Metadata supporting the article: Development and validation for research assessment of Oncotype DX® Breast Recurrence Score, EndoPredict® and Prosigna®
Creators: name: Buus, Richard
name: Szijgyarto, Zsolt
name: Schuster, Eugene F.
name: Xiao, Hui
name: Haynes, Ben P.
name: Sestak, Ivana
name: Cuzick, Jack
name: Paré, Laia
name: Seguí, Elia
name: Chic, Nuria
name: Prat, Aleix
name: Dowsett, Mitch
name: Cheang, Maggie Chon U.
Publisher: name: figshare
Year: 2021
Subjects: Computational Biology
Cancer
Biological Techniques
Biological Sciences not elsewhere classified
Gene Expression (incl. Microarray and other genome-wide approaches)
Medicine
Cancer Genetics

description: Multi-gene prognostic signatures including the Oncotype® DX Recurrence Score (RS), EndoPredict® (EP) and Prosigna® (ROR) are widely used to predict the likelihood of distant recurrence in patients with oestrogen-receptor positive (ER+), HER2-negative breast cancer. In this study, the authors describe the development and validation of methods to recapitulate RS, EP and ROR scores from NanoString expression data.<br><b>Data access</b>: The datasets that support the findings of this study are subject to third party restrictions due to contractual agreements, and are therefore not publicly available. The datasets will be made available upon reasonable request. Data access requests are subject to approval, and should be addressed to Dr. Mitch Dowsett, email address: <b>mitch.dowsett@icr.ac.uk</b>, and Dr. Aleix Prat, email address: <b>alprat@clinic.cat</b>. <br><b>Study approval and patient consent</b>: This study was approved by the South-East London Research Ethics Committee and all patients included gave informed consent.<br><b>Study aims and methodology</b>: Over 80% of breast cancer patients in the developed western world have oestrogen receptor (ER)–positive disease; their treatment normally includes surgery and adjuvant endocrine therapy, and sometimes chemotherapy which greatly improves outcome. However, a substantial risk remains for relapse.Multi-parameter gene-expression based prognostic signatures are often used to estimate the residual risk of recurrence after surgery to guide patient management. Amongst the most widely used prognostic signatures in ER+ breast cancer are the Oncotype DX Recurrence Score (RS),EndoPredict (EP/EPclin) and Prosigna® Risk Of Recurrence score (ROR). Each of these have been endorsed for prognostic use in authoritative guidelines.In this study, the authors describe the derivation and validation of RS and EP and ROR scores within ER+/HER2-tumours using gene expression data simultaneously generated by the NanoString nCounter System.They also provide a step-by-step protocol as reference and a guide for computing these scores to allow other researchers to assess the prognostic and predictive value of these scores in research studies.RNA was available from 107 tumours from postmenopausal women with early-stage, ER+, HER2- breast cancer from the translational Arimidex, Tamoxifen, Alone or in Combination study (TransATAC) where previously these signatures had been assessed with commercial methodology.Gene expression was measured using NanoString nCounter. The patients were selected for the current study with the aim to represent low, intermediate and high risk patients where the risk assessments were based on the commercial prognostic tests available in these patients.For more details on the methodology, please read the related article.<br><b>Datasets supporting the findings in the article: </b>Data supporting figure 1: <b>RS_EP_validation_48TA.txt</b>Data supporting figure 2: <b>ROR_validation_48TA_143SP.txt</b>Data supporting table 1: <b>RS_EP_ROR_48TA_143SP.txt</b>Data supporting table 2: <b>RS_genes_conv_factors_59TA.txt</b>Data supporting table 3: <b>EP_genes_conv_factors_59TA.txt</b>Data supporting supplementary table 1: <b>RS_genes_validation_assessment.txt</b>Data supporting supplementary table 2: <b>EP_genes_validation_assessment.txt</b>Data supporting supplementary table 3: <b>RS_scores_validation.txt</b>Data supporting supplementary table 4: <b>ROR_subtypes_TA_training_validation.txt</b>Data supporting supplementary table 6: <b>ROR_229ERPosHER2Neg_raw.txt</b>Data supporting supplementary table 7: <b>ROR_59TA_training_229ERPosHER2Neg.txt</b><br>All the datasets are in <b>text (.txt) </b>file format.<br>