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Mosaicism in human embryos: Etiology and pregnancy outcome
Santi Munné
disclosure
- Chief Scientific Officer of CooperGenomics
- Founder @ Reprogenetics (PGS/PGD company), sold to Cooper
- Founder @ Recombine (Carrier Screening company), sold to Cooper
- Founder @ Phosphorus (genomics as a service)
- Founder @ MedAnswers (DTC advise on genetics and infertility)
- Board advisors PreVivo (alternative to IVF)
Learning objectives
• What is the best technique to detect mosaicism?
• What is the incidence of mosaicism?
• Different types of mosaics have different ongoing potential?
• What is the chance of mosaics to reach term?
PGS v.2 PGD v.2
Methods for Comprehensive Chromosome Screening
Comparison of current PGS platforms
% embryos FISH qPCR aCGH
Embryo Vu
SNP array hr-NGS
Total Independent Data Signals* 11 96 2,700 26,000 32,000 700,000
Resolution in Mb arm 20M 6M 20M 6M 3M
Misdiagnosis aneuploides (a-f) 7% 1% 2% 3% d 2% 0%
Unbalanced translocations (g) 2% custom no yes no yes yes
Partial aneuploidies 5% no no yes some yes yes
Polyploidy 2% yes no no no yes yes
Mosaicism (h, i) 20% 20% no 4% no no 20%
Miscarriage rate (j, k) 10-20% 20% 13% unk unk 11%
a Gutierrez-Mateo et al (2011) Fertil Steril, b Scott et al. (2012), c Treff et al. (2012) Fertil Steril 97:819–24, d unpublished 7 misdiagnoses of 265 samples; e Kung et al. (2015) Reprod Biomed Online, , f Wells et al. (2014) J Med Genet, g Yeobah et al. (2015) ASRM, h Greco et al
(2016) NEJM, i Tormasi et al (2015) PGDIS, ASRM. J Rodriguez-Purata et al. (2016) JARG; k Friedenthal et al. (2017) ESHRE * 24M reads per run, 24 samples per run, 30% reads lost = 700,000 reads per sample
Evolution of PGS techniques
Year
Pro
ce
du
res
PGS data from Reprogenetics (2002 – 2016) + Genesis Genetics (2017) (*) annualized
0
5000
10000
15000
20000
25000
02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17(*)
NGS
aCGH
FISH
High Resolution NGS
Normal Chromosome 8
ATTAGACTTAGCCTAGATTCCAATGACTGA
Thousands of DNA fragments are mapped to each chromosome
High Resolution Next Generation Sequencing (hr-NGS)
Chromosome 10 Chromosome 9
Enables Reliable Detection of Mosaicism
Chromosome 8 Normal Trisomy Mosaic
High Resolution Next Generation Sequencing (hr-NGS)
Validation of hr-NGS by reanalysis of blastocysts
Kung, Munné, Wells et al. (2015) Biomed Reprod Online; Fiorentino et al. (2014) F&S;
Wells, Kung, Munné et al. (2014) J Med Genetics;
Original Analysis method
Reanalysis method
Sample Confirmed Euploid
Confirmed abnormal
TOTAL
Kung et al. 2015 (Reprogenetics)
aCGH NGS Same biopsy
44/44 108/108 152/152
Fiorentino et al. 2014
aCGH NGS Same biopsy
67/67 141/141 208/208
Wells et al. 2014 (Reprogenetics)
aCGH NGS
Separate biopsy
13/13 28/28 41/41
Total 100% Sensitivity
100% Specificity
0% Error rate
Comparison between NGS and aCGH: by Type of Abnormality
(1) Ribustello et al. 2016, PGDIS, (2) Ribustello (2015) ESHRE (3) Bauckman (2016) ESHRE
Reanalysis by aCGH
Original (NGS) Euploid Aneuploid Segmental Ref
Euploid 196 0 0 1,2
Aneuploid 0 222 0 1,2
Mosaic 16 4 0 2
Polyploid 6 4 0 2
Segmental Translocation 0 0 69 3
NGS advantages
• Detection of triploidy 69,XYY and 69,XXY
• Higher resolution than other techniques (1.5Mb)
• Detection of mosaics (20-80% range of abnormal cells or 1/5)
Higher dynamic range than other techniques allows:
Detection of Mosaicism By hr-NGS
30% of day-3 embryos were mosaic by FISH. The majority with all cells abnormal: <49% abnormal 40 50-99% abnormal 124 100% abnormal 528
Mosaicism: Common in day 3 embryos
Munné S, Grifo J, Cohen J, Weier HUG Am J Hum Genet 1994; 55:150-159.
Munné S, Weier HUG, Grifo J, Cohen J Biol. Reprod. 1994; 51:373-379
Colls et al. Fertil Steril 2007;88:53–61
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]
Higher dynamic range allows NGS to detect mosaics
hr NGS
aCGH
Higher
dynamic
range
Example: Mosaic trisomy 5
Full trisomy limit
Full monosomy limit
Mosaicism by NGS: Mixing Experiment
46,XX
46,XY: -16, +18
90% 46,XX: 10% 46,XY; -16, +18
80% 46,XX: 20% 46,XY; -16, +18
70% 46,XX: 30% 46,XY; -16, +18
60% 46,XX: 40% 46,XY; -16, +18
50% 46,XX: 50% 46,XY; -16, +18
40% 46,XX: 60% 46,XY; -16, +18
30% 46,XX: 70% 46,XY; -16, +18
20% 46,XX: 80% 46,XY; -16, +18
10% 46,XX: 90% 46,XY; -16, +18
Walmsley et al. (2016) ESHRE * Each embryo was biopsied 2-5 times, each time is a tissue
• ICM plus 2-5 TE biopsies per embryo analyzed, analyzed by NGS
Multiple tissue analysis of Blastocysts (Walmsley et al. 2016)
% euploid ICM
% euploid tissues*
71 embryos 252 tissues
complete aneuploid 0% 4%
complete segmental 0% 20%
Mosaic complex 11% 42%
mosaic segmental 41% 60%
mosaic aneuploid 44% 37%
Huang et al. (2017) J Assist Reprod Genet (2017) 34:487–493 * Each embryo was biopsied 4 times, each time is a tissue
• ICM plus 3 TE biopsies per embryo analyzed by aCGH
Multiple tissue analysis of Blastocysts (Huang et al. 2017)
% euploid ICM
% euploid tissues*
complete aneuploid (n=26) 0% 0%
complete segmental (n=33) 0% 3%
Mosaics (n=0) N/A N/A
Mosaicism rates by high resolution NGS Data from >100,000 embryos
• Mosaics are MITOTIC and therefore do not increase with age • Mosaics + Aneuploid and Mosaic show constant rates through age
Egg
donor <35 35-37 38-40 41-42 >42
Normal 59% 53% 44% 31% 19% 14% Mosaic 16% 18% 17% 13% 10% 8% Aneuploid (± mosaic) 18% 20% 28% 38% 41% 33% Complex (*) 7% 8% 10% 17% 28% 44% Polyploid 1% 1% 1% 1% 1% 1%
Total embryos analyzed 5659 10734 6697 6211 2656 1279
N = 103,405 embryos. Reprogenetics and Genesis Genetics data to 1/2017
* Complex: >2 full abnormalities
Meiotic errors: Difference statistically significant at P<0.05 and P<0.0001
Mitotic errors: No difference detected (P>0.05)
0%
20%
40%
60%
80%
100%
< 34 35-39 > 40
15%
36%
52%
24% 24% 26%
Maternal Age
Meiotic Errors
Mitotic Errors
Types of abnormalities and maternal age by karyomapping analysis
Center ID #
N Euploid % Aneuploid % Mosaic %
3 12 41% 37% 16%
4 41 57% 26% 18%
1 17 43% 30% 24%
8 40 45% 25% 29%
5 15 49% 17% 31%
2 26 53% 17% 31%
6 19 38% 27% 33%
7 21 47% 19% 34%
9 15 36% 18% 44%
206 p= 0.001
Significant difference in mosaicism between centers in egg donor embryos
Sachdev et al. (2016) ASRM
Clinical outcome of mosaics
Misdiagnosed by aCGH
• PGS: normal by aCGH • POC: Trisomy 16. • PGS reanalysis: normal by aCGH, Mosaic Trisomy 16 (70%) by NGS
aCGH
h-r NGS
Mosaics by hr-NGS Miscarry more
* miscarriage rate after aCGH about 10%. Grifo et al. (2015) ASRM
MISCARRIAGES COULD BE FURTHER REDUCED BY 54% USING hr-NGS *
hr-NGS result Total
Euploid 46%
Triploid 4%
Mosaic euploid / aneuploid 29%
Mosaic euploid / segmental 13%
Mosaic euploid / Complex abnormal 8%
52 loses after aCGH “euploid” embryo transfers were reanalyzed by hr-NGS:
Mosaic embryos by hr-NGS implant less
Fragouli et al. (2016) PGDIS
Mosaics can progress to term but significantly less than euploid embryos
Mosaic (n=44)
Euploid (n=52)
Implantation 38% 58% p<0.001
Ongoing implantation 27% 46% p<0.001
4% of mosaic embryos detected by aCGH 33% (6/18) ongoing pregnancies
Mosaic embryos can result in healthy pregnancies (Greco et al. 2015)
Geco, Minasa, Fiorentino (2015) New Eng. J. Med
OPR of euploid vs. mosaic: One center experience (NYU)
MOSAIC EUPLOID
transferred 18 569
implantated 50% 72% p=0.06
miscarried 56% 12% p=0.006
ongoing 22% 63% p=0.001
Besser, Maxwell, Friedenthal, Munné, 𝑀cCaffrey, Grifo (data from NYU, submitted)
OPR by type of mosaic or by % abnormal cells (multicentric data)
Munné et al. (2017) Fertil Steril + Fiorentino (Genoma) data
• No difference
between monosomy
and trisomy
• Complex have the
worse OPR
• Not enough embryos
replaced with >40%
abnormal cells
Mosaic type % abnormal replaced ongoing
Complex mosaics
any 32 6% p<0.001
Aneuploid mosaics
20-40% 102 50% p<0.05
>40% 44 30%
Segmental mosacis
any 43 37%
Total mosaics
221 37%
p<0.001
Euploid <20% All IVF
centers 50-70%
High resolution NGS vs. aCGH: Two center experiences
Friedenthal, Maxwell, MD, Munné, Kramer, McCulloh, McCaffrey, Grifo (2017) ESHRE
aCGH NGS p-value
SET transfers 421 579
Age 35.9 35.6 NS
IR (%) 63.9% 71.6% 0.01
OPR (%) 53.1% 61.9% 0.009
aCGH NGS p-value
transfers 390 38
Age 36.0 36.0 NS
IR (%) 41.6% 57.8% <0.05
OPR (%) 44.0% 65.7% <0.02
Macer, Barritt, Surrey, Danzer, Ghadir, Wang, Pisarska (submitted PCRS)
Paradigm shift
Current: • Classify embryos as normal or abnormal • Error rate 2-10% • False positives, False negatives occur
New: • Classify embryos as normal, mosaic or abnormal • Minimal error rate • Deprioritize mosaics
PGDIS, COGEN Recommendations (outdated?)
• Report <20% as normal and >80% as abnormal (resolution limit)
• High priority mosaics: those with <40% abnormal cells
• Low priority mosaics: chaotic mosaics or those with >40% abnormal cells
• Low priority mosaics: - with chromosomes X, Y, 13, 18, 21 (live born viability) - with chromosomes 14, 15 (risk of UPD) - with chromosomes 2, 7, 16 (intrauterine growth retardation) But there is no evidence that mosaics at blastocyst
level have the same impact as mosaics in first trimester
Risk of embryonic mosaics producing fetal mosaics or full trisomies
Risk of a trisomic baby from mosaic embryos is <1%:
- 24/106 pregnancies miscarried (no data on aneuploid SABs)
- 82/106 pregnancies were ongoing and 100% euploid
(data from CooperGenomics + Genoma, unpublished)
2.1% of CVS are mosaic
Grati et al (in press) – data on n=72,472 CVS
Is Mosaicism at blastocyst stage and fetal mosaicism caused by different mechanisms?
- Bolton et al: implantation of mice mosaic blastocysts depends on abnormal cell load
- Munne et al: human embryos with higher abnormal cell load perform worse
- Weier et al: confined placental mosaicism arises in the placenta and not the embryo
hr-NGS mosaics: Summary
• NGS detects mosaicism better than other methods
• Mosaics miscarry more (only 4% euploid by hr-NGS miscarry)
• Mosaics implant less than euploid embryos (specially complex mosaics)
Recommended: • Transfer euploid first followed by mosaics • do Prenatal diagnosis (Amnio) for mosaic transfers
• 21% of blastocysts are mosaics
• 40% of mosaics can result in an ongoing pregnancy compared to 50-70% euploid
Scientists Santi Munné, PhD, CSO (US) Mark Hughes, MD, PhD (US) Jacques Cohen, PhD (US) Dagan Wells, PhD (UK) Elpida Fragouli, PhD (UK) Joson Horcajadas, PhD (LATAM) M. Konstantinidis, PhD (US) Samer Alfarawati, PhD (UK) Tomas Escudero, MSc (US) Josh Blazek, PhD (US) Mike Large, PhD (US) Katharina Spath, PhD (UK) Ryan Subaran, PhD (US) Sarthak Sawarkar, MSc (US) Dhruti Babariya, PhD (UK)
Pere Colls, PhD (US) Tony Gordon, PhD (UK) John Kitchen, PhD (US) Carles Gimenez, PhD (Spain) Mireia Sandalinas, PhD (Spain) Sophia Tormasi, MSc (US) Lia Ribustello, MSc (US) Katie Bauckman, MSc (US) Renata Prates, MSc (US) Luis Guzman, PhD (Peru) Muriel Roche, PhD (Japan) Dr. Araki, PhD (Japan)
Bioinformatics, VC scientists Arun Manoharan, PhD (US) Avinash Shanmugan, PhD (US) Ursula Schick, PhD (US) Lauren Hurd, PhD (US) Jim Hayes, PhD (US) Eric Proffitt, PhD (US) Genetic Councilors (R&D) Amy Jordan Erin Mills Rachael Cabey Dina Goldberg Haley Nisson