Clinical Utility of Actionable Genome Information in Precision Oncology Clinic

Transcription

1 Indian Ocean Rim 2017 Laboratory Haematology Congress , Singapore Clinical Utility of Actionable Genome Information in Precision Oncology Clinic

2 Reimbursement program for NGS panel tests in Korea 1. Authorization of medical devices (Ministry of Food and Drug Safety) IonPGMDx, MiSeqDx and IonS5 Accredited NGS labs for RUO sequencers (HiSeq, NextSeq, NovaSeq etc) 2. Reimbursement program (Ministry of Health and Welfares) Germline variations (one test per person) 1 Genetic disorders (e.g. pathogenic genes for retinitis pigmentosa) 2 Inherited cancers (e.g. BRCA1 and BRCA2) Somatic variation (two tests at maximum per person) 1 Blood tumors, Level I (<50 genes) 2 Blood tumors, Level II (>50 genes) 3 Solid tumors, Level I (<50 genes) 4 Solid tumors, Level II (>50 genes) Small panel (<50 genes) in $700 and large panel (>50 genes) in $1, Registration for clinical service (Ministry of Health and Welfares) Clinical services available at 22 institutions including SMC Starting clinical services from June, 2017 in SMC

3 Snapshot of Samsung Medical Center Happiness through Healthcare Innovation with the aim of becoming a global leading hospital by 2020

4 Clinical sequencing platform in Samsung Genome Institute MFDS accredited NGS clinical lab (Pathology) Major resources 8 sequencers(3hiseq,2 Miseq, 2 IonPGM, I IonS5) 992 core, 1.2TB ram, 1.6PB storage-size server 31 bioinformatics specialists. Disease-specific diagnostic panels (Pathology/Lab Medicine) Integrated analysis of genomic and clinical data 7,442 CancerSCAN tests. 5,557 cases with clinical follow-up data in SMC and 267 cases on serial samples

5 Detection of variants by next generation sequencing Accurate detection of variants is essential for precision cancer medicine. We need to balance the sensitivity for a specific variant type and the diversity of variant types with different sequencing methods such as panel sequencing, WES and WGS. Shin HT et al. manuscript in review

6 Impact of sequencing depth on SNV detection Shin HT et al. manuscript in review

7 Influence of sequencing depth to background errors Park GH and Shin SH et al. manuscript in revision

8 CancerSCAN, NGS panel for precision oncology Targeted deep sequencing on 381 cancer-related genes SNVs, Indels, CNVs (375) Gene fusions (22), promoter (1) Coverage: ~1,000X Public and custom algorithms for variant detection Customized CNV caller (Nam J et al. Brief Bioinform, 2015) MSI caller (Lee S, Cortes-Ciriano I et al. Nature Commun, 2017)

9 Personalized treatment based on actionable mutation Samsung Genome Institute

10 Cancer types for CancerSCAN tests

11 Landscape of actionable alterations in SMC cohort Shin HT et al. manuscript in review

12 Frequency of actionable variants found in SMC cohort

13 Actionable genome analysis on refractory cancer patients

14 Low tumor purity in clinical samples Shin HT et al. manuscript in review

15 Variant allele fraction distribution of actionable mutations (overall) Frequency of actionable variants with low VAF 20% : 47.7% (969/2032) 10% : 24.3% (494/2032) 5% : 12.8% (260/2032) Shin HT et al. manuscript in review

16 Variant allele fraction distribution of actionable mutations (gene) Shin HT et al. manuscript in review

17 Frequency of actionable mutations in pre and post-chemotherapy Shin HT et al. manuscript in review

18 Frequency of EGFR mutations in longitudinal samples of NSCLC Shin HT et al. manuscript in review

19 Clinical outcome of patients with low VAF actionable mutations Samsung Genome Institute

20 Summary (1) Significant fraction of clinically-actionable variants have low VAFs in clinical samples treatment-induced mutations low tumor purity/heterogeneity 5%: total 12.8%, EGFR T790M 24%, PIK3CA E545 17%, and KRAS G12 12% Clinical utility of low VAF actionable variants no significant survival difference between the patients with low vs high VAF variants who receive targeted therapy

21 Integration of clinical and genomic data

22 Genomic and clinical landscape of NSCLC

23 Genomic and clinical landscape of stomach cancer

24 Genomic and clinical landscape of breast cancer

25 Genomic and clinical landscape of colorectal cancer

26 Machine learning framework to predict overall survival based on integrated clinical and genomic information Feature selection by LASSO Gradient boosting Superior performance Consider variable interaction Handle continuous and sparse data

27 C-index Performance of prognostic model with integrated data Cancer type: colorectal

28 Top 12 most contributing variables for four major cancer types Colorectal cancer Breast cancer Gastric cancer Non-small cell lung cancer

29 Contributions of predictor variables for prognostic model Colorectal cancer Breast cancer Gastric cancer Non-small cell lung cancer

30 Predict clinical outcomes PatientMatcher CancerSCAN data warehouse (n=~7,000) Create distance metric using a machine learning algorithm Analyze clinical outcomes Select nearest neighbors based on clinical and genomic profile

31 Prognostic impact Working example of PatientMatcher Subject Top 20 closest patients KM plot of top 20 closest patients Dissimilarity Neighbors share genetic similar profile

32 Performance evaluation of PatientMatcher Non small cell lung cancer Gastric cancer Predict survival rate using PatientMatcher Evaluate accuracy Breast cancer Colorectal cancer PatientMatcher Stage

33 Overall survival probability A case of poor prognosis in stage 1 NSCLC Reference curve (stage 1) Predicted curve Died at 1.2 years

34 Overall survival probability A case of good prognosis in stage 4 NSCLC Predicted curve Reference curve (stage 4) Survived until 7 years

35 Summary (2) 1. Machine learning approach captures how clinical and genomic variables influence the treatment outcome. 2. Genomic data add predictive value when combined with clinical information. 3. Through machine learning methods, PatientMatcher might provide patient-specific prognostic model with CancerSCAN data.

36 Acknowledgements Samsung Medical Center Genome Institute Hae-Ock Lee Kyu-Tae Kim Woosung Chung Hye-Hyun Eom Areum Cho Daeun Ryu Donghyun Park Gahee Park Kyung Yeon Han Changeun Yoo Heesung Moon Jegun Joung Jinho Kim Hyun Tae Shin Jae Yong Nam Chung Lee Nayoung KIm Breast Cancer Center, SMC Yeon Hee Park Jeong-Eon Lee Jong-Han Yu Ji Yeon Kim Seok Jin Nam Young Hyuck Im Lung Cancer Center, SMC Keunchil Park Myoung Joo Ahn Sehoon Lee Jin Seok Ahn Hematology, SMC Young Hye Ko WonSeok Kim Seok Jin Kim Ki Hyun Kim Seoul National University Hospital Won-Shik Han Han-Byeol Lee Harvard Medical School, USA Peter Kharchenko Soo H Lee Gene Wang Peter Park Alice E. Lee University of Pennsylvania, USA Junhyong Kim

37 Open platform for precision oncology Integrated clinical and genomic data Clinical grade sequencing data Structured clinical information Matched samples in biobank Global data sharing Providing open platform for Diagnostics development and service CDSS modeling for precision medicine Biomarker discovery for diagnostics Drug development on new targets Global partners