Munne pgs overviewpgdchina200911withsound 12588814598727-phpapp02 (1)

USA: Livingston, NJ Europe: Barcelona, Spain Oxford, UK Hamburg, Germany

Asia: Kobe, Japan

South America: Lima, Peru

Santiago MunnéSantiago Munné

PGD: an overview

Indications to be discussed:Indications to be discussed:

1.1. PGD for advanced maternal agePGD for advanced maternal age2.2. PGD for recurrent pregnancy lossPGD for recurrent pregnancy loss3.3. PGD for translocationsPGD for translocations4.4. PGD for gene defectsPGD for gene defects

- High rate of chromosome abnormalities in embryosHigh rate of chromosome abnormalities in embryos- Hypothesis: PGD should improve ART outcomeHypothesis: PGD should improve ART outcome- Results are not consistentResults are not consistent- Proposed reasons:Proposed reasons: a) Mosaicisma) Mosaicism b) Self-correctionb) Self-correction

c) Biopsy damagec) Biopsy damaged) Methods usedd) Methods used

- Optimized methods can produce good results- Optimized methods can produce good results- New methods (CGH, aCGH) may produce better resultsNew methods (CGH, aCGH) may produce better results- New methods can also be used for gene defect detectionNew methods can also be used for gene defect detection

PGD for advanced maternal age: summaryPGD for advanced maternal age: summary

High rates of High rates of Chromosome abnormalitiesChromosome abnormalities

embryos analyzed: 6054. Morphologically normal embryos: 3751. Source: Munnembryos analyzed: 6054. Morphologically normal embryos: 3751. Source: Munnéé et al. 2007. et al. 2007.Similar results also found by Munne et al 1995, Marquez et al. 2000, Magli et al. 2007.Similar results also found by Munne et al 1995, Marquez et al. 2000, Magli et al. 2007.

% chromosomally abnormal embryos

56%

Maternal age

Morphology:

The majority of embryos with ‘good’ The majority of embryos with ‘good’ morphology are chromosomally abnormalmorphology are chromosomally abnormal

Trisomy 21

Despite large studies indicating the Despite large studies indicating the

advantages of aneuploidy screening, the advantages of aneuploidy screening, the

notion that PGS for infertility is beneficial is notion that PGS for infertility is beneficial is

not yet shared uniformly.not yet shared uniformly.

Contradicting PGD results Contradicting PGD results using day 3 biopsy and FISHusing day 3 biopsy and FISH

Positive effectGianaroli et al. 1999Munne et al 1999Gianaroli et al 2001aGianaroli et al. 2001bMunne et al. 2003Gianaroli et al. 2004Munne et al. 2005Munne et al 2006Verlinsky et al. 2005Colls et al. 2007Garrisi et al. 2009Rubio et al. 2009

No effect (small) Werlin et al. 2003Jansen et al. 2008Mersereau et al. 2008Schoolcraft et al. 2009

No effect (Large)Staessen et al. 2004Platteau et al. 2005

Negative effect Mastenbroek et al. 2007Hardarson et al. 2008

Contradicting PGD results Contradicting PGD results using day 3 biopsy and FISHusing day 3 biopsy and FISH

Proposed explanations:

1) Mosaicism

2) Embryo biopsy damage

3) Sub-optimal PGD methods

MosaicismMosaicism

592 embryos found abnormal by PGD were reanalyzed and found to be: 592 embryos found abnormal by PGD were reanalyzed and found to be: normal normal 1313 mosaic <49% abnormalmosaic <49% abnormal 2727 mosaic 50-99% abnormal mosaic 50-99% abnormal 124124 mosaic 100% abnormal mosaic 100% abnormal 297297 homogeneously abnormal homogeneously abnormal 131131

Colls et al. (2007)Colls et al. (2007)

Mosaicism produces <7% misdiagnosisMosaicism produces <7% misdiagnosis

1[13]1[16]2[18]2[21]1[22]

2[13]1[16]2[18]2[21]2[22]

1[13]1[16]2[18]2[21]1[22]

1[16] 2[13]2[16]2[18]2[21]2[22]

2[13]1[16]2[18]1[21]1[22]

2[13]3[18]1[21]1[22]

3[13]1[16]2[18]1[21]3[22]

1[13]1[16]1[18]1[21]1[22]3[13]1[16]2[18]1[21]3[22]

6.8%6.8%

The FISH error of the assay translates into a The FISH error of the assay translates into a

clinical misdiagnosis rate of 0.5%clinical misdiagnosis rate of 0.5%

NEGATIVE PREDICTIVE VALUE: NEGATIVE PREDICTIVE VALUE: 99.5%99.5%- Data from >150 IVF centers referring to ReprogeneticsData from >150 IVF centers referring to Reprogenetics

- 10 aneuploid abortions of 2148 implanted fetuses 10 aneuploid abortions of 2148 implanted fetuses

- Average maternal age = 37 years- Average maternal age = 37 years

Clinical misdiagnosisClinical misdiagnosis

Reasons for Reasons for Contradictory results:Contradictory results:

Not optimized methodsNot optimized methods

Optimal PGSOptimal PGS Questionable PGSQuestionable PGS

Biopsy mediaBiopsy media with aminoacidswith aminoacids simple mediasimple media

Biopsy time / embryoBiopsy time / embryo 1 min1 min > 5 min> 5 min

# cells biopsied# cells biopsied oneone twotwo

Fixation methodFixation method Carnoy’sCarnoy’s Tween 20Tween 20

# chromosomes tested# chromosomes tested ≥8≥8 ≤≤ 66

# analysts / case# analysts / case 22 11

Use of NRR*Use of NRR* yesyes nono

Large experienceLarge experience yesyes nono

Error rateError rate <10%<10% 10-50%10-50%

Number of zygotesNumber of zygotes >5>5 ≤≤ 5 5

*NRR: No result rescue, or re-testing of dubious chromosome with different probes.

Optimal PGS methodsOptimal PGS methods

“ The data presented here clearly indicates that two cell biopsy significantly impacts clinical outcome. Our previous report providing no arguments in favour of PGS (Staessen et al., 2004) was criticised by others arguing that PGS might have been beneficial if only one cell had been removed (Cohen et al., 2007). In respect to the present findings, this criticism seems justified”.

P < 0.001

Two cell biopsy is detrimentalTwo cell biopsy is detrimental

CONTROL 2-CELLS BIOPSIED

Staessen et al (2004)

11.5%

17.1%

Implantation Staessen et al. (2004):

- No significant differences- But 2 cells biopsied

Studies using two-cell biopsyStudies using two-cell biopsy

1)1) 20% of 20% of cyclescycles undiagnosed (literature: 1-3% of undiagnosed (literature: 1-3% of embryos embryos *)*)2) 59% implantation reduction due to biopsy:2) 59% implantation reduction due to biopsy:

3) 3) Average number of embryos analyzed was only 5Average number of embryos analyzed was only 54) Chromosomes 15 and 22 (21% abnormalities) not analyzed4) Chromosomes 15 and 22 (21% abnormalities) not analyzed

59% reduction59% reduction

implantationimplantation Control Control 14.7% 14.7% Biopsied, no PGDBiopsied, no PGD 6.0%6.0% Biopsied and PGDBiopsied and PGD 16.8% 16.8%

* 1% Gianaroli et al. (2004), 3.1% Colls et al. (2007)

PGD for AMA: randomized studiesPGD for AMA: randomized studiesMastenbroek et al. (2007)Mastenbroek et al. (2007)

Twin 20 method Carnoy’s methodVelilla et al. 2002

Optimal method: modified Carnoy’sOptimal method: modified Carnoy’s• Provides largest nuclear diameterProvides largest nuclear diameter• The least overlaps The least overlaps • The lowest error (10% vs. 30%) The lowest error (10% vs. 30%)

Optimal fixation methodsOptimal fixation methods

p13p12

p11.2

q11.2

q21

q22.1

q22.2

q22.3

p13p12

p11.2

q11.2

q21

q22.1

q22.2

q22.3

Colls et al. (2007)

ProbeLSI 21

ProbeTel 21q

No Result Rescue (NRR): alternative-locus No Result Rescue (NRR): alternative-locus probes to resolve unclear resultsprobes to resolve unclear results

NoNo False +False +ResultsResults ErrorsErrors

Without NRRWithout NRR 7.5% 7.5% 7.2%7.2%NRR NRR 3.2% 3.2% 4.7%4.7%

NRR: No Result Rescue, from: Colls et al. (2007)NRR: No Result Rescue, from: Colls et al. (2007)

No Result Rescue (NRR): alternative-locus No Result Rescue (NRR): alternative-locus probes to resolve unclear resultsprobes to resolve unclear results

Analysis of remaining cells of embryos previously analyzed by PGD:Analysis of remaining cells of embryos previously analyzed by PGD:

studystudy error rate error rate

Baart et al 2004 Baart et al 2004 50.0%50.0% Li et al. 2005 Li et al. 2005 40.0%40.0% Gleicher et al. 2009Gleicher et al. 2009 15-20%15-20% Munne et al. 2002 Munne et al. 2002 7.2% 7.2% Colls et al., 2007 Colls et al., 2007 4.7% 4.7% Magli et al. 2007 Magli et al. 2007 3.7% 3.7%

Error rate should be <10%Error rate should be <10%

At least 9 chromosomes should be tested:At least 9 chromosomes should be tested:

# chromosomes# chromosomes % abnormal % abnormal analyzedanalyzed fetuses detectable fetuses detectable

5 5 28%28% 6 6 47%47% 9 9 70%70%12 12 80%80%2424 100% 100%

Number of chromosomes analyzedNumber of chromosomes analyzed

PGD results using PGD results using optimized methodsoptimized methods

Aneuploidy rates for chromosomes X,Y,13,18,21. Munne et al. 2006 and Reprogenetics data up to 10/2007. Average age 37, Observed: Based on 2300 pregnancies after PGD, Expected: Eiben et

al. 1994. Observed and expected adjusted by maternal ages

P<0.001

Significant reduction inSignificant reduction inTrisomic offspringTrisomic offspring

Gianaroli 1999 Munne 2003

Control PGS Control PGS

# cases 135 127 103 103

Mean age 36 36 40 40

Fetal heart 12% 24% 10% 20%

Prospective matched studies. Chromosomes analyzed: X,Y,13,18,21 + Prospective matched studies. Chromosomes analyzed: X,Y,13,18,21 + 15,16,2215,16,22

P<0.001 P<0.005

Gianaroli et al 1999 Munne et al. 2003

Improved implantation rateImproved implantation rateAfter PGDAfter PGD

39.4%39.4%

SART-ASRM (2005)

53.3%53.3%

13.3%13.3%17.7%17.7%

26.2%26.2%

Pregnancy loss in IVFPregnancy loss in IVF

Reduction in pregnancyReduction in pregnancy loss after PGDloss after PGD

Munne et al.

P <0.05 P <0.001

Patients 38-42, FISH with 9-probes, 1 cell biopsied, SART data of 5 centers with >10% PGD cases, 2003-2005, Munné et al. (2007) SART; a= p<0.01 b= p<0.001

Controls PGD

Clinic cycles Loss rate

Live birth

cycles Loss rate

Live birth

1 505 27% 35% 70 22% 40%

2 210 36% 14% 72 27% 15%

3 1204 34% 12% 120 15% 23%

4 509 29% 15% 236 26% 22%

5 191 25% 17% 208 16% 25%

Total 2619 30%a 18%b 706 21%a 24%b

Ongoing pregnancy rates after PGD:Ongoing pregnancy rates after PGD:Data from five IVF centersData from five IVF centers

PGD for RecurrentPGD for RecurrentPregnancy Loss (RPL)Pregnancy Loss (RPL)

• Defined as 3 or more lost pregnanciesDefined as 3 or more lost pregnancies

• It occurs in 1% of fertile populationIt occurs in 1% of fertile population

• Attributed to anatomic, endocrine, Attributed to anatomic, endocrine,

immunological or genetic problems but …immunological or genetic problems but …

• ……>50% of RPL cases are UNEXPLAINED>50% of RPL cases are UNEXPLAINED

BACKGROUND OF RPLBACKGROUND OF RPL

• Werlin L, et al. (2003) Preimplantation genetic diagnosis (PGD) as both a therapeutic and diagnostic tool in assisted reproductive technology. Fertil Steril, 80:467

• Munné et al. (2005) Preimplantation genetic diagnosis reduces pregnancy loss in women 35 and older with a history of recurrent miscarriages. Fertil Steril 84:331

• Munné et al. (2006) PGD for recurrent pregnancy loss can be effective in all age groups. Abstract PGDIS

• Garrisi et al. (2008) Preimplantation genetic diagnosis (PGD) effectively reduces idiopathic recurrent pregnancy loss (RPL) among patients with up to 5 previous consecutive miscarriages after natural conceptions. Fertil. Steril in press

Idiopathic RPL :

All controlled PGD studies on idiopathic All controlled PGD studies on idiopathic RPL show a decrease in miscarriagesRPL show a decrease in miscarriages

• Rubio et al. (in press) Prognosis factors for Preimplantation Genetic Screening in repeated pregnancy loss. Reprod Biomed Online, in press

N=122N=122With ≥3 With ≥3 previous previous losseslosses

*Munné et al. 2005 and unpublished data, **Brigham et al. 1999*Munné et al. 2005 and unpublished data, **Brigham et al. 1999

P<0.05 P<0.001 P<0.001

8%16%

12%

33%

44%39%

94%85%

89%

***

Reduction in miscarriages Reduction in miscarriages in RPL after PGDin RPL after PGD

PGD results according to previous number of miscarriagesPGD results according to previous number of miscarriages

# previous # previous % loss% loss % loss% loss miscarriagesmiscarriages cycles cycles expectedexpected after PGDafter PGD

22 76 76 31%31% 13%13% NSNS

3-53-5 179179 40%40% 14%14% p<0.025p<0.025

>5>5 24 24 48%48% 29%29% NSNS

Garrisi et al. (2008) Garrisi et al. (2008)

Reduction in miscarriages Reduction in miscarriages in RPL after PGDin RPL after PGD

PGD results according to age when previous number of PGD results according to age when previous number of miscarriages is 3-5miscarriages is 3-5

maternal maternal % loss% loss % loss% loss ageage cycles cycles expectedexpected after PGDafter PGD

<38<38 83 83 36%36% 14%14% p<0.05p<0.05

≥≥3838 96 96 44%44% 14%14% p<0.005p<0.005

Garrisi et al. (2008)Garrisi et al. (2008)

Reduction in miscarriages Reduction in miscarriages in RPL after PGDin RPL after PGD

PGD results according to fertilityPGD results according to fertility

methodmethod cyclescycles % loss% loss % loss% loss % %

conceptionconception expectedexpected after PGDafter PGD pp to termto term

IVFIVF 115115 35%35% 14%14% p<0.01p<0.01 34% 34%

naturalnatural 124124 41%41% 15%15% p<0.005p<0.005 37% 37%

Average maternal age: 37.5Average maternal age: 37.5Garrisi et al. (2008)Garrisi et al. (2008)

Reduction in miscarriages Reduction in miscarriages in RPL after PGDin RPL after PGD

• Munné et al (1998). Spontaneous abortions are reduced after pre-conception diagnosis of translocations. J Assited Reprod Genet 290:

• Munné S et al. (2000) Outcome of Preimplantation Genetic Diagnosis of translocations. Fertil Steril. 73:1209

• Verlinsky et al. (2005) Preimplantation testing for chromosomal disorders improves reproductive outcome of poor prognosis patients. Reprod Biomed Online 11:219

• Otani et al.(2006) Preimplantation genetic diagnosis significantly improves the pregnancy outcome of translocation carriers with a history of recurrent miscarriage and failing to produce a live birth. Reprod Biomed Online 13: 879

RPL due to translocations:

• Munné S (2006) Preimplantation genetic diagnosis for translocations. Hum Reprod 21:839

All PGD studies on RPL for translocations All PGD studies on RPL for translocations show a decrease in miscarriagesshow a decrease in miscarriages

PatientsPatients successfulsuccessful Risk ofRisk of time time pregnancies pregnancies miscarriagemiscarriage frameframe(cumulative)(cumulative)

with PGDwith PGD11 43 43 33%33% 17%17% 1.4 cycles (<4 months) 1.4 cycles (<4 months) 22 200 200 26%26% 11 %11 % 1.4 cycles (<4 months)1.4 cycles (<4 months)33 52 52 52%52% 18%18% 1.4 cycles 1.4 cycles (<4 months)(<4 months)without PGDwithout PGD44 4747 32%32% 68%68% 11.5 months11.5 months 55 4747 68%68% 65%65% 23.3 months23.3 months66 41 41 74% 74% 26%26% 6 years6 years77 28 28 64%64% 38%38% 4.2 years4.2 years

1: Lim et al. 1: Lim et al. (2004), 2: Munne et al. (2006d) and unpublished, 3: (2004), 2: Munne et al. (2006d) and unpublished, 3: Otani et al. (2006) and unpublished results, 4: Sugiura-Otani et al. (2006) and unpublished results, 4: Sugiura-Ogasawara et al. (2004)(a), 5: Sugiura-Ogasawara et al. (2004)(b), 6: Goddjin et al (2004)(e); 7: Stephenson and sierra (2006)Ogasawara et al. (2004)(a), 5: Sugiura-Ogasawara et al. (2004)(b), 6: Goddjin et al (2004)(e); 7: Stephenson and sierra (2006)

PGD for translocations is better option PGD for translocations is better option than natural cyclethan natural cycle

Strategies for full Strategies for full chromosome countchromosome count

• Sequential FISH with 24 probesSequential FISH with 24 probes

• Comparative Genome Hybridization (CGH)Comparative Genome Hybridization (CGH)

• Array CGHArray CGH

• Single Nucleotide Polymorphism (SNP) arraySingle Nucleotide Polymorphism (SNP) array

– Interpretation: standardInterpretation: standard

– Interpretation: bioinformaticsInterpretation: bioinformatics

– Interpretation: Karyomapping Interpretation: Karyomapping

Different strategies forDifferent strategies forAnalyzing all chromosomesAnalyzing all chromosomes

Kallioniemi et al. (1992), applied to single cells by Wells et al. (1999)

• Technique related to FISHTechnique related to FISH

• Allows the copy number of every chromosome to be determinedAllows the copy number of every chromosome to be determined

NormalNormal TrisomyTrisomy MonosomyMonosomy

Normal DNANormal DNATest DNATest DNA

Comparative Genome Comparative Genome Hybridization (CGH)Hybridization (CGH)

CGH on blastocyst biopsies:CGH on blastocyst biopsies:AdvantagesAdvantages

Disadvantages:-Time consuming: requires embryo freezing

Advantages:- Reduced impact of embryo biopsy- More cells biopsied: more robust diagnosis- Less risk of mosaicism misdiagnosis- 100% detected aneuploidies- No need for fixing cells or testing parental DNA

CyclesCycles Mat.Mat. Prev.Prev. embryosembryos pregnancy implant. pregnancy implant.ageage failedfailed replaced replaced rate rate rate rate

cyclescycles

Test : Test : 70 70 37.737.7 2.4 2.4 2.0 2.0 81%81% 62% 62%

control : control : 272272 37.137.1 1.2 1.2 2.7 2.7 57%57% 35% 35%

p<0.0001p<0.0001 p<0.001 p<0.001

Schoolcraft et al. (2009, ASRM)Schoolcraft et al. (2009, ASRM)

24 chromosome analysis in24 chromosome analysis inblastocyst biopsies: clinical resultsblastocyst biopsies: clinical results

CGH-based DNA CGH-based DNA Microarray (aCGH)Microarray (aCGH)

Test DNA

Normal DNA

2700 probesSame band resolution as karyotype

47,XY+2

aCGH advantagesaCGH advantages

• aCGH Results in aCGH Results in 2424 hours, allowing either PB, day 3 or hours, allowing either PB, day 3 or blastocyst biopsyblastocyst biopsy

• parental DNA parental DNA notnot needed: procedure can be decided on needed: procedure can be decided on same day of biopsysame day of biopsy

• Is quantitative detecting mitotic-origin aneuploidy and Is quantitative detecting mitotic-origin aneuploidy and MII mitotic errors without crossing-over (SNP array MII mitotic errors without crossing-over (SNP array methods that are only qualitative do not detect them).methods that are only qualitative do not detect them).

46,XX-10 +16

aCGH detected aCGH detected 50%50% more abnormalities than FISH-12 and more abnormalities than FISH-12 and 20%20% more abnormal embryos (Colls et al. 2009)more abnormal embryos (Colls et al. 2009)

Detection of abnormalities: Detection of abnormalities: aCGH vs FISH-12aCGH vs FISH-12

aCGH validation: error rateaCGH validation: error rate

• Validation method: Reanalysis of the rest of the embryo Validation method: Reanalysis of the rest of the embryo by FISH with 12 chromosomes plus those found abnormal by FISH with 12 chromosomes plus those found abnormal by aCGHby aCGH

• Error rate (biopsy day 3): Error rate (biopsy day 3): 6% 6% (same as expected by (same as expected by mosaicism)mosaicism)

• Cells with no results: Cells with no results: 1%1%

SNP and CNP arrays:SNP and CNP arrays:For diagnosis of aneuploidyFor diagnosis of aneuploidy

• SNP: Single Nucleotide PolymorphismSNP: Single Nucleotide Polymorphism

• They are normally occurring genetic variantThey are normally occurring genetic variant

• 1,854,000 identified so far 1,854,000 identified so far

• 99% inherited 99% inherited

What are SNPs?What are SNPs?

Microarray Example: Microarray Example: Affimetrix SNP 6.0 chip can measure Affimetrix SNP 6.0 chip can measure simultaneously 900,000 SNPs and CNPs at simultaneously 900,000 SNPs and CNPs at 1.8 million genome locations1.8 million genome locations

Analysis with karyomapping: Analysis with karyomapping: Handyside’ teamHandyside’ team

• SNP array method based on mapping crossovers between parental haplotypes

• Requires previous genotyping of parents and family member(s) with SNP arrays

• Mendelian analysis of parental and grandparental haplotypes

• Construction of karyomaps identifying the parental and grandparental origin of each chromosome or chromosome segment in each embryo

• Developed by Handyside, Thornhill, Harton, Mariani, Affara and Griffin

Slide adapted from A.Handyside

Monosomia 11

Trisomia 21

Comparison ofComparison ofdifferent approachesdifferent approaches

Comparison of platformsComparison of platforms

FISH 12FISH 12 CGHCGH arrayarray SNPSNPprobesprobes CGHCGH arraysarrays

Day 3 biopsy/ day 5 resultsDay 3 biopsy/ day 5 results yesyes nono yesyes yesyesParental DNA neededParental DNA needed nono nono nono yesyesDetect gene defectsDetect gene defects nono nono nono yesyesDetect polyploidy (risk error)Detect polyploidy (risk error) yesyes no (0.2%)no (0.2%) no (0.2%)no (0.2%) yesyesDetect MII errors w/o crossoverDetect MII errors w/o crossover yesyes yesyes yesyes no?no?Detect mitotic errorsDetect mitotic errors yesyes yesyes yesyes no?no?Error rate Error rate 77%% 8%8% 6%6% unkunkNo Results RateNo Results Rate 3.2%3.2% 9%9% 0%0% unkunk

Increased implantation rate Increased implantation rate ++ + + ++ + + unkunk unkunk

Reprogenetics data.Reprogenetics data.

BiopsyBiopsy AnalysisAnalysis cyclecycle implantation Commentsimplantation Comments teamteamdayday replacedreplaced improvementimprovement

PBPB FISHFISH samesame ++ only MI and MII abnormalitiesonly MI and MII abnormalities RGIRGIPBPB CGHCGH samesame ++ applied to young donorsapplied to young donors SherSher

day 3day 3 FISHFISH samesame ++ 9-12 chromosomes, 5% errors9-12 chromosomes, 5% errors Reprogenetics Reprogenetics day 3day 3 FISHFISH samesame ++ 9-12 chromosomes, 5% errors9-12 chromosomes, 5% errors SISMErSISMErday 3 day 3 FISH FISH same same - - - -- - - - severe biopsy damage, etc, etcsevere biopsy damage, etc, etc MastenbroekMastenbroekday 3day 3 CGHCGH samesame ++ / / -- SherSherday 3day 3 aCGHaCGH samesame pre-clinicalpre-clinical Reprogenetics Reprogeneticsday 3day 3 SNP arraySNP array samesame pre-clinicalpre-clinical Some MII errors, mitotic not Some MII errors, mitotic not RMA / BridgeRMA / Bridge

detected? detected? Kearns / GSNKearns / GSN

blastocystblastocyst FISHFISH samesame ++ / / -- 5 probes/ scoring too stringent? 5 probes/ scoring too stringent? Sydney IVF Sydney IVF blastocyst blastocyst CGHCGH nextnext + + + + + + BEST RESULTS SO FARBEST RESULTS SO FAR Reprogenetics /Reprogenetics /

SchoolcraftSchoolcraft

What matters most? What matters most? Biopsy or information quality?Biopsy or information quality?

PGD for PGD for gene defectsgene defects

PGD for gene disordersPGD for gene disorders

Disease tested: Acetil Co Oxidase type I defficiency, Adrenoleucodistrophy, Alpha-thalassemia, Alport syndrome, Autosomal Dominant Polycystic Kidney Disease (ADPKD), Autosomal Recesive Polycystic Kidney Disease (ARPKD), Beta-thalassemia, Branchio-Oto-Renal syndrome (BOR), BRCA1 breast cancer predisposition, BRCA2 breast cancer predisposition, CanavanCharcot-Marie-Tooth type IA (CMT1a), Choroideremia, Congenital adrenal hyperplasia (CAH), Congenital neutropenia, Connexin 26 hearing loss, Cystic fibrosis, Duchenne/Becker Muscular Dystrophy (DMD), Ectrodactyly, Ectodermal dysplasia, and Cleft lip/palate syndrome (EEC1), Fabry Disease, Familial adenomatous poliposis coli (FAP), Familial dysautonomia, Familial intrahepatic cholestasis 2, Fanconi anemia, Fragile site mental retardation , Gangliosidosis type 1 (GM1), Gaucher disease, Glomuvenous malformations (GVM), Glycogen-storage disease type I (GSD1), Glycosylation type 1C, Hemoglobin SC disease, Hemophilia A, Hemophilia B, Hereditary nonpolyposis colon cancer (HNPCC), Hereditary pancreatitis, HLA matching Huntington disease, Hurler syndrome, Hypophosphatasia, Incontinential pigmenti, Krabbe disease (Globoid cell leukodystrophy), Long QT syndrome, Marfan syndrome, Meckle gruber, Metachromatic leukodystrophy (MLD), Methylmalonic aciduria cblC type (MMACHC), Myotonic Dystrophy 1, Myotubular myopathy, Neurofibromatosis 1, Neurofibromatosis 2, Niemann-Pick Disease, Noonan syndrome, Oculocutaneous albinism 1 (OCA1), Ornithine carbamoyltransferase deficiency (OTC), Osteogenesis Imperfecta 1, Rapp Hodgkin ectodermal dysplasia, Retinitis pigmentosa, Retinoblastoma, Sickle Cell Anemia, Smith-Lemli-Opitz syndrome (SLOS), Spinal bulbar muscular atrophy (SBMA), Spinal Muscular Atrophy Type 1 (SMA1), Tay Sachs, Tuberous sclerosis 1 (TSC1), Tuberous sclerosis 2 (TSC2), Von Hippel-Lindau Syndrome (vHL), X-linked dominant Charcot–Marie–Tooth (CMTX), etc…… (see review Gutierrez et al. (2008))

We can do PGD for any disease with known mutation

accurate accurate amplification amplification of both lociof both loci

ADO affecting ADO affecting normal allelenormal allele

ADO affecting ADO affecting mutation sitemutation site

Mutation siteMutation site

Polymorphic Polymorphic sitesite

Conventional approach to PGD of gene Conventional approach to PGD of gene defects to avoid Allele Drop Out (ADO)defects to avoid Allele Drop Out (ADO)

Maternal DNAMaternal DNA

Paternal DNAPaternal DNA

PossiblePossible

embryoembryo

genotypesgenotypes

ContaminationContamination

Maternal contaminantMaternal contaminant

(e.g. cumulus cell)(e.g. cumulus cell)

Unknown contaminantUnknown contaminant

(e.g. skin cells from (e.g. skin cells from lab staff)lab staff)

DNA fingerprinting against contaminationDNA fingerprinting against contamination

Gene mutation

♂ ♀

Slide adapted from A.Handyside

Detection of gene mutations by SNP arrays

Santiago Munné, PhD, DirectorSantiago Munné, PhD, DirectorJacques Cohen, PhD, DirectorJacques Cohen, PhD, Director

[email protected] [email protected] www.reprogenetics.comwww.reprogenetics.com

Pere Colls, PhDPere Colls, PhDDagan Wells, PhDDagan Wells, PhDGeorge Pieczenik, PhDGeorge Pieczenik, PhD Cristina Gutiérrez, PhDCristina Gutiérrez, PhD Jorge Sanchez, PhDJorge Sanchez, PhD John Zheng, MDJohn Zheng, MD Tomas Escudero, MSTomas Escudero, MSKelly Ketterson, MSKelly Ketterson, MSJill Fischer, MSJill Fischer, MS

Jessica Vega, MSJessica Vega, MS Tim Schimmel, BSTim Schimmel, BSSasha Sadowy, BSSasha Sadowy, BSSophia Tormasi, BSSophia Tormasi, BSN-neka Goodall, BSN-neka Goodall, BSRenata Prates, BSRenata Prates, BS Piedad GarzonPiedad Garzon Laurie FerraraLaurie FerraraBekka Sellon-WrightBekka Sellon-WrightMaria FeldhausMaria Feldhaus

USAUSA

SpainSpainMireia Sandalinas, MSMireia Sandalinas, MSCarles Giménez, PhDCarles Giménez, PhD

César Arjona, MSCésar Arjona, MSAna Jiménez, PhDAna Jiménez, PhDElena Garcia, MS Elena Garcia, MS

JapanJapanTetsuo Otani, MDTetsuo Otani, MD

Muriel RocheMuriel RocheMiho MizuikeMiho Mizuike

UKUKDagan Wells, PhDDagan Wells, PhD

Elpida Fragouli, PhDElpida Fragouli, PhDSamer Alfarawati, MSSamer Alfarawati, MS

South AmericaSouth AmericaPaul Lopez, BSPaul Lopez, BS

Luis Alberto Guzman, BSLuis Alberto Guzman, BSFrancisco Parera, PhDFrancisco Parera, PhD

GermanyGermanyKarsten Held, MDKarsten Held, MD