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Global Trends of Digital Healthcare Industry in 2017

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Yoon Sup Choi
Yoon Sup Choi

Digital Healthcare Partners is a digital health accelerator in Korea that discovers, cultivates, invests in, and connects digital health startups. It provides mentoring, business development support, clinical validation, and investment to early-stage startups. Recent deals include a seed investment in 3billion, a company developing genetic diagnosis services for rare diseases using genome analysis. Global trends in digital health funding in Q1 2017 included large deals in areas like population health, EHR, and e-commerce. The largest deal was Grail's $900M series B for its liquid biopsy cancer diagnostic technology.

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최윤섭 디지털헬스케어 연구소 소장 최윤섭, PhD Global Trends of Digital Healthcare Industry The first half of 2017
The Convergence of IT, BT and Medicine
http://www.yoonsupchoi.com
Disclaimer: Conflict of Interest
Digital Healthcare Partners (DHP) 는 국내 유일의 디지털 헬스케어 전문 스타트업 엑셀러레이터입니다. 글로벌 한국 일반 의료/ 헬스케어
DHP는 디지털 헬스케어 전문 엑셀러레이터로서, 
 디지털 헬스케어/의료 스타트업을 발굴, 육성, 연결하고 투자합니다. 발굴 • 세상을 바꿀 수 있는 혁신적인 헬스케어 스타트업 및 예비 창업팀을 발굴합니다. • 발굴을 위해 DHP Office Hour, 해커톤, 자체 행사 개최 등의 다방면의 채널을 활용합니다. 육성 • 의료/헬스케어 전문가들로 이루어진 파트너 및 자문가들이 초기 스타트업을 멘토링합니다. • 사업 개발, 아이템 검증, 임상 연구, 인허가 관련 자문 등 전방위적으로 지원합니다. 투자 • 초기 스타트업 및 예비 창업팀에게 정해진 원칙에 따라 지분 투자를 집행합니다. • 스타트업을 성장시켜 지분 가치의 상승에 따라서 재무적 수익을 추구합니다. 연결 • 초기 스타트업을 병원, 규제기관, 보험사, VC, 대학 등 다양한 이해관계자들과 연결합니다. • 파트너와 자문가들의 네트워크를 적극 활용하여 스타트업을 의료계 이너서클로 끌어들입니다.
DHP는 최고의 의료 전문가들이 초기 헬스케어 스타트업에 의학 자문, 의료 기관 연계, 임상 검증, 투자 유치 등을 지원합니다. 최윤섭 대표파트너 정지훈 파트너 김치원 파트너 • 성균관대학교 디지털헬스학과 교수 • 최윤섭 디지털 헬스케어 연구소 소장 • VUNO, Zikto, 녹십자홀딩스 등 자문 • 저서: ‘헬스케어 이노베이션’ • 전) 서울대학교 의과대학 암연구소 교수 • 전) 서울대학교병원 의생명연구원 교수 • 포항공대 전산생물학 이학박사 • 포항공대 컴퓨터공학/생명과학 학사 • 경희사이버대학 미디어커뮤니케이션학과 교수 • 빅뱅엔젤스 파트너 • Lunit, 매직에코, 휴레이포지티브 등 자문 • 저서: ‘제 4의 불', ‘거의 모든 IT의 역사’ 등 • 전) 명지병원 IT융합연구소장 • 한양대학교 의과대학 의학사 • 서울대학교 보건정책관리학 석사 • USC 의공학박사 • 내과전문의, 서울와이즈요양병원 원장 • 성균관대학교 디지털 헬스학과 교수 • Noom, Zikto, Future Play 등 자문 • 저서: ‘의료, 미래를 만나다’ • 전) 맥킨지 서울사무소 경영컨설턴트 • 전) 삼성서울병원 의료관리학과 교수 • 서울대학교 의과대학 졸업 • 연세대학교 보건대학원 석사
많은 언론들에서 디지털 헬스케어 파트너스를 주목해주셨습니다.
DHP는 유전체 분석 기반의 희귀질환 진단 서비스를 개발하는 3billion에 시드 투자 및 엑셀러레이션을 시작하였습니다. • 마크로젠의 유전체 분석 전문가들이 2016년 11월 스핀오프 • 대표 이사 금창원은 유전체 분석 전문가이자 연쇄 창업가 • 유전체 분석으로 4,000여개 희귀 유전 질환을 한 번에 진단 • 해외 시장 타겟, 2,000불의 비용으로 2-3주 내 진단 • 2017년 2월 시제품 출시 • http://3billion.io
Contents • 2017 1Q 미국 VC 투자 동향 • ‘Liquid Biopsy’: Illumina and Grail • 23andMe의 DTC 서비스 FDA 인허가 확대 • IBM Watson for Oncology 도입 광풍(?) • 의사를 능가하는 Deep Learning 연구 결과들 • 의학적 효용을 증명한 헬스케어 스타트업의 증가
2017 1Q 미국 VC 투자 동향
http://rockhealth.com/2015/01/digital-health-funding-tops-4-1b-2014-year-review/
https://rockhealth.com/reports/2016-year-end-funding-report-a-reality-check-for-digital-health •2016년 디지털 헬스케어 스타트업 펀딩 규모는 $4.2b 으로 전년도에 비해서 8% 감소 •반면 투자를 받은 기업의 수는 273개에서 296개로 약 10% 증가 •총 451개 VC 및 CVC가 투자를 집행 •그 중 237개는 디지털 헬스케어 기업에 '처음' 투자한 곳 (화이자 포함)
https://rockhealth.com/reports/2016-year-end-funding-report-a-reality-check-for-digital-health • The six largest deals of 2016 made up 19% of all digital health funding. • Despite laying off 15% of its global workforce, Jawbone raised $165M in 2016. • The most funded digital health company of all time at nearly a billion dollars
•펀딩을 가장 많이 받은 분야는 Genomics and Sequencing 분야 •Human Longevity ($220M), Color Genomics ($45M), Seven Bridges Genomics ($45M) •Pathway Genomics ($40M), Emulate ($28M) https://rockhealth.com/reports/2016-year-end-funding-report-a-reality-check-for-digital-health
https://rockhealth.com/reports/digital-health-funding-2015-year-in-review/
•총 451개 VC 및 CVC가 투자를 집행 •3개 이상의 deal 을 한 곳은 40개 투자자 •총 투자자 중 1/3 정도는 ‘하나의’ deal 만 진행 •237개는 디지털 헬스케어 기업에 '처음' 투자한 곳 (화이자 포함) https://rockhealth.com/reports/2016-year-end-funding-report-a-reality-check-for-digital-health
•최근 3년 동안 Merk, J&J, GSK 등의 제약사들의 디지털 헬스케어 분야 투자 급증 •2015-2016년 총 22건의 deal (=2010-2014년의 5년간 투자 건수와 동일) •Merck 가 가장 활발: 2009년부터 Global Health Innovation Fund 를 통해 24건 투자 ($5-7M) •GSK 의 경우 2014년부터 6건 (via VC arm, SR One): including Propeller Health •Grail: cancer diagnostic spin-off from Illumina (Liquid biopsy) •$900m Series B, in March 2017 •가장 많은 제약사가 참여한 투자: J&J, Merck, Bristol-Myers-Squibb
•2017 1Q에 총 71건의 deal; $1B funding 으로 strong start •트럼프 정부의 의료 및 규제 정책의 불확실성이 리스크로 보였으나, 크게 영향을 미치지는 않은 것으로 보임 •Rock Health의 경우, •Digital Healthcare 분야의 정의가 보수적 (ie. 진단회사인 Grail은 누락) •미국 내의 $20m 이상의 deal 만을 조사
•Startup Health의 분석 •Digital Healthcare 분야의 정의가 더 넓고 (Grail 포함), $20m 이하의 deal 도 포함 •총 124 deal 에 $2.5B 가 투자 •2011년 이후 1분기 투자 횟수는 최하이지만, •개별 deal의 규모는 상승: $500m-900m startuphealth.com/reports 2010 2011 2012 2013 2014 2015 2016 2017 YTD Q1 Q2 Q3 Q4 158 300 499 668 589 526 606 124 Deal Count $1.1B $2.0B $1.5B $629M$572M$391M$192M $8.2B $6.0B $7.1B $2.9B $2.4B $2.0B $1.1B DIGITAL HEALTH FUNDING SNAPSHOT: YEAR OVER YEAR 5Source: StartUp Health Insights | startuphealth.com/insights Note: Report based on public data on seed, venture, corporate venture and private equity funding only. © 2017 StartUp Health LLC $2.5B $2.5B GRAIL’s $914 million Series B may be an outlier and skewed the overall funding numbers this quarter keeping it on track for another strong year overall, and turning an otherwise modest first quarter into a record-breaker. While Q1 2017 had the lowest deal volume since 2011 - with only 124 deals this quarter - we’re seeing more and more $500-900M deals. What do less deals and more money mean? Even though VCs are betting less, they’re betting bigger. Also, the lines are blurring quickly as expected between “digital” and all other categories of health and healthcare. “AI, virtual reality, mobile connectivity, genomics, and analytics are coming to change healthcare, and that is creating a wave of innovation like we’ve never seen.” -Unity Stoakes, President, StartUp Health
•Grail 이 $900M Series B funding으로 압도적인 1위 •이외에 상위권은 Rock Health - Startup Health 가 거의 비슷 •Alignment Healthcare: Population Health Management (병원, 보험사 대상) •PatientsLikeMe: Patients Community (제약회사 대상) •Nuna: Big Data Analytics (정부, 보험사 대상) startuphealth.com/reports Company $ Invested Subsector Notable Investor 1 $914M Big Data/Analytics 2 $115M Population Health 3 $100M Patient/Consumer Experience 4 $90M Big Data/Analytics 5 $85M EHR 6 $65M Research 7 $55M E-Commerce 8 $52M Population Health 9 $50M Medical Device 10 $41M Research THE TOP 10 LARGEST DEALS OF 2017 8Source: StartUp Health Insights | startuphealth.com/insights Note: Report based on public data on seed, venture, corporate venture and private equity funding only. © 2017 StartUp Health LLC The top 10 deals of Q1 2017 included companies working in sectors in which big deals have been rare. What does this suggest? 2017 might be a breakout year in terms of funding for solutions focusing on population health, EHR innovation, and e-commerce.
‘Liquid Biopsy’, and Grail
Tumor Heterogeneity Meric-Bernstam F, Mills GB. Nat Rev Clin Oncol. 2012 Sep;9(9):542-8.
in the understanding of tumour heterogeneity; second, the role of surgery as a therapeutic modality in the era of targeted therapy; third, the use of personalized therapy in the perioperative period and, finally, the possibilities of personalization of surgical procedures according to lung cancer subtypes. VATS lobectomy showed that intraoperative blood loss was significantly reduced in the VATS group compared with open lobectomy in nine studies; however, no differ- ence was observed in five studies and the values were not reported in seven studies.12 Hospital stay was also signifi- cantly shorter in VATS group in five studies. Park et al.,13 Heterogeneity in patients with adenocarcinoma of the lung according to driver oncogenes Heterogeneity within patients with EGFR mutation Heterogeneity in resistance mechanisms in one patient HER2 3% EGFR ~40% in Asians ~15% in Caucasians ALK ~5% KRAS ~15% in Asians ~30% in Caucasians RET ~1% ROS1 ~1% BRAF ~1% PIK3CA ~1% NRAS ~1% MET <5% Others? Exon 19del ~50% L858R ~40% Sensitive Inherent resistance CRKL ~3% BIM 20–40% IκB ~30% Inherent T790M ~2% by sequencing ~30% by sensitive method Further heterogeneity EGFR-TKI Drug X T790M MET a cb T790M Heterogeneity in patients with adenocarcinoma of the lung according to driver oncogenes Heterogeneity within patients with EGFR mutation Heterogeneity resistance mecha in one patien HER2 3% EGFR ~40% in Asians ~15% in Caucasians ALK ~5% KRAS ~15% in Asians ~30% in Caucasians RET ~1% ROS1 ~1% BRAF ~1% PIK3CA ~1% NRAS ~1% MET <5% Others? Exon 19del ~50% L858R ~40% Sensitive Inherent resistance CRKL ~3% BIM 20–40% IκB ~30% Inherent T790M ~2% by sequencing ~30% by sensitive method Further heterogeneity EGFR-TKI Drug T790M ME a cb T790M Figure 1 | Various classes of tumour heterogeneity in adenocarcinoma of the lung. a | Heterogeneity in patients with adenocarcinoma of the lung according to driver oncogenes that are crucial for selecting targeted drugs for treatment.2,76 Number of people reflects approximate incidence.2,76 b | Heterogeneity in patients with EGFR mutations, resulting in MitsudomiT, Suda K,YatabeY. Nat Rev Clin Oncol. 2013 Apr;10(4):235-44. Heterogeneity in Lung Adenocarcinoma
Tumor Heterogeneity Meric-Bernstam F, Mills GB. Nat Rev Clin Oncol. 2012 Sep;9(9):542-8.
Intratumor Heterogeneity Revealed by multiregion Sequencing B Regional Distribution of Mutations C Phylogenetic Relationships of Tumor Regions D Ploidy Profiling A Biopsy Sites R2 R4 R9 R8 R5 R1 R3 R2 PreP PreM R1 R2 R3 R5 R8 R9 R4 M1 M2a M2b C2orf85 WDR7 SUPT6H CDH19 LAMA3 DIXDC1 HPS5 NRAP KIAA1524 SETD2 PLCL1 BCL11A IFNAR1 DAMTS10 C3 KIAA1267 RT4 CD44 ANKRD26 TM7SF4 SLC2A1 DACH2 MMAB ZNF521 HMG20A DNMT3A RLF MAMLD1 MAP3K6 HDAC6 PHF21B FAM129B RPS8 CIB2 RAB27A SLC2A12 DUSP12 ADAMTSL4 NAP1L3 USP51 KDM5C SBF1 TOM1 MYH8 WDR24 ITIH5 AKAP9 FBXO1 LIAS TNIK SETD2 C3orf20 MR1 PIAS3 DIO1 ERCC5 KL ALKBH8 DAPK1 DDX58 SPATA21 ZNF493 NGEF DIRAS3 LATS2 ITGB3 FLNA SATL1 KDM5C KDM5C RBFOX2 NPHS1 SOX9 CENPN PSMD7 RIMBP2 GALNT11 ABHD11 UGT2A1 MTOR PPP6R2 ZNF780A WSCD2 CDKN1B PPFIA1 TH SSNA1 CASP2 PLRG1 SETD2 CCBL2 SESN2 MAGEB16 NLRP7 IGLON5 KLK4 WDR62 KIAA0355 CYP4F3 AKAP8 ZNF519 DDX52 ZC3H18 TCF12 NUSAP1 X4 KDM2B MRPL51 C11orf68 ANO5 EIF4G2 MSRB2 RALGDS EXT1 ZC3HC1 PTPRZ1 INTS1 CCR6 DOPEY1 ATXN1 WHSC1 CLCN2 SSR3 KLHL18 SGOL1 VHL C2orf21 ALS2CR12 PLB1 FCAMR IFI16 BCAS2 IL12RB2 PrivateUbiquitous Shared primary Shared metastasis Ubiquitous Lung metastases Chest-wall metastasis Perinephric metastasis M1 10 cm R7 (G4) R5 (G4) R9 R3 (G4) R1 (G3) R2 (G3) R4 (G1) R6 (G1) Hilum R8 (G4) Primary tumor Shared primary Shared metastasis M2b M2a Intratumor Heterogeneity Revealed by Multiregion Sequencing Gerlinger M et al. N Engl J Med. 2012 Mar 8;366(10):883-92
Nat Genet. 2014 Feb 26;46(3):214-5. Intratumoral heterogeneity in kidney cancer
Nat Genet. 2014 Mar;46(3):225-33. E S 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat map indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutation tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat ma indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutatio tumor. An asterisk indicates where VHL methylation was included in the analysis. Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat map indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutation tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat ma indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutatio tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat m indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutati tumor. An asterisk indicates where VHL methylation was included in the analysis. Regional distribution of nonsynonymous mutations in ten ccRCC tumors Heat maps indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 probable driver mutations are highlighted in magenta.
E S 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat map indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutation tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat ma indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutatio tumor. An asterisk indicates where VHL methylation was included in the analysis. Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat map indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutation tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat ma indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutatio tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat m indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutati tumor. An asterisk indicates where VHL methylation was included in the analysis. Regional distribution of nonsynonymous mutations in ten ccRCC tumors Heat maps indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 probable driver mutations are highlighted in magenta. Nat Genet. 2014 Mar;46(3):225-33.
A RT I C L E S We determined the regional distribution f nonsynonymous mutations on the basis of ata from ultra-deep amplicon sequencing. We called a mutation as being present in a umor region if a nucleotide substitution was etected in 0.5% of reads or an indel was etected in 1% of reads. We chose these hresholds on the basis of the error rate of he sequencing platform13. The regional istribution of 28 mutations for which ltra-deep sequencing data were not avail- ble was inferred from the exome sequenc- ng data. Exome sequencing of EV001 and EV002 has previously been reported2 and was ncluded in this analysis. On average, 67% range of 28–92%) of the nonsynonymous omatic mutations were heterogeneous and ot detectable across all sampled regions of n individual tumor (Fig. 1). The presence f somatic mutational heterogeneity in all 10/10) treatment-naive or pretreated cases indicates that ITH, char- cterized by the spatial separation of subclones, is a common feature n stage T2–T4 ccRCCs. To identify the optimal number of biopsies that can reliably detect he majority of nonsynonymous somatic mutations in a tumor, we alculated the number of mutations that would have been detected heterogeneity specifically in EV003 and EV006. No other clinical or pathological characteristic seemed to correlate with mutational ITH, and larger series will be required to determine the biological basis for the diversity in ccRCC phylogenetic structures. Identification of intraregional subclones R4b GL VHL SETD2 SETD2 KDM5C MTOR R8 KDM5C R4a R5 R3 R2 R1 R9 M1 M2a M2b SETD2 EV001 EV003 R6 R7 R1 R5 GL R9 VHL (methylation) PBRM1 EV005 R6dom R7 R1R5 R3 R4, R6min R2 GL VHL PBRM1 PIK3CA PIK3CA SF3B1 EV006 EV007 RMH002 R6 R7 R1 R2 R3 PBRM1 BAP1 TP53 RMH004 R8 R10 R2 GL VT R4 VHL PBRM1 ATM PTEN SMARCA4 R3 MSH6 PBRM1 ARID1A RMH008 R4min R5, R7 R6min R8 GL R1 R2 R3 VHL BAP1 TSC2 BAP1 BAP1 R6dom R4dom RK26 PBRM1 TP53 BAP1 R3, R4 R11 R9 GL R1 R2 VHL R5min R10 R7 R5dom R8 R6 10 non synonymous mutations Trunk Internal branch Terminal branch EV002 R7 R1 R3 R6 GL R9 VHL PBRM1 SETD2 TP53 R4 M PTEN PTEN SETD2 R3 GL GL GL R4 R7 VHL VHL VHL LN1a, LN1b R2R6 R1 R1 R15 R9min R9dom R3min BAP1 SETD2 R5,R7 R2, R3dom R6 PIK3CA SETD2 TP53 R4 R3R4 R2 igure 3 Phylogenetic trees generated by maximum parsimony from M-seq data for ten cRCC tumors. Trees for EV001 and EV002 re adapted from Gerlinger et al.2. Branch nd trunk lengths are proportional to the umber of nonsynonymous mutations acquired n the corresponding branch or trunk. Driver mutations were acquired by the indicated enes in the branches the arrows indicate. river mutations defining parallel evolution vents are highlighted by color. Trees are ooted at the germline (GL) DNA sequence, etermined by exome sequencing of DNA from eripheral blood. Phylogenetic trees generated for ten ccRCC tumors Mutational processes change during tumor evolution ccRCCs can traverse different evolutionary routes simultaneously
Br J Cancer. 2010 Oct 12;103(8):1139-43. resistance develops. A further obstacle for the interpretation of large-scale somatic mutation analyses is that fitness effects of the vast majority of mutations are unknown. The RNA interference- based functional genomic screening approaches can experimen- tally test the phenotypic effect of silencing large numbers of genes individually and may support the interpretation of mutation data sets by identifying genes that influence cellular fitness or drug sensitivity. cells in vitro (Duesberg et al recurrence after drug treatmen 2010). The clinically importan geneity could accelerate evolu enhance biological fitness to pressures could in turn favour t unstable cancer cells by can advantages conferred by genom must be balanced against the s result from the generation o deleterious mutations or tumou chromosomal instability in anim Importantly, evolutionary mod instability can be positively selec advantage in environments (e.g. during chemotherapy) in cycle arrest after DNA damage cells that are negatively selected cell cycle arrest and have a lower Wodarz, 2003). Thus, it is conceivable that the instability required to accelerate of cancers and that excessive tumour. Results from animal tu excessive chromosomal instabili role leads to the tantalising prop genome instability provides intervention (Weaver et al, 2007 EVIDENCE FOR DRUG RE EVOLUTION The harsh clinical reality is th almost invariably occurs in adva leading to disease progression an examples highlight how Darwi tumoural genetic heterogeneity pressure of systemic cancer t resistance from a Darwinian Genetic heterogeneity Time Bottleneck Drug treatment Cancercellpopulation Figure 1 Schematic view of tumour heterogeneity during tumour progression and treatment. Acquired mutations in daughter cells of a single founder cell (left) promote diversion into subclones (different colours reflect different clones). Some new mutations lead to accelerated growth (for example yellow and orange clones). Fitness reducing mutations lead to negative selection (cells with brown cytoplasm). Drug treatment leads to selective survival of a drug resistant clone (pink) and generates an evolutionary bottleneck that reduces genetic heterogeneity transiently. Heterogeneity is re-established rapidly through acquisition of mutations by daughter cells of the resistant clone. Darwinian evolution of tumor elucidate clonal heterogeneity • Acquired mutations in daughter cells of a single founder cell (left) promote diversion into subclones • Drug treatment leads to selective survival of a drug resistant clone (pink) and generates an evolutionary bottleneck that reduces genetic heterogeneity transiently. • Heterogeneity is re-established rapidly through acquisition of mutations by daughter cells of the resistant clone.
P E R S P E C T I V E Fig. 1. A trunk-branch model of intratumor heterogeneity. (A) The development of intratumor heterogeneity is analogous to a growing tree. The trunk harbors the founding ubiquitous driver mutations of a cancer present in every tumor subclone and region. The sprouting branches represent different geographically separated regions of the tumor or subclones present within single biopsies that carry heterogeneous mutations that are not present in every tumor cell or tumor region. Such mutations may distinguish the biological behavior of subclones and harbor the potential to become driver mutations under distinct selection pressures. Ubiquitous genetic events present in the trunk may provide more tractable biomarkers and therapeutic targets than heterogeneous events in the branches. We describe three levels of complexity: level 1, the trunk carries driver events, whereas the branches carry neutral mutations; level 2, the trunk carries driver events, whereas the branches carry neutral or additional driver events that may harbor convergent phenotypes (for example, distinct mutations in SETD2 or PTEN occur in different regions of the same renal cancer and converge on the same pathway resulting in its inactivation) (4); level 3, level 1, and level 2 events plus neutral mutations in the branches (or trunk) that become driver events under selection pressures (11, 17–20). With level 1 complexity, one biomarker can be developed against one target; with level 2 and 3 complexity, a single biomarker is unlikely to be sufficient. The risk of drug resistance may increase with each level of complexity. (B) Clonal ar- chitecture as a biomarker.The polygenic nature of drug resistance and intratumor heterogeneity may exacerbate difficulties in predicting therapeutic outcome. Consideration of tumor growth within a Darwinian evolutionary tree framework may support the identification of new predictive biomark- Level 1 complexity Level 2 complexity Level 3 complexity Clonal architecture as a biomarker A BTrunk-branch hypothesis onApril4,2012stm.sciencemag.org A trunk-branch model of intratumor heterogeneity • The trunk harbors the founding ubiquitous driver mutations of a cancer present in every tumor subclone and region. • The sprouting branches represent different geographically separated regions of the tumor or subclones present within single biopsies that carry heterogeneous mutations that are not present in every tumor cell or tumor region. Sci Transl Med. 2012 Mar 28;4(127):127
biopsy
Release and extraction of cfDNA from the blood •cfDNA 는 건강한 세포가 사멸할 때뿐만 아니라, 암 세포가 사멸할 때도 혈액 속으로 나온다. •Liquid biopsy (액체 생검) •혈액 속에서 cfDNA를 추출하여 암세포에서 나온 DNA를 detection 하고 분석 •암의 재발 유무 조기 발견, 항암제의 약효 파악, 암 세포의 유전 변이 파악 등에 활용 http://www.nature.com/nrclinonc/journal/v10/n8/full/nrclinonc.2013.110.html
http://www.nature.com/nrclinonc/journal/v10/n8/full/nrclinonc.2013.110.html Monitoring tumour-specific aberrations to detect recurrence and resistance •a. 암이 수술 이후에 조기 재발했는지에 대한 모니터링 •b. 표적 항암제 투여 이후에 내성이 있는 새로운 암세포(clone)가 자라는지 검사 •Red: 새로운 clone 이 생성하여 재발 •blue: 기저에 줄어들었던 원래 clone이 새로운 mutation 을 얻어서 재발
Importantly, the data provided by these tests indicate that these genotypes are not common in the plasma of individuals that are presumably cancer-free (Thress et al., 2015). It is worth noting that tu- mor-derived RNA and DNA methylation patterns can also be detected in the with highly conserved biology, a popula- tion of cancer patients behaves as a het- erogeneous collection of many diseases, each of which carries additional heteroge- neity in its own right. Therefore, identifying a finite number of protein or nucleic acid biomarkers that are highly sensitive and ctDNA molecules to reliably measure them in a background of mostly non-tu- mor-derived cfDNA. We estimate that such a broad and deep sequencing approach could require orders of magni- tude more sequence data than liquid bi- opsy assays currently use (Table 1). To Table 1. Comparison of ctDNA Liquid Biopsy Test to Potential Cancer Screening Test Indication Tumor Liquid Biopsy (Genotyping, Monitoring) Early Cancer Detection Target population Patients with known diagnosis of cancer Asymptomatic individuals Tissue reference Can be informed by tissue analyses No prior knowledge of tissue Key performance characteristics Sensitivity and specificity for specific actionable genotypes d Sensitivity and specificity for clinically detectable cancer d Premium on specificity in individuals without detectable cancer d Tissue of origin needed to guide workup Clinical Endpoint for Utility Therapeutic benefit with specific therapies Net outcome improvement with early detection and local treatment of cancer Genes Covered 10-50 100-1000s ctDNA Limit of Detection 0.1% <0.01% Importance of Novel Variant Detection Low High Amount of Sequencing 1x 100X Study Size for Clinical Validity and Utility 100’s 10,000 - 100,000 s Next-Generation Sequencing of Circulating Tumor DNA for Early Cancer Detection Cell 168, February 9, 2017
C A N C E R Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer Jeanne Tie,1,2,3,4 *† Yuxuan Wang,5† Cristian Tomasetti,6,7 Lu Li,6 Simeon Springer,5 Isaac Kinde,8 Natalie Silliman,5 Mark Tacey,9 Hui-Li Wong,1,3,4 Michael Christie,1,3,10 Suzanne Kosmider,2 Iain Skinner,2 Rachel Wong,1,11,12 Malcolm Steel,11 Ben Tran,1,2,3,4 Jayesh Desai,1,3,4 Ian Jones,4,13 Andrew Haydon,14 Theresa Hayes,15 Tim J. Price,16 Robert L. Strausberg,17 Luis A. Diaz Jr.,5 Nickolas Papadopoulos,5 Kenneth W. Kinzler,5 Bert Vogelstein,5 *† Peter Gibbs1,2,3,4,17 *† Detection of circulating tumor DNA (ctDNA) after resection of stage II colon cancer may identify patients at the highest risk of recurrence and help inform adjuvant treatment decisions. We used massively parallel sequencing–based assays to evaluate the ability of ctDNA to detect minimal residual disease in 1046 plasma samples from a prospective cohort of 230 patients with resected stage II colon cancer. In patients not treated with adjuvant chemotherapy, ctDNA was detected postoperatively in 14 of 178 (7.9%) patients, 11 (79%) of whom had recurred at a median follow-up of 27 months; recurrence occurred in only 16 (9.8 %) of 164 patients with negative ctDNA [hazard ratio (HR), 18; 95% confidence interval (CI), 7.9 to 40; P < 0.001]. In patients treated with chemotherapy, the presence of ctDNA after completion of chemotherapy was also associated with an inferior recurrence-free survival (HR, 11; 95% CI, 1.8 to 68; P = 0.001). ctDNA detection after stage II colon cancer resection provides direct evidence of residual disease and identifies patients at very high risk of recurrence. INTRODUCTION About 1.3 million cases of colorectal cancer are diagnosed annually worldwide (1). In patients with stage II colon cancer (~25% of all colorectal cancer), management after surgical resection remains a clinical dilemma, with about 80% cured by surgery alone (2). The cur- rent approach to defining recurrence risk for patients with early- tus in the tumor defines a low-risk group in which adjuvant chemo- therapy is not beneficial (6, 7). Most recently, multiple tissue-based gene signatures have been shown to have prognostic significance, but again with modest hazard ratios (HRs) of 1.4 to 3.7 (8–11). In practice, adjuvant chemotherapy is more frequently offered to high-risk stage II patients, with the justification that high-risk R E S E A R C H A R T I C L E http://stm.sciencemag.orgDownloadedfrom Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer postoperative adjuvant chemotherapy 를 받지 않은 환자군에 대해서, ctDNA 양성/음성 기반으로 RFS 을 효과적으로 구분할 수 있음 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer RFS를 ctDNA 여부에 의해서 판단하는 것이 (A) 기존의 T stage, LN yield, LVI 등 기반의 (clinicopathogic) 위험군 분류(B)보다 더욱 효과적일 가능성이 있음 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer 기존의 위험군 분류 기준에 의해서 저위험군(C)과 고위험군(D)을 따로 나눠서 ctDNA의 검출 여부로 보게 되더라도, 그 중에서도 RFS 예후 예측을 효과적으로 할 수 있음 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer postoperative adjuvant chemo therapy 를 받은 환자의 항암제 치료 도중과 이후의 ctDNA 변화와 이후 재발여부의 관계 A, B의 경우 •chemo 시작시에는 ctDNA가 positive였다가, •chemo 받는 동안에는 negative가 되고, •chemo 끝난 후에는 증가해서 결국 재발 •이 과정에서 기존의 표준 바이오마커인 CEA는 detection 에 실패 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer C, D 환자는 chemo 받는 동안 ctDNA가 negative가 되고 이후에도 유지되어서, 이후 f/u 에서도 재발하지 않음 이 환자들의 경우에는 CEA도 결과는 동일 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer E, F 환자의 경우에는 ctDNA가 각각 false negative, false polisive 결과 •E 환자: 수술 후 10개월 경에 재발하였으나, ctDNA 수치는 negative •F 환자: ctDNA는 계속 들쭉날쭉 했는데 36개월까지 재발을 하지 않음 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
pointing to cancers th by R (i.e., those with t account for cancer in seem particularly we miologic investigation appear unavoidable n they will become avo are at least four sourc cells: quantum effects o made by polymerase tion of bases (32), and produced reactive oxy olites (33). The last o be reduced by the dant drugs (34). The principle, be reduced cient repair genes int or through other crea As a result of the ulation, cancer is tod of death in the world the best way to reduc of a third contributo does not diminish t prevention but emph can be prevented by a factors (Figs. 2 and 3 vention is not the on exists or can be im ondary prevention, i.e vention, can also be which all mutations a Fig. 3. Etiology of driver gene mutations in women with cancer. For each of 18 representative cancer types, the schematic depicts the proportion of mutations that are inherited, due to environmental factors, or due to errors in DNA replication (i.e., not attributable to either heredity or environment).The sum of these three proportions is 100%. The color codes for hereditary, replicative, and environmental factors are identical and span white (0%) to brightest red (100%). The numerical values used to construct this figure, as well as the values for 14 other cancer types not shown in the figure, are provided in table S6. B, brain; Bl, bladder; Br, breast; C, cervical; CR, colorectal; E, esophagus; HN, head and neck; K, kidney; Li, liver; Lk, leukemia; Lu, lung; M, melanoma; NHL, non-Hodgkin lymphoma; O, ovarian; P, pancreas; S, stomach; RESEARCH | REPORTEtiology of driver gene mutations in women with cancer Cristian Tomasetti , Science 2017 유전적 요인(Hereditary), 환경적 요인(Environmental)에 비해서, 
 DNA replication에 의한 driver mutation (Replicative)의 비율이 암종의 구분 없이 매우 높다. 따라서, 암의 조기 발견의 중요성이 더욱 높아지고 있음.
https://www.illumina.com/content/dam/illumina-marketing/documents/company/investor-relations/investor_presentations/illumina_investor_presentation.pdf Product MiniSeq ™ MiSeq ® NextSeq HiSeq ® HiSeq ® X 4000 Five Ten Output per run 7.5 Gb 15 Gb 120 Gb 1.5 Tb 1.8 Tb 1.8 Tb Instrument price $49.5K $99K $275K $900K $6M1 $10M1 Utilization2 $20K–$25K $40K–$45K $100K–$150K $300K–$350K $625K–$725K Installed base3 370 ~5,300 ~1,800 ~1,900 ~400 Sequencing Power for Every Scale The broadest portfolio offering available 1. Based on purchase of 5 and 10 units for HiSeq X Five and HiSeq X Ten, respectively 2. Company’s projected annual instrument utilization per installed instrument; HiSeq and HiSeq X utilization to be combined later in • 2014년 1월 출시 • 기기 하나에 약 10억원 • 10개 번들 판매로 최소 구입 단위는 100억원 • 미국의 브로드 연구소, 호주의 가반의학연구소, 한국의 마크로젠
6 Shipping Q1 2017 $985K Shipping Early 2018 $850K NovaSeq 6000NovaSeq 5000 NovaSeq 5000 Flow Cells NovaSeq 6000 Flow Cells 1 Tb* 2 Tb 4 Tb* 6 Tb*Output/Run: NovaSeq System Scalable throughput to complete studies faster and more economically *S1 and S4 flow cells expected to begin shipping in Q3 2017; S3 flow cell expected to begin shipping in early 2018 https://www.illumina.com/content/dam/illumina-marketing/documents/company/investor-relations/investor_presentations/illumina_investor_presentation.pdf
• 2017년 1월 NovaSeq 5000, 6000 발표 • 몇년 내로 $100로 WES 를 실현하겠다고 공언 • 2일에 60명의 WES 가능 (한 명당 한 시간 이하)
http://privateoffice.investec.co.uk/research-and-insights/insights/vision_next_generation_sequencing.html Next Generation Sequencing (NGS) Market Share
http://privateoffice.investec.co.uk/research-and-insights/insights/vision_next_generation_sequencing.html Next Generation Sequencing (NGS) Market Share • 일루미나는 현재 전세계 DNA의 90%를 생산 • 전세계 인구의 0.01% 밖에 아직 DNA 서열 분석을 하지 않았음
Value Chain of Sequencing Industry Sequencing Analysis Diagnosis Treatment Consumer Service
Illumina tries to eat everything in sequencing market Sequencing Analysis Diagnosis Treatment Consumer Service 개인유전정보 앱스토어$100 m funding, co-founding (2015) NIPT(비침습 태아 산전진단) $350m 인수 (2013) Analysis Liquid Biopsy (액체 생검) Spin-off (2016.1)/ $100m 빌게이츠, 제프 베조스 등 투자
• 일루미나는 NGS 기기를 만드는 하드웨어 기업으로 시작 • 시퀀싱 시장 점유를 기반으로 value chain 후반의 진단, 소비자 서비스 시장으로 진출 중 • (via 인수, 투자, 공동 설립)
 • 과거 인터넷 산업에 비유하자면, • 초기에는 Cisco 같은 네트워크 인프라를 구축하는 기업이 수익 • 나중에는 인프라를 활용한 서비스 제공 기업이 성장 (구글, 페이스북…) • 일루미나는 그 둘을 모두 하겠다는 것 Illumina tries to eat everything in sequencing market Sequencing Analysis Diagnosis Treatment Consumer Service 개인유전정보 앱스토어$100 m funding, co-founding (2015) NIPT(비침습 태아 산전진단) $350m 인수 (2013) Analysis Liquid Biopsy (액체 생검) Spin-off (2016.1)/ $100m 빌게이츠, 제프 베조스 등 투자
•Series A: $100m •Series B: $900m •Biotech funding round 사상 최고액으로 평가 •ARCH Venture Partners led the round



with participation from J&J, Amazon, BMS, Celgene, Varian, and Merck. •Liquid Biopsy의 임상 연구에 활용할 계획
• Grail 이 발표한 최초의 대규모 임상 연구 (2016년 12월): Mayo Clinic, MSKCC 등 50여개 병원 참여 • 10,000명의 환자의 혈액을 분석으로 시작 (추후 확대 예정) • 7,000명의 암 환자 • 3,000명의 정상인 • 정상인 혈액과 암 환자의 cell free genome profile 을 파악하기 위한 연구 • 정상인의 cf genome의 heterogeneity 역시 연구: 정상인 - 암환자 구분에 도움 • ‘high intensitiy’ sequencing: ultra-deep sequencing & ultra-wide sequencing 을 사용하게 될 것
• 2017년 4월 대규모 유방암 환자 임상시험 STRIVE를 개시한다고 공표 • 유방암 조기 발견을 위한 blood test 의 개발 목적 • 120,000명 규모 • Mayo Clinic 과 Sutter Health 에서 유방암 정기검사 (mammography)를 받는 환자들 대상 • ultra-deep sequencing & ultra-wide sequencing 을 사용하게 될 것 • 이 임상 결과를 바탕으로 pan-cancer test 의 개발에도 사용하게 될 것
Leading Edge Commentary Next-Generation Sequencing of Circulating Tumor DNA for Early Cancer Detection Alexander M. Aravanis,1,2 Mark Lee,1,2 and Richard D. Klausner1,* 1GRAIL, Menlo Park, CA 94402, USA 2Co-first author *Correspondence: klausner.rick@gmail.com http://dx.doi.org/10.1016/j.cell.2017.01.030 Curative therapies are most successful when cancer is diagnosed and treated at an early stage. We advocate that technological advances in next-generation sequencing of circulating, tumor-derived nucleic acids hold promise for addressing the challenge of developing safe and effective cancer screening tests. Cancer-specific mortality from most types of solid tumors has barely decreased in decades, despite an expo- nential increase in our knowledge about cancer pathogenesis and significant in- vestments in the development of effective treatments. The past few years have witnessed a dramatic success of immu- notherapies in treating a subgroup of patients with a variety of tumor types, including lung, bladder, and kidney, as well as Hodgkin’s lymphoma and mela- noma. While such breakthroughs offer the hope of prolonged survival for some patients with advanced cancers, finding cancers earlier would still afford the great- est chance for cure, given that the survival rates for patients with early diagnoses are five to ten times higher compared with late stage disease (Cho et al., 2014). By tion algorithms that either miss a large number of invasive cancers or make the costly trade-off of over-diagnosing and consequently over treating. For instance, high false-positive rates from mammog- raphy in breast cancer screening, low- dose CT in lung cancer screening, and prostate-specific antigen (PSA) screening (Nelson et al., 2016a; Aberle et al., 2011; Chou et al., 2011) represent a significant cost to the healthcare system, with result- ing mental and physical morbidity, and even mortality in some cases (Nelson et al., 2016b). Even where cancer screening has pro- duced significant stage shifts, as with breast and prostate cancer screening, the impact on cancer-specific mortality has not been a predictable outcome (Berry, 2014). Multiple explanations may cancers are in a pre-metastatic state and thus still curable. This kinetic aspect of cancer progression is poorly understood, but it is essential to informing effective screening intervals. It is worth noting that mammography and PSA are only sur- rogate measures of cancer, which have poor specificity and provide little insight into tumor biology. We would argue that for successful screening, we need a platform that provides direct, sensitive, and specific measures of cancer and its attributes, which have bearing on clinical behavior. Circulating Tumor DNA Profiling of a tumor’s somatic alterations has become routine, and many clinical tests are now available that interrogate anywhere from a few genes to the whole
•국내에서도 삼성유전체연구소를 비롯한 몇몇 그룹이 Liquid Biopsy 를 연구 •삼성유전체연구소에서 LiquidScan을 개발했다고 발표 (2017.4) •(기사 제목처럼) 피 한 방울은 아니고, 20ml 정도 필요 •현재 췌장암 및 유방암 연구 중 •췌장암의 경우 LiquidScan을 통해 기존 방식보다 2-3개월 미리 재발 여부파악 가능
23andMe의 DTC 서비스 FDA 인허가 확대
Results within 6-8 weeksA little spit is all it takes! DTC Genetic TestingDirect-To-Consumer
• Direct-to-Consumer 방식의 서비스를 고집 • 데이터 소유권 이슈: “환자 본인에게 raw data 를 주겠다”
120 Disease Risk 21 Drug Response 49 Carrier Status 57Traits $99
• 질병 위험도 검사: BRCA로 유방암 위험도 예측 • 보인자 검사: 부모가 블룸증후군 유전자 보인자인지 판별 • 약물 민감도 검사: 와파린 등에 대한 민감도 검사 • 일반적 특징 검사: 곱슬머리, 카페인 대사, 유당 분해 능력 등 • 조상 분석: 내 조상이 어느 대륙에서 왔는가 Tests of 23andMe
Health Risks
Health Risks
Health Risks
Drug Response
Inherited Conditions 혈색소증은 유전적 원인으로 철에 대한 체내 대사에 이상이 생겨 음식을 통 해 섭취한 철이 너무 많이 흡수되는 질환입니다. 너무 많이 흡수된 철은 우 리 몸의 여러 장기, 특히 간, 심장 및 췌장에 과다하게 축적되며 이들 장기 를 손상시킴으로써 간질환, 심장질환 및 악성종양을 유발합니다.
Traits 음주 후 얼굴이 붉어지는가 쓴 맛을 감지할 수 있나 귀지 유형 눈 색깔 곱슬머리 여부 유당 분해 능력 말라리아 저항성 대머리가 될 가능성 근육 퍼포먼스 혈액형 노로바이러스 저항성 HIV 저항성 흡연 중독 가능성
Ancestry Composition
Neanderthal Ancestry
• 질병 위험도 검사: BRCA로 유방암 위험도 예측 • 보인자 검사: 부모가 블룸증후군 유전자 보인자인지 판별 • 약물 민감도 검사: 와파린 등에 대한 민감도 검사 • 일반적 특징 검사: 곱슬머리, 카페인 대사, 유당 분해 능력 등 • 조상 분석: 내 조상이 어느 대륙에서 왔는가 Tests of 23andMe (until Nov 2013)
• 제한적 유전정보: 일부분의 유전정보 (SNP) 만을 분석 • 환경적 요인 고려 불가: 대부분의 질병은 환경+유전 요인 작용 • So What? : 유전적 위험도를 알아도 대비책이 없거나 불분명 Personal Genome Service 의 한계
1,200,000 1,000,000 900,000 850,000 650,000 500,000 400,000 300,000 250,000 180,000 100,000 2007-11 2011-06 2011-10 2012-04 2012-10 2013-04 2013-06 2013-09 2013-12 2014-10 2015-02 2015-05 2015-06 2016-02 0 Customer growth of 23andMe by Changwon Keum (Macrogen)
 http://goldbio.blogspot.kr/2014/12/pg-100.html
• 의사를 통하지 않는 DTC 방식에 대한 우려 • 이러한 서비스의 정확성 및 안정성에 대한 우려 • 결과를 받은 사용자들이 제대로 이해할 수 있을지, 오남용에 대한 우려 • 특히, BRCA 유전자에 대한 검사 • Analytic & clinical validation data 제출 지연
• 질병 위험도 검사: BRCA로 유방암 위험도 예측 • 보인자 검사: 부모가 블룸증후군 유전자 보인자인지 판별 • 약물 민감도 검사: 와파린 등에 대한 민감도 검사 • 일반적 특징 검사: 곱슬머리, 카페인 대사, 유당 분해 능력 등 • 조상 분석: 내 조상이 어느 대륙에서 왔는가 Tests of 23andMe (Nov 2013 - Oc 2015)
2015.2.19
http://www.fastcompany.com/3051973/behind-the-brand/23andme-and-the-fda-reached-a-pivotal-genetic-testing-agreement
• 질병 위험도 검사: BRCA로 유방암 위험도 예측 • 보인자 검사: 부모가 블룸증후군 유전자 보인자인지 판별 • 약물 민감도 검사: 와파린 등에 대한 민감도 검사 • 일반적 특징 검사: 곱슬머리, 카페인 대사, 유당 분해 능력 등 • 조상 분석: 내 조상이 어느 대륙에서 왔는가 Tests of 23andMe (Oct 2015 - April 2017)
2017년 4월 6일 FDA가 23andMe의 질병 위험도 예측 서비스의 
 DTC (Direct-to-Consumer) 판매를 허가
FDA의 23andMe 질병 위험도 예측 DTC 서비스 허가 •아래와 같은 10가지 질병의 위험도 예측에 대해서 DTC 허가 •파킨슨병 (Parkinson’s disease) •알츠하이머 (Late-onset Alzheimer’s disease) •셀리악병(Celiac disease) •알파-1 항트립신 결핍증 (Alpha-1 antitrypsin deficiency) •조발성 1차성 근긴장이상증 (Early-onset primary dystonia) •XI 혈액응고인자 결핍증 (혈우병C) (Factor XI deficiency, a blood clotting disorder) •제 1형 고셔병 (Gaucher disease type 1) •포도당-6-인산탈수소효소(G6PD) 결핍증 (Glucose-6-Phosphate Dehydrogenase deficiency) •유전성 혈색소침착증(Hereditary hemochromatosis) •유전적 혈전 기호증(Hereditary thrombophilia) •FDA는 향후 다른 질병 위험도 예측 검사에 대해서 시장 출시 전 심사(premarket review)를 면제
FDA의 23andMe 질병 위험도 예측 DTC 서비스 허가 •임상 연구를 통하여 인허가를 위한 근거 자료 마련 •분석적 타당성(analytical validity) •임상적 타당성(clinical validity) •임상적 유용성(clinical utility)
 
 •23andMe의 타액 키트를 통해서 정확하고 일관적으로 유전 변이를 발견할 수 있다는 것을 증명 •검사하는 유전적 변이가 개별 질병의 위험도에 영향을 준다는 명확한 연구 결과. •환자의 DTC 결과 오남용에 대한 반박 •영국에서 25,000명에게 질병 위험도 예측 서비스를 DTC로 제공한 결과, 
 
 자해 등 위험한 결과가 한 건도 발생하지 않았음 •사용자들이 질병 위험도 예측의 결과 레포트의 90% 이상을 이해
• 질병 위험도 검사 • 보인자 검사: 부모가 블룸증후군 유전자 보인자인지 판별 • 약물 민감도 검사: 와파린 등에 대한 민감도 검사 • 일반적 특징 검사: 곱슬머리, 카페인 대사, 유당 분해 능력 등 • 조상 분석: 내 조상이 어느 대륙에서 왔는가 Tests of 23andMe (April 2017- 현재)
$115m 펀딩 100만 명 돌파 2006 23andMe 창업 20162007 2012 2013 2014 2015 구글 벤처스 360만 달러 투자 2008 $99 로 가격 인하 FDA 판매 중지 명령 영국에서 DTC 서비스 시작 FDA 블룸증후군 DTC 서비스 허가 FDA에 블룸증후군 테스트 승인 요청 FDA에 510(k) 제출 FDA 510(k) 철회 보인자 등 DTC 서비스 재개 ($199) 캐나다에서 DTC 서비스 시작 Genetech, pFizer가 23andMe 데이터 구입 자체 신약 개발 계획 발표 120만 명 돌파 $399 로 가격 인하 23andMe Chronicle Business Regulation 애플 리서치키트와 데이터 수집 협력 50만 명 돌파30만 명 돌파 TV 광고 시작 2017 FDA의 질병위험도 검사 DTC 서비스 허가 + 관련 규제 면제 프로세스 확립 Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
1,200,000 1,000,000 900,000 850,000 650,000 500,000 400,000 300,000 250,000 180,000 100,000 2007-11 2011-06 2011-10 2012-04 2012-10 2013-04 2013-06 2013-09 2013-12 2014-10 2015-02 2015-05 2015-06 2016-02 0 Customer growth of 23andMe 2017-04 2,000,000 Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
생명윤리법 개정안 및 DTC 허용 계획 •2015년 12월 9일, 국회에서 ‘생명윤리법 개정안’ 의결 •‘비의료기관은 보건복지부장관이 정하는 경우에만 의료기관의 의뢰 없이도 
 질병 예방 목적의 유전자 검사를 제한적으로 직접할 수 있도록 허용한다' •보건복지부 2016년 업무보고: 유전자 검사 제도 개선 •질병 예방 목적의 일부 유전자/유전체 검사를 비의료기관에서 직접 실시 (2016년 6월) •최적 치료법에 필요한 유전자/유전체 검사의 경우 건강보험 적용 (2016년 11월) •표적치료제 선택 검사 확대 •약물반응예측검사 추가
11
Category 분류기준 I 건강보험 요양급여 등재 혹은 신의료기술평가를 통해 안정성 및 유효성이 인정된 유전자 검사 로 임상적 사용목적(Intended use)이 동일한 경우 II 아직까지 건강보험 요양급여의 등재 혹은 신의료기술평가를 통해 안정성 및 유효성이 인정되 지 않았지만, 임상적 유효성 근거가 있는 검사로 임상적 사용 목적이 동일한 경우 III 건강보험 요양급여 등재 혹은 신의료기술평가를 통해 안정성, 유효성이 인정 받은 검사 및 임 상적 유효성의 근거가 있는 유전자 검사를 건강인에게 시행하는 경우 IV 건강보험 요양급여 등재 혹은 신의료기술평가를 통해 안정성, 유효성이 인정 받은 검사 및 임 상적 유효성의 근거가 있는 유전자 검사를 적절한 임상적 사용 목적 외에 의학적 근거가 부족 한 용도로 사용하는 경우 V 건강보험 요양급여 미등재 혹은 안정성 유효성에 관한 신의료기술평가를 받지 않은 검사로 과 학적 타당성의 입증이 불확실하거나, 검사대상자를 오도할 우려가 있는 신체 외관, 성격 등의 형질에 관한 검사 VI 건강보험 요양급여 미등재 혹은 안정성, 유효성에 관한 신의료기술평가를 받지 않은 검사로 임상적 유효성에 대한 근거가 부족한 검사 유전자 검사평가원에서 제안한 유전자 검사 분류표
한국 DTC 유전정보 분석 제한적 허용 (2016.6.30) • 「비의료기관 직접 유전자검사 실시 허용 관련 고시 제정, 6.30일시행」 • 2015년 12월「생명윤리 및 안전에 관한 법률」개정(‘15.12.29개정, ’16.6.30시행)과 제9차 무역투자진흥회의(’16.2월) 시 발표한 규제 개선의 후속조치 일환으로 추진 • 민간 유전자검사 업체에서는 혈당, 혈압, 피부노화, 체질량지수 등 12개 검사항목과 관련된 46개 유전자를 직접 검사 가능 http://www.mohw.go.kr/m/noticeView.jsp?MENU_ID=0403&cont_seq=333112&page=1 검사항목 (유전자수) 유전자명 1 체질량지수(3) FTO, MC4R, BDNF 2 중성지방농도(8) GCKR, DOCK7, ANGPTL3, BAZ1B, TBL2, MLXIPL, LOC105375745, TRIB1 3 콜레스테롤(8) CELSR2, SORT1, HMGCR, ABO, ABCA1, MYL2, LIPG, CETP 4 혈 당(8) CDKN2A/B, G6PC2, GCK, GCKR, GLIS3, MTNR1B, DGKB-TMEM195, SLC30A8 5 혈 압(8) NPR3, ATP2B1, NT5C2, CSK, HECTD4, GUCY1A3, CYP17A1, FGF5 6 색소 침착(2) OCA2, MC1R 7 탈 모(3) chr20p11(rs1160312, rs2180439), IL2RA, HLA-DQB1 8 모발 굵기(1) EDAR 9 피부 노화(1) AGER 10 피부 탄력(1) MMP1 11 비타민C농도(1) SLC23A1(SVCT1) 12 카페인대사(2) AHR, CYP1A1-CYP1A2
DTC 유전정보 분석 서비스 미국 vs. 한국 Table 1 분석 항목 분석 항목 예시 DTC (미국) DTC (한국) 개인유전정보 분석 질병 위험도 유방암(안젤리나 졸리) O 불가 약물 민감도 와파린 민감도 X X 열성유전질환 보인자 블룸 증후군 O X 웰니스 카페인 분해, 대머리 O 12개만 가능 조상 분석 O 불명확 •미국에서 허용된 보인자 검사, 질병 위험도 예측 검사 DTC 서비스는 여전히 한국에서 불법 •더 큰 문제는 잣대 자체가 FDA 등 글로벌 규제 기조나 산업계에서 통용되는 기준과 다름. 
 질병/약물/보인자/웰니스/조상 분석 등의 업계에서 받아들여지는 분류를 무시하고 있음. •글로벌 수준에 발맞추기는 커녕, 한국에서만 통용되는 자체적인 별도 규제 분류 체계를 
 갈수록 더 만들어가면서, 국내 산업의 갈라파고스화를 심화 시키고 있음
IBM Watson for Oncology 도입 광풍(?)
600,000 pieces of medical evidence 2 million pages of text from 42 medical journals and clinical trials 69 guidelines, 61,540 clinical trials IBM Watson on Medicine Watson learned... + 1,500 lung cancer cases physician notes, lab results and clinical research + 14,700 hours of hands-on training
Empowering the Oncology Community for Cancer Care Genomics Oncology Clinical Trial Matching Watson Health’s oncology clients span more than 35 hospital systems “Empowering the Oncology Community for Cancer Care” Andrew Norden, KOTRA Conference, March 2017, “The Future of Health is Cognitive”
IBM Watson Health Watson for Clinical Trial Matching (CTM) 18 1. According to the National Comprehensive Cancer Network (NCCN) 2. http://csdd.tufts.edu/files/uploads/02_-_jan_15,_2013_-_recruitment-retention.pdf© 2015 International Business Machines Corporation Searching across eligibility criteria of clinical trials is time consuming and labor intensive Current Challenges Fewer than 5% of adult cancer patients participate in clinical trials1 37% of sites fail to meet minimum enrollment targets. 11% of sites fail to enroll a single patient 2 The Watson solution • Uses structured and unstructured patient data to quickly check eligibility across relevant clinical trials • Provides eligible trial considerations ranked by relevance • Increases speed to qualify patients Clinical Investigators (Opportunity) • Trials to Patient: Perform feasibility analysis for a trial • Identify sites with most potential for patient enrollment • Optimize inclusion/exclusion criteria in protocols Faster, more efficient recruitment strategies, better designed protocols Point of Care (Offering) • Patient to Trials: Quickly find the right trial that a patient might be eligible for amongst 100s of open trials available Improve patient care quality, consistency, increased efficiencyIBM Confidential
Watson Genomics Overview 20 Watson Genomics Content • 20+ Content Sources Including: • Medical Articles (23Million) • Drug Information • Clinical Trial Information • Genomic Information Case Sequenced VCF / MAF, Log2, Dge Encryption Molecular Profile Analysis Pathway Analysis Drug Analysis Service Analysis, Reports, & Visualizations
At HIMSS 2017, provided Hye Jin Kam of Asan Medical Center
At HIMSS 2017, provided Hye Jin Kam of Asan Medical Center
식약처 인공지능 가이드라인 초안 Medtronic과 혈당관리 앱 시연 2011 2012 2013 2014 2015 Jeopardy! 우승 뉴욕 MSK암센터 협력 (Lung cancer) MD앤더슨 협력 (Leukemia) MD앤더슨 Pilot 결과 발표 @ASCO Watson Fund, WellTok 에 투자 ($22m) The NewYork Genome Center 협력 (Glioblastoma 분석) GeneMD, Watson Mobile Developer Challenge의 winner 선정 Watson Fund, Pathway Genomics 투자 Cleveland Clinic 협력 (Cancer Genome Analysis) 한국 IBM Watson 사업부 신설 Watson Health 출범 Phytel & Explorys 인수 J&J,Apple, Medtronic 협력 Epic & Mayo Clinic 제휴 (EHR data 분석) 동경대 도입 (oncology) 14 Cancer Center 제휴 (Cancer Genome Analysis) Mayo Clinic 협력 (clinical trail matching) Watson Fund, Modernizing Medicine 투자 Academia Business Pathway Genomics OME closed alpha 시작 TurvenHealth 인수 Apple ResearchKit 통한 수면 연구 시작 2017 가천대 길병원 Watson 도입 (oncology) Medtronic Sugar.IQ 출시 제약사 Teva와 제휴 인도 Manipal Hospital Watson 도입 태국 Bumrungrad  International Hospital, Watson 도입 최윤섭 디지털헬스케어 연구소, 소장 (주)디지털 헬스케어 파트너스, 대표파트너 최윤섭, Ph.D. yoonsup.choi@gmail.com IBM Watson in Healthcare Merge Healthcare 인수 2016 Under Amour 제휴 Broad 연구소 협력 발표 (유전체 분석-항암제 내성) Manipal Hospital의 WFO 정확성 발표 대구가톨릭병원 대구동산병원 WFO 도입 건양대병원 Watson 도입 (oncology) 부산대학병원 Watson 도입 (oncology/ genomics)
식약처 인공지능 가이드라인 초안 Medtronic과 혈당관리 앱 시연 2011 2012 2013 2014 2015 Jeopardy! 우승 뉴욕 MSK암센터 협력 (Lung cancer) MD앤더슨 협력 (Leukemia) MD앤더슨 Pilot 결과 발표 @ASCO Watson Fund, WellTok 에 투자 ($22m) The NewYork Genome Center 협력 (Glioblastoma 분석) GeneMD, Watson Mobile Developer Challenge의 winner 선정 Watson Fund, Pathway Genomics 투자 Cleveland Clinic 협력 (Cancer Genome Analysis) 한국 IBM Watson 사업부 신설 Watson Health 출범 Phytel & Explorys 인수 J&J,Apple, Medtronic 협력 Epic & Mayo Clinic 제휴 (EHR data 분석) 동경대 도입 (oncology) 14 Cancer Center 제휴 (Cancer Genome Analysis) Mayo Clinic 협력 (clinical trail matching) Watson Fund, Modernizing Medicine 투자 Academia Business Pathway Genomics OME closed alpha 시작 TurvenHealth 인수 Apple ResearchKit 통한 수면 연구 시작 2017 가천대 길병원 Watson 도입 (oncology) Medtronic Sugar.IQ 출시 제약사 Teva와 제휴 인도 Manipal Hospital Watson 도입 태국 Bumrungrad  International Hospital, Watson 도입 최윤섭 디지털헬스케어 연구소, 소장 (주)디지털 헬스케어 파트너스, 대표파트너 최윤섭, Ph.D. yoonsup.choi@gmail.com IBM Watson in Healthcare Merge Healthcare 인수 2016 Under Amour 제휴 Broad 연구소 협력 발표 (유전체 분석-항암제 내성) Manipal Hospital의 WFO 정확성 발표 대구가톨릭병원 대구동산병원 WFO 도입 건양대병원 Watson 도입 (oncology) 부산대학병원 Watson 도입 (oncology/ genomics)
한국에서도 Watson을 볼 수 있을까? 2015.7.9. 서울대학병원
• 부산대학병원 (2017년 1월) • Watson의 솔루션 두 가지를 도입 • Watson for Oncology • Watson for Genomics
• 건양대학병원 Watson for Oncology 도입 • 2017년 3월 • “최원준 건양대병원장은 "지역 환자들은 수도권의 여러 병원을 찾아다닐 필요가 없어질 것"이라며 "병 원의 우수한 협진 팀과 인공지능 의료 시스템의 시 너지를 바탕으로 암 환자에게 최상의 의료 서비스를 제공하겠다"고 약속했다."
IBM Watson Health Organizations Leveraging Watson Watson for Oncology Best Doctors (second opinion) Bumrungrad International Hospital Confidential client (Bangladesh and Nepal) Gachon University Gil Medical Center (Korea) Hangzhou Cognitive Care – 50+ Chinese hospitals Jupiter Medical Center Manipal Hospitals – 16 Indian Hospitals MD Anderson (**Oncology Expert Advisor) Memorial Sloan Kettering Cancer Center MRDM - Zorg (Netherlands) Pusan National University Hospital Clinical Trial Matching Best Doctors (second opinion) Confidential – Major Academic Center Highlands Oncology Group Froedtert & Medical College of Wisconsin Mayo Clinic Multiple Life Sciences pilots 24 Watson Genomic Analytics Ann & Robert H Lurie Children’s Hospital of Chicago BC Cancer Agency City of Hope Cleveland Clinic Columbia University, Irwing Cancer Center Duke Cancer Institute Fred & Pamela Buffett Cancer Center Fleury (Brazil) Illumina 170 Gene Panel NIH Japan McDonnell Institute at Washington University in St. Louis New York Genome Center Pusan National University Hospital Quest Diagnostics Stanford Health University of Kansas Cancer Center University of North Carolina Lineberger Cancer Center University of Southern California University of Washington Medical Center University of Tokyo Yale Cancer Center Andrew Norden, KOTRA Conference, March 2017, “The Future of Health is Cognitive” Watson for Oncology 는 현재 전세계 70여개 병원에 도입
• 인공지능으로 인한 인간 의사의 권위 약화 • 환자의 자기 결정권 및 권익 증대 • 의사의 진료 방식 및 교육 방식의 변화 필요 http://news.donga.com/3/all/20170320/83400087/1
• 의사와 Watson의 판단이 다른 경우? • NCCN 가이드라인과 다른 판단을 주기는 것으로 보임 • 100 여명 중에 5 case. 
 • 환자의 판단이 합리적이라고 볼 수 있는가? • Watson의 정확도는 검증되지 않았음 • ‘제 4차 산업혁명’ 등의 buzz word의 영향으로 보임 • 임상 시험이 필요하지 않은가? • 환자들의 선호는 인공지능의 adoption rate 에 영향 • 병원 도입에 영향을 미치는 요인들 • analytical validity • clinical validity/utility • 의사들의 인식/심리적 요인 • 환자들의 인식/심리적 요인 • 규제 환경 (인허가, 수가 등등) • 결국 환자가 원하면 (그것이 의학적으로 타당한지를 떠나서) 병원 도입은 더욱 늘어날 수 밖에 없음
• Watson에 대한 환자 반응이 생각보다 매우 좋음 • 도입 2개월만에 85명 암 환자 진료 • 기존의 길병원 예측보다는 더 빠른 수치일 듯 • Big5 에서도 길병원으로 전원 문의 증가 한다는 후문 • 교수들이 더 열심히 상의하고 환자 본다고 함
• Trained by 400 cases of historical patients cases • Assessed accuracy OEA treatment suggestions 
 using MD Anderson’s physicians’ decision as benchmark • When 200 leukemia cases were tested, • False positive rate=2.9% • False negative rate=0.4% • Overall accuracy of treatment recommendation=82.6% • Conclusion: Suggested personalized treatment option showed reasonably high accuracy MDAnderson’s Oncology ExpertAdvisor Powered by IBM Watson :AWeb-Based Cognitive Clinical Decision Support Tool Koichi Takahashi, MD (ASCO 2014)
Annals of Oncology (2016) 27 (suppl_9): ix179-ix180. 10.1093/annonc/mdw601 Validation study to assess performance of IBM cognitive computing system Watson for oncology with Manipal multidisciplinary tumour board for 1000 consecutive cases: 
 An Indian experience • MMDT(Manipal multidisciplinary tumour board) treatment recommendation and data of 1000 cases of 4 different cancers breast (638), colon (126), rectum (124) and lung (112) which were treated in last 3 years was collected. • Of the treatment recommendations given by MMDT, WFO provided 
 
 50% in REC, 28% in FC, 17% in NREC • Nearly 80% of the recommendations were in WFO REC and FC group • 5% of the treatment provided by MMDT was not available with WFO • The degree of concordance varied depending on the type of cancer • WFO-REC was high in Rectum (85%) and least in Lung (17.8%) • high with TNBC (67.9%); HER2 negative (35%)
 • WFO took a median of 40 sec to capture, analyze and give the treatment.
 
 (vs MMDT took the median time of 15 min)
Sung Won Park,APFCP, 2017 Assessing the performance of Watson for Oncology using colon cancer cases treated with surgery and adjuvant chemotherapy 
 at Gachon University Gil Medical Center • Stage II with high risk and stage III colon cancer patients (N=162) • Retrospective study: From September 1, 2014 to August 31, 2016 • Gachon University Gil Medical Center (GMC) • Generally accepted by GMC-recommendation in 83.3% • Concordant with • WFO-Rec: 53.1% • WFO-FC: 30.2% • WFO-NREC: 13.0% • Not included: 3.7%
WHY? • 국가별 가이드라인의 차이 • WFO는 기본적으로 MSKCC 기준 • 인종적 차이, 인허가 약물의 차이, 보험 제도의 차이 • NCCN 가이드라인의 업데이트 • 암종별 치료 가능한 옵션의 다양성 차이 • 폐암: 다양함 vs 직장암: 다양하지 않음 • TNBC: 다양하지 않음 vs HER2 (-): 다양함
원칙이 필요하다 •어떤 환자의 경우, 왓슨에게 의견을 물을 것인가? •왓슨을 (암종별로) 얼마나 신뢰할 것인가? •왓슨의 의견을 환자에게 공개할 것인가? •왓슨과 의료진의 판단이 다른 경우 어떻게 할 것인가? •왓슨에게 보험 급여를 매길 수 있는가? 이러한 기준에 따라 의료의 질/치료효과가 달라질 수 있으나, 현재 개별 병원이 개별적인 기준으로 활용하게 됨
의사를 능가하는 Deep Learning 연구 결과들
Deep Learning http://theanalyticsstore.ie/deep-learning/
Detection of Diabetic Retinopathy
당뇨성 망막병증 • 당뇨병의 대표적 합병증: 당뇨병력이 30년 이상 환자 90% 발병 • 안과 전문의들이 안저(안구의 안쪽)를 사진으로 찍어서 판독 • 망막 내 미세혈관 생성, 출혈, 삼출물 정도를 파악하여 진단
Copyright 2016 American Medical Association. All rights reserved. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD; Martin C. Stumpe, PhD; Derek Wu, BS; Arunachalam Narayanaswamy, PhD; Subhashini Venugopalan, MS; Kasumi Widner, MS; Tom Madams, MEng; Jorge Cuadros, OD, PhD; Ramasamy Kim, OD, DNB; Rajiv Raman, MS, DNB; Philip C. Nelson, BS; Jessica L. Mega, MD, MPH; Dale R. Webster, PhD IMPORTANCE Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. OBJECTIVE To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. DESIGN AND SETTING A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. EXPOSURE Deep learning–trained algorithm. MAIN OUTCOMES AND MEASURES The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. RESULTS TheEyePACS-1datasetconsistedof9963imagesfrom4997patients(meanage,54.4 years;62.2%women;prevalenceofRDR,683/8878fullygradableimages[7.8%]);the Messidor-2datasethad1748imagesfrom874patients(meanage,57.6years;42.6%women; prevalenceofRDR,254/1745fullygradableimages[14.6%]).FordetectingRDR,thealgorithm hadanareaunderthereceiveroperatingcurveof0.991(95%CI,0.988-0.993)forEyePACS-1and 0.990(95%CI,0.986-0.995)forMessidor-2.Usingthefirstoperatingcutpointwithhigh specificity,forEyePACS-1,thesensitivitywas90.3%(95%CI,87.5%-92.7%)andthespecificity was98.1%(95%CI,97.8%-98.5%).ForMessidor-2,thesensitivitywas87.0%(95%CI,81.1%- 91.0%)andthespecificitywas98.5%(95%CI,97.7%-99.1%).Usingasecondoperatingpoint withhighsensitivityinthedevelopmentset,forEyePACS-1thesensitivitywas97.5%and specificitywas93.4%andforMessidor-2thesensitivitywas96.1%andspecificitywas93.9%. CONCLUSIONS AND RELEVANCE In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment. JAMA. doi:10.1001/jama.2016.17216 Published online November 29, 2016. Editorial Supplemental content Author Affiliations: Google Inc, Mountain View, California (Gulshan, Peng, Coram, Stumpe, Wu, Narayanaswamy, Venugopalan, Widner, Madams, Nelson, Webster); Department of Computer Science, University of Texas, Austin (Venugopalan); EyePACS LLC, San Jose, California (Cuadros); School of Optometry, Vision Science Graduate Group, University of California, Berkeley (Cuadros); Aravind Medical Research Foundation, Aravind Eye Care System, Madurai, India (Kim); Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India (Raman); Verily Life Sciences, Mountain View, California (Mega); Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts (Mega). Corresponding Author: Lily Peng, MD, PhD, Google Research, 1600 Amphitheatre Way, Mountain View, CA 94043 (lhpeng@google.com). Research JAMA | Original Investigation | INNOVATIONS IN HEALTH CARE DELIVERY (Reprinted) E1 Copyright 2016 American Medical Association. All rights reserved.
Training Set / Test Set • CNN으로 후향적으로 128,175개의 안저 이미지 학습 • 미국의 안과전문의 54명이 3-7회 판독한 데이터 • 우수한 안과전문의들 7-8명의 판독 결과와 인공지능의 판독 결과 비교 • EyePACS-1 (9,963 개), Messidor-2 (1,748 개)a) Fullscreen mode b) Hit reset to reload this image. This will reset all of the grading. c) Comment box for other pathologies you see eFigure 2. Screenshot of the Second Screen of the Grading Tool, Which Asks Graders to Assess the Image for DR, DME and Other Notable Conditions or Findings
Inception-v3 (aka GoogleNet) https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html https://arxiv.org/abs/1512.00567
• EyePACS-1 과 Messidor-2 의 AUC = 0.991, 0.990 • 7-8명의 안과 전문의와 sensitivity, specificity 가 동일한 수준 • F-score: 0.95 (vs. 인간 의사는 0.91) Additional sensitivity analyses were conducted for sev- eralsubcategories:(1)detectingmoderateorworsediabeticreti- effects of data set size on algorithm performance were exam- ined and shown to plateau at around 60 000 images (or ap- Figure 2. Validation Set Performance for Referable Diabetic Retinopathy 100 80 60 40 20 0 0 70 80 85 95 90 75 0 5 10 15 20 25 30 100806040 Sensitivity,% 1 – Specificity, % 20 EyePACS-1: AUC, 99.1%; 95% CI, 98.8%-99.3%A 100 High-sensitivity operating point High-specificity operating point 100 80 60 40 20 0 0 70 80 85 95 90 75 0 5 10 15 20 25 30 100806040 Sensitivity,% 1 – Specificity, % 20 Messidor-2: AUC, 99.0%; 95% CI, 98.6%-99.5%B 100 High-specificity operating point High-sensitivity operating point Performance of the algorithm (black curve) and ophthalmologists (colored circles) for the presence of referable diabetic retinopathy (moderate or worse diabetic retinopathy or referable diabetic macular edema) on A, EyePACS-1 (8788 fully gradable images) and B, Messidor-2 (1745 fully gradable images). The black diamonds on the graph correspond to the sensitivity and specificity of the algorithm at the high-sensitivity and high-specificity operating points. In A, for the high-sensitivity operating point, specificity was 93.4% (95% CI, 92.8%-94.0%) and sensitivity was 97.5% (95% CI, 95.8%-98.7%); for the high-specificity operating point, specificity was 98.1% (95% CI, 97.8%-98.5%) and sensitivity was 90.3% (95% CI, 87.5%-92.7%). In B, for the high-sensitivity operating point, specificity was 93.9% (95% CI, 92.4%-95.3%) and sensitivity was 96.1% (95% CI, 92.4%-98.3%); for the high-specificity operating point, specificity was 98.5% (95% CI, 97.7%-99.1%) and sensitivity was 87.0% (95% CI, 81.1%-91.0%). There were 8 ophthalmologists who graded EyePACS-1 and 7 ophthalmologists who graded Messidor-2. AUC indicates area under the receiver operating characteristic curve. Research Original Investigation Accuracy of a Deep Learning Algorithm for Detection of Diabetic Retinopathy Results
Skin Cancer
ABCDE check-list
0 0 M O N T H 2 0 1 7 | V O L 0 0 0 | N A T U R E | 1 LETTER doi:10.1038/nature21056 Dermatologist-level classification of skin cancer with deep neural networks Andre Esteva1 *, Brett Kuprel1 *, Roberto A. Novoa2,3 , Justin Ko2 , Susan M. Swetter2,4 , Helen M. Blau5 & Sebastian Thrun6 Skin cancer, the most common human malignancy1–3 , is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs)4,5 show potential for general and highly variable tasks across many fine-grained object categories6–11 . Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets12 —consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care. There are 5.4 million new cases of skin cancer in the United States2 every year. One in five Americans will be diagnosed with a cutaneous malignancy in their lifetime. Although melanomas represent fewer than 5% of all skin cancers in the United States, they account for approxi- mately 75% of all skin-cancer-related deaths, and are responsible for over 10,000 deaths annually in the United States alone. Early detection is critical, as the estimated 5-year survival rate for melanoma drops from over 99% if detected in its earliest stages to about 14% if detected in its latest stages. We developed a computational method which may allow medical practitioners and patients to proactively track skin lesions and detect cancer earlier. By creating a novel disease taxonomy, and a disease-partitioning algorithm that maps individual diseases into training classes, we are able to build a deep learning system for auto- mated dermatology. Previous work in dermatological computer-aided classification12,14,15 has lacked the generalization capability of medical practitioners owing to insufficient data and a focus on standardized tasks such as dermoscopy16–18 and histological image classification19–22 . Dermoscopy images are acquired via a specialized instrument and histological images are acquired via invasive biopsy and microscopy; whereby both modalities yield highly standardized images. Photographic images (for example, smartphone images) exhibit variability in factors such as zoom, angle and lighting, making classification substantially more challenging23,24 . We overcome this challenge by using a data- driven approach—1.41 million pre-training and training images make classification robust to photographic variability. Many previous techniques require extensive preprocessing, lesion segmentation and extraction of domain-specific visual features before classification. By contrast, our system requires no hand-crafted features; it is trained end-to-end directly from image labels and raw pixels, with a single network for both photographic and dermoscopic images. The existing body of work uses small datasets of typically less than a thousand images of skin lesions16,18,19 , which, as a result, do not generalize well to new images. We demonstrate generalizable classification with a new dermatologist-labelled dataset of 129,450 clinical images, including 3,374 dermoscopy images. Deep learning algorithms, powered by advances in computation and very large datasets25 , have recently been shown to exceed human performance in visual tasks such as playing Atari games26 , strategic board games like Go27 and object recognition6 . In this paper we outline the development of a CNN that matches the performance of dermatologists at three key diagnostic tasks: melanoma classification, melanoma classification using dermoscopy and carcinoma classification. We restrict the comparisons to image-based classification. We utilize a GoogleNet Inception v3 CNN architecture9 that was pre- trained on approximately 1.28 million images (1,000 object categories) from the 2014 ImageNet Large Scale Visual Recognition Challenge6 , and train it on our dataset using transfer learning28 . Figure 1 shows the working system. The CNN is trained using 757 disease classes. Our dataset is composed of dermatologist-labelled images organized in a tree-structured taxonomy of 2,032 diseases, in which the individual diseases form the leaf nodes. The images come from 18 different clinician-curated, open-access online repositories, as well as from clinical data from Stanford University Medical Center. Figure 2a shows a subset of the full taxonomy, which has been organized clinically and visually by medical experts. We split our dataset into 127,463 training and validation images and 1,942 biopsy-labelled test images. To take advantage of fine-grained information contained within the taxonomy structure, we develop an algorithm (Extended Data Table 1) to partition diseases into fine-grained training classes (for example, amelanotic melanoma and acrolentiginous melanoma). During inference, the CNN outputs a probability distribution over these fine classes. To recover the probabilities for coarser-level classes of interest (for example, melanoma) we sum the probabilities of their descendants (see Methods and Extended Data Fig. 1 for more details). We validate the effectiveness of the algorithm in two ways, using nine-fold cross-validation. First, we validate the algorithm using a three-class disease partition—the first-level nodes of the taxonomy, which represent benign lesions, malignant lesions and non-neoplastic 1 Department of Electrical Engineering, Stanford University, Stanford, California, USA. 2 Department of Dermatology, Stanford University, Stanford, California, USA. 3 Department of Pathology, Stanford University, Stanford, California, USA. 4 Dermatology Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA. 5 Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. 6 Department of Computer Science, Stanford University, Stanford, California, USA. *These authors contributed equally to this work. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
LETTERH his task, the CNN achieves 72.1±0.9% (mean±s.d.) overall he average of individual inference class accuracies) and two gists attain 65.56% and 66.0% accuracy on a subset of the set. Second, we validate the algorithm using a nine-class rtition—the second-level nodes—so that the diseases of have similar medical treatment plans. The CNN achieves two trials, one using standard images and the other using images, which reflect the two steps that a dermatologist m to obtain a clinical impression. The same CNN is used for a Figure 2b shows a few example images, demonstrating th distinguishing between malignant and benign lesions, whic visual features. Our comparison metrics are sensitivity an Acral-lentiginous melanoma Amelanotic melanoma Lentigo melanoma … Blue nevus Halo nevus Mongolian spot … Training classes (757)Deep convolutional neural network (Inception v3) Inference classes (varies by task) 92% malignant melanocytic lesion 8% benign melanocytic lesion Skin lesion image Convolution AvgPool MaxPool Concat Dropout Fully connected Softmax Deep CNN layout. Our classification technique is a Data flow is from left to right: an image of a skin lesion e, melanoma) is sequentially warped into a probability over clinical classes of skin disease using Google Inception hitecture pretrained on the ImageNet dataset (1.28 million 1,000 generic object classes) and fine-tuned on our own 29,450 skin lesions comprising 2,032 different diseases. ning classes are defined using a novel taxonomy of skin disease oning algorithm that maps diseases into training classes (for example, acrolentiginous melanoma, amelanotic melano melanoma). Inference classes are more general and are comp or more training classes (for example, malignant melanocytic class of melanomas). The probability of an inference class is c summing the probabilities of the training classes according to structure (see Methods). Inception v3 CNN architecture repr from https://research.googleblog.com/2016/03/train-your-ow classifier-with.html GoogleNet Inception v3 • 129,450개의 피부과 병변 이미지 데이터를 자체 제작 • 미국의 피부과 전문의 18명이 데이터 curation • CNN (Inception v3)으로 이미지를 학습 • 피부과 전문의들 21명과 인공지능의 판독 결과 비교 • 표피세포 암 (keratinocyte carcinoma)과 지루각화증(benign seborrheic keratosis)의 구분 • 악성 흑색종과 양성 병변 구분 (표준 이미지 데이터 기반) • 악성 흑색종과 양성 병변 구분 (더마토스코프로 찍은 이미지 기반)
Skin cancer classification performance of the CNN and dermatologists. LETT a b 0 1 Sensitivity 0 1 Specificity Melanoma: 130 images 0 1 Sensitivity 0 1 Specificity Melanoma: 225 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 111 dermoscopy images 0 1 Sensitivity 0 1 Specificity Carcinoma: 707 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 1,010 dermoscopy images Algorithm: AUC = 0.94 0 1 Sensitivity 0 1 Specificity Carcinoma: 135 images Algorithm: AUC = 0.96 Dermatologists (25) Average dermatologist Algorithm: AUC = 0.94 Dermatologists (22) Average dermatologist Algorithm: AUC = 0.91 Dermatologists (21) Average dermatologist cancer classification performance of the CNN and 21명 중에 인공지능보다 정확성이 떨어지는 피부과 전문의들이 상당수 있었음 피부과 전문의들의 평균 성적도 인공지능보다 좋지 않았음
Skin cancer classification performance of the CNN and dermatologists. LETT a b 0 1 Sensitivity 0 1 Specificity Melanoma: 130 images 0 1 Sensitivity 0 1 Specificity Melanoma: 225 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 111 dermoscopy images 0 1 Sensitivity 0 1 Specificity Carcinoma: 707 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 1,010 dermoscopy images Algorithm: AUC = 0.94 0 1 Sensitivity 0 1 Specificity Carcinoma: 135 images Algorithm: AUC = 0.96 Dermatologists (25) Average dermatologist Algorithm: AUC = 0.94 Dermatologists (22) Average dermatologist Algorithm: AUC = 0.91 Dermatologists (21) Average dermatologist cancer classification performance of the CNN and
Digital Pathologist
Figure 4. Participating Pathologists’ Interpretations of Each of the 240 Breast Biopsy Test Cases 0 25 50 75 100 Interpretations, % 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 Case Benign without atypia 72 Cases 2070 Total interpretations A 0 25 50 75 100 Interpretations, % 218 220 222 224 226 228 230 232 234 236 238 240 Case Invasive carcinoma 23 Cases 663 Total interpretations D 0 25 50 75 100 Interpretations, % 147 145 149 151 153 155 157 159 161 163 165 167 169 171 173 175 177 179 181 183 185 187 189 191 193 195 197 199 201 203 205 207 209 211 213 215 217 Case DCIS 73 Cases 2097 Total interpretations C 0 25 50 75 100 Interpretations, % 74 76 78 80 82 84 86 88 90 92 94 96 98 100 102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 144 Case Atypia 72 Cases 2070 Total interpretations B Benign without atypia Atypia DCIS Invasive carcinoma Pathologist interpretation DCIS indicates ductal carcinoma in situ. Diagnostic Concordance in Interpreting Breast Biopsies Original Investigation Research Elmore etl al. JAMA 2015 Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens The overall agreement between the individual pathologists’ interpretations and the expert consensus–derived reference diagnoses was 75.3% (total 240 cases)
Elmore etl al. JAMA 2015 Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens • Concordance noted in 5194 of 6900 case interpretations or 75.3%. • Reference diagnosis was obtained from consensus of 3 experienced breast pathologists. spentonthisactivitywas16(95%CI,15-17);43participantswere awarded the maximum 20 hours. Pathologists’ Diagnoses Compared With Consensus-Derived Reference Diagnoses The 115 participants each interpreted 60 cases, providing 6900 total individual interpretations for comparison with the con- sensus-derived reference diagnoses (Figure 3). Participants agreed with the consensus-derived reference diagnosis for 75.3% of the interpretations (95% CI, 73.4%-77.0%). Partici- pants (n = 94) who completed the CME activity reported that Patient and Pathologist Characteristics Associated With Overinterpretation and Underinterpretation The association of breast density with overall pathologists’ concordance (as well as both overinterpretation and under- interpretation rates) was statistically significant, as shown in Table 3 when comparing mammographic density grouped into 2 categories (low density vs high density). The overall concordance estimates also decreased consistently with increasing breast density across all 4 Breast Imaging- Reporting and Data System (BI-RADS) density categories: BI-RADS A, 81% (95% CI, 75%-86%); BI-RADS B, 77% (95% Figure 3. Comparison of 115 Participating Pathologists’ Interpretations vs the Consensus-Derived Reference Diagnosis for 6900 Total Case Interpretationsa Participating Pathologists’ Interpretation ConsensusReference Diagnosisb Benign without atypia Atypia DCIS Invasive carcinoma Total Benign without atypia 1803 200 46 21 2070 Atypia 719 990 353 8 2070 DCIS 133 146 1764 54 2097 Invasive carcinoma 3 0 23 637 663 Total 2658 1336 2186 720 6900 DCIS indicates ductal carcinoma in situ. a Concordance noted in 5194 of 6900 case interpretations or 75.3%. b Reference diagnosis was obtained from consensus of 3 experienced breast pathologists. Diagnostic Concordance in Interpreting Breast Biopsies Original Investigation Research Comparison of 115 Participating Pathologists’ Interpretations vs 
 the Consensus-Derived Reference Diagnosis for 6900 Total Case Interpretations
Constructing higher-level contextual/relational features: Relationships between epithelial nuclear neighbors Relationships between morphologically regular and irregular nuclei Relationships between epithelial and stromal objects Relationships between epithelial nuclei and cytoplasm Characteristics of stromal nuclei and stromal matrix Characteristics of epithelial nuclei and epithelial cytoplasm Building an epithelial/stromal classifier: Epithelial vs.stroma classifier Epithelial vs.stroma classifier B Basic image processing and feature construction: H&E image Image broken into superpixels Nuclei identified within each superpixel A Relationships of contiguous epithelial regions with underlying nuclear objects Learning an image-based model to predict survival Processed images from patients Processed images from patients C D onNovember17,2011stm.sciencemag.orgwnloadedfrom TMAs contain 0.6-mm-diameter cores (median of two cores per case) that represent only a small sample of the full tumor. We acquired data from two separate and independent cohorts: Nether- lands Cancer Institute (NKI; 248 patients) and Vancouver General Hospital (VGH; 328 patients). Unlike previous work in cancer morphom- etry (18–21), our image analysis pipeline was not limited to a predefined set of morphometric features selected by pathologists. Rather, C-Path measures an extensive, quantitative feature set from the breast cancer epithelium and the stro- ma (Fig. 1). Our image processing system first performed an automated, hierarchical scene seg- mentation that generated thousands of measure- ments, including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. The pipeline consisted of three stages (Fig. 1, A to C, and tables S8 and S9). First, we used a set of processing steps to separate the tissue from the background, partition the image into small regions of coherent appearance known as superpixels, find nuclei within the superpixels, and construct Constructing higher-level contextual/relational features: Relationships between epithelial nuclear neighbors Relationships between morphologically regular and irregular nuclei Relationships between epithelial and stromal objects Relationships between epithelial nuclei and cytoplasm Characteristics of stromal nuclei and stromal matrix Characteristics of epithelial nuclei and epithelial cytoplasm Epithelial vs.stroma classifier Epithelial vs.stroma classifier Relationships of contiguous epithelial regions with underlying nuclear objects Learning an image-based model to predict survival Processed images from patients alive at 5 years Processed images from patients deceased at 5 years L1-regularized logisticregression modelbuilding 5YS predictive model Unlabeled images Time P(survival) C D Identification of novel prognostically important morphologic features basic cellular morphologic properties (epithelial reg- ular nuclei = red; epithelial atypical nuclei = pale blue; epithelial cytoplasm = purple; stromal matrix = green; stromal round nuclei = dark green; stromal spindled nuclei = teal blue; unclassified regions = dark gray; spindled nuclei in unclassified regions = yellow; round nuclei in unclassified regions = gray; background = white). (Left panel) After the classification of each image object, a rich feature set is constructed. (D) Learning an image-based model to predict survival. Processed images from patients alive at 5 years after surgery and from patients deceased at 5 years after surgery were used to construct an image-based prog- nostic model. After construction of the model, it was applied to a test set of breast cancer images (not used in model building) to classify patients as high or low risk of death by 5 years. www.ScienceTranslationalMedicine.org 9 November 2011 Vol 3 Issue 108 108ra113 2 onNovember17,2011stm.sciencemag.orgDownloadedfrom Digital Pathologist Sci Transl Med. 2011 Nov 9;3(108):108ra113
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017
Global Trends of Digital Healthcare Industry in 2017

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Global Trends of Digital Healthcare Industry in 2017

  • 1. 최윤섭 디지털헬스케어 연구소 소장 최윤섭, PhD Global Trends of Digital Healthcare Industry The first half of 2017
  • 2. The Convergence of IT, BT and Medicine
  • 4.
  • 6.
  • 7. Digital Healthcare Partners (DHP) 는 국내 유일의 디지털 헬스케어 전문 스타트업 엑셀러레이터입니다. 글로벌 한국 일반 의료/ 헬스케어
  • 8. DHP는 디지털 헬스케어 전문 엑셀러레이터로서, 
 디지털 헬스케어/의료 스타트업을 발굴, 육성, 연결하고 투자합니다. 발굴 • 세상을 바꿀 수 있는 혁신적인 헬스케어 스타트업 및 예비 창업팀을 발굴합니다. • 발굴을 위해 DHP Office Hour, 해커톤, 자체 행사 개최 등의 다방면의 채널을 활용합니다. 육성 • 의료/헬스케어 전문가들로 이루어진 파트너 및 자문가들이 초기 스타트업을 멘토링합니다. • 사업 개발, 아이템 검증, 임상 연구, 인허가 관련 자문 등 전방위적으로 지원합니다. 투자 • 초기 스타트업 및 예비 창업팀에게 정해진 원칙에 따라 지분 투자를 집행합니다. • 스타트업을 성장시켜 지분 가치의 상승에 따라서 재무적 수익을 추구합니다. 연결 • 초기 스타트업을 병원, 규제기관, 보험사, VC, 대학 등 다양한 이해관계자들과 연결합니다. • 파트너와 자문가들의 네트워크를 적극 활용하여 스타트업을 의료계 이너서클로 끌어들입니다.
  • 9. DHP는 최고의 의료 전문가들이 초기 헬스케어 스타트업에 의학 자문, 의료 기관 연계, 임상 검증, 투자 유치 등을 지원합니다. 최윤섭 대표파트너 정지훈 파트너 김치원 파트너 • 성균관대학교 디지털헬스학과 교수 • 최윤섭 디지털 헬스케어 연구소 소장 • VUNO, Zikto, 녹십자홀딩스 등 자문 • 저서: ‘헬스케어 이노베이션’ • 전) 서울대학교 의과대학 암연구소 교수 • 전) 서울대학교병원 의생명연구원 교수 • 포항공대 전산생물학 이학박사 • 포항공대 컴퓨터공학/생명과학 학사 • 경희사이버대학 미디어커뮤니케이션학과 교수 • 빅뱅엔젤스 파트너 • Lunit, 매직에코, 휴레이포지티브 등 자문 • 저서: ‘제 4의 불', ‘거의 모든 IT의 역사’ 등 • 전) 명지병원 IT융합연구소장 • 한양대학교 의과대학 의학사 • 서울대학교 보건정책관리학 석사 • USC 의공학박사 • 내과전문의, 서울와이즈요양병원 원장 • 성균관대학교 디지털 헬스학과 교수 • Noom, Zikto, Future Play 등 자문 • 저서: ‘의료, 미래를 만나다’ • 전) 맥킨지 서울사무소 경영컨설턴트 • 전) 삼성서울병원 의료관리학과 교수 • 서울대학교 의과대학 졸업 • 연세대학교 보건대학원 석사
  • 10. 많은 언론들에서 디지털 헬스케어 파트너스를 주목해주셨습니다.
  • 11. DHP는 유전체 분석 기반의 희귀질환 진단 서비스를 개발하는 3billion에 시드 투자 및 엑셀러레이션을 시작하였습니다. • 마크로젠의 유전체 분석 전문가들이 2016년 11월 스핀오프 • 대표 이사 금창원은 유전체 분석 전문가이자 연쇄 창업가 • 유전체 분석으로 4,000여개 희귀 유전 질환을 한 번에 진단 • 해외 시장 타겟, 2,000불의 비용으로 2-3주 내 진단 • 2017년 2월 시제품 출시 • http://3billion.io
  • 12. Contents • 2017 1Q 미국 VC 투자 동향 • ‘Liquid Biopsy’: Illumina and Grail • 23andMe의 DTC 서비스 FDA 인허가 확대 • IBM Watson for Oncology 도입 광풍(?) • 의사를 능가하는 Deep Learning 연구 결과들 • 의학적 효용을 증명한 헬스케어 스타트업의 증가
  • 13. 2017 1Q 미국 VC 투자 동향
  • 15. https://rockhealth.com/reports/2016-year-end-funding-report-a-reality-check-for-digital-health •2016년 디지털 헬스케어 스타트업 펀딩 규모는 $4.2b 으로 전년도에 비해서 8% 감소 •반면 투자를 받은 기업의 수는 273개에서 296개로 약 10% 증가 •총 451개 VC 및 CVC가 투자를 집행 •그 중 237개는 디지털 헬스케어 기업에 '처음' 투자한 곳 (화이자 포함)
  • 16. https://rockhealth.com/reports/2016-year-end-funding-report-a-reality-check-for-digital-health • The six largest deals of 2016 made up 19% of all digital health funding. • Despite laying off 15% of its global workforce, Jawbone raised $165M in 2016. • The most funded digital health company of all time at nearly a billion dollars
  • 17.
  • 18. •펀딩을 가장 많이 받은 분야는 Genomics and Sequencing 분야 •Human Longevity ($220M), Color Genomics ($45M), Seven Bridges Genomics ($45M) •Pathway Genomics ($40M), Emulate ($28M) https://rockhealth.com/reports/2016-year-end-funding-report-a-reality-check-for-digital-health
  • 20. •총 451개 VC 및 CVC가 투자를 집행 •3개 이상의 deal 을 한 곳은 40개 투자자 •총 투자자 중 1/3 정도는 ‘하나의’ deal 만 진행 •237개는 디지털 헬스케어 기업에 '처음' 투자한 곳 (화이자 포함) https://rockhealth.com/reports/2016-year-end-funding-report-a-reality-check-for-digital-health
  • 21. •최근 3년 동안 Merk, J&J, GSK 등의 제약사들의 디지털 헬스케어 분야 투자 급증 •2015-2016년 총 22건의 deal (=2010-2014년의 5년간 투자 건수와 동일) •Merck 가 가장 활발: 2009년부터 Global Health Innovation Fund 를 통해 24건 투자 ($5-7M) •GSK 의 경우 2014년부터 6건 (via VC arm, SR One): including Propeller Health •Grail: cancer diagnostic spin-off from Illumina (Liquid biopsy) •$900m Series B, in March 2017 •가장 많은 제약사가 참여한 투자: J&J, Merck, Bristol-Myers-Squibb
  • 22. •2017 1Q에 총 71건의 deal; $1B funding 으로 strong start •트럼프 정부의 의료 및 규제 정책의 불확실성이 리스크로 보였으나, 크게 영향을 미치지는 않은 것으로 보임 •Rock Health의 경우, •Digital Healthcare 분야의 정의가 보수적 (ie. 진단회사인 Grail은 누락) •미국 내의 $20m 이상의 deal 만을 조사
  • 23. •Startup Health의 분석 •Digital Healthcare 분야의 정의가 더 넓고 (Grail 포함), $20m 이하의 deal 도 포함 •총 124 deal 에 $2.5B 가 투자 •2011년 이후 1분기 투자 횟수는 최하이지만, •개별 deal의 규모는 상승: $500m-900m startuphealth.com/reports 2010 2011 2012 2013 2014 2015 2016 2017 YTD Q1 Q2 Q3 Q4 158 300 499 668 589 526 606 124 Deal Count $1.1B $2.0B $1.5B $629M$572M$391M$192M $8.2B $6.0B $7.1B $2.9B $2.4B $2.0B $1.1B DIGITAL HEALTH FUNDING SNAPSHOT: YEAR OVER YEAR 5Source: StartUp Health Insights | startuphealth.com/insights Note: Report based on public data on seed, venture, corporate venture and private equity funding only. © 2017 StartUp Health LLC $2.5B $2.5B GRAIL’s $914 million Series B may be an outlier and skewed the overall funding numbers this quarter keeping it on track for another strong year overall, and turning an otherwise modest first quarter into a record-breaker. While Q1 2017 had the lowest deal volume since 2011 - with only 124 deals this quarter - we’re seeing more and more $500-900M deals. What do less deals and more money mean? Even though VCs are betting less, they’re betting bigger. Also, the lines are blurring quickly as expected between “digital” and all other categories of health and healthcare. “AI, virtual reality, mobile connectivity, genomics, and analytics are coming to change healthcare, and that is creating a wave of innovation like we’ve never seen.” -Unity Stoakes, President, StartUp Health
  • 24. •Grail 이 $900M Series B funding으로 압도적인 1위 •이외에 상위권은 Rock Health - Startup Health 가 거의 비슷 •Alignment Healthcare: Population Health Management (병원, 보험사 대상) •PatientsLikeMe: Patients Community (제약회사 대상) •Nuna: Big Data Analytics (정부, 보험사 대상) startuphealth.com/reports Company $ Invested Subsector Notable Investor 1 $914M Big Data/Analytics 2 $115M Population Health 3 $100M Patient/Consumer Experience 4 $90M Big Data/Analytics 5 $85M EHR 6 $65M Research 7 $55M E-Commerce 8 $52M Population Health 9 $50M Medical Device 10 $41M Research THE TOP 10 LARGEST DEALS OF 2017 8Source: StartUp Health Insights | startuphealth.com/insights Note: Report based on public data on seed, venture, corporate venture and private equity funding only. © 2017 StartUp Health LLC The top 10 deals of Q1 2017 included companies working in sectors in which big deals have been rare. What does this suggest? 2017 might be a breakout year in terms of funding for solutions focusing on population health, EHR innovation, and e-commerce.
  • 26. Tumor Heterogeneity Meric-Bernstam F, Mills GB. Nat Rev Clin Oncol. 2012 Sep;9(9):542-8.
  • 27. in the understanding of tumour heterogeneity; second, the role of surgery as a therapeutic modality in the era of targeted therapy; third, the use of personalized therapy in the perioperative period and, finally, the possibilities of personalization of surgical procedures according to lung cancer subtypes. VATS lobectomy showed that intraoperative blood loss was significantly reduced in the VATS group compared with open lobectomy in nine studies; however, no differ- ence was observed in five studies and the values were not reported in seven studies.12 Hospital stay was also signifi- cantly shorter in VATS group in five studies. Park et al.,13 Heterogeneity in patients with adenocarcinoma of the lung according to driver oncogenes Heterogeneity within patients with EGFR mutation Heterogeneity in resistance mechanisms in one patient HER2 3% EGFR ~40% in Asians ~15% in Caucasians ALK ~5% KRAS ~15% in Asians ~30% in Caucasians RET ~1% ROS1 ~1% BRAF ~1% PIK3CA ~1% NRAS ~1% MET <5% Others? Exon 19del ~50% L858R ~40% Sensitive Inherent resistance CRKL ~3% BIM 20–40% IκB ~30% Inherent T790M ~2% by sequencing ~30% by sensitive method Further heterogeneity EGFR-TKI Drug X T790M MET a cb T790M Heterogeneity in patients with adenocarcinoma of the lung according to driver oncogenes Heterogeneity within patients with EGFR mutation Heterogeneity resistance mecha in one patien HER2 3% EGFR ~40% in Asians ~15% in Caucasians ALK ~5% KRAS ~15% in Asians ~30% in Caucasians RET ~1% ROS1 ~1% BRAF ~1% PIK3CA ~1% NRAS ~1% MET <5% Others? Exon 19del ~50% L858R ~40% Sensitive Inherent resistance CRKL ~3% BIM 20–40% IκB ~30% Inherent T790M ~2% by sequencing ~30% by sensitive method Further heterogeneity EGFR-TKI Drug T790M ME a cb T790M Figure 1 | Various classes of tumour heterogeneity in adenocarcinoma of the lung. a | Heterogeneity in patients with adenocarcinoma of the lung according to driver oncogenes that are crucial for selecting targeted drugs for treatment.2,76 Number of people reflects approximate incidence.2,76 b | Heterogeneity in patients with EGFR mutations, resulting in MitsudomiT, Suda K,YatabeY. Nat Rev Clin Oncol. 2013 Apr;10(4):235-44. Heterogeneity in Lung Adenocarcinoma
  • 28. Tumor Heterogeneity Meric-Bernstam F, Mills GB. Nat Rev Clin Oncol. 2012 Sep;9(9):542-8.
  • 29. Intratumor Heterogeneity Revealed by multiregion Sequencing B Regional Distribution of Mutations C Phylogenetic Relationships of Tumor Regions D Ploidy Profiling A Biopsy Sites R2 R4 R9 R8 R5 R1 R3 R2 PreP PreM R1 R2 R3 R5 R8 R9 R4 M1 M2a M2b C2orf85 WDR7 SUPT6H CDH19 LAMA3 DIXDC1 HPS5 NRAP KIAA1524 SETD2 PLCL1 BCL11A IFNAR1 DAMTS10 C3 KIAA1267 RT4 CD44 ANKRD26 TM7SF4 SLC2A1 DACH2 MMAB ZNF521 HMG20A DNMT3A RLF MAMLD1 MAP3K6 HDAC6 PHF21B FAM129B RPS8 CIB2 RAB27A SLC2A12 DUSP12 ADAMTSL4 NAP1L3 USP51 KDM5C SBF1 TOM1 MYH8 WDR24 ITIH5 AKAP9 FBXO1 LIAS TNIK SETD2 C3orf20 MR1 PIAS3 DIO1 ERCC5 KL ALKBH8 DAPK1 DDX58 SPATA21 ZNF493 NGEF DIRAS3 LATS2 ITGB3 FLNA SATL1 KDM5C KDM5C RBFOX2 NPHS1 SOX9 CENPN PSMD7 RIMBP2 GALNT11 ABHD11 UGT2A1 MTOR PPP6R2 ZNF780A WSCD2 CDKN1B PPFIA1 TH SSNA1 CASP2 PLRG1 SETD2 CCBL2 SESN2 MAGEB16 NLRP7 IGLON5 KLK4 WDR62 KIAA0355 CYP4F3 AKAP8 ZNF519 DDX52 ZC3H18 TCF12 NUSAP1 X4 KDM2B MRPL51 C11orf68 ANO5 EIF4G2 MSRB2 RALGDS EXT1 ZC3HC1 PTPRZ1 INTS1 CCR6 DOPEY1 ATXN1 WHSC1 CLCN2 SSR3 KLHL18 SGOL1 VHL C2orf21 ALS2CR12 PLB1 FCAMR IFI16 BCAS2 IL12RB2 PrivateUbiquitous Shared primary Shared metastasis Ubiquitous Lung metastases Chest-wall metastasis Perinephric metastasis M1 10 cm R7 (G4) R5 (G4) R9 R3 (G4) R1 (G3) R2 (G3) R4 (G1) R6 (G1) Hilum R8 (G4) Primary tumor Shared primary Shared metastasis M2b M2a Intratumor Heterogeneity Revealed by Multiregion Sequencing Gerlinger M et al. N Engl J Med. 2012 Mar 8;366(10):883-92
  • 30. Nat Genet. 2014 Feb 26;46(3):214-5. Intratumoral heterogeneity in kidney cancer
  • 31. Nat Genet. 2014 Mar;46(3):225-33. E S 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat map indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutation tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat ma indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutatio tumor. An asterisk indicates where VHL methylation was included in the analysis. Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat map indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutation tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat ma indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutatio tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat m indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutati tumor. An asterisk indicates where VHL methylation was included in the analysis. Regional distribution of nonsynonymous mutations in ten ccRCC tumors Heat maps indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 probable driver mutations are highlighted in magenta.
  • 32. E S 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat map indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutation tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat ma indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutatio tumor. An asterisk indicates where VHL methylation was included in the analysis. Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat map indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutation tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE G Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat ma indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 p driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutatio tumor. An asterisk indicates where VHL methylation was included in the analysis. 226 VOLUME 46 | NUMBER 3 | MARCH 2014 NATURE Figure 1 Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat m indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutati tumor. An asterisk indicates where VHL methylation was included in the analysis. Regional distribution of nonsynonymous mutations in ten ccRCC tumors Heat maps indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 probable driver mutations are highlighted in magenta. Nat Genet. 2014 Mar;46(3):225-33.
  • 33. A RT I C L E S We determined the regional distribution f nonsynonymous mutations on the basis of ata from ultra-deep amplicon sequencing. We called a mutation as being present in a umor region if a nucleotide substitution was etected in 0.5% of reads or an indel was etected in 1% of reads. We chose these hresholds on the basis of the error rate of he sequencing platform13. The regional istribution of 28 mutations for which ltra-deep sequencing data were not avail- ble was inferred from the exome sequenc- ng data. Exome sequencing of EV001 and EV002 has previously been reported2 and was ncluded in this analysis. On average, 67% range of 28–92%) of the nonsynonymous omatic mutations were heterogeneous and ot detectable across all sampled regions of n individual tumor (Fig. 1). The presence f somatic mutational heterogeneity in all 10/10) treatment-naive or pretreated cases indicates that ITH, char- cterized by the spatial separation of subclones, is a common feature n stage T2–T4 ccRCCs. To identify the optimal number of biopsies that can reliably detect he majority of nonsynonymous somatic mutations in a tumor, we alculated the number of mutations that would have been detected heterogeneity specifically in EV003 and EV006. No other clinical or pathological characteristic seemed to correlate with mutational ITH, and larger series will be required to determine the biological basis for the diversity in ccRCC phylogenetic structures. Identification of intraregional subclones R4b GL VHL SETD2 SETD2 KDM5C MTOR R8 KDM5C R4a R5 R3 R2 R1 R9 M1 M2a M2b SETD2 EV001 EV003 R6 R7 R1 R5 GL R9 VHL (methylation) PBRM1 EV005 R6dom R7 R1R5 R3 R4, R6min R2 GL VHL PBRM1 PIK3CA PIK3CA SF3B1 EV006 EV007 RMH002 R6 R7 R1 R2 R3 PBRM1 BAP1 TP53 RMH004 R8 R10 R2 GL VT R4 VHL PBRM1 ATM PTEN SMARCA4 R3 MSH6 PBRM1 ARID1A RMH008 R4min R5, R7 R6min R8 GL R1 R2 R3 VHL BAP1 TSC2 BAP1 BAP1 R6dom R4dom RK26 PBRM1 TP53 BAP1 R3, R4 R11 R9 GL R1 R2 VHL R5min R10 R7 R5dom R8 R6 10 non synonymous mutations Trunk Internal branch Terminal branch EV002 R7 R1 R3 R6 GL R9 VHL PBRM1 SETD2 TP53 R4 M PTEN PTEN SETD2 R3 GL GL GL R4 R7 VHL VHL VHL LN1a, LN1b R2R6 R1 R1 R15 R9min R9dom R3min BAP1 SETD2 R5,R7 R2, R3dom R6 PIK3CA SETD2 TP53 R4 R3R4 R2 igure 3 Phylogenetic trees generated by maximum parsimony from M-seq data for ten cRCC tumors. Trees for EV001 and EV002 re adapted from Gerlinger et al.2. Branch nd trunk lengths are proportional to the umber of nonsynonymous mutations acquired n the corresponding branch or trunk. Driver mutations were acquired by the indicated enes in the branches the arrows indicate. river mutations defining parallel evolution vents are highlighted by color. Trees are ooted at the germline (GL) DNA sequence, etermined by exome sequencing of DNA from eripheral blood. Phylogenetic trees generated for ten ccRCC tumors Mutational processes change during tumor evolution ccRCCs can traverse different evolutionary routes simultaneously
  • 34. Br J Cancer. 2010 Oct 12;103(8):1139-43. resistance develops. A further obstacle for the interpretation of large-scale somatic mutation analyses is that fitness effects of the vast majority of mutations are unknown. The RNA interference- based functional genomic screening approaches can experimen- tally test the phenotypic effect of silencing large numbers of genes individually and may support the interpretation of mutation data sets by identifying genes that influence cellular fitness or drug sensitivity. cells in vitro (Duesberg et al recurrence after drug treatmen 2010). The clinically importan geneity could accelerate evolu enhance biological fitness to pressures could in turn favour t unstable cancer cells by can advantages conferred by genom must be balanced against the s result from the generation o deleterious mutations or tumou chromosomal instability in anim Importantly, evolutionary mod instability can be positively selec advantage in environments (e.g. during chemotherapy) in cycle arrest after DNA damage cells that are negatively selected cell cycle arrest and have a lower Wodarz, 2003). Thus, it is conceivable that the instability required to accelerate of cancers and that excessive tumour. Results from animal tu excessive chromosomal instabili role leads to the tantalising prop genome instability provides intervention (Weaver et al, 2007 EVIDENCE FOR DRUG RE EVOLUTION The harsh clinical reality is th almost invariably occurs in adva leading to disease progression an examples highlight how Darwi tumoural genetic heterogeneity pressure of systemic cancer t resistance from a Darwinian Genetic heterogeneity Time Bottleneck Drug treatment Cancercellpopulation Figure 1 Schematic view of tumour heterogeneity during tumour progression and treatment. Acquired mutations in daughter cells of a single founder cell (left) promote diversion into subclones (different colours reflect different clones). Some new mutations lead to accelerated growth (for example yellow and orange clones). Fitness reducing mutations lead to negative selection (cells with brown cytoplasm). Drug treatment leads to selective survival of a drug resistant clone (pink) and generates an evolutionary bottleneck that reduces genetic heterogeneity transiently. Heterogeneity is re-established rapidly through acquisition of mutations by daughter cells of the resistant clone. Darwinian evolution of tumor elucidate clonal heterogeneity • Acquired mutations in daughter cells of a single founder cell (left) promote diversion into subclones • Drug treatment leads to selective survival of a drug resistant clone (pink) and generates an evolutionary bottleneck that reduces genetic heterogeneity transiently. • Heterogeneity is re-established rapidly through acquisition of mutations by daughter cells of the resistant clone.
  • 35. P E R S P E C T I V E Fig. 1. A trunk-branch model of intratumor heterogeneity. (A) The development of intratumor heterogeneity is analogous to a growing tree. The trunk harbors the founding ubiquitous driver mutations of a cancer present in every tumor subclone and region. The sprouting branches represent different geographically separated regions of the tumor or subclones present within single biopsies that carry heterogeneous mutations that are not present in every tumor cell or tumor region. Such mutations may distinguish the biological behavior of subclones and harbor the potential to become driver mutations under distinct selection pressures. Ubiquitous genetic events present in the trunk may provide more tractable biomarkers and therapeutic targets than heterogeneous events in the branches. We describe three levels of complexity: level 1, the trunk carries driver events, whereas the branches carry neutral mutations; level 2, the trunk carries driver events, whereas the branches carry neutral or additional driver events that may harbor convergent phenotypes (for example, distinct mutations in SETD2 or PTEN occur in different regions of the same renal cancer and converge on the same pathway resulting in its inactivation) (4); level 3, level 1, and level 2 events plus neutral mutations in the branches (or trunk) that become driver events under selection pressures (11, 17–20). With level 1 complexity, one biomarker can be developed against one target; with level 2 and 3 complexity, a single biomarker is unlikely to be sufficient. The risk of drug resistance may increase with each level of complexity. (B) Clonal ar- chitecture as a biomarker.The polygenic nature of drug resistance and intratumor heterogeneity may exacerbate difficulties in predicting therapeutic outcome. Consideration of tumor growth within a Darwinian evolutionary tree framework may support the identification of new predictive biomark- Level 1 complexity Level 2 complexity Level 3 complexity Clonal architecture as a biomarker A BTrunk-branch hypothesis onApril4,2012stm.sciencemag.org A trunk-branch model of intratumor heterogeneity • The trunk harbors the founding ubiquitous driver mutations of a cancer present in every tumor subclone and region. • The sprouting branches represent different geographically separated regions of the tumor or subclones present within single biopsies that carry heterogeneous mutations that are not present in every tumor cell or tumor region. Sci Transl Med. 2012 Mar 28;4(127):127
  • 37.
  • 38. Release and extraction of cfDNA from the blood •cfDNA 는 건강한 세포가 사멸할 때뿐만 아니라, 암 세포가 사멸할 때도 혈액 속으로 나온다. •Liquid biopsy (액체 생검) •혈액 속에서 cfDNA를 추출하여 암세포에서 나온 DNA를 detection 하고 분석 •암의 재발 유무 조기 발견, 항암제의 약효 파악, 암 세포의 유전 변이 파악 등에 활용 http://www.nature.com/nrclinonc/journal/v10/n8/full/nrclinonc.2013.110.html
  • 39. http://www.nature.com/nrclinonc/journal/v10/n8/full/nrclinonc.2013.110.html Monitoring tumour-specific aberrations to detect recurrence and resistance •a. 암이 수술 이후에 조기 재발했는지에 대한 모니터링 •b. 표적 항암제 투여 이후에 내성이 있는 새로운 암세포(clone)가 자라는지 검사 •Red: 새로운 clone 이 생성하여 재발 •blue: 기저에 줄어들었던 원래 clone이 새로운 mutation 을 얻어서 재발
  • 40. Importantly, the data provided by these tests indicate that these genotypes are not common in the plasma of individuals that are presumably cancer-free (Thress et al., 2015). It is worth noting that tu- mor-derived RNA and DNA methylation patterns can also be detected in the with highly conserved biology, a popula- tion of cancer patients behaves as a het- erogeneous collection of many diseases, each of which carries additional heteroge- neity in its own right. Therefore, identifying a finite number of protein or nucleic acid biomarkers that are highly sensitive and ctDNA molecules to reliably measure them in a background of mostly non-tu- mor-derived cfDNA. We estimate that such a broad and deep sequencing approach could require orders of magni- tude more sequence data than liquid bi- opsy assays currently use (Table 1). To Table 1. Comparison of ctDNA Liquid Biopsy Test to Potential Cancer Screening Test Indication Tumor Liquid Biopsy (Genotyping, Monitoring) Early Cancer Detection Target population Patients with known diagnosis of cancer Asymptomatic individuals Tissue reference Can be informed by tissue analyses No prior knowledge of tissue Key performance characteristics Sensitivity and specificity for specific actionable genotypes d Sensitivity and specificity for clinically detectable cancer d Premium on specificity in individuals without detectable cancer d Tissue of origin needed to guide workup Clinical Endpoint for Utility Therapeutic benefit with specific therapies Net outcome improvement with early detection and local treatment of cancer Genes Covered 10-50 100-1000s ctDNA Limit of Detection 0.1% <0.01% Importance of Novel Variant Detection Low High Amount of Sequencing 1x 100X Study Size for Clinical Validity and Utility 100’s 10,000 - 100,000 s Next-Generation Sequencing of Circulating Tumor DNA for Early Cancer Detection Cell 168, February 9, 2017
  • 41. C A N C E R Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer Jeanne Tie,1,2,3,4 *† Yuxuan Wang,5† Cristian Tomasetti,6,7 Lu Li,6 Simeon Springer,5 Isaac Kinde,8 Natalie Silliman,5 Mark Tacey,9 Hui-Li Wong,1,3,4 Michael Christie,1,3,10 Suzanne Kosmider,2 Iain Skinner,2 Rachel Wong,1,11,12 Malcolm Steel,11 Ben Tran,1,2,3,4 Jayesh Desai,1,3,4 Ian Jones,4,13 Andrew Haydon,14 Theresa Hayes,15 Tim J. Price,16 Robert L. Strausberg,17 Luis A. Diaz Jr.,5 Nickolas Papadopoulos,5 Kenneth W. Kinzler,5 Bert Vogelstein,5 *† Peter Gibbs1,2,3,4,17 *† Detection of circulating tumor DNA (ctDNA) after resection of stage II colon cancer may identify patients at the highest risk of recurrence and help inform adjuvant treatment decisions. We used massively parallel sequencing–based assays to evaluate the ability of ctDNA to detect minimal residual disease in 1046 plasma samples from a prospective cohort of 230 patients with resected stage II colon cancer. In patients not treated with adjuvant chemotherapy, ctDNA was detected postoperatively in 14 of 178 (7.9%) patients, 11 (79%) of whom had recurred at a median follow-up of 27 months; recurrence occurred in only 16 (9.8 %) of 164 patients with negative ctDNA [hazard ratio (HR), 18; 95% confidence interval (CI), 7.9 to 40; P < 0.001]. In patients treated with chemotherapy, the presence of ctDNA after completion of chemotherapy was also associated with an inferior recurrence-free survival (HR, 11; 95% CI, 1.8 to 68; P = 0.001). ctDNA detection after stage II colon cancer resection provides direct evidence of residual disease and identifies patients at very high risk of recurrence. INTRODUCTION About 1.3 million cases of colorectal cancer are diagnosed annually worldwide (1). In patients with stage II colon cancer (~25% of all colorectal cancer), management after surgical resection remains a clinical dilemma, with about 80% cured by surgery alone (2). The cur- rent approach to defining recurrence risk for patients with early- tus in the tumor defines a low-risk group in which adjuvant chemo- therapy is not beneficial (6, 7). Most recently, multiple tissue-based gene signatures have been shown to have prognostic significance, but again with modest hazard ratios (HRs) of 1.4 to 3.7 (8–11). In practice, adjuvant chemotherapy is more frequently offered to high-risk stage II patients, with the justification that high-risk R E S E A R C H A R T I C L E http://stm.sciencemag.orgDownloadedfrom Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
  • 42. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer postoperative adjuvant chemotherapy 를 받지 않은 환자군에 대해서, ctDNA 양성/음성 기반으로 RFS 을 효과적으로 구분할 수 있음 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
  • 43. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer RFS를 ctDNA 여부에 의해서 판단하는 것이 (A) 기존의 T stage, LN yield, LVI 등 기반의 (clinicopathogic) 위험군 분류(B)보다 더욱 효과적일 가능성이 있음 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
  • 44. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer 기존의 위험군 분류 기준에 의해서 저위험군(C)과 고위험군(D)을 따로 나눠서 ctDNA의 검출 여부로 보게 되더라도, 그 중에서도 RFS 예후 예측을 효과적으로 할 수 있음 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
  • 45. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer postoperative adjuvant chemo therapy 를 받은 환자의 항암제 치료 도중과 이후의 ctDNA 변화와 이후 재발여부의 관계 A, B의 경우 •chemo 시작시에는 ctDNA가 positive였다가, •chemo 받는 동안에는 negative가 되고, •chemo 끝난 후에는 증가해서 결국 재발 •이 과정에서 기존의 표준 바이오마커인 CEA는 detection 에 실패 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
  • 46. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer C, D 환자는 chemo 받는 동안 ctDNA가 negative가 되고 이후에도 유지되어서, 이후 f/u 에서도 재발하지 않음 이 환자들의 경우에는 CEA도 결과는 동일 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
  • 47. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer E, F 환자의 경우에는 ctDNA가 각각 false negative, false polisive 결과 •E 환자: 수술 후 10개월 경에 재발하였으나, ctDNA 수치는 negative •F 환자: ctDNA는 계속 들쭉날쭉 했는데 36개월까지 재발을 하지 않음 Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
  • 48. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer Tie, J., Wang, Y., Tomasetti, C., Li, L., Springer, S. et al. (2016). Sci. Transl. Med. 8, 346ra92.
  • 49. pointing to cancers th by R (i.e., those with t account for cancer in seem particularly we miologic investigation appear unavoidable n they will become avo are at least four sourc cells: quantum effects o made by polymerase tion of bases (32), and produced reactive oxy olites (33). The last o be reduced by the dant drugs (34). The principle, be reduced cient repair genes int or through other crea As a result of the ulation, cancer is tod of death in the world the best way to reduc of a third contributo does not diminish t prevention but emph can be prevented by a factors (Figs. 2 and 3 vention is not the on exists or can be im ondary prevention, i.e vention, can also be which all mutations a Fig. 3. Etiology of driver gene mutations in women with cancer. For each of 18 representative cancer types, the schematic depicts the proportion of mutations that are inherited, due to environmental factors, or due to errors in DNA replication (i.e., not attributable to either heredity or environment).The sum of these three proportions is 100%. The color codes for hereditary, replicative, and environmental factors are identical and span white (0%) to brightest red (100%). The numerical values used to construct this figure, as well as the values for 14 other cancer types not shown in the figure, are provided in table S6. B, brain; Bl, bladder; Br, breast; C, cervical; CR, colorectal; E, esophagus; HN, head and neck; K, kidney; Li, liver; Lk, leukemia; Lu, lung; M, melanoma; NHL, non-Hodgkin lymphoma; O, ovarian; P, pancreas; S, stomach; RESEARCH | REPORTEtiology of driver gene mutations in women with cancer Cristian Tomasetti , Science 2017 유전적 요인(Hereditary), 환경적 요인(Environmental)에 비해서, 
 DNA replication에 의한 driver mutation (Replicative)의 비율이 암종의 구분 없이 매우 높다. 따라서, 암의 조기 발견의 중요성이 더욱 높아지고 있음.
  • 50.
  • 51.
  • 52. https://www.illumina.com/content/dam/illumina-marketing/documents/company/investor-relations/investor_presentations/illumina_investor_presentation.pdf Product MiniSeq ™ MiSeq ® NextSeq HiSeq ® HiSeq ® X 4000 Five Ten Output per run 7.5 Gb 15 Gb 120 Gb 1.5 Tb 1.8 Tb 1.8 Tb Instrument price $49.5K $99K $275K $900K $6M1 $10M1 Utilization2 $20K–$25K $40K–$45K $100K–$150K $300K–$350K $625K–$725K Installed base3 370 ~5,300 ~1,800 ~1,900 ~400 Sequencing Power for Every Scale The broadest portfolio offering available 1. Based on purchase of 5 and 10 units for HiSeq X Five and HiSeq X Ten, respectively 2. Company’s projected annual instrument utilization per installed instrument; HiSeq and HiSeq X utilization to be combined later in • 2014년 1월 출시 • 기기 하나에 약 10억원 • 10개 번들 판매로 최소 구입 단위는 100억원 • 미국의 브로드 연구소, 호주의 가반의학연구소, 한국의 마크로젠
  • 53. 6 Shipping Q1 2017 $985K Shipping Early 2018 $850K NovaSeq 6000NovaSeq 5000 NovaSeq 5000 Flow Cells NovaSeq 6000 Flow Cells 1 Tb* 2 Tb 4 Tb* 6 Tb*Output/Run: NovaSeq System Scalable throughput to complete studies faster and more economically *S1 and S4 flow cells expected to begin shipping in Q3 2017; S3 flow cell expected to begin shipping in early 2018 https://www.illumina.com/content/dam/illumina-marketing/documents/company/investor-relations/investor_presentations/illumina_investor_presentation.pdf
  • 54. • 2017년 1월 NovaSeq 5000, 6000 발표 • 몇년 내로 $100로 WES 를 실현하겠다고 공언 • 2일에 60명의 WES 가능 (한 명당 한 시간 이하)
  • 56. http://privateoffice.investec.co.uk/research-and-insights/insights/vision_next_generation_sequencing.html Next Generation Sequencing (NGS) Market Share • 일루미나는 현재 전세계 DNA의 90%를 생산 • 전세계 인구의 0.01% 밖에 아직 DNA 서열 분석을 하지 않았음
  • 57. Value Chain of Sequencing Industry Sequencing Analysis Diagnosis Treatment Consumer Service
  • 58. Illumina tries to eat everything in sequencing market Sequencing Analysis Diagnosis Treatment Consumer Service 개인유전정보 앱스토어$100 m funding, co-founding (2015) NIPT(비침습 태아 산전진단) $350m 인수 (2013) Analysis Liquid Biopsy (액체 생검) Spin-off (2016.1)/ $100m 빌게이츠, 제프 베조스 등 투자
  • 59. • 일루미나는 NGS 기기를 만드는 하드웨어 기업으로 시작 • 시퀀싱 시장 점유를 기반으로 value chain 후반의 진단, 소비자 서비스 시장으로 진출 중 • (via 인수, 투자, 공동 설립)
 • 과거 인터넷 산업에 비유하자면, • 초기에는 Cisco 같은 네트워크 인프라를 구축하는 기업이 수익 • 나중에는 인프라를 활용한 서비스 제공 기업이 성장 (구글, 페이스북…) • 일루미나는 그 둘을 모두 하겠다는 것 Illumina tries to eat everything in sequencing market Sequencing Analysis Diagnosis Treatment Consumer Service 개인유전정보 앱스토어$100 m funding, co-founding (2015) NIPT(비침습 태아 산전진단) $350m 인수 (2013) Analysis Liquid Biopsy (액체 생검) Spin-off (2016.1)/ $100m 빌게이츠, 제프 베조스 등 투자
  • 60.
  • 61. •Series A: $100m •Series B: $900m •Biotech funding round 사상 최고액으로 평가 •ARCH Venture Partners led the round



with participation from J&J, Amazon, BMS, Celgene, Varian, and Merck. •Liquid Biopsy의 임상 연구에 활용할 계획
  • 62. • Grail 이 발표한 최초의 대규모 임상 연구 (2016년 12월): Mayo Clinic, MSKCC 등 50여개 병원 참여 • 10,000명의 환자의 혈액을 분석으로 시작 (추후 확대 예정) • 7,000명의 암 환자 • 3,000명의 정상인 • 정상인 혈액과 암 환자의 cell free genome profile 을 파악하기 위한 연구 • 정상인의 cf genome의 heterogeneity 역시 연구: 정상인 - 암환자 구분에 도움 • ‘high intensitiy’ sequencing: ultra-deep sequencing & ultra-wide sequencing 을 사용하게 될 것
  • 63. • 2017년 4월 대규모 유방암 환자 임상시험 STRIVE를 개시한다고 공표 • 유방암 조기 발견을 위한 blood test 의 개발 목적 • 120,000명 규모 • Mayo Clinic 과 Sutter Health 에서 유방암 정기검사 (mammography)를 받는 환자들 대상 • ultra-deep sequencing & ultra-wide sequencing 을 사용하게 될 것 • 이 임상 결과를 바탕으로 pan-cancer test 의 개발에도 사용하게 될 것
  • 64. Leading Edge Commentary Next-Generation Sequencing of Circulating Tumor DNA for Early Cancer Detection Alexander M. Aravanis,1,2 Mark Lee,1,2 and Richard D. Klausner1,* 1GRAIL, Menlo Park, CA 94402, USA 2Co-first author *Correspondence: klausner.rick@gmail.com http://dx.doi.org/10.1016/j.cell.2017.01.030 Curative therapies are most successful when cancer is diagnosed and treated at an early stage. We advocate that technological advances in next-generation sequencing of circulating, tumor-derived nucleic acids hold promise for addressing the challenge of developing safe and effective cancer screening tests. Cancer-specific mortality from most types of solid tumors has barely decreased in decades, despite an expo- nential increase in our knowledge about cancer pathogenesis and significant in- vestments in the development of effective treatments. The past few years have witnessed a dramatic success of immu- notherapies in treating a subgroup of patients with a variety of tumor types, including lung, bladder, and kidney, as well as Hodgkin’s lymphoma and mela- noma. While such breakthroughs offer the hope of prolonged survival for some patients with advanced cancers, finding cancers earlier would still afford the great- est chance for cure, given that the survival rates for patients with early diagnoses are five to ten times higher compared with late stage disease (Cho et al., 2014). By tion algorithms that either miss a large number of invasive cancers or make the costly trade-off of over-diagnosing and consequently over treating. For instance, high false-positive rates from mammog- raphy in breast cancer screening, low- dose CT in lung cancer screening, and prostate-specific antigen (PSA) screening (Nelson et al., 2016a; Aberle et al., 2011; Chou et al., 2011) represent a significant cost to the healthcare system, with result- ing mental and physical morbidity, and even mortality in some cases (Nelson et al., 2016b). Even where cancer screening has pro- duced significant stage shifts, as with breast and prostate cancer screening, the impact on cancer-specific mortality has not been a predictable outcome (Berry, 2014). Multiple explanations may cancers are in a pre-metastatic state and thus still curable. This kinetic aspect of cancer progression is poorly understood, but it is essential to informing effective screening intervals. It is worth noting that mammography and PSA are only sur- rogate measures of cancer, which have poor specificity and provide little insight into tumor biology. We would argue that for successful screening, we need a platform that provides direct, sensitive, and specific measures of cancer and its attributes, which have bearing on clinical behavior. Circulating Tumor DNA Profiling of a tumor’s somatic alterations has become routine, and many clinical tests are now available that interrogate anywhere from a few genes to the whole
  • 65. •국내에서도 삼성유전체연구소를 비롯한 몇몇 그룹이 Liquid Biopsy 를 연구 •삼성유전체연구소에서 LiquidScan을 개발했다고 발표 (2017.4) •(기사 제목처럼) 피 한 방울은 아니고, 20ml 정도 필요 •현재 췌장암 및 유방암 연구 중 •췌장암의 경우 LiquidScan을 통해 기존 방식보다 2-3개월 미리 재발 여부파악 가능
  • 66. 23andMe의 DTC 서비스 FDA 인허가 확대
  • 67.
  • 68. Results within 6-8 weeksA little spit is all it takes! DTC Genetic TestingDirect-To-Consumer
  • 69. • Direct-to-Consumer 방식의 서비스를 고집 • 데이터 소유권 이슈: “환자 본인에게 raw data 를 주겠다”
  • 70. 120 Disease Risk 21 Drug Response 49 Carrier Status 57Traits $99
  • 71. • 질병 위험도 검사: BRCA로 유방암 위험도 예측 • 보인자 검사: 부모가 블룸증후군 유전자 보인자인지 판별 • 약물 민감도 검사: 와파린 등에 대한 민감도 검사 • 일반적 특징 검사: 곱슬머리, 카페인 대사, 유당 분해 능력 등 • 조상 분석: 내 조상이 어느 대륙에서 왔는가 Tests of 23andMe
  • 76. Inherited Conditions 혈색소증은 유전적 원인으로 철에 대한 체내 대사에 이상이 생겨 음식을 통 해 섭취한 철이 너무 많이 흡수되는 질환입니다. 너무 많이 흡수된 철은 우 리 몸의 여러 장기, 특히 간, 심장 및 췌장에 과다하게 축적되며 이들 장기 를 손상시킴으로써 간질환, 심장질환 및 악성종양을 유발합니다.
  • 77. Traits 음주 후 얼굴이 붉어지는가 쓴 맛을 감지할 수 있나 귀지 유형 눈 색깔 곱슬머리 여부 유당 분해 능력 말라리아 저항성 대머리가 될 가능성 근육 퍼포먼스 혈액형 노로바이러스 저항성 HIV 저항성 흡연 중독 가능성
  • 80. • 질병 위험도 검사: BRCA로 유방암 위험도 예측 • 보인자 검사: 부모가 블룸증후군 유전자 보인자인지 판별 • 약물 민감도 검사: 와파린 등에 대한 민감도 검사 • 일반적 특징 검사: 곱슬머리, 카페인 대사, 유당 분해 능력 등 • 조상 분석: 내 조상이 어느 대륙에서 왔는가 Tests of 23andMe (until Nov 2013)
  • 81. • 제한적 유전정보: 일부분의 유전정보 (SNP) 만을 분석 • 환경적 요인 고려 불가: 대부분의 질병은 환경+유전 요인 작용 • So What? : 유전적 위험도를 알아도 대비책이 없거나 불분명 Personal Genome Service 의 한계
  • 83.
  • 84. • 의사를 통하지 않는 DTC 방식에 대한 우려 • 이러한 서비스의 정확성 및 안정성에 대한 우려 • 결과를 받은 사용자들이 제대로 이해할 수 있을지, 오남용에 대한 우려 • 특히, BRCA 유전자에 대한 검사 • Analytic & clinical validation data 제출 지연
  • 85. • 질병 위험도 검사: BRCA로 유방암 위험도 예측 • 보인자 검사: 부모가 블룸증후군 유전자 보인자인지 판별 • 약물 민감도 검사: 와파린 등에 대한 민감도 검사 • 일반적 특징 검사: 곱슬머리, 카페인 대사, 유당 분해 능력 등 • 조상 분석: 내 조상이 어느 대륙에서 왔는가 Tests of 23andMe (Nov 2013 - Oc 2015)
  • 88. • 질병 위험도 검사: BRCA로 유방암 위험도 예측 • 보인자 검사: 부모가 블룸증후군 유전자 보인자인지 판별 • 약물 민감도 검사: 와파린 등에 대한 민감도 검사 • 일반적 특징 검사: 곱슬머리, 카페인 대사, 유당 분해 능력 등 • 조상 분석: 내 조상이 어느 대륙에서 왔는가 Tests of 23andMe (Oct 2015 - April 2017)
  • 89. 2017년 4월 6일 FDA가 23andMe의 질병 위험도 예측 서비스의 
 DTC (Direct-to-Consumer) 판매를 허가
  • 90. FDA의 23andMe 질병 위험도 예측 DTC 서비스 허가 •아래와 같은 10가지 질병의 위험도 예측에 대해서 DTC 허가 •파킨슨병 (Parkinson’s disease) •알츠하이머 (Late-onset Alzheimer’s disease) •셀리악병(Celiac disease) •알파-1 항트립신 결핍증 (Alpha-1 antitrypsin deficiency) •조발성 1차성 근긴장이상증 (Early-onset primary dystonia) •XI 혈액응고인자 결핍증 (혈우병C) (Factor XI deficiency, a blood clotting disorder) •제 1형 고셔병 (Gaucher disease type 1) •포도당-6-인산탈수소효소(G6PD) 결핍증 (Glucose-6-Phosphate Dehydrogenase deficiency) •유전성 혈색소침착증(Hereditary hemochromatosis) •유전적 혈전 기호증(Hereditary thrombophilia) •FDA는 향후 다른 질병 위험도 예측 검사에 대해서 시장 출시 전 심사(premarket review)를 면제
  • 91. FDA의 23andMe 질병 위험도 예측 DTC 서비스 허가 •임상 연구를 통하여 인허가를 위한 근거 자료 마련 •분석적 타당성(analytical validity) •임상적 타당성(clinical validity) •임상적 유용성(clinical utility)
 
 •23andMe의 타액 키트를 통해서 정확하고 일관적으로 유전 변이를 발견할 수 있다는 것을 증명 •검사하는 유전적 변이가 개별 질병의 위험도에 영향을 준다는 명확한 연구 결과. •환자의 DTC 결과 오남용에 대한 반박 •영국에서 25,000명에게 질병 위험도 예측 서비스를 DTC로 제공한 결과, 
 
 자해 등 위험한 결과가 한 건도 발생하지 않았음 •사용자들이 질병 위험도 예측의 결과 레포트의 90% 이상을 이해
  • 92. • 질병 위험도 검사 • 보인자 검사: 부모가 블룸증후군 유전자 보인자인지 판별 • 약물 민감도 검사: 와파린 등에 대한 민감도 검사 • 일반적 특징 검사: 곱슬머리, 카페인 대사, 유당 분해 능력 등 • 조상 분석: 내 조상이 어느 대륙에서 왔는가 Tests of 23andMe (April 2017- 현재)
  • 93. $115m 펀딩 100만 명 돌파 2006 23andMe 창업 20162007 2012 2013 2014 2015 구글 벤처스 360만 달러 투자 2008 $99 로 가격 인하 FDA 판매 중지 명령 영국에서 DTC 서비스 시작 FDA 블룸증후군 DTC 서비스 허가 FDA에 블룸증후군 테스트 승인 요청 FDA에 510(k) 제출 FDA 510(k) 철회 보인자 등 DTC 서비스 재개 ($199) 캐나다에서 DTC 서비스 시작 Genetech, pFizer가 23andMe 데이터 구입 자체 신약 개발 계획 발표 120만 명 돌파 $399 로 가격 인하 23andMe Chronicle Business Regulation 애플 리서치키트와 데이터 수집 협력 50만 명 돌파30만 명 돌파 TV 광고 시작 2017 FDA의 질병위험도 검사 DTC 서비스 허가 + 관련 규제 면제 프로세스 확립 Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
  • 95. 생명윤리법 개정안 및 DTC 허용 계획 •2015년 12월 9일, 국회에서 ‘생명윤리법 개정안’ 의결 •‘비의료기관은 보건복지부장관이 정하는 경우에만 의료기관의 의뢰 없이도 
 질병 예방 목적의 유전자 검사를 제한적으로 직접할 수 있도록 허용한다' •보건복지부 2016년 업무보고: 유전자 검사 제도 개선 •질병 예방 목적의 일부 유전자/유전체 검사를 비의료기관에서 직접 실시 (2016년 6월) •최적 치료법에 필요한 유전자/유전체 검사의 경우 건강보험 적용 (2016년 11월) •표적치료제 선택 검사 확대 •약물반응예측검사 추가
  • 96. 11
  • 97. Category 분류기준 I 건강보험 요양급여 등재 혹은 신의료기술평가를 통해 안정성 및 유효성이 인정된 유전자 검사 로 임상적 사용목적(Intended use)이 동일한 경우 II 아직까지 건강보험 요양급여의 등재 혹은 신의료기술평가를 통해 안정성 및 유효성이 인정되 지 않았지만, 임상적 유효성 근거가 있는 검사로 임상적 사용 목적이 동일한 경우 III 건강보험 요양급여 등재 혹은 신의료기술평가를 통해 안정성, 유효성이 인정 받은 검사 및 임 상적 유효성의 근거가 있는 유전자 검사를 건강인에게 시행하는 경우 IV 건강보험 요양급여 등재 혹은 신의료기술평가를 통해 안정성, 유효성이 인정 받은 검사 및 임 상적 유효성의 근거가 있는 유전자 검사를 적절한 임상적 사용 목적 외에 의학적 근거가 부족 한 용도로 사용하는 경우 V 건강보험 요양급여 미등재 혹은 안정성 유효성에 관한 신의료기술평가를 받지 않은 검사로 과 학적 타당성의 입증이 불확실하거나, 검사대상자를 오도할 우려가 있는 신체 외관, 성격 등의 형질에 관한 검사 VI 건강보험 요양급여 미등재 혹은 안정성, 유효성에 관한 신의료기술평가를 받지 않은 검사로 임상적 유효성에 대한 근거가 부족한 검사 유전자 검사평가원에서 제안한 유전자 검사 분류표
  • 98. 한국 DTC 유전정보 분석 제한적 허용 (2016.6.30) • 「비의료기관 직접 유전자검사 실시 허용 관련 고시 제정, 6.30일시행」 • 2015년 12월「생명윤리 및 안전에 관한 법률」개정(‘15.12.29개정, ’16.6.30시행)과 제9차 무역투자진흥회의(’16.2월) 시 발표한 규제 개선의 후속조치 일환으로 추진 • 민간 유전자검사 업체에서는 혈당, 혈압, 피부노화, 체질량지수 등 12개 검사항목과 관련된 46개 유전자를 직접 검사 가능 http://www.mohw.go.kr/m/noticeView.jsp?MENU_ID=0403&cont_seq=333112&page=1 검사항목 (유전자수) 유전자명 1 체질량지수(3) FTO, MC4R, BDNF 2 중성지방농도(8) GCKR, DOCK7, ANGPTL3, BAZ1B, TBL2, MLXIPL, LOC105375745, TRIB1 3 콜레스테롤(8) CELSR2, SORT1, HMGCR, ABO, ABCA1, MYL2, LIPG, CETP 4 혈 당(8) CDKN2A/B, G6PC2, GCK, GCKR, GLIS3, MTNR1B, DGKB-TMEM195, SLC30A8 5 혈 압(8) NPR3, ATP2B1, NT5C2, CSK, HECTD4, GUCY1A3, CYP17A1, FGF5 6 색소 침착(2) OCA2, MC1R 7 탈 모(3) chr20p11(rs1160312, rs2180439), IL2RA, HLA-DQB1 8 모발 굵기(1) EDAR 9 피부 노화(1) AGER 10 피부 탄력(1) MMP1 11 비타민C농도(1) SLC23A1(SVCT1) 12 카페인대사(2) AHR, CYP1A1-CYP1A2
  • 99. DTC 유전정보 분석 서비스 미국 vs. 한국 Table 1 분석 항목 분석 항목 예시 DTC (미국) DTC (한국) 개인유전정보 분석 질병 위험도 유방암(안젤리나 졸리) O 불가 약물 민감도 와파린 민감도 X X 열성유전질환 보인자 블룸 증후군 O X 웰니스 카페인 분해, 대머리 O 12개만 가능 조상 분석 O 불명확 •미국에서 허용된 보인자 검사, 질병 위험도 예측 검사 DTC 서비스는 여전히 한국에서 불법 •더 큰 문제는 잣대 자체가 FDA 등 글로벌 규제 기조나 산업계에서 통용되는 기준과 다름. 
 질병/약물/보인자/웰니스/조상 분석 등의 업계에서 받아들여지는 분류를 무시하고 있음. •글로벌 수준에 발맞추기는 커녕, 한국에서만 통용되는 자체적인 별도 규제 분류 체계를 
 갈수록 더 만들어가면서, 국내 산업의 갈라파고스화를 심화 시키고 있음
  • 100. IBM Watson for Oncology 도입 광풍(?)
  • 101.
  • 102. 600,000 pieces of medical evidence 2 million pages of text from 42 medical journals and clinical trials 69 guidelines, 61,540 clinical trials IBM Watson on Medicine Watson learned... + 1,500 lung cancer cases physician notes, lab results and clinical research + 14,700 hours of hands-on training
  • 103.
  • 104.
  • 105.
  • 106. Empowering the Oncology Community for Cancer Care Genomics Oncology Clinical Trial Matching Watson Health’s oncology clients span more than 35 hospital systems “Empowering the Oncology Community for Cancer Care” Andrew Norden, KOTRA Conference, March 2017, “The Future of Health is Cognitive”
  • 107. IBM Watson Health Watson for Clinical Trial Matching (CTM) 18 1. According to the National Comprehensive Cancer Network (NCCN) 2. http://csdd.tufts.edu/files/uploads/02_-_jan_15,_2013_-_recruitment-retention.pdf© 2015 International Business Machines Corporation Searching across eligibility criteria of clinical trials is time consuming and labor intensive Current Challenges Fewer than 5% of adult cancer patients participate in clinical trials1 37% of sites fail to meet minimum enrollment targets. 11% of sites fail to enroll a single patient 2 The Watson solution • Uses structured and unstructured patient data to quickly check eligibility across relevant clinical trials • Provides eligible trial considerations ranked by relevance • Increases speed to qualify patients Clinical Investigators (Opportunity) • Trials to Patient: Perform feasibility analysis for a trial • Identify sites with most potential for patient enrollment • Optimize inclusion/exclusion criteria in protocols Faster, more efficient recruitment strategies, better designed protocols Point of Care (Offering) • Patient to Trials: Quickly find the right trial that a patient might be eligible for amongst 100s of open trials available Improve patient care quality, consistency, increased efficiencyIBM Confidential
  • 108. Watson Genomics Overview 20 Watson Genomics Content • 20+ Content Sources Including: • Medical Articles (23Million) • Drug Information • Clinical Trial Information • Genomic Information Case Sequenced VCF / MAF, Log2, Dge Encryption Molecular Profile Analysis Pathway Analysis Drug Analysis Service Analysis, Reports, & Visualizations
  • 109. At HIMSS 2017, provided Hye Jin Kam of Asan Medical Center
  • 110. At HIMSS 2017, provided Hye Jin Kam of Asan Medical Center
  • 111. 식약처 인공지능 가이드라인 초안 Medtronic과 혈당관리 앱 시연 2011 2012 2013 2014 2015 Jeopardy! 우승 뉴욕 MSK암센터 협력 (Lung cancer) MD앤더슨 협력 (Leukemia) MD앤더슨 Pilot 결과 발표 @ASCO Watson Fund, WellTok 에 투자 ($22m) The NewYork Genome Center 협력 (Glioblastoma 분석) GeneMD, Watson Mobile Developer Challenge의 winner 선정 Watson Fund, Pathway Genomics 투자 Cleveland Clinic 협력 (Cancer Genome Analysis) 한국 IBM Watson 사업부 신설 Watson Health 출범 Phytel & Explorys 인수 J&J,Apple, Medtronic 협력 Epic & Mayo Clinic 제휴 (EHR data 분석) 동경대 도입 (oncology) 14 Cancer Center 제휴 (Cancer Genome Analysis) Mayo Clinic 협력 (clinical trail matching) Watson Fund, Modernizing Medicine 투자 Academia Business Pathway Genomics OME closed alpha 시작 TurvenHealth 인수 Apple ResearchKit 통한 수면 연구 시작 2017 가천대 길병원 Watson 도입 (oncology) Medtronic Sugar.IQ 출시 제약사 Teva와 제휴 인도 Manipal Hospital Watson 도입 태국 Bumrungrad  International Hospital, Watson 도입 최윤섭 디지털헬스케어 연구소, 소장 (주)디지털 헬스케어 파트너스, 대표파트너 최윤섭, Ph.D. yoonsup.choi@gmail.com IBM Watson in Healthcare Merge Healthcare 인수 2016 Under Amour 제휴 Broad 연구소 협력 발표 (유전체 분석-항암제 내성) Manipal Hospital의 WFO 정확성 발표 대구가톨릭병원 대구동산병원 WFO 도입 건양대병원 Watson 도입 (oncology) 부산대학병원 Watson 도입 (oncology/ genomics)
  • 112. 식약처 인공지능 가이드라인 초안 Medtronic과 혈당관리 앱 시연 2011 2012 2013 2014 2015 Jeopardy! 우승 뉴욕 MSK암센터 협력 (Lung cancer) MD앤더슨 협력 (Leukemia) MD앤더슨 Pilot 결과 발표 @ASCO Watson Fund, WellTok 에 투자 ($22m) The NewYork Genome Center 협력 (Glioblastoma 분석) GeneMD, Watson Mobile Developer Challenge의 winner 선정 Watson Fund, Pathway Genomics 투자 Cleveland Clinic 협력 (Cancer Genome Analysis) 한국 IBM Watson 사업부 신설 Watson Health 출범 Phytel & Explorys 인수 J&J,Apple, Medtronic 협력 Epic & Mayo Clinic 제휴 (EHR data 분석) 동경대 도입 (oncology) 14 Cancer Center 제휴 (Cancer Genome Analysis) Mayo Clinic 협력 (clinical trail matching) Watson Fund, Modernizing Medicine 투자 Academia Business Pathway Genomics OME closed alpha 시작 TurvenHealth 인수 Apple ResearchKit 통한 수면 연구 시작 2017 가천대 길병원 Watson 도입 (oncology) Medtronic Sugar.IQ 출시 제약사 Teva와 제휴 인도 Manipal Hospital Watson 도입 태국 Bumrungrad  International Hospital, Watson 도입 최윤섭 디지털헬스케어 연구소, 소장 (주)디지털 헬스케어 파트너스, 대표파트너 최윤섭, Ph.D. yoonsup.choi@gmail.com IBM Watson in Healthcare Merge Healthcare 인수 2016 Under Amour 제휴 Broad 연구소 협력 발표 (유전체 분석-항암제 내성) Manipal Hospital의 WFO 정확성 발표 대구가톨릭병원 대구동산병원 WFO 도입 건양대병원 Watson 도입 (oncology) 부산대학병원 Watson 도입 (oncology/ genomics)
  • 113. 한국에서도 Watson을 볼 수 있을까? 2015.7.9. 서울대학병원
  • 114.
  • 115. • 부산대학병원 (2017년 1월) • Watson의 솔루션 두 가지를 도입 • Watson for Oncology • Watson for Genomics
  • 116. • 건양대학병원 Watson for Oncology 도입 • 2017년 3월 • “최원준 건양대병원장은 "지역 환자들은 수도권의 여러 병원을 찾아다닐 필요가 없어질 것"이라며 "병 원의 우수한 협진 팀과 인공지능 의료 시스템의 시 너지를 바탕으로 암 환자에게 최상의 의료 서비스를 제공하겠다"고 약속했다."
  • 117.
  • 118. IBM Watson Health Organizations Leveraging Watson Watson for Oncology Best Doctors (second opinion) Bumrungrad International Hospital Confidential client (Bangladesh and Nepal) Gachon University Gil Medical Center (Korea) Hangzhou Cognitive Care – 50+ Chinese hospitals Jupiter Medical Center Manipal Hospitals – 16 Indian Hospitals MD Anderson (**Oncology Expert Advisor) Memorial Sloan Kettering Cancer Center MRDM - Zorg (Netherlands) Pusan National University Hospital Clinical Trial Matching Best Doctors (second opinion) Confidential – Major Academic Center Highlands Oncology Group Froedtert & Medical College of Wisconsin Mayo Clinic Multiple Life Sciences pilots 24 Watson Genomic Analytics Ann & Robert H Lurie Children’s Hospital of Chicago BC Cancer Agency City of Hope Cleveland Clinic Columbia University, Irwing Cancer Center Duke Cancer Institute Fred & Pamela Buffett Cancer Center Fleury (Brazil) Illumina 170 Gene Panel NIH Japan McDonnell Institute at Washington University in St. Louis New York Genome Center Pusan National University Hospital Quest Diagnostics Stanford Health University of Kansas Cancer Center University of North Carolina Lineberger Cancer Center University of Southern California University of Washington Medical Center University of Tokyo Yale Cancer Center Andrew Norden, KOTRA Conference, March 2017, “The Future of Health is Cognitive” Watson for Oncology 는 현재 전세계 70여개 병원에 도입
  • 119. • 인공지능으로 인한 인간 의사의 권위 약화 • 환자의 자기 결정권 및 권익 증대 • 의사의 진료 방식 및 교육 방식의 변화 필요 http://news.donga.com/3/all/20170320/83400087/1
  • 120. • 의사와 Watson의 판단이 다른 경우? • NCCN 가이드라인과 다른 판단을 주기는 것으로 보임 • 100 여명 중에 5 case. 
 • 환자의 판단이 합리적이라고 볼 수 있는가? • Watson의 정확도는 검증되지 않았음 • ‘제 4차 산업혁명’ 등의 buzz word의 영향으로 보임 • 임상 시험이 필요하지 않은가? • 환자들의 선호는 인공지능의 adoption rate 에 영향 • 병원 도입에 영향을 미치는 요인들 • analytical validity • clinical validity/utility • 의사들의 인식/심리적 요인 • 환자들의 인식/심리적 요인 • 규제 환경 (인허가, 수가 등등) • 결국 환자가 원하면 (그것이 의학적으로 타당한지를 떠나서) 병원 도입은 더욱 늘어날 수 밖에 없음
  • 121. • Watson에 대한 환자 반응이 생각보다 매우 좋음 • 도입 2개월만에 85명 암 환자 진료 • 기존의 길병원 예측보다는 더 빠른 수치일 듯 • Big5 에서도 길병원으로 전원 문의 증가 한다는 후문 • 교수들이 더 열심히 상의하고 환자 본다고 함
  • 122. • Trained by 400 cases of historical patients cases • Assessed accuracy OEA treatment suggestions 
 using MD Anderson’s physicians’ decision as benchmark • When 200 leukemia cases were tested, • False positive rate=2.9% • False negative rate=0.4% • Overall accuracy of treatment recommendation=82.6% • Conclusion: Suggested personalized treatment option showed reasonably high accuracy MDAnderson’s Oncology ExpertAdvisor Powered by IBM Watson :AWeb-Based Cognitive Clinical Decision Support Tool Koichi Takahashi, MD (ASCO 2014)
  • 123. Annals of Oncology (2016) 27 (suppl_9): ix179-ix180. 10.1093/annonc/mdw601 Validation study to assess performance of IBM cognitive computing system Watson for oncology with Manipal multidisciplinary tumour board for 1000 consecutive cases: 
 An Indian experience • MMDT(Manipal multidisciplinary tumour board) treatment recommendation and data of 1000 cases of 4 different cancers breast (638), colon (126), rectum (124) and lung (112) which were treated in last 3 years was collected. • Of the treatment recommendations given by MMDT, WFO provided 
 
 50% in REC, 28% in FC, 17% in NREC • Nearly 80% of the recommendations were in WFO REC and FC group • 5% of the treatment provided by MMDT was not available with WFO • The degree of concordance varied depending on the type of cancer • WFO-REC was high in Rectum (85%) and least in Lung (17.8%) • high with TNBC (67.9%); HER2 negative (35%)
 • WFO took a median of 40 sec to capture, analyze and give the treatment.
 
 (vs MMDT took the median time of 15 min)
  • 124. Sung Won Park,APFCP, 2017 Assessing the performance of Watson for Oncology using colon cancer cases treated with surgery and adjuvant chemotherapy 
 at Gachon University Gil Medical Center • Stage II with high risk and stage III colon cancer patients (N=162) • Retrospective study: From September 1, 2014 to August 31, 2016 • Gachon University Gil Medical Center (GMC) • Generally accepted by GMC-recommendation in 83.3% • Concordant with • WFO-Rec: 53.1% • WFO-FC: 30.2% • WFO-NREC: 13.0% • Not included: 3.7%
  • 125. WHY? • 국가별 가이드라인의 차이 • WFO는 기본적으로 MSKCC 기준 • 인종적 차이, 인허가 약물의 차이, 보험 제도의 차이 • NCCN 가이드라인의 업데이트 • 암종별 치료 가능한 옵션의 다양성 차이 • 폐암: 다양함 vs 직장암: 다양하지 않음 • TNBC: 다양하지 않음 vs HER2 (-): 다양함
  • 126. 원칙이 필요하다 •어떤 환자의 경우, 왓슨에게 의견을 물을 것인가? •왓슨을 (암종별로) 얼마나 신뢰할 것인가? •왓슨의 의견을 환자에게 공개할 것인가? •왓슨과 의료진의 판단이 다른 경우 어떻게 할 것인가? •왓슨에게 보험 급여를 매길 수 있는가? 이러한 기준에 따라 의료의 질/치료효과가 달라질 수 있으나, 현재 개별 병원이 개별적인 기준으로 활용하게 됨
  • 127. 의사를 능가하는 Deep Learning 연구 결과들
  • 129. Detection of Diabetic Retinopathy
  • 130. 당뇨성 망막병증 • 당뇨병의 대표적 합병증: 당뇨병력이 30년 이상 환자 90% 발병 • 안과 전문의들이 안저(안구의 안쪽)를 사진으로 찍어서 판독 • 망막 내 미세혈관 생성, 출혈, 삼출물 정도를 파악하여 진단
  • 131. Copyright 2016 American Medical Association. All rights reserved. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD; Martin C. Stumpe, PhD; Derek Wu, BS; Arunachalam Narayanaswamy, PhD; Subhashini Venugopalan, MS; Kasumi Widner, MS; Tom Madams, MEng; Jorge Cuadros, OD, PhD; Ramasamy Kim, OD, DNB; Rajiv Raman, MS, DNB; Philip C. Nelson, BS; Jessica L. Mega, MD, MPH; Dale R. Webster, PhD IMPORTANCE Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. OBJECTIVE To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. DESIGN AND SETTING A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. EXPOSURE Deep learning–trained algorithm. MAIN OUTCOMES AND MEASURES The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. RESULTS TheEyePACS-1datasetconsistedof9963imagesfrom4997patients(meanage,54.4 years;62.2%women;prevalenceofRDR,683/8878fullygradableimages[7.8%]);the Messidor-2datasethad1748imagesfrom874patients(meanage,57.6years;42.6%women; prevalenceofRDR,254/1745fullygradableimages[14.6%]).FordetectingRDR,thealgorithm hadanareaunderthereceiveroperatingcurveof0.991(95%CI,0.988-0.993)forEyePACS-1and 0.990(95%CI,0.986-0.995)forMessidor-2.Usingthefirstoperatingcutpointwithhigh specificity,forEyePACS-1,thesensitivitywas90.3%(95%CI,87.5%-92.7%)andthespecificity was98.1%(95%CI,97.8%-98.5%).ForMessidor-2,thesensitivitywas87.0%(95%CI,81.1%- 91.0%)andthespecificitywas98.5%(95%CI,97.7%-99.1%).Usingasecondoperatingpoint withhighsensitivityinthedevelopmentset,forEyePACS-1thesensitivitywas97.5%and specificitywas93.4%andforMessidor-2thesensitivitywas96.1%andspecificitywas93.9%. CONCLUSIONS AND RELEVANCE In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment. JAMA. doi:10.1001/jama.2016.17216 Published online November 29, 2016. Editorial Supplemental content Author Affiliations: Google Inc, Mountain View, California (Gulshan, Peng, Coram, Stumpe, Wu, Narayanaswamy, Venugopalan, Widner, Madams, Nelson, Webster); Department of Computer Science, University of Texas, Austin (Venugopalan); EyePACS LLC, San Jose, California (Cuadros); School of Optometry, Vision Science Graduate Group, University of California, Berkeley (Cuadros); Aravind Medical Research Foundation, Aravind Eye Care System, Madurai, India (Kim); Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India (Raman); Verily Life Sciences, Mountain View, California (Mega); Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts (Mega). Corresponding Author: Lily Peng, MD, PhD, Google Research, 1600 Amphitheatre Way, Mountain View, CA 94043 (lhpeng@google.com). Research JAMA | Original Investigation | INNOVATIONS IN HEALTH CARE DELIVERY (Reprinted) E1 Copyright 2016 American Medical Association. All rights reserved.
  • 132. Training Set / Test Set • CNN으로 후향적으로 128,175개의 안저 이미지 학습 • 미국의 안과전문의 54명이 3-7회 판독한 데이터 • 우수한 안과전문의들 7-8명의 판독 결과와 인공지능의 판독 결과 비교 • EyePACS-1 (9,963 개), Messidor-2 (1,748 개)a) Fullscreen mode b) Hit reset to reload this image. This will reset all of the grading. c) Comment box for other pathologies you see eFigure 2. Screenshot of the Second Screen of the Grading Tool, Which Asks Graders to Assess the Image for DR, DME and Other Notable Conditions or Findings
  • 134. • EyePACS-1 과 Messidor-2 의 AUC = 0.991, 0.990 • 7-8명의 안과 전문의와 sensitivity, specificity 가 동일한 수준 • F-score: 0.95 (vs. 인간 의사는 0.91) Additional sensitivity analyses were conducted for sev- eralsubcategories:(1)detectingmoderateorworsediabeticreti- effects of data set size on algorithm performance were exam- ined and shown to plateau at around 60 000 images (or ap- Figure 2. Validation Set Performance for Referable Diabetic Retinopathy 100 80 60 40 20 0 0 70 80 85 95 90 75 0 5 10 15 20 25 30 100806040 Sensitivity,% 1 – Specificity, % 20 EyePACS-1: AUC, 99.1%; 95% CI, 98.8%-99.3%A 100 High-sensitivity operating point High-specificity operating point 100 80 60 40 20 0 0 70 80 85 95 90 75 0 5 10 15 20 25 30 100806040 Sensitivity,% 1 – Specificity, % 20 Messidor-2: AUC, 99.0%; 95% CI, 98.6%-99.5%B 100 High-specificity operating point High-sensitivity operating point Performance of the algorithm (black curve) and ophthalmologists (colored circles) for the presence of referable diabetic retinopathy (moderate or worse diabetic retinopathy or referable diabetic macular edema) on A, EyePACS-1 (8788 fully gradable images) and B, Messidor-2 (1745 fully gradable images). The black diamonds on the graph correspond to the sensitivity and specificity of the algorithm at the high-sensitivity and high-specificity operating points. In A, for the high-sensitivity operating point, specificity was 93.4% (95% CI, 92.8%-94.0%) and sensitivity was 97.5% (95% CI, 95.8%-98.7%); for the high-specificity operating point, specificity was 98.1% (95% CI, 97.8%-98.5%) and sensitivity was 90.3% (95% CI, 87.5%-92.7%). In B, for the high-sensitivity operating point, specificity was 93.9% (95% CI, 92.4%-95.3%) and sensitivity was 96.1% (95% CI, 92.4%-98.3%); for the high-specificity operating point, specificity was 98.5% (95% CI, 97.7%-99.1%) and sensitivity was 87.0% (95% CI, 81.1%-91.0%). There were 8 ophthalmologists who graded EyePACS-1 and 7 ophthalmologists who graded Messidor-2. AUC indicates area under the receiver operating characteristic curve. Research Original Investigation Accuracy of a Deep Learning Algorithm for Detection of Diabetic Retinopathy Results
  • 137. 0 0 M O N T H 2 0 1 7 | V O L 0 0 0 | N A T U R E | 1 LETTER doi:10.1038/nature21056 Dermatologist-level classification of skin cancer with deep neural networks Andre Esteva1 *, Brett Kuprel1 *, Roberto A. Novoa2,3 , Justin Ko2 , Susan M. Swetter2,4 , Helen M. Blau5 & Sebastian Thrun6 Skin cancer, the most common human malignancy1–3 , is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs)4,5 show potential for general and highly variable tasks across many fine-grained object categories6–11 . Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets12 —consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care. There are 5.4 million new cases of skin cancer in the United States2 every year. One in five Americans will be diagnosed with a cutaneous malignancy in their lifetime. Although melanomas represent fewer than 5% of all skin cancers in the United States, they account for approxi- mately 75% of all skin-cancer-related deaths, and are responsible for over 10,000 deaths annually in the United States alone. Early detection is critical, as the estimated 5-year survival rate for melanoma drops from over 99% if detected in its earliest stages to about 14% if detected in its latest stages. We developed a computational method which may allow medical practitioners and patients to proactively track skin lesions and detect cancer earlier. By creating a novel disease taxonomy, and a disease-partitioning algorithm that maps individual diseases into training classes, we are able to build a deep learning system for auto- mated dermatology. Previous work in dermatological computer-aided classification12,14,15 has lacked the generalization capability of medical practitioners owing to insufficient data and a focus on standardized tasks such as dermoscopy16–18 and histological image classification19–22 . Dermoscopy images are acquired via a specialized instrument and histological images are acquired via invasive biopsy and microscopy; whereby both modalities yield highly standardized images. Photographic images (for example, smartphone images) exhibit variability in factors such as zoom, angle and lighting, making classification substantially more challenging23,24 . We overcome this challenge by using a data- driven approach—1.41 million pre-training and training images make classification robust to photographic variability. Many previous techniques require extensive preprocessing, lesion segmentation and extraction of domain-specific visual features before classification. By contrast, our system requires no hand-crafted features; it is trained end-to-end directly from image labels and raw pixels, with a single network for both photographic and dermoscopic images. The existing body of work uses small datasets of typically less than a thousand images of skin lesions16,18,19 , which, as a result, do not generalize well to new images. We demonstrate generalizable classification with a new dermatologist-labelled dataset of 129,450 clinical images, including 3,374 dermoscopy images. Deep learning algorithms, powered by advances in computation and very large datasets25 , have recently been shown to exceed human performance in visual tasks such as playing Atari games26 , strategic board games like Go27 and object recognition6 . In this paper we outline the development of a CNN that matches the performance of dermatologists at three key diagnostic tasks: melanoma classification, melanoma classification using dermoscopy and carcinoma classification. We restrict the comparisons to image-based classification. We utilize a GoogleNet Inception v3 CNN architecture9 that was pre- trained on approximately 1.28 million images (1,000 object categories) from the 2014 ImageNet Large Scale Visual Recognition Challenge6 , and train it on our dataset using transfer learning28 . Figure 1 shows the working system. The CNN is trained using 757 disease classes. Our dataset is composed of dermatologist-labelled images organized in a tree-structured taxonomy of 2,032 diseases, in which the individual diseases form the leaf nodes. The images come from 18 different clinician-curated, open-access online repositories, as well as from clinical data from Stanford University Medical Center. Figure 2a shows a subset of the full taxonomy, which has been organized clinically and visually by medical experts. We split our dataset into 127,463 training and validation images and 1,942 biopsy-labelled test images. To take advantage of fine-grained information contained within the taxonomy structure, we develop an algorithm (Extended Data Table 1) to partition diseases into fine-grained training classes (for example, amelanotic melanoma and acrolentiginous melanoma). During inference, the CNN outputs a probability distribution over these fine classes. To recover the probabilities for coarser-level classes of interest (for example, melanoma) we sum the probabilities of their descendants (see Methods and Extended Data Fig. 1 for more details). We validate the effectiveness of the algorithm in two ways, using nine-fold cross-validation. First, we validate the algorithm using a three-class disease partition—the first-level nodes of the taxonomy, which represent benign lesions, malignant lesions and non-neoplastic 1 Department of Electrical Engineering, Stanford University, Stanford, California, USA. 2 Department of Dermatology, Stanford University, Stanford, California, USA. 3 Department of Pathology, Stanford University, Stanford, California, USA. 4 Dermatology Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA. 5 Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. 6 Department of Computer Science, Stanford University, Stanford, California, USA. *These authors contributed equally to this work. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
  • 138. LETTERH his task, the CNN achieves 72.1±0.9% (mean±s.d.) overall he average of individual inference class accuracies) and two gists attain 65.56% and 66.0% accuracy on a subset of the set. Second, we validate the algorithm using a nine-class rtition—the second-level nodes—so that the diseases of have similar medical treatment plans. The CNN achieves two trials, one using standard images and the other using images, which reflect the two steps that a dermatologist m to obtain a clinical impression. The same CNN is used for a Figure 2b shows a few example images, demonstrating th distinguishing between malignant and benign lesions, whic visual features. Our comparison metrics are sensitivity an Acral-lentiginous melanoma Amelanotic melanoma Lentigo melanoma … Blue nevus Halo nevus Mongolian spot … Training classes (757)Deep convolutional neural network (Inception v3) Inference classes (varies by task) 92% malignant melanocytic lesion 8% benign melanocytic lesion Skin lesion image Convolution AvgPool MaxPool Concat Dropout Fully connected Softmax Deep CNN layout. Our classification technique is a Data flow is from left to right: an image of a skin lesion e, melanoma) is sequentially warped into a probability over clinical classes of skin disease using Google Inception hitecture pretrained on the ImageNet dataset (1.28 million 1,000 generic object classes) and fine-tuned on our own 29,450 skin lesions comprising 2,032 different diseases. ning classes are defined using a novel taxonomy of skin disease oning algorithm that maps diseases into training classes (for example, acrolentiginous melanoma, amelanotic melano melanoma). Inference classes are more general and are comp or more training classes (for example, malignant melanocytic class of melanomas). The probability of an inference class is c summing the probabilities of the training classes according to structure (see Methods). Inception v3 CNN architecture repr from https://research.googleblog.com/2016/03/train-your-ow classifier-with.html GoogleNet Inception v3 • 129,450개의 피부과 병변 이미지 데이터를 자체 제작 • 미국의 피부과 전문의 18명이 데이터 curation • CNN (Inception v3)으로 이미지를 학습 • 피부과 전문의들 21명과 인공지능의 판독 결과 비교 • 표피세포 암 (keratinocyte carcinoma)과 지루각화증(benign seborrheic keratosis)의 구분 • 악성 흑색종과 양성 병변 구분 (표준 이미지 데이터 기반) • 악성 흑색종과 양성 병변 구분 (더마토스코프로 찍은 이미지 기반)
  • 139. Skin cancer classification performance of the CNN and dermatologists. LETT a b 0 1 Sensitivity 0 1 Specificity Melanoma: 130 images 0 1 Sensitivity 0 1 Specificity Melanoma: 225 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 111 dermoscopy images 0 1 Sensitivity 0 1 Specificity Carcinoma: 707 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 1,010 dermoscopy images Algorithm: AUC = 0.94 0 1 Sensitivity 0 1 Specificity Carcinoma: 135 images Algorithm: AUC = 0.96 Dermatologists (25) Average dermatologist Algorithm: AUC = 0.94 Dermatologists (22) Average dermatologist Algorithm: AUC = 0.91 Dermatologists (21) Average dermatologist cancer classification performance of the CNN and 21명 중에 인공지능보다 정확성이 떨어지는 피부과 전문의들이 상당수 있었음 피부과 전문의들의 평균 성적도 인공지능보다 좋지 않았음
  • 140. Skin cancer classification performance of the CNN and dermatologists. LETT a b 0 1 Sensitivity 0 1 Specificity Melanoma: 130 images 0 1 Sensitivity 0 1 Specificity Melanoma: 225 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 111 dermoscopy images 0 1 Sensitivity 0 1 Specificity Carcinoma: 707 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 1,010 dermoscopy images Algorithm: AUC = 0.94 0 1 Sensitivity 0 1 Specificity Carcinoma: 135 images Algorithm: AUC = 0.96 Dermatologists (25) Average dermatologist Algorithm: AUC = 0.94 Dermatologists (22) Average dermatologist Algorithm: AUC = 0.91 Dermatologists (21) Average dermatologist cancer classification performance of the CNN and
  • 142. Figure 4. Participating Pathologists’ Interpretations of Each of the 240 Breast Biopsy Test Cases 0 25 50 75 100 Interpretations, % 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 Case Benign without atypia 72 Cases 2070 Total interpretations A 0 25 50 75 100 Interpretations, % 218 220 222 224 226 228 230 232 234 236 238 240 Case Invasive carcinoma 23 Cases 663 Total interpretations D 0 25 50 75 100 Interpretations, % 147 145 149 151 153 155 157 159 161 163 165 167 169 171 173 175 177 179 181 183 185 187 189 191 193 195 197 199 201 203 205 207 209 211 213 215 217 Case DCIS 73 Cases 2097 Total interpretations C 0 25 50 75 100 Interpretations, % 74 76 78 80 82 84 86 88 90 92 94 96 98 100 102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 144 Case Atypia 72 Cases 2070 Total interpretations B Benign without atypia Atypia DCIS Invasive carcinoma Pathologist interpretation DCIS indicates ductal carcinoma in situ. Diagnostic Concordance in Interpreting Breast Biopsies Original Investigation Research Elmore etl al. JAMA 2015 Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens The overall agreement between the individual pathologists’ interpretations and the expert consensus–derived reference diagnoses was 75.3% (total 240 cases)
  • 143. Elmore etl al. JAMA 2015 Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens • Concordance noted in 5194 of 6900 case interpretations or 75.3%. • Reference diagnosis was obtained from consensus of 3 experienced breast pathologists. spentonthisactivitywas16(95%CI,15-17);43participantswere awarded the maximum 20 hours. Pathologists’ Diagnoses Compared With Consensus-Derived Reference Diagnoses The 115 participants each interpreted 60 cases, providing 6900 total individual interpretations for comparison with the con- sensus-derived reference diagnoses (Figure 3). Participants agreed with the consensus-derived reference diagnosis for 75.3% of the interpretations (95% CI, 73.4%-77.0%). Partici- pants (n = 94) who completed the CME activity reported that Patient and Pathologist Characteristics Associated With Overinterpretation and Underinterpretation The association of breast density with overall pathologists’ concordance (as well as both overinterpretation and under- interpretation rates) was statistically significant, as shown in Table 3 when comparing mammographic density grouped into 2 categories (low density vs high density). The overall concordance estimates also decreased consistently with increasing breast density across all 4 Breast Imaging- Reporting and Data System (BI-RADS) density categories: BI-RADS A, 81% (95% CI, 75%-86%); BI-RADS B, 77% (95% Figure 3. Comparison of 115 Participating Pathologists’ Interpretations vs the Consensus-Derived Reference Diagnosis for 6900 Total Case Interpretationsa Participating Pathologists’ Interpretation ConsensusReference Diagnosisb Benign without atypia Atypia DCIS Invasive carcinoma Total Benign without atypia 1803 200 46 21 2070 Atypia 719 990 353 8 2070 DCIS 133 146 1764 54 2097 Invasive carcinoma 3 0 23 637 663 Total 2658 1336 2186 720 6900 DCIS indicates ductal carcinoma in situ. a Concordance noted in 5194 of 6900 case interpretations or 75.3%. b Reference diagnosis was obtained from consensus of 3 experienced breast pathologists. Diagnostic Concordance in Interpreting Breast Biopsies Original Investigation Research Comparison of 115 Participating Pathologists’ Interpretations vs 
 the Consensus-Derived Reference Diagnosis for 6900 Total Case Interpretations
  • 144. Constructing higher-level contextual/relational features: Relationships between epithelial nuclear neighbors Relationships between morphologically regular and irregular nuclei Relationships between epithelial and stromal objects Relationships between epithelial nuclei and cytoplasm Characteristics of stromal nuclei and stromal matrix Characteristics of epithelial nuclei and epithelial cytoplasm Building an epithelial/stromal classifier: Epithelial vs.stroma classifier Epithelial vs.stroma classifier B Basic image processing and feature construction: H&E image Image broken into superpixels Nuclei identified within each superpixel A Relationships of contiguous epithelial regions with underlying nuclear objects Learning an image-based model to predict survival Processed images from patients Processed images from patients C D onNovember17,2011stm.sciencemag.orgwnloadedfrom TMAs contain 0.6-mm-diameter cores (median of two cores per case) that represent only a small sample of the full tumor. We acquired data from two separate and independent cohorts: Nether- lands Cancer Institute (NKI; 248 patients) and Vancouver General Hospital (VGH; 328 patients). Unlike previous work in cancer morphom- etry (18–21), our image analysis pipeline was not limited to a predefined set of morphometric features selected by pathologists. Rather, C-Path measures an extensive, quantitative feature set from the breast cancer epithelium and the stro- ma (Fig. 1). Our image processing system first performed an automated, hierarchical scene seg- mentation that generated thousands of measure- ments, including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. The pipeline consisted of three stages (Fig. 1, A to C, and tables S8 and S9). First, we used a set of processing steps to separate the tissue from the background, partition the image into small regions of coherent appearance known as superpixels, find nuclei within the superpixels, and construct Constructing higher-level contextual/relational features: Relationships between epithelial nuclear neighbors Relationships between morphologically regular and irregular nuclei Relationships between epithelial and stromal objects Relationships between epithelial nuclei and cytoplasm Characteristics of stromal nuclei and stromal matrix Characteristics of epithelial nuclei and epithelial cytoplasm Epithelial vs.stroma classifier Epithelial vs.stroma classifier Relationships of contiguous epithelial regions with underlying nuclear objects Learning an image-based model to predict survival Processed images from patients alive at 5 years Processed images from patients deceased at 5 years L1-regularized logisticregression modelbuilding 5YS predictive model Unlabeled images Time P(survival) C D Identification of novel prognostically important morphologic features basic cellular morphologic properties (epithelial reg- ular nuclei = red; epithelial atypical nuclei = pale blue; epithelial cytoplasm = purple; stromal matrix = green; stromal round nuclei = dark green; stromal spindled nuclei = teal blue; unclassified regions = dark gray; spindled nuclei in unclassified regions = yellow; round nuclei in unclassified regions = gray; background = white). (Left panel) After the classification of each image object, a rich feature set is constructed. (D) Learning an image-based model to predict survival. Processed images from patients alive at 5 years after surgery and from patients deceased at 5 years after surgery were used to construct an image-based prog- nostic model. After construction of the model, it was applied to a test set of breast cancer images (not used in model building) to classify patients as high or low risk of death by 5 years. www.ScienceTranslationalMedicine.org 9 November 2011 Vol 3 Issue 108 108ra113 2 onNovember17,2011stm.sciencemag.orgDownloadedfrom Digital Pathologist Sci Transl Med. 2011 Nov 9;3(108):108ra113