Journal Article
. 2013 Oct;14().
doi: 10.1186/1471-2164-14-690.

Analytical validation of the Oncotype DX prostate cancer assay - a clinical RT-PCR assay optimized for prostate needle biopsies

Dejan Knezevic 1 Audrey D Goddard  Nisha Natraj  Diana B Cherbavaz  Kim M Clark-Langone  Jay Snable  Drew Watson  Sara M Falzarano  Cristina Magi-Galluzzi  Eric A Klein  Christopher Quale  
  • PMID: 24103217
  •     21 References
  •     110 citations


Background: The Oncotype DX Prostate Cancer Assay is a multi-gene RT-PCR expression assay that was developed for use with fixed paraffin-embedded (FPE) diagnostic prostate needle biopsies containing as little as 1 mm of prostate tumor in the greatest dimension. The assay measures expression of 12 cancer-related genes representing four biological pathways and 5 reference genes which are algorithmically combined to calculate the Genomic Prostate Score (GPS). This biopsy-based assay has been analytically and subsequently clinically validated as a predictor of aggressive prostate cancer. The aim of this study was to validate the analytical performance of the Oncotype DX Prostate Cancer Assay using predefined acceptance criteria.

Results: The lowest quartile of RNA yields from prostate needle biopsies (six 5 μm sections) was between 19 and 34 ng. Analytical validation of the process requiring as little as 5 ng of RNA met all pre-defined acceptance criteria. Amplification efficiencies, analytical sensitivity, and accuracy of gene assays were measured by serially diluting an RNA sample and analyzing features of the linear regression between RNA expression measured by the crossing point (Cp) versus the log2 of the RNA input per PCR assay well. Gene assays were shown to accurately measure expression over a wide range of inputs (from as low as 0.005 ng to 320 ng). Analytical accuracy was excellent with average biases at qPCR inputs representative of patient samples <9.7% across all assays while amplification efficiencies were within ±6% of the median. Assessments of reproducibility and precision were performed by testing 10 prostate cancer RNA samples over multiple instruments, reagent lots, operators, days (precision), and RNA input levels (reproducibility) using appropriately parameterized linear mixed models. The standard deviations for analytical precision and reproducibility were 1.86 and 2.11 GPS units (100-unit scale) respectively.

Conclusions: The Oncotype DX Prostate Cancer Assay, a clinical RT-PCR assay specifically designed for use with prostate needle biopsies, has been analytically validated using very limited RNA inputs. The assay requirements and analytical performance will provide physicians with test results from a robust and reliable assay which will enable improved treatment decisions for men diagnosed with early-stage prostate cancer.

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PLoS One, 2019 Jun 28; 14(6). PMID: 31247038    Free PMC article.
Genomic Evaluation of Multiparametric Magnetic Resonance Imaging-visible and -nonvisible Lesions in Clinically Localised Prostate Cancer.
Marina A Parry, Shambhavi Srivastava, +18 authors, Esther Baena.
Eur Urol Oncol, 2019 Apr 02; 2(1). PMID: 30929837    Free PMC article.
17-Gene Genomic Prostate Score Test Results in the Canary Prostate Active Surveillance Study (PASS) Cohort.
Daniel W Lin, Yingye Zheng, +18 authors, Lisa F Newcomb.
J Clin Oncol, 2020 Mar 05; 38(14). PMID: 32130059    Free PMC article.
Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens.
Kunal Nagpal, Davis Foote, +23 authors, Craig H Mermel.
JAMA Oncol, 2020 Jul 24; 6(9). PMID: 32701148    Free PMC article.
Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer.
Shea P Connell, Eve O'Reilly, +9 authors, Daniel S Brewer.
Prostate, 2020 Mar 11; 80(7). PMID: 32153047    Free PMC article.
A 17-Gene Panel Genomic Prostate Score Has Similar Predictive Accuracy for Adverse Pathology at Radical Prostatectomy in African American and European American Men.
Adam B Murphy, Samuel Carbunaru, +8 authors, Peter Gann.
Urology, 2020 Apr 12; 142. PMID: 32277993    Free PMC article.
Genetic alterations in the 3q26.31-32 locus confer an aggressive prostate cancer phenotype.
Benjamin S Simpson, Niedzica Camacho, +6 authors, Hayley C Whitaker.
Commun Biol, 2020 Aug 17; 3(1). PMID: 32796921    Free PMC article.
AXIN2 expression predicts prostate cancer recurrence and regulates invasion and tumor growth.
Brian R Hu, Adrian S Fairey, +17 authors, Amir Goldkorn.
Prostate, 2016 Jan 16; 76(6). PMID: 26771938    Free PMC article.
Oncogenic Gene-Expression Programs in Leiomyosarcoma and Characterization of Conventional, Inflammatory, and Uterogenic Subtypes.
Matthew L Hemming, Changyu Fan, +4 authors, Suzanne George.
Mol Cancer Res, 2020 Jun 11; 18(9). PMID: 32518213    Free PMC article.
Big Data Approaches for Modeling Response and Resistance to Cancer Drugs.
Peng Jiang, William R Sellers, X Shirley Liu.
Annu Rev Biomed Data Sci, 2019 Jul 26; 1. PMID: 31342013    Free PMC article.
Second-Generation Antiandrogens: From Discovery to Standard of Care in Castration Resistant Prostate Cancer.
Meghan A Rice, Sanjay V Malhotra, Tanya Stoyanova.
Front Oncol, 2019 Sep 27; 9. PMID: 31555580    Free PMC article.
The role of radiomics in prostate cancer radiotherapy.
Rodrigo Delgadillo, John C Ford, +3 authors, Radka Stoyanova.
Strahlenther Onkol, 2020 Aug 22; 196(10). PMID: 32821953    Free PMC article.
Molecular Biomarkers in the Clinical Management of Prostate Cancer.
Aaron M Udager, Scott A Tomlins.
Cold Spring Harb Perspect Med, 2018 Jan 10; 8(11). PMID: 29311125    Free PMC article.
Analysing the nanoparticle-protein corona for potential molecular target identification.
Chandra Kumar Elechalawar, Md Nazir Hossen, +2 authors, Priyabrata Mukherjee.
J Control Release, 2020 Mar 14; 322. PMID: 32165239    Free PMC article.
Revealing Prognosis-Related Pathways at the Individual Level by a Comprehensive Analysis of Different Cancer Transcription Data.
Jingya Fang, Cong Pian, +5 authors, Liangyun Zhang.
Genes (Basel), 2020 Nov 04; 11(11). PMID: 33138076    Free PMC article.
Meaningful end points and outcomes in men on active surveillance for early-stage prostate cancer.
Christopher J Welty, Matthew R Cooperberg, Peter R Carroll.
Curr Opin Urol, 2014 Mar 13; 24(3). PMID: 24614347    Free PMC article.
Are localized prostate cancer biomarkers useful in the clinical practice?
Arie Carneiro, Paulo Priante Kayano, +8 authors, Bianca Bianco.
Tumour Biol, 2018 Sep 12; 40(9). PMID: 30204063    Free PMC article.
Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management.
Claire Tonry, Stephen Finn, John Armstrong, Stephen R Pennington.
Clin Proteomics, 2020 Dec 10; 17(1). PMID: 33292167    Free PMC article.
A Prognostic Gene-Expression Signature and Risk Score for Meningioma Recurrence After Resection.
William C Chen, Harish N Vasudevan, +16 authors, David R Raleigh.
Neurosurgery, 2020 Aug 30; 88(1). PMID: 32860417    Free PMC article.
A new risk stratification system of prostate cancer to identify high-risk biochemical recurrence patients.
Xiangkun Wu, Daojun Lv, +5 authors, Yongda Liu.
Transl Androl Urol, 2021 Jan 19; 9(6). PMID: 33457230    Free PMC article.
The expression of YWHAZ and NDRG1 predicts aggressive outcome in human prostate cancer.
Sofia Lage-Vickers, Juan Bizzotto, +10 authors, Geraldine Gueron.
Commun Biol, 2021 Jan 24; 4(1). PMID: 33483585    Free PMC article.
Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges.
Dimitrios Doultsinos, Ian G Mills.
Cancers (Basel), 2021 Feb 03; 13(3). PMID: 33525365    Free PMC article.
Effective Prediction of Prostate Cancer Recurrence through the IQGAP1 Network.
Yan Gu, Xiaozeng Lin, +3 authors, Damu Tang.
Cancers (Basel), 2021 Jan 28; 13(3). PMID: 33498739    Free PMC article.