1. New method for embryo selection: NGS plus MitoGrade™
Santiago Munné, PhD
Reprogenetics, a CooperSurgical Company
N. America: Livingston (NJ), Los Angeles, Chicago, Portland (OR), Miami, Vancouver / Europe: Barcelona, Oxford, Hamburg / Asia: Kobe, Tokyo, Macao, Abu Dhabi / S. America: Lima , Buenos Aires, Sao Paulo 1

2. Delayed reproduction = infertility by aneuploidy
Women are reproducing later:
Birth to first-time mothers by age
(per 1000 women)
Infertility increases with age due to Chromosome abnormalities
Source: CDC
Source: Reprogenetics (N = 18,000 PGS cycles of aCGH), CDC

3. Why PGS?
The problem:
Aneuploid increases from 30% to 90% with maternal age
Aneuploid embryos miscarry or do not implant
The PGS v2 solution:
Comprehensive chromosome analysis: <2% error, 100% aneuploidies
Blastocyst biopsy: non-detrimental
Results:
Improves ongoing implantation rates
Eliminates the maternal age effect on implantation

4. PGS by high resolution Next Generation Sequencing (hr-NGS)

5. Original Analysis method
High resolution NGS validation:
reanalysis of blastocysts
Original Analysis method
Reanalysis method
Sample
Confirmed Euploid
Confirmed abnormal
TOTAL
Kung et al
(Reprogenetics)
aCGH
NGS
Same biopsy
44/44
108/108
152/152
Fiorentino et al. 2014
67/67
141/141
208/208
Wells et al. 2014
Separate biopsy
13/13
28/28
41/41
Total
100%
Sensitivity
Specificity 0%
Error rate
DW: get ready for Treff and others to critisize this slide and the next one. In particular, the use of the same SurePlex product for confirmation is a weak spot. However you can point to our paper from earlier this year Wells et al 2014, where confirmation utilised a separate biopsy
SM: add another column indicating how the embryo was reanalyzed, if by same SurePlex product or by re-biopsy in all this papers, and another column about what technique was used for the reanalysis, if NGS/FISH, NGS/aCGH or NGS/NGS in all these papers
AK: Removed the Wang paper, they were blastomeres.
Kung, Munne, Wells et al. (2015) Biomed Reprod Online; Fiorentino et al. (2014) F&S;
Wells, Kung, Munne et al. (2014) J Med Genetics

6. Mosaicism: Common in day 3 embryos
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
1[13]1[16]2[18]2[21]1[22]
2[13]1[16]2[18]2[21]2[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]
Munné S, Grifo J, Cohen J, Weier HUG Am J Hum Genet 1994; 55:
Munné S, Weier HUG, Grifo J, Cohen J Biol. Reprod. 1994; 51:
Colls et al. Fertil Steril 2007;88:53–61

7. Comparison of current PGS platforms Frequency abnormalities
aCGH
qPCR .
NGS
Frequency abnormalities
Misdiagnosis aneuploidy
2%a
1%bc
0%ef
Minimal Resolution (in Mb) 6
>20 3
Translocations
yes no 2%
Partial aneuploidy (g) 5%
Mosaicism detected (h, i) * 4%
21%
>20%
a Gutierrez-Mateo et al (2011) Fertil Steril, b Scott et al. (2012), c Treff et al. (2012) Fertil Steril 97:819–24, dGood Start Genetics: 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 Reprogenetics data 43/45 pregnancies ongoing.

8. High-res NGS calls mosaics better
hr NGS
aCGH

9. Mosaicism detected by high resolution NGS
Egg donor
<35
35-37
38-40
41-42
>42
Total
Normal
61%
48%
43%
33%
17%
11%
Mosaic
27%
21%
18%
14%
Abnormal + Mosaic 7% 9%
16%
Abnormal
15%
35%
53%
57%
Mosaics are mostly MITOTIC and therefore do not increase with age
N = 6980 embryos, and 1518 cycles, Reprogenetics data to 9/2015

10. Pregnancy outcome of mosaics
Euploid by aCGH
70% Euploid by hr-NGS
30% Mosaic by hr-NGS
66% ongoing
4% miscarriage
39% ongoing
12% miscarriage
(a)
9% of 1st trimester still mosaic
56% all miscarriages
(c)
(b)
a: Maxwell et al (submitted ESHRE), b: Fragouli et al. (2015) ASRM, c: Huang et al. (2009) F.S. reported 1% of all pregnancies are mosaic / 30% x 39%

12. Mosaics: a third category
aCGH
hr-NGS
impact
Normal (61%)
Normal (43%)
100% concordance
Mosaic (18%)
Improved selection against low implantation, high miscarriage risk embryos
Abnormal (39%)
Mosaic (3%)
some chance of making a baby
Abnormal (36%)
21%

13. hr-NGS mosaics: Summary
NGS detects mosaicism better than other methods
21% of blastocysts are mosaics
Mosaics miscarry more (only 4% euploid by hr-NGS miscarry)
They implant less than euploid embryos (specially complex mosaics)
40% of their ICMs are euploid, the same as undiagnosed embryos
Therefore some mosaics make babies
Recommended:
Prioritize euploid embryos for transfer
Prenatal diagnosis (Amnio)

14. Euploidy decreases with age but not with cohort size (hr-NGS)
# of embryos
% euploid blastocysts *
egg donors
< years
years
years
years
>42 years
1-3
61%
48%
44%
30%
14% 9%
4-6
55%
49%
45%
37%
21%
10%
7-10
63%
47%
43%
31%
17%
13%
>10
68%
35%
11%
Total
33%
N = 6980 embryos, and 1518 cycles, Reprogenetics data to 9/2015,
* excludes mosaics

15. Prognosis depending on age and ovarian response
# of embryos
% of patients with normal blastocysts
egg donors
< years
years
years
years
>42 years
1-3
83%
80%
71%
57%
36%
22%
4-6
97%
95%
92%
82%
59%
43%
7-10
99%
98%
96%
89%
74%
50%
10-17
100%
88%
banked
64%
>17
97% banked
87%
N = 10,852 cycles and 58,798 embryos, up to 5/2015 with array CGH.
Ata, Munne et al. (2012) Reprod Biomed Online and Reprogenetics unpublished data.

16. Overall clinical results
PGS v.2
Overall clinical results

17. PGS is proven 1) Three Randomized Clinical trials prove it:
Implantation rates
Control
PGS
Yang et al (aCGH)
46%
69%
Scott et al (qPCR)
63%
80%
Forman et al (qPCR)
40%
58%
TOTAL
53%
73%
P<0.001
2) Two Meta-analyses says so:
- Dahdouh, Balayla, Garcia-Velasco (2015) Reprod Biomed Online
- Lee et al. (2015) Human Reprod.
3) The CDC agrees: Chang et al. (2015) Fertil. Steril.

18. CDC report: PGS reduces miscarriage and increases ongoing pregnancy
FERTIL STERIL, IN PRESS

19. PGS by aCGH eliminates the maternal
age effect on implantation (update)
Ave: 67%
Implantation rate
Maternal age
* Harton, Munné et al. (2013) Fertil Steril. And unpublished data to 8/2015. N= 2532 followed up cycles of PGS by aCGH. ** SART 2013

20. Miscarriage rate after blastocyst biopsy and aCGH do not increase with age
PGS **
No PGS *
Maternal age
*SART, ** Harton et al. (2013) Fertil Steril, and Reprogenetics unpublished data

21. STAR Trial (ClinicalTrials.gov NCT02268786)
Goals
prospective, international,
multi-center,
Blinded
randomized-controlled trial 1.
hr-NGS
vs.
morphology 2.
End point:
Ongoing pregnancy 3.

22. STAR Trial (ClinicalTrials.gov NCT02268786)
Locations
Protocol
9 Laboratories
34 Clinical Sites
Aus, Can, UK, USA
Randomization (day 5/6)
Intervention Arm
TE biopsy + hr-NGS
Comparator Arm
Morphology assessment
Patient Details
Vitrification
Women aged 25–40 years
Moderate prognosis: ≥2 blastocysts
300 test and 300 control patients
Single Embryo Transfer

23. MitoGrade™
Selection of the most viable euploid blastocyst by mitochondrial DNA quantification

24. Blastocyst Mitochondria
Mitochondria are cytoplasmic organelles that generate energy for the cell
Mitochondria are all maternal in origin, and contain one or more copies of their genome
At cleavage stage the mitochondria are still those inherited from the egg
The embryo produces its own mitochondria during blastocyst formation
Due to a bottleneck mitochondria in the egg and in the blastocyst can be different.
Therefore mitochondria competence should be tested in blastocysts, not PBs, or day 3 embryos.

25. blastocyst implantation ability Relative quantity of mtDNA
mtDNA quantity and
blastocyst implantation ability
Reprogenetics has discovered that elevated mtDNA is associated with failure to implant
Above a threshold of mtDNA fewer euploid blastocysts implant.
Relative quantity of mtDNA
15% of euploid blastocysts had high levels of mtDNA and no dot implant
Fragouli et al. (2015) PLOS
Implantation
No Implantation

26. independent biomarker
Mitochondria is an
independent biomarker
There was no association with blastocyst morphology
Only mild association with maternal age and aneuploidy
No difference in mutations between high and low level mtDNA blastocysts
mtDNA quantification represents a new independent biomarker of embryo viability
Fragouli et al., PLOS

27. MitoGrade™ is not related to morphology
Implantation is higher if the blastocyst is:
Euploid, Mitograde™ normal, morphology BB
Than
Euploid, Mitograde™ Elevated, morphology AA AA BB
preg
Non preg
preg
Non preg

28. Mitochondria quantification
Blastocyst biopsy
WGA
PGS by aCGH or NGS
Quantify mtDNA by qPCR (soon by NGS)
Targeting multiple mitochondria sites
Normalize cell number by comparing to a multi-copy nuclear sequence
Mitochondrial genome (kb)
Sequence reads (depth of coverage)
Fragouli et al. (2015) PLOS

29. SET after PGS + Mitograde™: Non-selection study, prospective, blinded
76%
(n=103)
P<0.001
implantation 0%
(n=8)
Fragouli et al (2016) ESHRE

30. SET after PGS + Mitograde™:
Retrospective data
74%
*, **
(n=204)
66% **
(n=283)
implantation
* P< 0.001
** P= 0.03
0% *
(n=33)
Ravichandran et al (2016) ESHRE

31. DET after PGS + Mitograde™:
Paired study
Design:
DET: 1 MitoGrade high + 1 MitoGrade normal euploid embryos
Both embryos of different gender to track their implantation
Multi-center study (still recruiting centers)
Preliminary results:
6 transfers
5 pregnancies: all single implantations of MitoGrade normal
1 not pregnant

32. Example patient 35 years old
PGS + MitoGrade™
Example patient 35 years old
12%
26%
46%
25%
74%
63%
Blastocysts available for transfer
14%
37%

33. MitoGrade and OvaScience Augment target different problems
Egg mitochondria. As old as the egg
Embryonic mitochondria (created after genome activation)
Mitochondria from egg precursor cells
Normal
Depleted
Treated

34. Summary hr-NGS detects mosaicism better than other techniques
Mosaics implant less and miscarry more but can make babies
We recommend to prioritize transfer of euploid embryos
PGS can be combined with MitoGrade
MitoGrade elevated embryos do not implant despite being euploid

35. Reprogenetics Laboratories
Scientists
Santiago Munné, PhD (US)
Dagan Wells, PhD (UK)
Jacques Cohen, PhD (US)
M. Konstantinidis, PhD (US)
Mireia Sandalinas, PhD (Spain)
Samer Alfarawati, PhD (UK)
Tomas Escudero (US)
Renata Prates (US)
J. Horcajadas, PhD (Latin Am.)
Luis Guzman, PhD (Peru)
N’Neka Goodall (US)
Sophia Tormasi (US)
Allen Kung (US)
Lia Ribustello (US)
Katharina Spath (UK)
Katie Bauckman (US)
Sarthak Sawarkar, PhD (US)
Lab & Medical Directors
Pere Colls, PhD (US)
Carles Gimenez, PhD (Spain)
Elpida Fragouli, PhD (UK)
Karsten Held, MD (Germany)
Tetsuo Otani, MD (Japan)
Muriel Roche, PhD (Japan)
Braulio Peramo, MD (UAE)
Souraya Jaroudi, PhD (UAE)
Ahmed Yesilyurt, MD (Turkey)
Xuezhong Zeng, MD (China)
Francisco Rocha, PhD (Mexico)
Embryologists
Kelly Ketterson
Catherine Welch
Tim Schimmel
Genetic Councilors
Amy Jordan
Erin Mills
Rachael Cabey
Dina Goldberg .