title: Development and verification of the PAM50-based Prosigna breast cancer gene signature assay.
abstract: 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.
authors: NanoString Technologies, Inc, 530 Fairview Avenue North, Suite 2000, Seattle, WA, 98109, USA. bwallden@nanostring.com.
authors: Brett
authors: B
authors: Wallden
authors: NanoString Technologies, Inc, 530 Fairview Avenue North, Suite 2000, Seattle, WA, 98109, USA. jstorhoff@nanostring.com.
authors: James
authors: J
authors: Storhoff
authors: Genetic Pathology Evaluation Centre, Vancouver Coastal Health Research Institute and British Columbia Cancer Agency, 2655 Oak St, Vancouver, BC, V5Z 1M9, Canada. torsten@mail.ubc.ca.
authors: Torsten
authors: T
authors: Nielsen
authors: NanoString Technologies, Inc, 530 Fairview Avenue North, Suite 2000, Seattle, WA, 98109, USA. ndowidar@nanostring.com.
authors: Naeem
authors: N
authors: Dowidar
authors: Statistical consultant, New York, NY, USA. carl@carlschaper.com.
authors: Carl
authors: C
authors: Schaper
authors: NanoString Technologies, Inc, 530 Fairview Avenue North, Suite 2000, Seattle, WA, 98109, USA. sferree@nanostring.com.
authors: Sean
authors: S
authors: Ferree
authors: Genetic Pathology Evaluation Centre, Vancouver Coastal Health Research Institute and British Columbia Cancer Agency, 2655 Oak St, Vancouver, BC, V5Z 1M9, Canada. Shuzhen.Liu@vch.ca.
authors: Shuzhen
authors: S
authors: Liu
authors: Genetic Pathology Evaluation Centre, Vancouver Coastal Health Research Institute and British Columbia Cancer Agency, 2655 Oak St, Vancouver, BC, V5Z 1M9, Canada. Samuel.Leung@vch.ca.
authors: Samuel
authors: S
authors: Leung
authors: NanoString Technologies, Inc, 530 Fairview Avenue North, Suite 2000, Seattle, WA, 98109, USA. ggeiss@nanostring.com.
authors: Gary
authors: G
authors: Geiss
authors: Washington University School of Medicine, 660 S Euclid, St. Louis, MO, 63110, USA. jsnider@pathology.wustl.edu.
authors: Jacqueline
authors: J
authors: Snider
authors: Washington University School of Medicine, 660 S Euclid, St. Louis, MO, 63110, USA. tvickery@wustl.edu.
authors: Tammi
authors: T
authors: Vickery
authors: Washington University School of Medicine, 660 S Euclid, St. Louis, MO, 63110, USA. daviess@wustl.edu.
authors: Sherri R
authors: SR
authors: Davies
authors: Washington University School of Medicine, 660 S Euclid, St. Louis, MO, 63110, USA. emardis@wustl.edu.
authors: Elaine R
authors: ER
authors: Mardis
authors: Department of Surgery and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria. michael.gnant@meduniwien.ac.at.
authors: Michael
authors: M
authors: Gnant
authors: Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Sq, London, EC1M 6BQ, UK. i.sestak@qmul.ac.uk.
authors: Ivana
authors: I
authors: Sestak
authors: Lester and Sue Smith Breast Center, Baylor College of Medicine, One Baylor Plaza, MS 600, Houston, TX, 77030, USA. Matthew.Ellis@bcm.edu.
authors: Matthew J
authors: MJ
authors: Ellis
authors: Lineberger Comprehensive Cancer Center, Department of Genetics, University of North Carolina at Chapel Hill, 450 West Drive, Chapel Hill, NC, 27599, USA. cperou@med.unc.edu.
authors: Charles M
authors: CM
authors: Perou
authors: Huntsman Comprehensive Cancer Center, Department of Pathology, 2000 Circle of Hope, Salt Lake City, UT, 84103, USA. phil.bernard@hci.utah.edu.
authors: Philip S
authors: PS
authors: Bernard
authors: Lineberger Comprehensive Cancer Center, Department of Genetics, University of North Carolina at Chapel Hill, 450 West Drive, Chapel Hill, NC, 27599, USA. parkerjs@email.unc.edu.
authors: Joel S
authors: JS
authors: Parker
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000235:genetics* / Q000473:pathology; D016000:Cluster Analysis; D005240:Feasibility Studies; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D052138:Genes, Neoplasm / Q000235:genetics*; D006801:Humans; D000069550:Machine Learning; D008875:Middle Aged; D011379:Prognosis; D012008:Recurrence; D012306:Risk; D013629:Tamoxifen / Q000627:therapeutic use
chemical_list: D013629:Tamoxifen