Mutations in Cancer CellsMutations in Cancer Cells

Ben Ho Park, M.D., Ph.D.Ben Ho Park, M.D., Ph.D.Johns Hopkins UniversityJohns Hopkins University

April 11,2007April 11,2007

No Relevant Financial Relationships with Commercial No Relevant Financial Relationships with Commercial InterestsInterests

Overview/ObjectivesOverview/Objectives

Cancer is a genetic diseaseCancer is a genetic disease Mutations found in human colorectal and Mutations found in human colorectal and

breast cancersbreast cancers Translation of findings toward clinical Translation of findings toward clinical

diagnosis, prognostic/predictive markers diagnosis, prognostic/predictive markers and therapyand therapy

DNA, Genes and CancerDNA, Genes and Cancer

DNA is a cellular “blueprint” using four DNA is a cellular “blueprint” using four different bases: Adenine, Cytosine, different bases: Adenine, Cytosine, Guanine, Thymine (A,C,G,T)Guanine, Thymine (A,C,G,T)

DNA DNA RNA RNA Protein Protein Two copies (generally) of DNA in each cellTwo copies (generally) of DNA in each cell

DNA, Genes and CancerDNA, Genes and Cancer

Organized units of DNA form genesOrganized units of DNA form genes Most cancers involve changes of Most cancers involve changes of

DNA/genes that are DNA/genes that are somaticsomatic i.e. only in the i.e. only in the cancer cellcancer cell Mutations, “epigenetics”, amplification/lossMutations, “epigenetics”, amplification/loss

Two general classes of genes involved Two general classes of genes involved with cancer formationwith cancer formation Tumor suppressor genes (brakes)Tumor suppressor genes (brakes) Oncogenes (accelerator)Oncogenes (accelerator)

Tumor suppressor genesTumor suppressor genes“the brakes”“the brakes”

GermlineInherited

Mutation ofOne copy

Somatic inactivationOf second copy

Somatic inactivationOf second copy

Park and VogelsteinCancer Medicine 6th ed 2006

Somatic mutationOf one copy

OncogenesOncogenes“the accelerator”“the accelerator”

Genes involved with “activating” growthGenes involved with “activating” growth Generally only one of two copies needs to Generally only one of two copies needs to

be mutatedbe mutated Examples: K-ras, PIK3CAExamples: K-ras, PIK3CA

Genetic Model ofGenetic Model ofColorectal Colorectal

CarcinogenesisCarcinogenesis

NormalSmall

Adenoma CancerLargeAdenoma

Metastasis

APCTGF-RII/Smad4 p53

K-RasPRL-3

Chromosomal or Microsatellite

Instability

EXONsEXONs

PrimersPrimers

How is sequencing done?How is sequencing done?

Sequencing “trace”Sequencing “trace”

Why look for mutated genes?Why look for mutated genes?

Because most human cancers arise from somatic mutations, Because most human cancers arise from somatic mutations, this makes a physical change in the cancer cell that is different this makes a physical change in the cancer cell that is different from normal cells, i.e. good target for therapyfrom normal cells, i.e. good target for therapy

Also because mutations leading to cancer are somatic, it can in Also because mutations leading to cancer are somatic, it can in theory be a marker of cancer and used for detection and theory be a marker of cancer and used for detection and possible prognosispossible prognosis

Sequencing involves amplifying via polymerase chain reaction (PCR) a Sequencing involves amplifying via polymerase chain reaction (PCR) a “coding” region of DNA, and then determining the base pairs that are “coding” region of DNA, and then determining the base pairs that are present, e.g. A, C, G, T using a high throughput machinepresent, e.g. A, C, G, T using a high throughput machine

Challenges of finding mutationsChallenges of finding mutations

Sequencing all known genes (>20,000)Sequencing all known genes (>20,000) SamplesSamples

Number of samples for analysisNumber of samples for analysis Amount of DNA needed to sequence all Amount of DNA needed to sequence all

genes per a given samplegenes per a given sample Need for a “normal” matched control from Need for a “normal” matched control from

same individual to verify mutations as somaticsame individual to verify mutations as somatic

Challenges of finding mutationsChallenges of finding mutations Sequencing all known genes (>20,000)Sequencing all known genes (>20,000)

As an initial first “pass”, we analyzed all known genes As an initial first “pass”, we analyzed all known genes in the largest most complete human genome data in the largest most complete human genome data base: the consensus coding sequences (CCDS) data base: the consensus coding sequences (CCDS) data base containing > 13,000 genesbase containing > 13,000 genes

Employed Employed automatedautomated design of polymerase chain design of polymerase chain reaction (PCR) amplifying primersreaction (PCR) amplifying primers

high-throughput (robotic) PCR, DNA sequencing, and high-throughput (robotic) PCR, DNA sequencing, and mutation analysis. This protocol allowed the efficient mutation analysis. This protocol allowed the efficient analysis of more than 135,000 amplicons analysis of more than 135,000 amplicons representing the coding sequences of greater than representing the coding sequences of greater than 13,000 genes, covering more than 500 Mb of tumor 13,000 genes, covering more than 500 Mb of tumor sequence. sequence.

Challenges of finding mutationsChallenges of finding mutations

SamplesSamples Needed enough samples of breast and colon Needed enough samples of breast and colon

cancers for statistically sound study, but cancers for statistically sound study, but balanced by small amounts of DNA in clinical balanced by small amounts of DNA in clinical specimens and costs of sequencingspecimens and costs of sequencing

Performed a two-tier approachPerformed a two-tier approach

Sequencing StrategySequencing Strategy

First used 11 breast and 11 colon cancer cell First used 11 breast and 11 colon cancer cell lines (unlimited source of DNA) to sequence lines (unlimited source of DNA) to sequence 13,000 genes: “Discovery Screen”13,000 genes: “Discovery Screen”

Any mutations found were confirmed as being Any mutations found were confirmed as being somatic, since normal DNA was available for somatic, since normal DNA was available for each of these cell lines, and then resequenced each of these cell lines, and then resequenced for verificationfor verification

After verification, only mutated genes from this After verification, only mutated genes from this discovery screen using patient samples of breast discovery screen using patient samples of breast and colon cancers (24 each) were sequenced and colon cancers (24 each) were sequenced for the “Validation Screen”for the “Validation Screen”

Discovery ScreenDiscovery Screen

Sjoblom et al. Science 2006

Validation ScreenValidation ScreenSjoblom et al. Science 2006

What we foundWhat we found

365 somatic mutations in 236 genes365 somatic mutations in 236 genes Unlike previously known genes mutated at Unlike previously known genes mutated at

relatively high frequencies, no such genes relatively high frequencies, no such genes were found in this analysis. Rather, many were found in this analysis. Rather, many different genes were found to mutated at different genes were found to mutated at low frequency hallmarking the low frequency hallmarking the heterogeneity of human cancersheterogeneity of human cancers

What we foundWhat we found

Most mutations were heterozygous Most mutations were heterozygous missense mutations (81%)missense mutations (81%)

Colon cancers have many C Colon cancers have many C T T mutations c/w breast cancers (59% vs. mutations c/w breast cancers (59% vs. 35%)35%)

Breast cancers have more C Breast cancers have more C G G mutations c/w colon cancers (29% vs. 7%)mutations c/w colon cancers (29% vs. 7%)

““Passenger” mutationsPassenger” mutations

Cancers have a high rate of mutationCancers have a high rate of mutation How to distinguish between functionally How to distinguish between functionally

important mutations vs. passenger important mutations vs. passenger mutations?mutations?

CaMP scores and CAN genesCaMP scores and CAN genes Statistical analysis that takes into account that Statistical analysis that takes into account that

mutations were validated in a two step processmutations were validated in a two step process Also factors in frequency of mutation and Also factors in frequency of mutation and

number of base pairs sequenced along with number of base pairs sequenced along with background rate of mutationbackground rate of mutation

Cancer Mutation Prevalence score (CaMP) Cancer Mutation Prevalence score (CaMP) equals likelihood gene is mutated at frequency equals likelihood gene is mutated at frequency higher than background, i.e. it is a cancer (CAN) higher than background, i.e. it is a cancer (CAN) genegene

CaMP score >1 predicted to have >90% CaMP score >1 predicted to have >90% likelihood of mutational frequency above likelihood of mutational frequency above backgroundbackground

Number and types of CAN genesNumber and types of CAN genes

Breast and colon cancers had 122 and 69 CAN Breast and colon cancers had 122 and 69 CAN genes, respectivelygenes, respectively

Breast cancers had on average 12 mutated CAN Breast cancers had on average 12 mutated CAN genes (range 4 to 23)genes (range 4 to 23)

Colon cancers had on average 9 mutated CAN Colon cancers had on average 9 mutated CAN genes (range 3 to 18)genes (range 3 to 18)

All previous known mutated genes in cancer All previous known mutated genes in cancer were found serving as internal controlwere found serving as internal control

Most genes were never known to be mutatedMost genes were never known to be mutated Some genes were never previously implicated Some genes were never previously implicated

with human cancerswith human cancers

Classes of CAN genesClasses of CAN genes

Sjoblom et al. Science 2006

Limitations of StudyLimitations of Study Technical issues prevented sequencing ~10% of Technical issues prevented sequencing ~10% of

CCDS genesCCDS genes Sequenced CCDS database (~2/3 of expressed Sequenced CCDS database (~2/3 of expressed

genes); likely that there are more mutations in genes); likely that there are more mutations in other genes not yet sequencedother genes not yet sequenced

Cannot detect other genetic alterations e.g. Cannot detect other genetic alterations e.g. amplifications, large deletions, epigenetic amplifications, large deletions, epigenetic silencing, silencing,

Starting samples for primary samples Starting samples for primary samples homogenous for single breast subtype, i.e. homogenous for single breast subtype, i.e. selection biasselection bias

Summary of studySummary of study

Feasibility/proof of principle: Study was Feasibility/proof of principle: Study was completed in less than 1 year with largely completed in less than 1 year with largely philanthropic and foundation supportphilanthropic and foundation support

High complexity and heterogeneity of the genes High complexity and heterogeneity of the genes involved with cancer; no cancer sample had involved with cancer; no cancer sample had more than 6 CAN genes mutated in common more than 6 CAN genes mutated in common with any other samplewith any other sample

Many new genes and pathways have been Many new genes and pathways have been discovered to be mutated/altered in breast and discovered to be mutated/altered in breast and colon cancerscolon cancers

Clinical ImplicationsClinical Implications

Prevention/early screening/diagnosisPrevention/early screening/diagnosis May be possible in the future to sequence genes that May be possible in the future to sequence genes that

are mutated “early” in the neoplastic process are mutated “early” in the neoplastic process Could be useful for detecting early lesions and/or Could be useful for detecting early lesions and/or

predicting likelihood of progressing to more malignant predicting likelihood of progressing to more malignant phenotype, e.g. in situ disease to invasive cancerphenotype, e.g. in situ disease to invasive cancer

May be helpful in categorizing subtypes of cancers May be helpful in categorizing subtypes of cancers based on mutations foundbased on mutations found

Not likely to happen until “early” genes are sorted out Not likely to happen until “early” genes are sorted out and costs of sequencing declinesand costs of sequencing declines

Clinical ImplicationsClinical Implications

Predictor/PrognosisPredictor/Prognosis Depending on gene/families mutated, may be Depending on gene/families mutated, may be

predictive of response to current and future predictive of response to current and future therapies, e.g. Her2/neu amplification and therapies, e.g. Her2/neu amplification and HerceptinHerceptin

May yield prognostic information e.g. May yield prognostic information e.g. likelihood of progressing/recurrence/deathlikelihood of progressing/recurrence/death

Would again take time to validateWould again take time to validate

Clinical ImplicationsClinical Implications

Predictor/PrognosisPredictor/Prognosis Could potentially be used for detecting distant Could potentially be used for detecting distant

sites of disease with single molecule DNA sites of disease with single molecule DNA detection of a mutation found in the primary detection of a mutation found in the primary samplesample

For example, if a breast cancer was found to For example, if a breast cancer was found to have a certain mutation, we could look for that have a certain mutation, we could look for that mutation in the lymph nodes or blood of the mutation in the lymph nodes or blood of the patient to determine spread of the diseasepatient to determine spread of the disease

Single Molecule Detection of Single Molecule Detection of Mutant DNAMutant DNA

Normal + Mutant Genes

Digital-PCR

All 1

1 2

2

Normal

Mutant

BEAMingBEAMing

Dressman et al., PNAS, 2003

Clinical Implications-TherapyClinical Implications-Therapy

Previous methods of finding drugs to treat Previous methods of finding drugs to treat cancers involved screening compounds cancers involved screening compounds that killed cancer cells but had minimal that killed cancer cells but had minimal effect on normal cellseffect on normal cells

Often was very toxic to other normal cell Often was very toxic to other normal cell typestypes

Unknown mechanism of anti-tumor effectUnknown mechanism of anti-tumor effect No way to control for differences between No way to control for differences between

various cancer and normal cellsvarious cancer and normal cells

High throughput drug screening High throughput drug screening using paired cell linesusing paired cell lines

p21 knock out cell linesp21 knock out cell lines

High throughput drug screening High throughput drug screening using paired cell linesusing paired cell lines

‘‘Selectivity’Selectivity’‘Normal genotype’ + drug

‘Normal genotype’ no drug’

‘Cancer genotype’ no drug’

‘Cancer genotype’ + drug’

0

10

20

30

40

50

60

70

80

90

100

110

120

0.0 1.4 4.3 12.9 38.6 115.7 347.2 1041.7 3125.0

Concentration (nM)

% S

urvi

val

ERIN

ERIKMW 356.46

A

A novel drug selective for p21-/-(ERIK) cells

(patent pending)

ConclusionsConclusions

Cancer is a genetic disease, largely of somatic Cancer is a genetic disease, largely of somatic mutationsmutations

Large scale genomic sequencing is feasible, Large scale genomic sequencing is feasible, though costs of scale need to be reduced for though costs of scale need to be reduced for individual cancer genomes to be sequenced individual cancer genomes to be sequenced efficientlyefficiently

Most cancers have a number of somatic Most cancers have a number of somatic mutations (9-12) but have striking mutational mutations (9-12) but have striking mutational heterogeneity even within the same organ typeheterogeneity even within the same organ type

ConclusionsConclusions

This leads to the conclusion that targeted This leads to the conclusion that targeted therapies against a given mutated gene will not therapies against a given mutated gene will not be pragmatic, and that targeting be pragmatic, and that targeting pathwayspathways may may be a more reasonable approachbe a more reasonable approach

As costs continue to decline, the ability to As costs continue to decline, the ability to sequence a patient’s cancer genome becomes a sequence a patient’s cancer genome becomes a reality and will lead to new and better means of reality and will lead to new and better means of detection, prognosis and treatmentdetection, prognosis and treatment

AcknowledgmentsAcknowledgments

AcknowledgmentsAcknowledgments Avon FoundationAvon Foundation FAMRIFAMRI V FoundationV Foundation Department of DefenseDepartment of Defense NIH/NCINIH/NCI The Maryland Cigarette The Maryland Cigarette

Restitution FundRestitution 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, RR017698, P30-CA43703, and CA109274, The Pew Charitable

Trusts, The Palmetto Health Foundation, The State of Ohio BiomedicalResearch and Technology Transfer Commission, The

Clayton Fund, The Blaustein Foundation, The NationalColorectal Cancer Research Alliance, Strang Cancer

Prevention Center.

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