
EarlyAdenoma
LateAdenoma
Carcinoma MetastasisIntermediate Adenoma
APC/ -cateninb K-RAS 18q p53Other
Changes?
30 to 40 years
Cancer is a genetic disease
Cancer genotype directed Cancer genotype directed therapiestherapies
Herceptin (trastuzumab)Herceptin (trastuzumab)– Breast Cancer (HER-2)Breast Cancer (HER-2)
Iressa (gefitinib) and Iressa (gefitinib) and Tarceva (erlotinib) (erlotinib)– NSCLC (EGFR)NSCLC (EGFR)
What we know about cancer genetics
High throughput sequencingHigh throughput sequencing
(>10 million bp per day)
+ =+ $$
Pre-genome Post-genome
Candidate approach High throughput
Methods to identify mutationsMethods to identify mutations
138 protein tyrosine kinases138 protein tyrosine kinases 16 phosphatidylinositol 3-kinases16 phosphatidylinositol 3-kinases 87 protein tyrosine phosphatases87 protein tyrosine phosphatases 200 chromosomal instability genes200 chromosomal instability genes 350 serine / threonine kinases350 serine / threonine kinases
Analyzed in a collection of colorectal and other human Analyzed in a collection of colorectal and other human tumorstumors
Mutational analysis of signaling Mutational analysis of signaling pathways in colorectal cancerpathways 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)
High frequency of mutations of the High frequency of mutations of the PI3-kinase PIK3CA in human cancerPI3-kinase PIK3CA in human cancer
C2
Tum or F raction m utatedC o lo n B ra in G a s tr ic B rea s t
1 /2 4 (4 % )
7 4 /2 3 4 (32 % ) 4 /1 5 (2 7 % )
3 /1 2 (2 5 % ) 1 /1 2 (8 % )
L u n g
8 % 4 7 % 3 3 %
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 cancer 13/53 27%Hepatocellular cancer 26/73 35%Brain cancer 4/15 27%Gastric cancer 3/12 25%Lung cancer 1/24 4%
Parsons et al. Nature 436: 792 (2005)
Mutations of PI3K pathway genes Mutations of PI3K pathway genes in colorectal cancerin colorectal cancer
Goals for “Cancer Genomics”Goals for “Cancer Genomics”
To develop a strategy for unbiased genome-wide To develop a strategy for unbiased genome-wide analyses of cancer genes in human tumors analyses of cancer genes in human tumors
To determine the spectrum and extent of somatic To determine the spectrum and extent of somatic mutations in human tumors of similar and different mutations in human tumors of similar and different histologic typeshistologic types
To identify new cancer genes for basic research and To identify new cancer genes for basic research and improvements in diagnosis, prevention, and therapyimprovements in diagnosis, prevention, and therapy
Find tumor-specific mutations
Dye terminator sequencing
PCR amplify coding exons from samples of tumor DNA
Validate mutated genes in largerpanel of additional tumors
Compare gene mutation frequency to expected
background
Candidate cancer genes
Genes with passenger mutations
Va
lida
tio
n S
cre
en
Driver Driver vs. vs. Passenger Passenger mutationsmutations
Driver mutations – provide a net Driver mutations – provide a net growth advantage and are positively growth advantage and are positively selected for during tumorigenesisselected for during tumorigenesis
Passenger mutations – neutral Passenger mutations – neutral mutations that provide no advantage mutations that provide no advantage to the tumorto the tumor
Mutation Prioritization
1. Frequency2. Type
3. Predicted effects4. Structural models
5. Analogous mutations6. Functional studies
Evaluating Genes based on Evaluating Genes based on Mutation FrequencyMutation Frequency
CaMP ScoreCaMP Score– Metric used to rank genes based on their mutation frequency Metric used to rank genes based on their mutation frequency
and typeand type– Takes account of number of mutations, length and nucleotide Takes account of number of mutations, length and nucleotide
content of gene, context of mutationscontent of gene, context of mutations Can use statistical methods to determine the likelihood Can use statistical methods to determine the likelihood
that genes with CaMP scores over a threshold are that genes with CaMP scores over a threshold are mutated at a frequency higher than backgroundmutated at a frequency higher than background
OverviewOverview
I. Background and overview of cancer genome studies
II. Lessons from prior analyses of cancer genomes
III. Results and implications of the current brain cancer study
What tumors? What tumors? Breast and Colon cancersBreast and Colon cancers
2004 Estimated US Cancer Cases*
*Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder.Source: American Cancer Society, 2004.
Men699,560
Women668,470
32% Breast
12% Lung & bronchus
11% Colon & rectum
6% Uterine corpus
4% Ovary
4% Non-Hodgkinlymphoma
4% Melanomaof skin
3% Thyroid
2% Pancreas
2% Urinary bladder
20% All Other Sites
Prostate 33%
Lung & bronchus 13%
Colon & rectum 11%
Urinary bladder 6%
Melanoma of skin 4%
Non-Hodgkinlymphoma 4%
Kidney 3%
Oral Cavity 3%
Leukemia 3%
Pancreas 2%
All Other Sites 18%
What genes?What genes?Protein-coding genes in CCDS and RefSeqProtein-coding genes in CCDS and RefSeq
RefSeq
Ensembl
ConsensusCoding
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
Lessons Lessons learned - 1Mutations and candidate cancer genes
Many genes are mutated in these solid tumorsMany genes are mutated in these solid tumors
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11
Tumor #
No
n-s
ilen
t m
uta
tio
ns Total mutations
Mutations per tumor
CAN-gene mutations
Lessons Lessons learned – 1 Mutations and candidate cancer genes
Many genes are mutated in these solid tumorsMany genes are mutated in these solid tumors Vast majority of previously known breast and Vast majority of previously known breast and
colon cancer genes were identifiedcolon cancer genes were identified
Genes known to be mutated in breast Genes known to be mutated in breast and colorectal cancers are and colorectal cancers are CANCAN-genes-genes
Mutation frequencyMutation frequency Breast cancersBreast cancers Colon cancersColon cancers
Lessons Lessons learned – 1 Mutations and candidate cancer genes
Many genes are mutated in these solid tumorsMany genes are mutated in these solid tumors Vast majority of previously known breast and Vast majority of previously known breast and
colon cancer genes were identifiedcolon cancer genes were identified Many new breast and colon Many new breast and colon CANCAN-genes were -genes were
discovered discovered New New CANCAN-genes are likely to exist in other -genes are likely to exist in other
tumor typestumor types
The majority ofThe majority of CAN- CAN-genes had not genes had not previously been implicated in cancerpreviously been implicated in cancer
20%
3%
12%
61%
1%
3%
Colon cancers(n=69 genes)
Breast cancers(n=122 genes)
Mutation
Translocation
Amplification
Deletion
Methylation
Expression
Not known
8%
18%
67%
3%
3%
1%
Lessons Lessons learned – 2Genomic landscape of cancers
More genes involved in cancer than previously More genes involved in cancer than previously anticipated – few “mountains”, many “hills”anticipated – few “mountains”, many “hills”
Top colon Top colon CAN-CAN-genesgenesGeneGene NameName
Landscape of colon cancersLandscape of colon cancers
Landscape of colon cancersLandscape of colon cancers
APC
KRAS
TP53PIK3CAFBXW7
Landscape of colon cancersLandscape of colon cancers
APC
KRAS
TP53PIK3CAFBXW7
Lessons Lessons learned – 2Genomic landscape of cancers
More genes involved in cancer than previously More genes involved in cancer than previously anticipated – few “mountains”, many “hills”anticipated – few “mountains”, many “hills”
There is significant heterogeneity between There is significant heterogeneity between individual tumors (even of the same type)individual tumors (even of the same type)
Landscape of a single colon cancerLandscape of a single colon cancer
APC
KRAS
TP53PIK3CAFBXW7
Landscape of a single colon cancerLandscape of a single colon cancer
APC
KRAS
TP53PIK3CAFBXW7
Lessons Lessons learned – 2Genomic landscape of cancers
More genes involved in cancer than previously More genes involved in cancer than previously anticipated – few “mountains”, many “hills”anticipated – few “mountains”, many “hills”
There is significant heterogeneity between There is significant heterogeneity between individual tumors (even of the same type)individual tumors (even of the same type)
Simpler gene groups and pathways emerge Simpler gene groups and pathways emerge when mutation data are considered as a wholewhen mutation data are considered as a whole
PI3K/AKT pathway is mutated in both breast and colorectal PI3K/AKT pathway is mutated in both breast and colorectal cancers, but the specific mutated genes are different.cancers, but the specific mutated genes are different.
OverviewOverview
I. Background and overview of cancer genome studies
II. Lessons from prior analyses of cancer genomes
III. Results and implications of the current brain cancer study
What genes?What genes?All available protein-coding genesAll available protein-coding genes
RefSeq
Ensembl
ConsensusCoding
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
Human Genome Reference and Ensembl Sequences23,219 transcripts from 20,661genes
Design primers for PCR-based amplification and sequencing of coding exons
208,311 passing primer pairs31.8 Mb coding sequence
Amplify and sequence DNA from 22 GBM samples689 Mb total tumor sequence
MUTATION ANALYSIS
Assemble sequence data and filter putative somatic mutations
Resequence tumor and normal DNA to confirm mutations and exclude germline variants
2325 somatic mutations in 2043 genes
COPY NUMBER ANALYSIS EXPRESSION ANALYSIS
Integrated bioinformatic analyses of altered genes
Identification of CAN-genes Identification of mutated pathways
Hybridisation to high density oligo arrays
1.06 million genomic loci
Serial analysis of gene expression using next generation sequencing
2 million tags / sample
134 homozygous deletions and 147 amplifications
Differential expression of genetically altered genes
Integration of expression analysesIntegration of expression analyses
Identification of potential target genes in Identification of potential target genes in previously-uncharacterized deletions and previously-uncharacterized deletions and amplificationsamplifications
Identification of differentially-expressed Identification of differentially-expressed genes in GBMs relative to normal braingenes in GBMs relative to normal brain
Analysis of expression changes in pathways Analysis of expression changes in pathways implicated by genetic alterationsimplicated by genetic alterations
Table 1. Summary of genomic analyses
Sequencing analysisNumber of genes successfully analyzed 20,661Number of transcripts successfully analyzed 23,219Number of exons successfully analyzed 175,471Primer pairs designed for amplification 219,229Fraction of passing amplicons* 95.0%Total number of nucleotides successfully sequenced 689,071,123Fraction of passing amplicon sequences successfully analyzed† 98.3%Fraction of targeted bases successfully analyzed† 93.0%Number of somatic mutations identified (n=22 samples) 2,325Number of somatic mutations (excluding Br27P) 993 Missense 622 Nonsense 43 Insertion 3 Deletion 46 Duplication 7 Splice site or UTR 27 Synonymous 245Average number of sequence alterations per sample 47.3
Copy number analysisTotal number of SNP loci assessed for copy number changes 1,069,688Number of copy number alterations identified (n=22 samples) 281 Amplifications 147 Homozygous deletions 134Average number of amplifications per sample 6.7Average number of homozygous deletions per sample 6.1
*Passing amplicons were defined as having PHRED20 scores or better over 90% of the target sequence in 75% of samples analyzed. †Fraction of nucleotides having PHRED20 scores or better (see Supporting Online Materials for additional information).
Table 2. Most frequently altered GBM CAN- genes
GeneNumber of
tumorsFraction of
tumorsNumber of
tumorsFraction of
tumorsNumber of
tumorsFraction of
tumors
Fraction of tumors with
any alteration
Passenger
Probability*
CDKN2A 0/22 0% 0/22 0% 11/22 50% 50% <0.01
TP53 37/105 35% 0/22 0% 1/22 5% 40% <0.01
EGFR 15/105 14% 5/22 23% 0/22 0% 37% <0.01
PTEN 27/105 26% 0/22 0% 1/22 5% 30% <0.01
NF1 16/105 15% 0/22 0% 0/22 0% 15% 0.04
CDK4 0/22 0% 3/22 14% 0/22 0% 14% <0.01
RB1 8/105 8% 0/22 0% 1/22 5% 12% 0.02
IDH1 12/105 11% 0/22 0% 0/22 0% 11% <0.01
PIK3CA 10/105 10% 0/22 0% 0/22 0% 10% 0.10
PIK3R1 8/105 8% 0/22 0% 0/22 0% 8% 0.10The most frequently-altered CAN- genes are listed; all CAN- genes are listed in Table S7. ^Fraction of tumors with point mutations indicates the fraction of mutated GBMs out of the 105 samples in the Discovery and Prevalence Screens. CDKN2A and CDK4 were not analyzed for point mutations in the Prevalence Screen because no sequence
alterations were detected in these genes in the Discovery Screen. &Fraction of tumors with amplifications and deletions indicates the number of tumors with these types of
alterations in the 22 Discovery Screen samples. *Passenger probability indicates the Passenger probability - Mid (12 ).
Point mutations^ Amplifications& Homozygous deletions&
Altered genes in GBM
Core genetic pathways in GBMsCore genetic pathways in GBMs
Table 3. Mutations of the TP53, PI3K, and RB1 pathways in GBM samples
* Mut, mutated; Amp, amplified; Del, deleted; Alt, altered #Fraction of affected tumors in 22 Discovery Screen samples
TP53 pathway PI3K Pathway RB1 pathway
G395A (R132H)
Normal
Br122X
Br104X
C394A (R132S)
IDH1 mutationsIDH1 mutations
Isocitrate dehydrogenases (IDHs)
Catalyze the oxidative carboxylation of isocitrate to -ketoglutarate
Isocitrate + NAD(P)+ ----------> -ketoglutarate + CO2 + NAD(P)H
Isocitrate binding site residues:
One subunit: Thr77, Ser94, Arg100, Arg109,Arg132, Tyr139, Asp275
Other subunit: Lys212, Thr214, Asp252
Five isocitrate dehydrogenase (IDH) genes reported
NAD(+) NADP(+)
(e- acceptor)
IDH3ACCDS10297.1
Chr 15
IDH3GCCDS14730.1
Chr XIDH3B
CCDS13031.1CCDS13032.1
Chr 20
-Form heterotetramer b-Catalyze rate-limiting
step of TCA cycle
IDH1CCDS2381.1
Chr 2IDH2CCDS10359.1
Chr 15Mitochondria Cytoplasm/peroxisomes
-Form homodimer-Regeneration of NADPH
for biosynthetic processes-Defense against oxidative
damage?
Isocitrate dehydrogenases (IDHs)
Catalyze the oxidative carboxylation of isocitrate to -ketoglutarate
Isocitrate + NAD(P)+ ----------> -ketoglutarate + CO2 + NAD(P)H
Isocitrate binding site residues:
One subunit: Thr77, Ser94, Arg100, Arg109,Arg132, Tyr139, Asp275
Other subunit: Lys212, Thr214, Asp252
Fig. 1. Structure of the active site of IDH1. The crystal structure of the human cytosolic NADP(+) -dependent IDH is shown in ribbon format (PDBID: 1T0L) (44). The active cleft of IDH1 consists of a NADP-binding site and the isocitrate-metal ion-binding site. The alpha-carboxylate oxygen and the hydroxyl group of isocitrate chelate the Ca2+ ion. NADP is colored in orange, isocitrate in purple and Ca2+ in blue. The Arg132 residue, displayed in yellow, forms hydrophilic interactions, shown in red, with the alpha-carboxylate of isocitrate. Displayed image was created with UCSF Chimera software version 1.2422
Characteristics of IDH1-mutated GBMsCharacteristics of IDH1-mutated GBMs Table 4. Characteristics of GBM patients with IDH1 mutations
Nucleotide Amino acid
Br10P 30 F No No 2.2 G395A R132H Yes No
Br11P 32 M No No 4.1 G395A R132H Yes No
Br12P 31 M No No 1.6 G395A R132H Yes No
Br104X 29 F No No 4.0 C394A R132S Yes No
Br106X 36 M No No 3.8 G395A R132H Yes No
Br122X 53 M No No 7.8 G395A R132H No No
Br123X 34 M No Yes 4.9 G395A R132H Yes No
Br237T 26 M No Yes 2.6 G395A R132H Yes No
Br211T 28 F No Yes 0.3 G395A R132H Yes No
Br27P 32 M Yes Yes 1.2 G395A R132H Yes No
Br129X 25 M Yes Yes 3.2 C394A R132S No No
Br29P 42 F Yes Unknown Unknown G395A R132H Yes No
IDH1 mutant patients (n=12)
33.2 67% M 25% 42% 3.8 100% 100% 83% 0%
IDH1 wildtype patients (n=93)
53.3 65% M 16% 1% 1.1 0% 0% 27% 60%
Mutation of TP53
Mutation of PTEN, RB1, EGFR, or NF1
*Patient age refers to age at which patient GBM sample was obtained. #Recurrent GBM designates a GBM which was resected >3 months after a prior diagnosis of GBM. ^Secondary GBM
designates a GBM which was resected > 1 year after a prior diagnosis of a lower grade glioma (WHO I-III). &Overall survival was calculated using date of GBM diagnosis and date of death or last patient contact: patients Br10P and Br11P were alive at last contact. Median survival for IDH1 mutant patients and IDH1 wildtype patients was calculated using logrank test. Previous pathologic diagnoses in secondary GBM patients were oligodendroglioma (WHO grade II) in Br123X, low grade glioma (WHO grade I-II) in Br237T and Br211T, anaplastic astrocytoma (WHO grade III) in Br27P, and anaplastic oligodendroglioma (WHO grade III) in Br129X. Abbreviations: GBM (glioblastoma multiforme, WHO grade IV), WHO (World Health Organization), M (male), F (female), mut (mutant). Mean age and median survival are listed for the groups of IDH1-mutated and IDH1-wildtype patients.
IDH1 MutationPatient age
(years)*SexPatient ID
Recurrent
GBM#
Secondary
GBM^
Overall survival
(years)&
IDH1 mutation and patient ageIDH1 mutation and patient age
0
10
20
30
40
50
60
70
80
Patients with mutated IDH1
Patients with wildtype IDH1
IDH1 mutation, age and tumor typeIDH1 mutation, age and tumor type
Age (years)
<20 0/12 0%
20-29 6/10 60%
30-39 8/16 50%
40-49 2/25 8%
50-59 2/36 6%
>59 0/50 0%
All 18/149 12%
IDH1 mutated All patients 18/149 12%
Patients < 35 years 13/32 41%
Patients 35+ years 5/117 4%
Secondary GBMs 8/10 80%
Total
Young adult patients Secondary GBMs
IDH1 mutation and patient survivalIDH1 mutation and patient survival
0 2 4 6 8 100
20
40
60
80
100
IDH1Wildtype
(n=79)
IDH1 Mutated(n=11)
p<0.001
Years
Ove
rall
Su
rviv
al (
%)
Conclusions Conclusions – 1Pathway analyses
Core set of pathways identified in GBMs using Core set of pathways identified in GBMs using integrated genomic data, including processes integrated genomic data, including processes specific to the nervous systemspecific to the nervous system
Conclusions Conclusions – 1 Pathway analyses
Core set of pathways identified in GBMs using Core set of pathways identified in GBMs using integrated genomic data, including processes integrated genomic data, including processes specific to the nervous systemspecific to the nervous system
Necessity for pathway or process-specific view Necessity for pathway or process-specific view to guide further analyses and therapeutic to guide further analyses and therapeutic designdesign
Conclusions Conclusions – 2Identification of IDH1
IDH1 was identified as a commonly mutated IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of GBM gene, particularly in specific subsets of patientspatients
Conclusions Conclusions – 2 Identification of IDH1
IDH1 was identified as a commonly mutated IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of GBM gene, particularly in specific subsets of patientspatients
IDH1-mutated GBMs have characteristic IDH1-mutated GBMs have characteristic clinical and genetic findingsclinical and genetic findings
Conclusions Conclusions – 2 Identification of IDH1
IDH1 was identified as a commonly mutated IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of GBM gene, particularly in specific subsets of patientspatients
IDH1-mutated GBMs have characteristic IDH1-mutated GBMs have characteristic clinical and genetic findingsclinical and genetic findings
Identifies IDH1 as a potentially-useful target for Identifies IDH1 as a potentially-useful target for diagnostics and therapeuticsdiagnostics and therapeutics
Conclusions Conclusions – 2 Identification of IDH1
IDH1 was identified as a commonly mutated IDH1 was identified as a commonly mutated GBM gene, particularly in specific subsets of GBM gene, particularly in specific subsets of patientspatients
IDH1-mutated GBMs have characteristic IDH1-mutated GBMs have characteristic clinical and genetic findingsclinical and genetic findings
Identifies IDH1 as a potentially-useful target for Identifies IDH1 as a potentially-useful target for diagnostics and therapeuticsdiagnostics and therapeutics
Further functional studies requiredFurther functional studies required
AcknowledgementsAcknowledgements
JHU participants in prior genome studies GBM study participants (JHU) GBM study participants (Duke)Tobias Sjoblom Sian Jones Hai YanLaura Wood Xiaosong Zhang Roger McLendonYardena Samuels Jimmy Lin B. Ahmed RasheedSteve Szabo Rebecca Leary Stephen KeirBen Ho Park Philipp Angenendt Darell BignerKurtis E. Bachman Parminder Mankoo
Hannah Carter GBM study (other collaborators)Additional JHU participants in current study I-Mei Sui Tatiana NikolskayaJanine Ptak Gary Gallia Yuri NikolskyNatalie Silliman Allesandro Olivi Dana BsamLisa Dobbyn Luis Diaz, Jr. Hanna TekleabMelissa Whalen Gregory Riggins James Hartigan
Rachel Karchin Doug SmithNick Papadopoulos Robert StrausbergGiovanni Parmigiani Sely Kazue Nagahashi MarieBert Vogelstein Sueli Mieko Oba ShinjoVictor VelculescuKen Kinzler
