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NanoString’s Prosigna Breast Cancer Assay

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Presentation on theme: "NanoString’s Prosigna Breast Cancer Assay"— Presentation transcript:

1 NanoString’s Prosigna Breast Cancer Assay
Bringing Genomic Testing Into Your Lab

2 Measuring Nucleic Acids in Clinical Samples
Current clinical practice Formalin-fixed, paraffin-embedded (FFPE) samples pose challenges to accurate and reproducible estimation of biomolecules Challenges of expression-profiling FFPE samples FFPE processed samples provide low and variable yields of mRNA Formalin fixation modifies and degrades mRNA, making it difficult to measure by traditional techniques Analytical need A robust and validated methodology is required to accurately estimate RNA expression levels in tumor FFPE samples The traditional clinical method of choice for processing and storing tumor samples is not biomolecule-friendly. While optimal for the original intended purpose (ie, histologic review), formaldehyde preservation hydrolyses phosphodiester linkages of polynucleotide chains, breaking long-chain nucleotide (nt) sequences into shorter strands Moreover, the protocols for formaldehyde fixation are not uniform across clinical centers, adding further variability to the extent of degradation of biomolecules Also, samples may vary in age

3 nCounter: Designed for Translational Research
Highly Multiplexed 800 targets per reaction No Amplification (for > 25ng RNA input) Tolerant of FFPE and no bias Precision Digital counting means unparalleled reproducibility over nearly 6 logs of dynamic range Simple and Fast Easier, faster, automated data production Reporter Probe Capture Probe Target Target-Probe Complex

4 nCounter Advantage for Quantitating Nucleic Acids in FFPE Samples
Direct measurement minimizes error/bias from enzyme-based processes (reverse transcriptase and polymerase) Ability to detect short chain RNA templates Linearity of measurement over a wide dynamic range ensures accurate quantitation of both rare and abundant transcripts Facilitated by solution phase hybridization Digital counting design Excess probes Allows for flexible range of sample input The nCounter platform has inherent advantages in accurate quantification of mRNA form FFPE derived samples over competing solid-phase GEP platforms. Amplification bias is eliminated As mentioned previously, chip-based protocols require c-DNA synthesis and amplification which can skew data particularly for relatively rare transcripts Dynamic range Allows for wider range of sample input minimizing potential dilution/concentration steps Solution phase hybridization In addition, the solution-phase hybridization protocol used in nCounter is far more efficient than solid-phase hybridization used in microarrays allowing for efficient capture and quantitation of both abundant and rare transcripts

5 nCounter Workflow: Hands-off Process Enables Decentralized Testing
Minimal HANDS-ON Minimal HANDS-ON Minimal HANDS-ON Step 1 12 HOURS OR OVERNIGHT Step 2 2.5 – 3.0 HOURS, AUTOMATED Step 3 3 – 4.5 HOURS, AUTOMATED nCounter® Prep Station nCounter® Digital Analyzer 1 Hybridize 2 Purify 3 Count Simple and fast workflow is well suited for qualified clinical laboratories

6 nCounter Dx Analysis System With Flex Configuration
A single instrument system can be used to Validate genomic discoveries from NGS or arrays Translate validated discoveries into clinical use Run clinical diagnostics

7 nCounter Elements: Enabling More Options for Dx Development
CodeSets Custom-built assays Standardized panels 300+ peer-reviewed papers Prosigna™ Prosigna Breast Cancer Assay CE Marked & launched in EU Expected Q U.S. launch FDA-cleared: September 9, 2013 Since 2009 Advanced Disease Research Global Diagnostic Kits

8 nCounter Elements: Enabling More Options for Dx Development
Advanced Disease Research Enable Clinical Testing Global Diagnostic Kits Since 2009 Commercial Release: February 11, 2014 FDA-cleared: September 9, 2013 CodeSets Custom-built assays Standardized panels 300+ peer-reviewed papers nCounter Elements™ Components to Develop Assays Registered with FDA Flexible Format Prosigna™ Prosigna Breast Cancer Assay CE Marked & launched in EU Expected Q U.S. launch

9 Development and Output
Prosigna Development and Output

10 Origins of Prosigna—PAM50 Gene Signature Deriving the Gene Set and Prognostic Algorithm
Determine optimal gene set Hierarchical clustering of 1,906 “intrinsic” genes defined by previous studies Optimized gene set (qRT-PCR data from 122 breast cancers) to 161 genes that passed FFPE performance criteria Cross-validation (random 10% left out in each of 50 cycles) by “N” t-test method selected 50 genes Develop algorithm Compared reproducibility of classification across 3 centroid-based prediction methods Selected Prediction Analysis of Microarray (PAM) Luminal A Basal-like Luminal B HER2-enriched UBE2C CCNB1 MYBL2 PTTG1 TYMS HSPC150 BIRC5 CEP55 KNTC2 MELK ORC6L RRM2 CDC6 EXO1 KIF2C ANLN CDC20 CDCA1 CENPF GRB7 CCNE1 MKI67 TMEM45B GPR160 BLVRA ERBB2 NAT1 MMP11 FOXA1 SLC39A6 ESR1 CXXC5 ACTR3B BAG1 PGR MIA KRT5 MYC FOXC1 KRT14 KRT17 SFRP1 CDH3 PHGDH BCL2 MDM2 FGFR4 EFGR MAPT MLPH Normal The PAM50 gene set is a subset of the expanded gene set that delineated the intrinsic subtypes of breast cancer based on the gene expression profile of breast tumor tissue. First, a broad technical optimization screen was used to identify transcripts in FFPE-preserved tissues that could accurately be measured by qRT-PCR Cross-validation with the prototypical intrinsic subtypes resulted in the optimal set of 50 genes which were then trained on the node-negative, untreated subset of the van de Vijver cohort1 Reference: 1. van de Vijver MJ, et al. N Engl J Med. 2002;347: Trained with a multivariable Cox model Risk of recurrence (ROR) score developed for test case using correlation to subtype alone: ROR-S = 0.05*basal *HER2 – 0.34*LumA * LumB or using subtype correlation plus tumor size: ROR-C= 0.05*basal *HER2 – 0.23*LumA *LumB * T Parker JS, et al. J Clin Oncol. 2009;27(8):

11 Subtype Predictions by PAM50 Shows Distinct Clinical Outcomes
Intrinsic subtypes assigned using PAM50 show clear differences in RFS in node- negative patients who did not receive systemic adjuvant therapy Similar differences in RFS by subtype were seen in the subset of patients with ER+ BC Patients who did not receive systemic adjuvant therapy Subset of patients with ER+ disease 1.0 1.0 0.8 0.8 0.6 0.6 Relapse-Free Survival (probability) PAM50-assigned intrinsic subtypes are associated with distinct risks of recurrence. An important finding from these analyses was that all intrinsic subtypes are present and clinically significant in terms of outcome predictions in cohorts of patients diagnosed with ER-positive disease Thus the molecular subtypes are not simply another method of classification that reflects ER status However, as shown in the next slide, the PAM50 gene set and algorithm provides clinically significant information beyond intrinsic subtype Relapse-Free Survival (probability) 0.4 0.4 Basal-like HER2-enriched Luminal A Luminal B Basal-like HER2-enriched Luminal A Luminal B 0.2 0.2 Log-rank P = 2.26e-12 Log-rank P = 1.89e-10 2 4 6 8 10 2 4 6 8 10 Time, years Time, years RFS, relapse-free survival. Parker JS, et al. J Clin Oncol. 2009;27(8):

12 PAM50 ROR-Score Provides Increased Accuracy in Outcome Predictions
Risk prediction based on the PAM50 gene set added significant information to pathologic staging, histologic grade, and standard clinical molecular markers Kaplan-Meier estimates of risk groups show clear separation of RFS 1.0 0.8 PAM50 risk scores discriminate the risk of recurrence within intrinsic subtypes, demonstrating the additive prognostic value. Relapse-Free Survival (probability) 0.6 0.4 High Low Log-rank P = 9.52e-11 Medium 2 4 6 8 10 Time, years Parker JS, et al. J Clin Oncol. 2009;27(8):

13 PAM50 Gene Signature Is Robust Validated by TCGA (Independent Dataset, Different Platform)
Semi-supervised hierarchical cluster analysis showed consensus across breast cancer intrinsic subtypes defined in independent datasets using 5 different genomic/ proteomic platformsa These data are independent of nCounter technology . The TCGA study in breast cancer shows that intrinsic subtype assignments obtained using the PAM50 gene expression profile were concordant with intrinsic subtype assignments derived from analyses of protein expression, miRNA, copy number variation, and methylation. Notably, these analyses are based on an independent data set and on measurements based on alternate high-throughput platforms, including that used for PAM50 (ie, Agilent 244K WGA rather than nCounter) Figure legend: Consensus clustering analysis of subtypes identified major groups (n = 348). The blue and white heat map displays sample consensus. Heat map display of subtypes defined independently by microRNAs, DNA methylation, copy number, PAM50 mRNA expression, and RPPA expression. Associations with molecular and clinical features. P values were calculated using a Chi-square test. a Heat map display of subtypes defined independently by microRNAs, DNA methylation, copy number, PAM50 mRNA expression, and RPPA expression. The Cancer Genome Atlas Network. Nature. 2012;490(7418):61-70.

14 Transitioning qRT-PCR–Based PAM50 to nCounter Platform (NanoString)
Objective Replace qRT-PCR protocol with an automated, multiplexed assay (nCounter) to assign risk score Endpoints Assess concordance between outputs: Expression measurements of each gene Assignment of intrinsic subtype Proliferation and risk scores Approach Assess concordance with above endpoints Retrain algorithm using nCounter-based measurements nCounter provides simplified up-stream and downstream processing to determine nCounter-PAM50 ROR score. However, to transition to the nCounter platform, it is necessary to establish concordance in expression measurements of each gene and to assess the effects of variation of these estimates on assignment of intrinsic subtype and evaluation of risk score

15 nCounter-Based Measurements Concordant With qRT-PCR
ROR scores or proliferation scores obtained using estimates of gene expression of the nCounter-PAM50 gene signature gene panel by qRT-PCR or by the nCounter platform were very similar nCounter results versus qRT-PCR ROR Score Proliferation Score 80 0.5 60 Estimates of PAM50 gene expression on nCounter were concordant with qRT-PCR–based estimates, as were the ROR scores. The concordance between qRT-PCR and nCounter data for the level of gene expression was presented in Liew M, et al. 0.0 qRT-PCR 40 qRT-PCR 20 –05 icc = 0.951 icc = 0.947 –1.0 20 40 60 80 –1.0 –0.5 0.0 0.5 NanoString NanoString Liew M, et al, AMP National Meeting, 2010.

16 Switching Analytic Platform to nCounter Retraining the Algorithm
Data from Parker et al1 were re-analyzed using the prototypical subtype centroids from nCounter training Subtypes showed a similar prognostic profile to the originally reported data All 1.0 0.8 0.6 0.4 0.2 0.0 2 4 6 8 Probability of Event Log-rank P = 1.14e-08 LumA 72/297 LumB 94/204 Her2 48/95 Basal 42/111 ER+ 1.0 0.8 0.6 0.4 0.2 0.0 2 4 6 8 10 Probability of Event Log-rank P = 4.16e-07 LumA 62/243 LumB 72/157 Her2 42/81 Basal 32/82 ER– 1.0 0.8 0.6 0.4 0.2 0.0 2 4 6 8 10 Probability of Event LumA 7/51 LumB 19/42 Her2 5/11 Basal 9/28 Log-rank P = Clinical outcomes in the nCounter-based subtypes were similar to those observed for qRT-PCR–based assignments. 10 Parker JS, et al. J Clin Oncol. 2009;27(8):

17 nCounter (NanoString) Subtype2
New Training as Robust as Published Classifier in the Intended Use Population The recurrence-free curves of the intrinsic subtype cohorts defined by qRT-PCR (left) and nCounter (right) are highly concordant qRT-PCR Subtype1 nCounter (NanoString) Subtype2 1.0 1.0 0.8 0.8 0.6 0.6 Probability of Event Probability of Event 0.4 0.4 The nCounter retrained algorithm was successfully validated in an independent data set. Test set of samples independent of training samples Community cohort of ER+ patients treated with tamoxifen only Her2 4/14 LumA 10/54 LumB 22/58 Normal 0/3 0.2 0.2 Her2-enriched 3/13 Luminal A 3/31 Luminal B 30/85 Log-rank P = .155 Log-rank P = .0336 0.0 0.0 5 10 15 5 10 15 1. Nielsen et al. Clin Cancer Res. 2010;16(21): 2. NanoString data on file.

18 Prosigna Assay Uses the expression profile of 50 classifier genes (and 8 housekeeping genes for normalization) to determine breast cancer subtype and probability of relapse Algorithm provides a continuous risk of recurrence (ROR) score; henceforth, referred to as the “Prosigna Score” Incorporates intrinsic subtype, proliferation score, and tumor size Uses different cut-points for probability of relapse in patients with node- negative and node-positive breast cancer Validated on the nCounter platform allowing decentralized and standardized breast cancer testing worldwide The nCounter-PAM50 gene signature assay integrates standard clinicopathologic characteristics with intrinsic subtype and proliferation score to provide an objective assessment of risk of recurrence.

19 Prosigna Algorithm Prosigna Score for Individual Patients
ROR score integrates intrinsic subtype, proliferation index, and tumor size (in the context of nodal status) to provide an integer score proportional to the risk of recurrence Determine intrinsic subtype through Pearson’s correlation to centroids Calculate ROR (Risk of Recurrence Score) Patient expression profile LumB LumA Basal-like HER2-enriched PAM50 centroids aRLumA+ bRLumB+ cRHer2e+ dRBasal+ ROR = Pearson’s correlation to centroids ROR score integrates intrinsic subtype, proliferation index, and tumor size (in the context of nodal status) to provide an integer score proportional to the risk of recurrence. Gene expression data are weighted with clinical variables to determine an integer score from 0 through 100 (ROR) indicative of the probability of disease recurrence ROR is based on the similarity of the gene expression profile to intrinsic subtypes, proliferation score, and tumor size Risk algorithm requires input of gross tumor size and nodal status eP+ fT Proliferation score Gross tumor size Gnant M, et al. SABCS. 2012; poster P 19

20 Prosigna Score Is a Continuous Risk Function
The probability of distant recurrence within 10 years increases as a function of ROR and nodal status based on data in the tested patient population Node-negative Node-positive (1-3 nodes) The probability of distant recurrence within 10 years increases as a function of ROR Score and nodal status based on data in the tested patient population. A change of 10 ROR units corresponds to an average change in 10-year distant recurrence free survival of 7% and 6% for node negative and node positive patients respectively Package Insert (US).

21 Prosigna Categorizes Risk Groups by Nodal Status
ROR Range Risk Categorization Node-negative 0 - 40 Low Intermediate High Node-positive (1 - 3 nodes) Risk classification groups are provided based on cutoffs related to clinical outcome in the tested patient populations: Low risk: 10-year probability of distant recurrence of < 10% High risk: 10-year probability of distant recurrence of > 20% ROR score discriminates risk groups within patients with node-negative disease Distinct ROR thresholds for node-negative and node-positive tumors define risk groups. This slide is for the US audience Package Insert (US). Gnant M, et al. Ann Oncol. 2014;25(2):

22 Prosigna Patient Report
The nCounter-PAM50 Gene Signature Assay is intended for use as a prognostic indicator for distant recurrence-free survival at 10 years Indicated for postmenopausal women with Stage I/II lymph node- negative or Stage II lymph node-positive (1 to 3 positive nodes) hormone receptor-positive breast cancer who have undergone surgery in conjunction with locoregional treatment consistent with standard of care Risk of Recurrence (ROR Score) Zero to 100 Relates to recurrence rate at 10 years on 5 years of endocrine therapy alone Risk Groups Node Negative Low, Intermediate, or High risk Based on ROR Score and nodal status Node Positive Low or High risk The nCounter-PAM50 Gene Signature patient report is readily interpretable. US Prosigna Patient Report; FDA 510K cleared.

23 Analytical Validation
Prosigna Analytical Validation

24 nCounter-PAM50 ROR Score
Analytic Reproducibility of nCounter-PAM50 Gene Signature Assay Evaluated in Two Studies Study 1: Reproducibility from tissue Study 2: Precision from RNA Extract RNA from FFPE tumor sample Run on the nCounter Dx Analysis System The analytical validation evaluated all procedural variables related to sample extraction, reagents, operator, and instrumentation. nCounter-PAM50 ROR Score

25 Evaluating Variation in nCounter Gene Expression Estimate
RNA Precision Study Assessed reproducibility of device, reagents, and operator-specific components using a common template RNA Measured variability between and within assay variables including Testing site (n = 3) Sample type (n = 5) Operator (n = 6) Reagent lot (n = 3) Assay run (n = 18/site) Site 1 Site 2 Site 3 Operator 1 Operator 2 Reagent Lot 1 Reagent Lot 2 Reagent Lot 3 This study design evaluated the effects of variation in the gene expression estimate and ROR score across sites. Run 1 Run 2 Run 3 Basal-like HER2-E Lum A Lum B1 Lum B2 X 2 Nielsen T, et al. BMC Cancer. 2014;14(1):177.

26 Reproducibility of nCounter-PAM50 Gene Signature Assay Concordance in Risk Group
Reproducibility Study For each tissue sample, macrodissection, RNA extraction, and processing with the assay were performed by a single operator at each site per standard operating procedures 43 tissues across all sites represent a wide range (94 units) of ROR scores and all risk categories Samples Site 1 Site 2 Site 3 Pathologist 1 Pathologist 2 Pathologist 3 Operator 1 Operator 2 Operator 3 This study design evaluated the effects of variation in the RNA input on evaluation of the ROR score. RNA Isolation Kit 1 RNA Isolation Kit 1 RNA Isolation Kit 1 Duplicate Run with Reagent Lot 1 Duplicate Run with Reagent Lot 1 Duplicate Run with Reagent Lot 1 The definition of “high reproducibility” was pre-specified to be a total SD<4.3 Nielsen T, et al. BMC Cancer. 2014;14(1):177.

27 Prosigna Analytically Validated for Decentralized Testing
Reproducibility from 43 FFPE Tissue Samples1 Precision from 5 Pooled RNA Samples1 Prosigna Score Prosigna Score Prosigna Score Average of 90% risk group concordance between sites Site-to-site or operator-to-operator <1% of variance Prosigna Score Standard Deviation = 2.9 100% concordance between risk groups Site-to-site or operator-to-operator <1% of variance Prosigna Score Standard Deviation = 0.67 Analytical Reproducibility of the Breast Cancer Intrinsic Subtyping Test and nCounter® Analysis System Using Formalin-Fixed Paraffin-Embedded (FFPE) Breast Tumor Specimens. T Nielsen, S McDonald, S Kulkarni, J Storhoff, C Schaper, B Wallden, S Ferree, S Liu, V Hucthagowder, K Deschryver, V Holtschlag, G Barry, M Evenson, N Dowidar, M Maysuria, D Gao. CAP 2013 Output of the U.S. version of Prosigna submitted for 510(k) does not report intrinsic subtype. Reporting intrinsic subtype in the U.S. will require a future PMA supported by additional clinical studies

28 Heterogeneity of Breast Cancer
The Benefit: Risk Equation

29 Heterogeneity of Breast Cancer
Clinicopathologic covariates and receptor status inform treatment decisions However, disease course and treatment outcomes in patients with similar baseline disease covariates vary considerably Advances in molecular understanding suggest underlying heterogeneity Traditional clinical characteristics of breast tumors do not adequately predict treatment outcomes, reflecting the underlying heterogeneity of the disease. Clinicopathologic covariates such as nodal status and tumor size are the primary considerations in determining treatment modality NCCN Guidelines1 recommend adjuvant therapy in all patients with node-positive disease regardless of HR and HER2 status of tumor (ie, HR+/HER2– disease or HR–/HER2– disease) However, clinical data show that these factors alone, or in combination, do not adequately reflect the risk of late recurrence, and disease course varies within patient subgroups defined by common clinicopathologic covariates For example, patients with positive nodal status may be at low risk for recurrence, and conversely, patients with node-negative disease may be at high risk for late recurrence As a result, treatment decisions are suboptimal Molecular approaches can provide objective tools to quantifying the individual patient’s risk of recurrence and help guide the treatment choice Molecular approaches to treatment selection will not only improve overall treatment outcomes but will likely also enhance patient quality of life Reference: 1. NCCN Clinical Practice Guidelines in Oncology: Breast Cancer. V

30 Dendrogram Intrinsic Classification

31 Time to distant metastasis, months
Defining Heterogeneity in Breast Cancer Predictive Value of Intrinsic Subtypes Clinical evidence suggests that intrinsic subtype is predictive of clinical outcomes1,2 1 Luminal A Luminal B Basal ERBB2+ 0.8 0.6 P < .01 Probability 0.4 Classification of patients into risk-categories on the basis of the prognosis profile may be a useful means of guiding adjuvant therapy decisions in patients with early stage breast cancer. (Note: the use for guiding treatment approaches the definition of ‘predictive’) The figure in the slide shows a reanalysis of the van’t Veer samples by Sorlie et al,1 and concludes that clinical outcomes were worst for basal (and ERBB2), best for luminal A, and intermediate for luminal B subtypes The van’t Veer data set2: 98 breast tumors were selected for analysis of which 34 patients developed distant metastases within 5 years 44 patients were disease-free for at least 5 years Data shown in the figure above was derived from 97 of these cases Reference: Sorlie T, et al. Proc Nat Acad Sci (USA). 2003;100(14): van’t Veer LJ, et al. Nature. 2002;415(6871): 0.2 24 48 72 96 120 144 168 192 Time to distant metastasis, months 1. Wirapati P, et al. Breast Cancer Res. 2008;10(4):R65. 2. Figure from Sorlie T, et al. Proc Nat Acad Sci (USA). 2003;100(14):

32 Tumor Size, Grade, Lymph Node Status, ER, PR, Her2 Tumor Boards-Good actor vs bad actor

33 Breast cancer prognostic classification in the molecular era: the role of histological grade
Histologic grade (NGS) is a robust, prognostic, with good to excellent reproducibility. The scrutiny applied to histologic grading has only recently been applied to molecular tests. 36% intermediate Histologic Grade; 40% of oncotype dx tests are Intermediate RS. Rare: Grade 1-Grade3. *Rakha EA, Reis-Filho JS, Baehner F, Dabbs DJ, Decker T, Eusebi V, Fox SB, Ichihara S, Jacquemier J, Lakhani SR, Palacios J, Richardson AL, Schnitt SJ, Schmitt FC, Tan PH, Tse GM, Badve S, Ellis IO. Breast Cancer Res ;12(4):207. doi: /bcr2607. Epub 2010 Jul 30. Review. PMID:

34 What your Pathologists Know – histological surrogates for the intrinsic subtypes
Luminal A: ER +, very high (HS ) PR + Very High (HS ) Ki67: Low 3-14% Grade: 1, few percent grade 2. Luminal B: ER + intermediate-low (HS 1-200) PR: negative or low (HS <100) Grade 2 or 3 Ki67 >15%, often much higher

35 Significant Clinical Impact of Oncotype-MammaPrint Discordance
Molecular Subtype (MP) Patients Oncotype Dx LUM A % Major Discordance 47 Low, 12 Int, 4 High LUM B 51 35% Major discordance 18 Low, 17 Int, 16 High Molecular Subtypes of Cases Discordant Between Risk Classification Assays in ER+ N0-N1 Patients: Shivers et al Miami Breast Conference March, 2014

36 Dabbs et al. ASCO-June, 2014 Molecular Subtype (MP) Patients
Oncotype Dx LUM A % Major Discordance 188 Low, 95 Int, 4 High LUM B % Major Discordance 16 Low, 47 Int, 27 High

37 Breast Cancer Gene Expression Profiling
Risk assessment profiles are not uniform. Platforms are not uniform. Information is often driven by marketing. Patient results show greater discordance for treatment purposes than the variation that is seen with pathologist grading of tumors. The need for FDA to regulate…..July 31, 2014.

38 Take Home Message..Why Magee is Offering Prosigna
New generation Nanostring platform. Prosigna (PAM50) has superior prognostic information compared to Oncotype.* Intrinsic subtype adds more information to differentiate risk assessment, decreasing intermediate scores by 25%. Low Risk for node negative and positive. 10 year DR prognostic information is unique. A transparent, FDA cleared platform for patient safety. Value-added pathology. *Sestak I et al. Factors Predicting Late Recurrence for ER+ breast Cancer JNCI 2013; 105(19): *Dowsett M et al. Comparison of PAM50 ROR with Oncotype Dx and IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol 2013; 31(22):

39 Tumor Biomarker Tests The Burden of Proof1
Analytical validation Robustness of hypothesis and algorithm Reproducibility of sampling and assay Clinical validation Prospective/retrospective study in the target population2,a Clinical utility Significant association of biomarker-defined patient subset with treatment outcome Value in guiding treatment decision Economic utility The economic impact of consequent effects on treatment decisions is within accepted regional norms Tumor biomarker-based assays must satisfy key technical and clinical criteria for the intended use (ie, analytical validity, clinical validity, and clinical utility) in addition to being economically viable. a Prospective analysis plan sufficient for Level 1 evidence. Hayes DF, et al. Breast. 2013;22(suppl 2):S22-S26. Simon RM, et al. J Natl Cancer Inst 2009;101(12):

40 Clinical Validation in HR+ Breast Cancer
TransATAC, ABCSG-8

41 nCounter-PAM50 Gene Signature Assay Clinically Validated in Two Studies Representative of the ‘Intended Use Population’ TransATAC Study (n=1017) N = 1,007 patients Presented at San Antonio Breast Cancer Symposium in December 2011 Published in Journal of Clinical Oncology1 ABCSG-8 Study (n=1478) Presented at San Antonio Breast Cancer Symposium in December 2012 Published in Annals of Oncology2 Prospectively defined analysis of two registration-quality databases with ≥10-yr median follow-up in postmenopausal women with HR+ early stage breast cancer treated with endocrine therapy alone Primary Objective: Validate published observations that the nCounter-PAM50 gene signature score provides additional prognostic information over and above standard clinical variables for DRFS at 10 years Primary Analysis: All patients Secondary Analysis: Node-negative, node-positive, and HER2-negative patients Secondary Objective: Validate observations that Luminal A and Luminal B patients have statistically significantly different DRFS at 10 yearsa The evidence for the clinical validation of the nCounter-PAM50 gene signature assay is robust and was conducted in a large patient sample from randomized controlled trials with eligibility criteria representative of the ‘intended use population’. The nCounter-PAM50 Gene Signature Assay is indicated in female breast cancer patients who have undergone either mastectomy or breast-conserving therapy in conjunction with locoregional treatment consistent with standard of care, either as: A prognostic indicator for distant recurrence-free survival at 10 years in post-menopausal women with Hormone Receptor- Positive (HR+), lymph node-negative, Stage I or II breast cancer to be treated with adjuvant endocrine therapy alone, when used in conjunction with other clinicopathological factors A prognostic indicator for distant recurrence-free survival at 10 years in post-menopausal women with Hormone Receptor-Positive (HR+), lymph node-positive (1-3 positive nodes, or 4 or more positive nodes), Stage II or IIIA breast cancer to be treated with adjuvant endocrine therapy alone, when used in conjunction with other clinicopathological factors ROR score was clinically validated in TransATAC and ABCSG-8 a Output of the U.S. version of the nCounter-PAM50 gene signature assay FDA cleared for 510(k) does not report intrinsic subtype. 1. Dowsett M, et al. J Clin Oncol. 2013;31(22): 2. Gnant M, et al. Ann Oncol. 2014;25(2):

42 TransATAC and ABCSG8 Studies: Met Primary and Secondary Objectives
Prosigna adds statistically significant prognostic information in addition to standard clinical-pathological variables1,2. Analyses Population TransATAC Number of Patients P-value ABCSG8 Primary All Evaluable 1,007 <0.0001 1,478 Secondary Node-negative 739 1,047 Node-positive (1-3 nodes) 268 0.002 382 HER2 Negative 888 1,397 Because the statistical analysis plan for ABCSG8 was so similar to that for TransATAC, and the results were so positive, I will move quickly through the same set of analyses that we have previously covered. As you can see from this table, ABCSG8 met its primary and secondary endpoints with similar significant results. The p-values here are all less than The relationship between Prosigna’s risk score and outcome is very strong. 1. Dowsett et al. JCO 2013 2. Gnant et al. Annals of Oncology 2014.

43 Node-negative Patients
ABCSG-8: Prosigna Score Discriminates Recurrence Risk Within Nodal Subgroups Node-negative Patients Node-positive Patients (1-3 nodes) The KM curves here show that we achieve good separation between high, intermediate, and low risk in the node-negative patients, and between high and low risk in the node-positive patients. The risk groups defined by Prosigna are similarly prognostic, and most importantly the patients categorized as low risk have excellent outcomes when treated with endocrine therapy alone. We believe that most physicians would spare these low risk women chemotherapy. Risk Group N (%) Events % without recurrence at 10 yr Low 487 (47%) 15 97% [94% - 98%] Intermediate 335 (32%) 28 90% [86% - 93%] High 225 (21%) 32 84% [78% - 89%] Total 1,047 Risk Group N (%) Events % without recurrence at 10 yr Low 158 (41%) 7 94% [88% - 97%] High 224 (59%) 46 76% [69% – 81%] Total 382 Source: Gnant et al. Annals of Oncology 2014.

44 TransATAC: Prosigna v RS: Fewer Intermediate Risk Node-Negative Patients
Risk groups prospectively defined based on predicted probability of 10-year distant recurrence (low < 10%, intermediate 10% to 20%, high > 20%) Clinical and pathologic variables excluded from analysis 100 90 80 70 60 Percent Without Distance Recurrence ROR scores categorize fewer patients in the intermediate risk group compared with 21 gene test. Clinical outcome between the intermediate risk groups classified by the nCounter-PAM50 Gene Signature Assay is acceptable. Patients classified as intermediate risk by the nCounter-PAM50 Gene Signature Assay do not require adjuvant therapy (acceptable risk) In contrast, the clinical outcome of the intermediate risk group defined by RS is higher (separates early and reaches ~15% at 10 years). Thus it is likely that most patients with RS-defined intermediate risk may opt for adjuvant treatment ROR, low RS, low ROR, intermediate RS, intermediate ROR, high RS, high Follow-Up Time (Years) * Risk groups prospectively defined based on predicted probability of 10-yr distant recurrence (Low <10%, Intermediate 10-20%, High >20%) Dowsett M, et al. J Clin Oncol. 2013;31(22):

45 Combined Analysis Clinical Validation in More Than 2,400 Patients
9,366 enrolled in ATAC 3,901 enrolled in ABCSG-8 5,880 eligible for TransATAC 3,714 eligible for ABCSG-8 2,006 tissue specimen received 1,620 re-consented or deceased 1,017 sufficient residual RNA 25 insufficient cancer in specimen 73 insufficient RNA isolated 44 failed QC specs for device 10 failed QC specs for device A combined analysis of patients included in the TransATAC and ABCSG-8 was conducted according to a prospectively defined analysis plan. Again, these analyses may be considered as Level 1 evidence The primary objective was evaluated in All patients (Primary Analysis) Node-negative/positive, HER2-negative patients (Secondary Analysis) The 2 trials explored different endocrine regimens TransATAC: TAM vs ANA vs TAM + ANA ABCSG-8: TAM followed by TAM vs TAM followed by ANA 1,007 evaluable RNA specimens 1,478 evaluable tissue specimens 2,485 evaluable specimens Gnant M, et al. ASCO 2013; Poster 506.

46 Update--Late Recurrence
Prosigna provides for additional information on the observed risk of distant recurrence in years 5-10 for node negative (1.3% Distant Recurrence) and node positive (3.7% Distant Recurrence). Patients in years 5-10 may avoid further endocrine therapy. FDA-cleared

47 5 years after endocrine therapy Node-negative
Prosigna identifies Low Risk node-negative patients that remain Low Risk in all time periods --Low Risk node-negative patients had a total 10-year risk of recurrence of 3.4%

48 5 years after endocrine therapy Node-positive (1-3 nodes)
Prosigna identifies Low Risk node-positive (1-3 nodes) patients that remain Low Risk in all time periods --Low Risk node-negative patients had a total 10-year risk of recurrence of 5.8%

49 Conclusions Across Clinical Studies
Support the clinical validity of Prosigna Score for the risk of late recurrence in postmenopausal women with HR+ ESBC Prosigna Score discriminates risk groups within subsets of patients defined by Nodal status (negative versus positive) Prosigna Score score provides clinically actionable information Identifies node negative and node-positive patients at sufficiently low risk to be spared chemotherapy Identifies patients at sufficiently low risk of late-recurrence to be spared extended endocrine therapy The results of the TransATAC and ABCSG8 studies provide Level 1 evidence for the clinical validity of the PAM50 test The nCounter-PAM50 Gene Signature Assay based risk groups in node-negative and node-positive patients provide clinically meaningful information to inform treatment decisions.

50 GEICAM Spanish Breast Cancer Group Decision Impact Study
Prosigna changed the treatment decision in 20% of cases --94% of physicians were confident with the prognosis --93% of physicians were confident with the intended treatment

51 Conclusions Prosigna has successfully undergone rigorous analytic validation Highly reproducible and suited for decentralized testing Prosigna has been clinically validated in 2 large trials of adjuvant endocrine therapy (TransATAC and ABCSG-8) The results of the TransATAC and ABCSG8 studies provide Level 1 evidence for the clinical validity of the PAM50 test. Prosigna Score provides more prognostic information in endocrine treated patients than RS, with better differentiation of intermediate and high risk groups. Prosigna Score has strong prognostic information in years 5-10, which may help select patients who could benefit most from hormonal therapy beyond 5 years of treatment. The nCounter-PAM50 Gene Signature Assay is based on a robust and reproducible analytic platform and process that provides meaningful information for determining treatment selection in patients with early stage, hormone-receptor–positive breast cancer. The modular algorithm (intrinsic subtype, proliferation score, clinical characteristics) may potentially be retrained/adapted to inform other types of treatment decisions in breast cancer.


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