Repair of DNA double-strand breaks and susceptibility to breast cancer

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Repair of DNA double-strand breaks and susceptibility to breast cancer Powered By Docstoc
					      Breast/Ovarian Family



       † 57                   † 49                 43




 54       51        48                   55             B 32
                                                        O 59


                         +           +
22             32        PO          PO       39
                         45          45
Inherited predisposition
   More BRCA-like genes

   Rare, moderately strong variants

   Common genetic variation
Role of normal genetic variation in
 determining individual risk.

How useful is this information in
 selection for screening and
 prevention?

How do we find the genes?

      Breast cancer as an example
 Evidence that genetic variation
          affects risk

Measure of variation = familial
 clustering

Risk in close blood relative compared to
 risk in population as a whole

            = roughly 2-fold.
 Is family clustering genetic?
                      Incidence % per year
MZ twin                      1.31

DZ twin                         0.5
Mother/sister                   0.36

Patient‟s contralateral breast         0.66

                   (Peto & Mack, Nat Genet 26, 411
 (2000))
How much genetic predisposition is there?
        How is it distributed?

            Determines potential for
         discriminating individual risks




                     risk
      Breast/Ovarian Family



       † 57                   † 49                 43




 54       51        48                   55             B 32
                                                        O 59


                         +           +
22             32        PO          PO       39
                         45          45
Familial clustering of breast cancer
                  OBS        EXP           Excess

                  177         106             71

Population



                   13         1.47            11.5
BRCA1/2
mutation
               Fraction of excess familial clustering
                attributable to BRCA1/2 = 15-20%
   Familial clustering of breast
              cancer

Risk to                                  Roughly 15-20%
1o relative                              due to BRCA1/2
              2
of case
                  Excess familial risk
                                              ATM
                                              Chk-2
              1                               Ha-ras
                                              PTEN
         What sort of genes may account
      for familial risk apart from BRCA1/2?
                 Common low-penetrant
                      genes




                                        BRCA3 etc    BRCA1, 2


                        1.5                   10                Relative risk


Allele freq.   XsFRR    Number      Allele freq.   XsFRR   Number
    1%            .25     350              0.2%         16       5
   10%            2.3      35
   30%            5.3      16
       Patterns of breast cancer in
                 families
1500 cases, population based
BRCA1/2 excluded




                 What model fits best?
Best fit = combined result of several
 factors,            individually of small
 effect

         = log-normal distribution of risk

             in population.
   Distribution of genotypes in
population and cases by genotype
                risk
 0.040
                                                        SD = 1.2


 0.030
         Population                          Cases

 0.020



 0.010



 0.000
     0.01             0.10       1.00           10.00       100.00
                             Relative risk
Proportion of population and cases
  above specified risk: SD = 1.2
                      100%
                                  88%
Proportion above given risk (x)




                                                                       Cases
                                                                       Population


                                  50%
                                  46%




                                  10%

                                  0%
                                        0% 3%   12%   20%              40%            60%   80%
                                                       Risk of breast cancer by age 70
   Effects of normal genetic
variation on breast cancer risks

 Population   10%              50%




 Cancers               46%
                    12%


 Individual risk
   by age 70            >1:8   < 1 : 30
Proportion of population and cases
  above specified risk: SD = 0.8
                                  100%
Proportion above given risk (x)



                                   80%
                                                                       Cases
                                                                       Population


                                  50%

                                  31%



                                  10%
                                   0%
                                         0% 4% 11%   20%            40%                60%   80%
                                                     Risk of breast cancer by age 70
Proportion of population and cases
  above specified risk: SD = 0.3
           100%
Proportion above given risk (x)




                              75%                             Cases
                                                              Population


                              50%



                              25%



                                  0%
                                       0%   20%             40%               60%   80%
                                            Risk of breast cancer by age 70
Gail model of breast cancer
               risk Analysis
     Nurses Health Study

Excellent prediction of breast cancer incidence in
  specified population.

Poor prediction of risk to individual.

     2.8-fold between upper and lower deciles
     cut-off for tamoxifen use defined 33% of
           population with 44% of cases.
                            (Rockhill, JNCI 93, 358 (2001))
                   - find genes
                   - interactions
                   - validation




1/5          1/5
       40x
      risk
      QuickTime™ and a
 Photo - JPEG decompressor
are need ed to see this picture.
How to find the genes?
     Association studies
      C        T          arg       cys

           V


indirect                             direct

           linkage disequilibrium
                        Problems:         recombination
                                          origins different time

                                          multiple origins
Common variant : common disease   Rare variants




    Marker
    Disease allele
 Candidate genes
Estrogen synthesis and degradation;
  ER
Cell cycle checkpoints
DNA repair
TGFb pathway
IGF pathway
Carcinogen metabolism
               Sample sets
Initial : 2000 cases, 2000 controls
Confirmatory : 2000 cases, 2000 controls

 Cases -   Population based, East Anglia
             simple epidemiology data, survival;
             paraffin blocks
 Controls - EPIC cohort, East Anglia
             extensive epidemiological data, follow-up,
             serum, mammography, bone density, etc
                                         Power
                         Percentage polygenic variance explained.          90% power
                                                                             p = 10-4
                                                                          multiplicative
              6000

              5000
Sample size




              4000                                                                 1%
                                                                                   2%
              3000
                                                                                   5%
              2000                                                                 10%

              1000

                0
                     0    0.05     0.1    0.15     0.2      0.25    0.3   0.35
                                         Allele Frequency

                                                         (Antoniou & Easton, submitted)
  Provisional positive associations :
            breast cancer
               98 snps   47 candidate genes
                                Risk Br Ca Fraction
                                to age 70 of excess
        Freq    OR       PAF      (5.7%)      RR

TGFb    14%      1.25    2.9%      6.8%       0.2%
BRCA2   7%       1.31    2.1%      7.4%       0.3%
XRCC3   15%      1.34    4.4%      7.4%       0.5%
ERa     20%      1.27    5%         6.8%        0.5%

Chk2    0.5%     2.4     0.6%      16%        0.5%

                                                ~2.0%
BRCA2 N372H association with breast cancer risk
                                  Finns HH


                                  HDB HH

                                  UK set 3 HH

                                  UK set 2 HH

                                  UK set 1 HH




                                  Joint HH

                                  Joint NH      p=0.02

                                  Joint NN

          0.1       1        10
 3133            Tee et al. In prep.

                 Fiegelson et al. 2001


 1081
                 Haiman et al. 1999               OR breast
                 Mitrunen et al. 2000
                                                  cancer
                 Kristensen et al. 1999
                                                  CYP17 t -34 c
                 Spurdle et al. 2000
 744                                              (cc Vs. tt)
                 Miyoshi et al. 2000

                 Kuligina et al. 2000         Conclusion:
                 Hamajima et al. 2000
                                              This SNP has no main effect
 310

                 Huang et al. 1999
                                              on breast cancer risk!
                 Helzlouler et al. 1998
 230

                 Weston et al. 1998


 226             Bergman-Jungestrom et al. 1999

                 Young et al. 1999
                                                          Ye & Parry, 2002
 N               Weston et al. 1998
                                                          Mutagenesis 17:119-126
0.1     1   10       100
Why a p value of p = 0.01 is not persuasive
                                  True              False
                                association       association

Prior probability of result
 (snp causing 1% of FRR,
 100,000 snps in genome)           1/1000          999/1000

Probability given result
     has p = 0.01                  99/100            1/100

                                 99/100,000         999/100,000

             Assuming random choice of „candidate‟ gene
                only ~ 10% results at p = 0.01 are true

                                    (~50%, at p = 0.001)
               Summary of results
                                96 snps, 47 genes
                             ~2000 cases, 2000 controls
0.001
 p-value




                        p = 0.01/0.0004 for comparison
                               of distributions
 0.01



 0.05
 0.10                        observed

               chance



 1.00
           0    10      20      30      40      50   60   70   80   90   100
                                             SNP
                % of excess FRR
                   explained




0.5   1   1.3         2   relative risk
       Some reasons why human
       association studies may be
                 difficult
Inappropriate genetic models eg rare/multiple alleles

Regulatory vs coding polymorphisms

Numbers : inadequate statistical power

Genetic background effects; interactions
      weak „main effect‟, high-order interactions
      „null‟ result = balance of susceptible and resistant on
                           different BG

Phenotypic heterogeneity eg ER+/ER-; histology

Cancer/no cancer endpoint lacks power
      Intermediate phenotypes
Serum estradiol and     Serum SHBG and SHBG
CYP19                   Exon 8 g>a or D356N
Exon 10 t>c 3‟UTR
 20                     60
 18                     50
 16
                        40
 14
 12                     30

 10                     20
       tt   tc     cc        gg    ga    aa

 P homogeneity =        P homogeneity = 0.006
 0.0005                 P trend = 0.006
 P trend <0.0001
                              (Ponder, Dowsett labs;
                                EPIC; unpublished)
Implications for breast cancer
             risk
2 fold increase in estradiol  30% increase
  in risk of breast cancer

tt genotype of CYP19 c>t associated with
   14% increase in estradiol: equivalent to
   1.04 fold increase in breast cancer risk
         Where next?
Empirical vs candidate approaches

Snp genotyping now ~17c/genotype :
    ? screen 600 “enriched” cases/600
 controls
         vs 1150 coding snps

                   ~$240,000
Candidate gene approaches

  Candidates from cell biology
  Epidemiology
  Regulatory variants
  Quantitative phenotypes
  Leads from mouse models
Mouse/human collaborations
1. Candidate susceptibility genes/regions

    mapped in susceptible/resistant crosses
    refined by amplicons/deletions in tumours
    allele-specific differences in expression/somatic change
         (easier in mouse because extended haplotypes)

    loci involved in control of gene regulation

    loci influencing intermediate phenotypes
      set up large cross and score multiple phenotypes
    How tightly should the
     region be defined?
                       300 kb



Say 5 genes
First pass = find all coding region snps at >5%
Construct haplotypes, select minimum snp set = ? 30 snps

Genotype 30 snps in 2000 cases/2000 controls = 120,000
  genotypes

Genotyping cost ~$20,000 @ 17c/genotype

BUT : currently requires ~1000 snps at a time
Mouse/human collaborations
2. Interactions

   Identification of interacting loci
          potentially approachable in mouse

   Develop and evaluate programmes to
         search for higher order
   interactions;
         ? applicability to man
 Mouse/human collaborations
3. Stages of cancer development

 ? Distinguish loci that influence
    multiplicity
    latency; progression
    invasion
    metastasis              and resistance to these

 ? Loci that affect treatment response
 Mouse/human collaborations

4. “End game” - which is the active gene,
  snp?

    strain comparisons of variants
    dissection of complex QTLs

    transgenic models
                 A new horizon in
                        medicine? before they
“„Risk factor‟ analysis will facilitate environmental modification,
  screening and therapeutic management of people
  develop symptoms”
                                                 (Bell, BMJ 1998)



“Differences in social structure, lifestyle and environment account
  for much larger proportions of disease than genetic differences
  …… Those who make medical and scientific policies ….. would do
  well to see beyond the hype”
                              (Holtzman & Marteau, NEJM 2000)
Strangeways Research Laboratories -
      University of Cambridge
Bruce Ponder       Doug Easton
Paul Pharoah       Antonis Antoniou   UCSF
Alison Dunning     Mitul Shah          Allan Balmain
Fabienne Lesueur   Julian Lipscombe     Mandy Toland
Bettina Kuschel                         Joe Gray
Annika Auranen     Nick Day; EPIC       Mark Sternlicht
Katie Healey                          NCI
Craig Luccarini                                Kent Hunter
Jenny He
Louise Tee                            Biochemistry, Cambridge
Gary Dew                                Jim Metcalfe

                          Cancer Research UK; MRC
                        TGFb
        t/c   Pro/Leu


       -509      10

                           PP vs LL OR 1.25 (1.1 - 1.4)
                                        p = 0.01
         t      P
                           tt vs cc   OR 1.30 (1.1 - 1.5)
0.25                                     p = 0.01
         c       P
0.11
         c       L
0.60
   Which SNP is the functional variant?

                                             tt ProPro
Pro10 homozygotes
have increased risk                          ct ProPro
regardless of c-509t
                                             cc ProPro
genotype


                                             ct LeuPro

                                             cc LeuPro



                                             cc LeuLeu

           0.1         1.0   Odds Ratio 10
        TGFb in vitro secretion
        End Point       Time Course
    4




    3                                           Pro10
TGFb1                                           Ratio P:L
ng/ml
    2




    1                                           Leu10

    0               0     6           12   18
                              hours


                              (Metcalfe, Ponder labs, 2002)
          Funnel Plot For TGFb L10P
                                  O R (PP Vs. LL)



                           N


                           4517                          ABC
                           875                           HDB
                           939                           Finn

                           404                           Hishido et al.

* Cohort study              *
                           3075                          Ziv et al.
146 cases 2929 controls
                           238                           Frei

                          0.1           1           10