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

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

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

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

Original Analysis method High resolution NGS validation: reanalysis of blastocysts 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 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

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; 55:150-159. Munné S, Weier HUG, Grifo J, Cohen J Biol. Reprod. 1994; 51:373-379 Colls et al. Fertil Steril 2007;88:53–61

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.

High-res NGS calls mosaics better hr NGS aCGH

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

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%

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%

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)

Euploidy decreases with age but not with cohort size (hr-NGS) # of embryos % euploid blastocysts * egg donors <35 years 35-37 years 38-40 years 41-42 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

Prognosis depending on age and ovarian response # of embryos % of patients with normal blastocysts egg donors <35 years 35-37 years 38-40 years 41-42 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.

Overall clinical results PGS v.2 Overall clinical results

PGS is proven 1) Three Randomized Clinical trials prove it: Implantation rates Control PGS Yang et al. 2012 (aCGH) 46% 69% Scott et al. 2013 (qPCR) 63% 80% Forman et al. 2013 (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.

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

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

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

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.

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

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

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.

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

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., 2015. PLOS

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

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) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Fragouli et al. (2015) PLOS

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

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

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

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

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

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

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 . santi@reprogenetics.com www.reprogenetics.com