Development and verification of the PAM50-based Prosigna breast cancer gene signature assay

BMC Medical Genomics, Aug 2015

Background The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories. Methods 514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies. Results The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online. Conclusions The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting.

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Development and verification of the PAM50-based Prosigna breast cancer gene signature assay

Wallden et al. BMC Medical Genomics Development and verification of the PAM50-based Prosigna breast cancer gene signature assay Brett Wallden 0 James Storhoff 0 Torsten Nielsen 3 Naeem Dowidar 0 Carl Schaper 2 Sean Ferree 0 Shuzhen Liu 3 Samuel Leung 3 Gary Geiss 0 Jacqueline Snider 7 Tammi Vickery 7 Sherri R. Davies 7 Elaine R. Mardis 7 Michael Gnant 6 Ivana Sestak 5 Matthew J. Ellis 4 Charles M. Perou 1 Philip S. Bernard 8 Joel S. Parker 1 0 NanoString Technologies, Inc , 530 Fairview Avenue North, Suite 2000, Seattle, WA 98109 , USA 1 Lineberger Comprehensive Cancer Center, Department of Genetics, University of North Carolina at Chapel Hill , 450 West Drive, Chapel Hill, NC 27599 , USA 2 Statistical consultant , New York, NY , USA 3 Genetic Pathology Evaluation Centre, Vancouver Coastal Health Research Institute and British Columbia Cancer Agency , 2655 Oak St, Vancouver, BC V5Z 1M9 , Canada 4 Lester and Sue Smith Breast Center, Baylor College of Medicine , One Baylor Plaza, MS 600, Houston, TX 77030 , USA 5 Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London , Charterhouse Sq, London EC1M 6BQ , UK 6 Department of Surgery and Comprehensive Cancer Center, Medical University of Vienna , Vienna , Austria 7 Washington University School of Medicine , 660 S Euclid, St. Louis, MO 63110 , USA 8 Huntsman Comprehensive Cancer Center, Department of Pathology , 2000 Circle of Hope, Salt Lake City, UT 84103 , USA Background: The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories. Methods: 514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies. Results: The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online. Conclusions: The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting. - Background A significant body of evidence gathered over the course of more than 10 years has repeatedly demonstrated the prognostic significance and predictive ability of the four intrinsic subtypes of breast cancer (Luminal A, Luminal B, HER2-enriched, and Basal-like) [1–8], which were first described in 2000 by Perou et al. [9]. These studies began with genome-wide gene expression profiling from microarray datasets and progressed to a PCR-based test with a curated list of 50 genes (the “PAM50” gene signature) to classify breast tumors into one of these four subtypes [10]. Recently, the NanoString nCounter Dx Analysis System has been shown to provide more precise and accurate measures of mRNA expression levels in formalin-fixed, paraffin-embedded (FFPE) tissue when compared to PCR [11]. Polymerase-based assays require excessive optimization from FFPE tissues and can introduce biases in amplification as mRNA from FFPE tissue is highly fragmented and cross-links to protein during fixation. The NanoString nCounter Dx Analysis System provides a digital profile of up to 800 genes in a single hybridization reaction using no enzymes and a simple workflow [12]. Several research groups have recently transitioned from profiling oncology biomark (...truncated)


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Brett Wallden, James Storhoff, Torsten Nielsen, Naeem Dowidar, Carl Schaper, Sean Ferree, Shuzhen Liu, Samuel Leung, Gary Geiss, Jacqueline Snider, Tammi Vickery, Sherri Davies, Elaine Mardis, Michael Gnant, Ivana Sestak, Matthew Ellis, Charles Perou, Philip Bernard, Joel Parker. Development and verification of the PAM50-based Prosigna breast cancer gene signature assay, BMC Medical Genomics, 2015, pp. 54, 8, DOI: 10.1186/s12920-015-0129-6