Prognostic Diagnosis for HR+/HER2 Early Breast Cancer Patients Based on the Algorithm through the Gene Expression Signature

Transcription

1 Prognostic Diagnosis for HR+/HER2 Early Breast Cancer Patients Based on the Algorithm through the Gene Expression Signature Young Kee Shin, M.D., Ph.D. Seoul National University College of Pharmacy N BIO (Institutes of Entrepreneurial BioConvergence), The center for Anti cancer Companion Diagnostics LOGONE Bio Convergence Research Foundation

2 Introduction To make an optimal treatment decision for early state breast cancer, it is important to identify risk of recurrence. Current clinicopathological parameters alone have limited predictive or prognostic value for recurrence risk in patients with early breast cancer. Gene expression based approaches provide significant prognostic or predictive information with respect to breast cancer.several commercial assays based on expression of multiple genes in frozen or FFPE samples have been developed Oncotype DX, PAM50, MammaPrint, and EndoPredict. However, none of the available assays provide a clear answer about prognostic information in HR+/HER2 early breast cancer because these assays do not differentiate patients with HR+/HER2 early breast cancer. There is an urgent clinical need to identify novel prognostic markers to provide a clear answer about prognostic value in HR+/HER2 early breast cancer. Here, we developed and validated a new prognostic model for predicting the risk of distant metastasis in patients with pn0 N1 HR+/HER2 breast cancer treated with hormone therapy alone

3 GenesWell BCT Overview

4 GenesWell BCT : Overview GenesWell BCT is a qrt PCR based in vitro diagnostic assay using RNA extracted from FFPE (Formalin Fixed Paraffin Embedded) samples of breast tumor tissues. The test uses gene expression data of 6 prognostic genes, 3 reference genes, and 3 breast cancer related genes weighted together with 2 clinical variables to generate BCT score through Algorithm, to assess a patient s risk of distant recurrence within 10 years by using. GenesWell BCT Report Form BCT Score Result High Risk 7 Probability of Distant Metastasis Within 10 years 100% 80% 60% 40% 20% 0% Low Risk High Risk BCT Score

5 GenesWell BCT : Competitive Advantage AQS Key technology of GenesWell BCT is customized and fully automated AQS (Automatic Quantification System) using FFPE Sample. Tissue Preparation System (TPS, SEIMENS) GENCURIX Automated Prognosis Diagnostic System (AQS: Automated Quantification System) 1 AQS Unit: 1TPS, 1ADS, 1RT PCR : 1 AQS Unit Capacity 24,000 Tests/year(96 tests/day) by 3 researchers ( 8,000 tests/researcher) : Superior efficiency than competitors Genomic Health: Manual method 115,000 tests/year by 144 researchers (800 tests/researcher) Great consistency by Automated System & expandability Price competitiveness due to mass processing with little manpower A fully automated solution for extraction of nucleic acids from FFPE as the first step MFD IVD approved Automatic Dispense System (ADS, Custom made by HAMILTON) GenesWell TM Diagnostic Kits Gene Amplifying & Analysis (LightCycler480 II, Roche) Custom made Automated Dispense System (ADS) MFDS IVD approved Probe/Primer Pre coated GenesWell Brand Kits: [Breast Cancer] Prognosis Diagnostics Kit [Lung & Colorectal Cancer] Companion Diagnostics Kit Providing the result of diagnostic analysis to clinical field using GENCURIX s own algorithm : Applying to diagnostic service MFDS IVD approved

6 GenesWell BCT : Request & Result forms

7 GenesWell BCT Developmental History

8 Developmental history of GenesWell TM BCT Identification of Novel reference genes 1) Biomarker discovery for prognostic diagnosis of breast cancer 2 3) Singapore Joint Grant Clinical Validation (AMC) 4) MFDS class III approval* Discovery data set (SMC+AMC) Algorithm design Development of GeneWell TM BCT Clinical validation Analytical validation 1) Kwon, Mi Jeong, et al. "Identification of novel reference genes using multiplatform expression data and their validation for quantitative gene expression analysis." PloS one 4.7 (2009): e ) Oh, Ensel, et al. "A prognostic model for lymph node negative breast cancer patients based on the integration of proliferation and immunity." Breast cancer research and treatment (2012): ) Han, Jinil et al. MMP11 and CD2 as Novel Prognostic Factors in Hormone Receptor Negative, HER2 Positive Breast Cancer. Breast cancer research and treatment (2017) (Accepted) 4) Gong, Gyungyub, et al. "A new molecular prognos c score for predic ng the risk of distant metastasis in pa ents with HR+/HER2 early breast cancer." Scientific Reports 7 (2017): *Class III: Critical impact for diagnostic and treatment decision

9 Identification of Novel Reference Genes Normalization of mrna levels using endogenous reference genes is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional endogenous reference genes such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Therefore, novel reference genes were needed for accurate measurement in quantitative methods. Biomarker I Reference Gene (House keeping gene) β Actin, GAPDH etc Gene expression ratio in Cancer cell Biomarker II Gene expression ratio in Normal cell

10 Identification of Novel Reference Genes 13 novel endogenous reference genes (nergs) were selected using human gene expression data from different platforms including EST, SAGE, and microarray Among them, 3 ERGs (CTBP1, CUL1, UBQLN1) were selected to normalize expression levels of prognostic genes High expression stability in FFPE tissues, frozen tissues and cell lines Lower expression levels than those of traditional endogenous reference genes <The mrna levels of tergs and 13 nergs in Cp values> Low expression High expression Red: traditional ERGs GAPDH, ACTB, B2M, PPIA, HPRT1, HMBS, TBP Blue: novel ERGs ZNF207, OAZ1, LUC7L2, CTBP1, TRIM27, GPBP1, UBQLN1, ARL8B, PAPOLA, CUL1, DIMT1L, FBXW2, SPG21 PLoS One Jul 7;4(7):e6162. Identification of novel reference genes using multiplatform expression data and their validation for quantitativegene expression analysis.

11 Novel Prognostic Genes in GenesWell BCT Prognostic genes of GenesWell TM BCT were selected to show clear difference of distant metastasis free survival between high risk group and low risk group through large scale analysis of early breast cancer patients. 6 prognostic genes Gene Group Gene Symbol Full name GO terms (biological process) UBE2C ubiquitin conjugating enzyme E2C cell division; mitotic cell cycle; mitotic spindle assembly checkp oint TOP2A topoisomerase (DNA) II alpha DNA topological change; mitotic cell cycle Proliferation RRM2 ribonucleotide reductase M2 G1/S transition of mitotic cell cycle; mitotic cell cycle FOXM1 forkhead box M1 G2/M transition of mitotic cell cycle; mitotic cell cycle MKI67 marker of proliferation Ki 67 DNA metabolic process; cell proliferation Immune Response BTN3A2 butyrophilin, subfamily 3, member A2 T cell mediated immunity; interferon gamma secretion

12 dataset summary GEO number Publi catio n Total cases # of N- Survival type Median follow-up time(yr) Sample sources Date of diagnosis Treatment Discovery data set GSE2034 [4] Distant meta 7.17 Erasmus medical center, the Netherlands 1980 ~ 1995 No systemic adjuvant therapy GSE6532 (1) [8,37] Distant meta 5.99 John Radcliffe Hospital, UK Guys Hospital, UK Uppsala Univ. Hospital, Sweden 1987~ 1989 tamoxifen only GSE7390 [13] Distant meta Institut Gustaye Roussy, France Karolinska Institute, Sweden Guys Hospital, UK John Radcliffe Hospital, UK Centre Rene Huguenin, France 1980~1998 No systematic adjuvant therapy GSE11121 [32] Distant meta 7.54 Johannes Guteberg Univ., Germany 1988 ~ 1998 No systematic adjuvant therapy GSE Distant meta 7.08 Ljubljana, Slovenia National Cancer Institute, Italy Technische Universitaet Muenchen, Germany Cleveland Clinic Foundation, US 1981 ~ 2000 tamoxifen only Validation data set 1 GSE1456 [9] GSE3494 [10] few Overall 7.05 Karolinska Hospital, Sweden 1994~ 1996 Tamoxifen + chemotherapy (104) Overall Uppsala Univ. Hospital, Sweden 1987~ 1989 For all node positive patients, chemotherapy or tamoxifen Validation data set 2 van t Veer et al Distant meta 6.61 Netherlands Cancer Institute, the Netherlands 1984~ 1995 node positive patients -Chemotherapy only (90) - tamoxifen only (20) - both (20) Validation data set3 GSE6532 (2) Distant meta 8.98 Guys Hospital, UK 1980~ all tamoxifen only

13 A Figure 1 Data preparation 7 independent data sets (1557 cases) Remove duplicated cases 1371 unique cases Preprocessing : RMA (.CEL) Normalized 1371 cases B Select differentially expressed genes (SAM) Define good outcome and poor outcome Good : > 10yrs distant-meta free Poor : < 5yrs develop distant-meta ER- Good : 71 samples Poor : 71 samples Good.up : 226 genes Poor.up : 23 genes (FDR < 0.1) 1104 samples Lymph node negative No chemotherapy Good : 281 samples Poor : 217 samples Pool of candidate genes (1019 unique genes) ER+ Good : 210 samples Poor : 146 samples Select differentially expressed genes (SAM) Good.up : 625 genes Poor.up : 247 genes (FDR = 0.000) Mining of a prognostic gene signature for early breast cancer patients Determine ER status with ESR1 expression levels (ER+ or ER-) Identify expression patterns K-means clustering (by genes) Visualization by TreeView ER+ : 1084 cases ER- : 287 cases Identify 2 distinct expression patterns Discovery 1104 cases (5 data sets) Validation set cases (2 data sets) Validation set cases Validation set cases Proliferation (61 genes) GO analysis (by pattern) Fold change IQR Average expression Immune response (93 genes) prognostic genes Proliferation : 8 genes Immune resp. : 8 genes

14 Method(2) survival data Type Status Total Survival time (yr) min 1Q 2Q 3Q max Dist. meta Yes (total 851) no relapse Yes (total 369) No overall Dead (total 267) alive Distant meta Relapse 74.0% 6.5% Poor Good Good Overall

15 p3 ER- Expression pattern of proliferation and immune response ER+ p2 High (p3) Intermediate (p2) Low (p1) proliferation Proliferation (p-genes) Immune response (i-genes) low high G1 G2 G3 T1 T2 T3 N- N+ ER- ER+ No data Distant meta within 5 yrs

16 symbol name location p-genes (proliferationrelated genes) UBE2C ubiquitin-conjugating enzyme E2C 20q13.12 ZWINT ZW10 interactor 10q21-q22 CKS2 CDC28 protein kinase regulatory subunit 2 9q22 RACGAP1 Rac GTPase activating protein 1 12q13 PRC1 protein regulator of cytokinesis 1 15q26.1 CCNB2 cyclin B2 15q21.3 PTTG1 pituitary tumor-transforming 1 5q35.1 CDC20 cell division cycle 20 homolog (S. cerevisiae) 1p34.1 i-genes (immune responserelated genes) CXCL13 chemokine (C-X-C motif) ligand 13 4q21 IGHM immunoglobulin heavy constant mu 14q32.32-q32.33 TRBC1 T cell receptor beta constant 1 7q34 GZMB granzyme B (granzyme 2, cytotoxic T-lymphocyteassociated 14q11.2 serine esterase 1) UBD ubiquitin D 6p21.3 LCP1 lymphocyte cytosolic protein 1 (L-plastin) 13q14.3 BTN3A3 butyrophilin, subfamily 3, member A3 6p22.1 STAT1 signal transducer and activator of transcription 1, 91kDa 2q32.2-q32.3

17 A KM survival curves of p-genes ER+ Proliferation (p-genes) ER- Proliferation (p-genes) Figure 4-1 Survival Probability Low (p1) Intermediate (p2) High (p3) HR (intermed/low) : 2.44 ( ) *** HR (high/low) : 5.82 ( ) *** 10 Low (p1) Intermediate (p2) High (p3) HR (intermed/low) : 5.03 ( ) HR (high/low) : 4.54 ( ) Time (yr) B KM survival curves of i-genes ER+ Immune response (i-genes) ER- Immune response (i-genes) Low (i1) Intermediate (i2) High (i3) HR (intermed/high) : 3.31 ( ) *** HR (low/high) : 4.54 ( ) *** HR (intermed/high) : 1.74 ( ) HR (low/high) : 2.82 ( ) **

18 Validation 1 (Agilent Hu25K) distant meta ER+ 70 genes p+i Hazard Ratio : 4.92 Hazard Ratio : 3.62 ER- 70 genes p+i Hazard Ratio : 0.93 Hazard Ratio : 2.16

19 Validation 2 (Affy U133A) overall survival ER+ ER Hazard Ratio : 4.5 Hazard Ratio : 1.48 Validation 3 (Affy U133Plus2) distant meta ER Hazard Ratio : 3.99

20 Discovery of Novel Prognostic Genes 384 candidate genes 30 candidate genes 16 candidate genes 6 prognostic genes Microarray dataset qrt PCR of 30 genes qrt PCR of 16 genes GenesWell BCT A) B) C) D) A) 684 breast cancer cases were collected from 3 independent microarray data sets i) All the cases were LN, and none of them received tamoxifen and adjuvant chemotherapy after surgery. B) According to the distribution of the DMFS, 212 cases were assigned to the good outcome group (no DM for more than 10 years), and 159 cases were assigned to the poor outcome group (DM within 5 years) C) A total of 384 genes showing a significant differential expression between two clinical outcome (good/poor) groups were selected as candidate prognostic genes i) Good Up: 159 genes ii) Poor Up: 225 genes D) The candidate genes were classified into two functional categories proliferation and immune response Breast cancer research and treatment, 132(2), A prognostic model for lymph node negative breast cancer patients based on the integration of proliferation and immunity.

21 Discovery of Novel Prognostic Genes 384 candidate genes identified based on public gene expression microarray datasets 30 candidate genes 16 candidate genes 6 prognostic genes High correlation with either proliferation or immune response High expression variability between samples (large interquartile range) High mean expression values qrt PCR of 30 genes in paired FFPE and frozen tissues from the same patients High expression correlation between FFPE and frozen tissues based on qrt PCR qrt PCR of 16 genes in breast cancer FFPE tissues Univariate analysis of 16 genes for DMFS according to subtypes of breast cancer Significant or marginally significant associated with distant metastasis in LN, HR+/HER2 breast cancer

22 Discovery of Novel Prognostic Genes 384 candidate genes 30 candidate genes 16 candidate genes 6 prognostic genes Microarray dataset qrt PCR of 30 genes qrt PCR of 16 genes GenesWell BCT qrt PCR of 30 genes in paired FFPE and frozen tissues from the same patients 16 candidate genes: High expression correlation between FFPE and frozen tissues based on qrt PCR

23 Discovery of Novel Prognostic Genes 384 candidate genes 30 candidate genes 16 prognostic genes 6 prognostic genes Microarray dataset qrt PCR of 30 genes qrt PCR of 16 genes qrt PCR of 16 genes in breast cancer FFPE tissues (n=819) Univariate analysis of 16 genes for DMFS according to subtypes of breast cancer GenesWell BCT All HR+/HER2 HR+/HER2+ HR /HER2+ TNBC HR 95% CI P value HR 95% CI P value HR 95% CI P value HR 95% CI P value HR 95% CI P value No. of patients (No. of events) 421 (64) 202 (29) 45 (7) 58 (12) 116 (16) p genes AURKA CCNB FOXM MKI MMP PTTG RACGAP RRM TOP2A UBE2C i genes BTN3A CCL CD CD HLADPA TRBC Breast cancer related genes ESR PGR ERBB Prognostic genes: Significant or marginally significant associated with distant metastasis in LN, HR+/HER2 breast cancer Breast cancer research and treatment (accepted). MMP11 and CD2 as Novel Prognostic Factors in Hormone Receptor Negative, HER2 Positive Breast Cancer

24 Development of Prognostic Model 384 candidate genes 30 candidate genes 16 prognostic genes 6 prognostic genes Microarray dataset qrt PCR of 30 genes qrt PCR of 16 genes GenesWell BCT Scientific Reports (accepted). A new molecular prognos c score for predic ng the risk of distant metastasis in pa ents with HR+/HER2 early breast cancer

25 Development of Prognostic Model Two clinical variables (pn status and tumor size) were identified as independent negative prognostic factors for distant metastasis in HR+/HER2 breast cancer patients. Univariate analysis Multivariate analysis HR 95% CI p value HR 95% CI p value Age pn Pathologic Stage I II Histologic Grade Tumor Size < NPI Abbreviations: HR, hazard ratio; CI, confidence interval; pn, pathological nodal status; NPI, Nottingham prognostic index; HRs with P values of less than 0.05 are marked in bold.

26 Development of Prognostic Model A new molecular predictor of distant metastasis (BCT Score) was developed based on the combination of six prognostic genes and two clinical variables. BCT Score = (0.63 Ct_UBE2C) + (0.32 Ct_TOP2A) + (0.13 Ct_RRM2) + (0.02 Ct_FOXM1) + (0.04 Ct_MKI67) (0.42 Ct_BTN3A2) + (0.89 Tumor_size) + (1.22 pn) 5 proliferation related genes 1 immune response related gene 2 clinical variables Higher values of the BCT score indicate a higher risk of distant recurrence. A cutoff BCT score value to distinguish patients with low and high risk for distant metastasis was set to 4, which maximized sum of sensitivity and specificity. Category BCT Score (BS, 0 10) Low risk BS<4 High risk BS 4

27 GenesWell BCT Clinical Performance

28 Characteristics of the patients Discovery cohort Validation cohort Hormone therapy alone (A) Hormone therapy alone (B) Hormone therapy plus che motherapy (C) No. of patient s (n=174) (n=222) (n=510) % No. of patient s % No. of patient s % P value betw een A and B P value betw een B and C DMFS rate at 10 years 92.0% (87.9%-96.3%) 92.2% (88.0%-96.6%) 84.7% (81.4%-88.2%) Median age (range), years ( ) ( ) ( ) c c Age, years a <0.001 a < % % % % % % Menopausal status a a Pre % % % Post % % % NA % % % pn a <0.001 a % % % % % % Tumor size, cm b <0.001 b % % % % % % > % % % Pathologic stage a <0.001 a IA % % % IIA % % % IIB % % % Histologic grade a a % % % % % % % % % NPI a <0.001 a % % % % % % % % %

29 Probabilities of DMFS The Kaplan Meier survival curve showed a statistically significant difference in DMFS between the low risk and high risk groups distinguished by the BCT score (p values<0.001). <Discovery set (n=174)> Samsung Medical Center & Asan Medical Center <Validation set (n=222)> Asan Medical Center Low risk: 97.1% Low risk: 96.2% P value < P value < High risk: 73.7% High risk Low risk High risk: 60.3% High risk Low risk Product Reference High Inter Low Low High Oncotype Dx Paik (2004) 69.5% 85.7% 93.2% 23.7% Prosigna 510(k) 84.3% 90.4% 96.6% 12.3% EndoPredict Filipits (2011) 72.0% 96.0% 24.0% 78.0% 96.0% 18.0% Discovery 60.3% 97.1% 36.8% GenesWell TM BCT Validation 73.7% 96.2% 22.5% Total 68.0% 96.9% 28.9%

30 Probabilities of DMFS (Early/Late stage) Discovery set Early stage ( 5 years) Late stage (>5 years) Low risk: 97.9% Low risk: 99.2% High risk: 85.7% High risk: 70.3% High risk Low risk P <0.001 High risk Low risk P <0.001 Validation set Low risk: 98.9% Low risk: 97.3% High risk: 85.1% High risk: 86.5% High risk High risk Low risk P <0.001 Low risk P <0.026

31 Probabilities of DFS The Kaplan Meier survival curve showed a statistically significant difference in DFS between the low risk and high risk groups distinguished by the BCT score (p values<0.001). <Discovery set (n=174)> Samsung Medical Center & Asan Medical Center <Validation set (n=222)> Asan Medical Center Low risk: 97.2% Low risk: 94.0% P value < High risk Low risk High risk: 53.9% P value < High risk Low risk High risk: 63.9%

32 Probabilities of OS The Kaplan Meier survival curve showed a statistically significant difference in OS between the low risk and high risk groups distinguished by the BCT score (p values<0.001). <Discovery set (n=174)> Samsung Medical Center & Asan Medical Center <Validation set (n=222)> Asan Medical Center Low risk: 96.1% Low risk: 98.9% High risk: 77.0% P value < High risk Low risk High risk: 63.4% P value < High risk Low risk

33 Multivariate analysis of the BCT score and clinicopathological parameters Multivariate analysis revealed that the BCT score was independently associated with distant metastasis

34 Comparison of prognostic performance BCT score showed the best performance in predicting the risk of distant metastasis with the highest C index. <Discovery Set> <Validation Set> The BCT score provides more significant prognostic information about risk ofdistantmetastasis than clinical variables and established prognostic models based on clinicopathological parameters.

35 Performance of the BCT score in chemotherapy treated patients A significant difference in 10 year distant metastasis rates between the high and low risk groups <Validation set (n=510)> Asan Medical Center & Samsung Medical Center Low risk: 90.7% High risk: 78.2% High-risk Low-risk P <0.001 BCT score can discriminate patients at high risk and low risk of distant metastasis after chemotherapy treatment.

36 Risk of Distant Metastasis in subgroups

37 Commercially available multigene expression signatures in breast cancer

38 Available multi gene expression signatures Product GenesWell BCT Oncotype DX MammaPrint Prosigna Endopredict Image Manufacturer Number of Genes Sample Type FFPE FFPE Fresh / FFPE FFPE FFPE Target patient Time to Launch Node(+/ ) ER/PR(+) HER2( ) Node( /+) ER(+) HER2( ) Node( ) ER(+/ ) HER2(+/ ) Node( /+) ER/PR(+) Node( /+) ER(+) HER2( ) Ready to launch Centralization Yes Yes Yes Yes No FDA Approval 510k pre sub MFDS approval CLIA 510K 510K N/A Service price $3,000 $4,510 $4,200 $3,500 $3,500 Turnaround Time Ground Technology 1 week 2weeks 2weeks 2~3weeks 1week qrt PCR qrt PCR Microarray (Solid hybridization) ncounter (Liquid hybridization) qrt PCR

39 Oncotype DX RS U = x HER2 Group Score 0.34 x ER Group Score x Proliferation Group Score x Invasion Group Score x CD x GSTM X BAG1 RS=0 if RS U <0 RS=20 x (RS U 6.7) if 0 RS U 100 RS=100 if RS U >100 NSABP B 14 LN, ER+ breast cancer retrospective result NSABP B 20 LN, ER+ breast cancer retrospective result Low risk High risk Paik et al., 2006 Paik et al., 2004

40 Oncotype DX Major clinical trials TAILORx trial HR+/HER2, LN breast cancer RxPONDER trial HR+/HER2, LN+ (1 3) breast cancer To determine in a randomized trial whether chemotherapy impacted clinical outcomes in patients with a mid range RS (11~25) To confirm that patients with a low RS (<11) had excellent outcomes with hormonal therapy alone To determine the effect of the addition of chemotherapy to hormonal therapy in patients with Node+ breast cancer who have RS 25 Patients with RS<11: very low rates of recurrence at 5 years with endocrine therapy alone Sparano et al., 2015 Reference cancer.oncotypedx.com/en US/Professional Invasive/Resources/ClinicalTrials

41 MammaPrint <Algorithm Development> TRNASBIG trial LN, no adjuvant therapy, size 5cm Buyse et al., (K) k RASTER trial ct1 3, N0M0, chemo+nonchemo van t Veer et al., 2010 Drukker et al., 2013

42 MammaPrint Prospective trial clow/glow clow/ghigh chigh/glow chigh/ghigh Chemo? Yes No Chemo? Yes No Lower boundary of the 95% C.I.>92% => Primayr object(dmfs rate>92%) was achieved The results from the primary test showed MammaPrint Low Risk patients had a 94.7% 5 year Distant Metastasis Free Survival (DMFS) without chemotherapy, even in presence of high risk clinical factors. Cardoso et al., 2016

43 MammaPrint Different results in Asian patients 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 63.2% 36.8% High risk Low risk 44.1% 55.9% 35.8% 64.2% 80.4% 86.1% Ethnic difference? age standardised rate per 100, % 10.0% 19.6% 13.9% 0.0% (2006) Buyse TRANSBIG (2013) Drukker RASTER (2016) Cardoso MINDACT (2010) Ishitobi (2011) Na 5 European centers Netherlands 9 European countries Japan Korea <Trends in incident from female breast cancer >

44 MammaPrint Different results in Asian patients Clinical outcomes of breast cancer patients according to intrinsic subtypes in Korea % 95.00% 35 years years >50 years % 95.00% 35 years years >50 years 90.00% 90.00% 85.00% 85.00% 80.00% 80.00% 75.00% 75.00% 70.00% 70.00% 65.00% 65.00% 60.00% 60.00% HR+/HER2 HR+/HER2+ HR /HER2+ TNBC HR+/HER2 HR+/HER2+ HR /HER2+ TNBC 5 year Recurrence Free Survival Rate (%) 5 year Overall Survival Rate (%) Park et al., 2015

45 Prosigna ROR (risk of relapse): a risk score based on correlation to subtypes The Prosigna Score is a numerical value on a 0 to 100 scale that correlates with the probability of distant recurrence within 10 years The gene expression profile of a patient s tumor is compared with each of the 4 PAM50 prototypical molecular profiles to determine the degree of similarity. The results in combination with a proliferation score and tumor size produce an individualized Prosigna Score Heatmap of the centroid models of subtype (PAM50) A ROR score was assigned to each test case using correlation to subtype alone (1) (ROR-S) or using subtype correlation along with tumor size (2) (ROR-C) Parker et al., 2009

46 Prosigna TransATAC trial Postmenopausal women with ER+ BC ABCSG 8 Postmenopausal women with HR+ BC Dowsett et al, 2013 Gnant et al, 2014

47 EndoPredict EP Score (s) Epclin Score (S clin ) Unscaled EP Score (s u ) s u =0.41 C t (BIRC5) 0.35 C t (RBBP8) C t (UBE2C) 0.31 C t (IL6ST) 0.26 C t (AZGP1) C t (DHCR7) 0.18 C t (MGP) 0.15 C t (STC2) 2.63 Rescaled EP Score (s) s=0, if 1.5 s u <0 s=15, 1.5 s u >15 s=1.5 s u , otherwise Clinical Validation ABCSG 6 Women with ER+/HER2 BC EPclin (S clin ) S clin =0.35 t n s, t (tumor size) 1 ( 1cm), 2(>1 to 2cm), 3 (>2 to 5cm), and 4: >5cm n (nodal status) 1 (negative), 2 (1 3 positive nodes), 3 (4 10 positive nodes), and 4 (>10 positive nodes) s (EP Score) 0 s 15 ABCSG 8 Women with ER+/HER2 BC EP Score EPclin Score EP Score EPclin Score Filipits et al., 2011

48 Clinical Performance of GenesWell BCT FDA Clearance Product Reference Clinical Trial Comparison between Risk groups Hazard ratio for Distant metastasis GenesWell TM BCT Gong et al., 2017 Discovery (AMC+SMC) High risk vs. Low risk ( ) Validation (AMC) High risk vs. Low risk 8.57 ( ) Cleared Prosigna 510(k) K ABCSG 8 Trial (pn0) High risk vs. Low risk 3.96 ( ) Inter. risk vs. Low risk 2.60 ( ) ABCSG 8 Trial (pn1) High risk vs. Low risk 4.22 ( ) Cleared MammaPrint 510(k) k TRANSBIG Trial High risk vs. Low risk 2.32 ( ) Cleared MammaPrint FFPE 510(k) k RASTER Trial High risk vs. Low risk 5.44 ( ) LDT Oncotype DX Dowsett et al., 2010 TransATAC Trial (Node ) TransATAC Trial (Node+) High risk vs. Low risk 5.20 ( ) Inter. risk vs. Low risk 2.60 ( ) High risk vs. Low risk 2.70 ( ) Inter. risk vs. Low risk 1.80 ( ) EndoPredict Filipits et al., 2010 ABCSG 6 Trial High risk vs. Low risk 7.97 ( ) ABCSG 8 Trial High risk vs. Low risk 4.27 ( ) H: High risk, IM: Intermediate risk, L: Low risk

49 GenesWell BCT Comparative Study

50 Histogram of risk scores Low risk (<4) High risk ( 4) Low risk (0~17) Inter. risk (18~30) High risk ( 31) GenesWell BCT Oncotype DX

51 NCC+SMC+AMC BCT Oncotype Inter. Low High ( 18, <3 (RS<18) (RS 31) 1) Sum Low (BS<4) High (BS 4) Sum Oncotype 기본분류 Low: 0~17 Inter.: 18~30 High: 31 or more BCT Oncotype Low (RS<18) High (RS 31) Sum Low (BS<4) High (BS 4) Sum OPA: 78.96% NPA: 80.43% PPA: 68.00% BCT Oncotype Low (RS<18) High (RS 18) Sum Low (BS<4) High (BS 4) Sum OPA: 60.49% NPA: 80.43% PPA: 34.29% BCT Oncotype Low (RS<31) High (RS 31) Sum Low (BS<4) High (BS 4) Sum OPA: 76.85% NPA: 77.59% PPA: 68.00% *OPA: 전체일치율, NPA: 음성일치율 (low risk 일치율 ), PPA: 양성일치율 (high risk 일치율 )

52 NCC+SMC+AMC BCT Low (RS<11) Oncotype Inter. ( 11, 25) High (RS>25) Sum Low (BS<4) High (BS 4) Sum TAILORx study 기준분류 Low: 0~10 Inter.: 11~25 High: more than 25 BCT Oncotype Low (RS<11) High (RS>25) Sum Low (BS<4) High (BS 4) Sum OPA: 76.84% NPA: 87.50% PPA: 61.54% BCT Oncotype Low (RS<11) High (RS 11) Sum Low (BS<4) High (BS 4) Sum OPA: 38.89% NPA: 87.50% PPA: 28.73% BCT Oncotype Low (RS 25) High (RS>25) Sum Low (BS<4) High (BS 4) Sum OPA: 76.85% NPA: 78.95% PPA: 61.54% *OPA: 전체일치율, NPA: 음성일치율 (low risk 일치율 ), PPA: 양성일치율 (high risk 일치율 )

53 OncotypeDX 기존분류기준적용결과비교 Samples Grouping OPA NPA PPA Reference n=34 (EP Score) Oncotype vs EndoPred ict n=34 (EPclin) n=739 (ROR*) Oncotype vs Prosigna n=739 (ROR) Oncotype vs Prosigna n=52 (All) Oncotype vs MammaP n=57 (All) rint Oncotype vs MammaP n=135 (All) rint Oncotype vs MammaP n=86 (All) rint Oncotype vs BCT Oncotype vs BCT Oncotype vs BCT Oncotype vs BCT n=91 (SMC) n=185 (AMC) n= (NCC) n=276 (SMC&AMC &NCC) Low vs Inter-Hi gh Low vs Inter-Hi gh Low vs Inter vs High Low vs Inter vs High Low vs Inter vs High Low-Inter vs Hi gh Low vs Inter-Hi gh Low-Inter vs Hi gh Low vs Inter-Hi gh Low-Inter vs Hi gh Low vs Inter-Hi gh Low-Inter vs Hi gh Low vs Inter-Hi gh Low-Inter vs Hi gh Low vs Inter-Hi gh Low-Inter vs Hi gh Low vs Inter-Hi gh Low-Inter vs Hi gh Low vs Inter-Hi gh 76.47% 60.00% 89.47% Varga, Zsuzsanna, et al. "Comparison of EndoPredict and Oncotype DX test results in 64.70% 73.33% 57.89% hormone receptor positive invasive breast cancer." PloS one 8.3 (2013): e % 72.20% 35.29% Dowsett, Mitch, et al. "Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy." Journ 66.44% 87.19% 34.15% al of Clinical Oncology (2013): JCO % 59.46% 33.33% Alvarado, Michael D., et al. "A prospective comparison of the 21-gene recurrence sco re and the pam50-based prosigna in estrogen receptor-positive early-stage breast c ancer." Advances in therapy (2015): % 61.82% 50.00% Poulet, Bruno, et al. "Risk classification of early stage breast cancer as assessed by M ammaprint and Oncotype DX genomic assays." San Antonio Breast Cancer Symposiu 61.40% 69.70% 50.00% m (SABCS) % 63.55% 85.71% Shivers, S. C., et al. "Abstract P : Direct comparison of risk classification betwe en MammaPrint, Oncotype DX and MammoStrat assays in patients with early s 68.89% 70.83% 66.67% tage breast cancer." (2013): P % 61.04% 77.78% Maroun, Ralph, et al. "A head-to-head comparison of Mammaprint and Oncotype Dx 60.47% 64.71% 54.29% : A McGill University Health Center Experience." (2015): % 79.55% 33.33% 64.84% 81.25% 25.93% 74.05% 74.40% 70.59% 60.00% 78.57% 39.08% 85.42% 86.05% 80.00% 54.17% 86.36% 26.91% 76.85% 77.59% 68.00% 60.49% 80.43% 34.29%

54 Correlation between risk score and Ki 67 SMC 결과 Low risk High risk <10 10 & <20 20 Ki 67 <10 10 & <20 20 Ki 67 Low risk Inter. risk High risk

55 Prediction of chemotherapy response in HR+/HER2 breast cancer patients

56 Response to medical therapy Endocrine ER % HER % TN 15% Chemotherapy Breast cancer Molecular subtypes Response to therapy

57 Biomarkers for adjuvant systemic therapy in early breast cancer Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early Stage Invasive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline Product Target Guide Type Oncotype DX EndoPredict MammaPrint PAM50 Breast Cancer Index Mammostrat IHC4 Evidence quality Strength of recommendation HR+,HER2- (node negative) use to guide decisions on adjuvant systemic chemotherapy Evidence-based High Strong HR+,HER2- (node positive) should not use the Oncotype DX to guide decisions on adjuvant systemic chemotherapy Evidence-based intermediate Moderate HER2+ or TNBC should not use the Oncotype DX to guide decisions on adjuvant systemic therapy informal consensus insufficient Strong HR+,HER2- (node negative) use EndoPredict to guide decisions on adjuvant systemic chemotherapy Evidence-based intermediate Moderate HR+,HER2- (node positive) should not use the EndoPredict to guide decisions on adjuvant systemic chemotherapy Evidence-based insufficient moderate HER2+ or TNBC should not use the EndoPredict to guide decisions on adjuvant systemic therapy informal consensus insufficient Strong HR+,HER2- (LN+ or LN-) should not use the MammaPrint to guide decisions on adjuvant systemic chemotherapy Evidence-based intermediate moderate HER2+ not use to guide decisions on adjuvant systemic therapy informal consensus low moderate TNBC not use to guide decisions on adjuvant systemic therapy informal consensus insufficient Strong HR+,HER2- (node negative) use the PAM50 to guide decisions on adjuvant systemic therapy Evidence-based High Strong HR+,HER2- (node positive) not use PAM50 to guide decisions on adjuvant systemic therapy Evidence-based intermediate Moderate HER2+ not use PAM50 to guide decisions on adjuvant systemic therapy informal consensus insufficient Strong TNBC not use PAM50 to guide decisions on adjuvant systemic therapy informal consensus insufficient Strong HR+,HER2- (node negative) use the Breast cancer Index to guide decisions on adjuvant systemic therapy Evidence-based intermediate Moderate HR+,HER2- (node positive) not use the Breast cancer Index to guide dicisions on adjuvant systemic therapy informal consensus insufficient Strong HER2+ not use the Breast cancer Index to guide dicisions on adjuvant systemic therapy informal consensus insufficient Strong HR+,HER2- (LN+ or LN-) not use the Mammostrat to guide decisions on adjuvant systemic therapy Evidence-based intermediate Moderate HER2+ not use the Mammostrat to guide decisions on adjuvant systemic therapy informal consensus insufficient Strong HR+,HER2- (LN+ or LN-) not use IHC4 to guide decisions on adjuvant systemic chemotherapy Evidence-based intermediate Moderate HER2+ or TNBC not use IHC4 to guide decisions on adjuvant systemic therapy informal consensus insufficient Strong Reference: +Therapy+in+Early+Breast+Cancer

58 Systemic adjuvant treatment HR+/HER2 disease ff Other rprognostic multigene assays may be considered to help assess risk of recurrence but have not been validated to predict response to chemotherapy.

59 Predictive information for HR+/HER2 breast cancer patients Assays such as Oncotype DX or Prosigna target ER+ breast cancer but do not discriminate between ER+/HER2 and ER+/HER2+ subtypes in all published validation results. Although both are subtypes of ER+ breast cancer, they have different prognoses after hormone therapy and show different responses to adjuvant chemotherapy. Oncotype DX NSABP B 20 Study Patients with LN & ER+ SWOG 8814 Study Patients with LN+ & ER+ Paik et al., Albain et al., 2010.

60 Predictive information for HR+/HER2 breast cancer patients Assays such as Oncotype DX or Prosigna target ER+ breast cancer but do not discriminate between ER+/HER2 and ER+/HER2+ subtypes in all published validation results. Although both are subtypes of ER+ breast cancer, they have different prognoses after hormone therapy and show different responses to adjuvant chemotherapy. MammaPrint Knauer et al., 2010.

61 GenesWell BCT: Chemotherapy benefit according to risk groups A GenesWell BCT prognostic signature for LN, HR+/HER2 breast cancer patients in a single center study GenesWell BCT risk All patients; only a 3% absolute CTx benefit Low risk patients; Little to no CTx benefit High risk patients; Large CTx benefit 94.5% 91.9% 96.4% 96.0% 91.9% 26.5% 65.4% P = n DM HTx + CTx HTx P = n DM HTx + CTx 86 3 HTx P = n DM HTx + CTx 57 4 HTx 23 7

62 GenesWell BCT: Chemotherapy benefit according to risk groups Clinical risk assessment according to modified Adjuvant! Online (LN, HR+/HER2 breast cancer) Modified Adjuvant! Online All patients; only a 3% absolute CTx benefit Clinical low risk; Little to no CTx benefit Clinical high risk; Little CTx benefit 94.5% 91.9% 94.8% 91.2% 96.4% 13.4% 83.0% P = n DM HTx + CTx HTx P = n DM HTx + CTx 50 4 HTx P = n DM HTx + CTx 93 3 HTx 44 6

63 GenesWell BCT Further Study

64 Further Study 1. Comparative study with Oncotype DX 1) Concordance between risk groups 2) Evaluation of prognostic values 2. Prospective validation study 3. Pro and Retro spective study for young breast cancer 4. Clinical trials for FDA 510(k) clearance

65 Further Studies Prospective validation study Pro and retro spective studies for young breast cancer Prof. Seok Jin Nam Prof. Jeong Eon Lee DCIS study Comparison study between FFPE and core biopsy Prof. Doo Ho Choi Prof. Gyung Yup Gong

66 GenesWell BCT Conclusion

67 Conclusion GenesWell TM BCT was developed to predict the risk of distant metastasis in patients with pn0 1, HR+/HER2 breast cancer and validated in independent cohorts of AMC. The BCT score of GenesWell TM BCT has a prognostic value for late distant metastasis. The BCT score provided better prognostic information about distant metastasis than other prognostic models based on traditional clinicopathological factors. The GenesWell TM BCT may help inform decisions about the need for additional adjuvant therapies in patients with pn0 N1, HR+/HER2 breast cancer.