PD18-07 A NON-INVASIVE URINE-BASED METHYLATION BIOMARKER PANEL FOR BLADDER CANCER DETECTION
Bibliographic record
Abstract
You have accessJournal of UrologyBladder Cancer: Non-invasive II (PD18)1 Apr 2019PD18-07 A NON-INVASIVE URINE-BASED METHYLATION BIOMARKER PANEL FOR BLADDER CANCER DETECTION Thomas Hermanns*, Andrea J. Savio, Ekaterina Olkhov-Mitsel, Andrea Mari, Karim Saba, Bimal Bhindi, Bethany Gill, Jenna Sykes, Cynthia Kuk, Cedric Poyet, Peter J. Wild, Aidan Noon, Shaheena Bashir, Tristan Juvet, Ricardo A. Rendon, David Waltregny, Theodorus van der Kwast, Antonio Finelli, Girish S. Kulkarni, Neil E. Fleshner, Kirk Lo, Bharati Bapat, and Alexandre R. Zlotta Thomas Hermanns*Thomas Hermanns* More articles by this author , Andrea J. SavioAndrea J. Savio More articles by this author , Ekaterina Olkhov-MitselEkaterina Olkhov-Mitsel More articles by this author , Andrea MariAndrea Mari More articles by this author , Karim SabaKarim Saba More articles by this author , Bimal BhindiBimal Bhindi More articles by this author , Bethany GillBethany Gill More articles by this author , Jenna SykesJenna Sykes More articles by this author , Cynthia KukCynthia Kuk More articles by this author , Cedric PoyetCedric Poyet More articles by this author , Peter J. WildPeter J. Wild More articles by this author , Aidan NoonAidan Noon More articles by this author , Shaheena BashirShaheena Bashir More articles by this author , Tristan JuvetTristan Juvet More articles by this author , Ricardo A. RendonRicardo A. Rendon More articles by this author , David WaltregnyDavid Waltregny More articles by this author , Theodorus van der KwastTheodorus van der Kwast More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Girish S. KulkarniGirish S. Kulkarni More articles by this author , Neil E. FleshnerNeil E. Fleshner More articles by this author , Kirk LoKirk Lo More articles by this author , Bharati BapatBharati Bapat More articles by this author , and Alexandre R. ZlottaAlexandre R. Zlotta More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555579.23708.62AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Aberrant DNA methylation is very common in bladder cancer (BC) pathogenesis and progression. These epigenetic alterations are detectable in voided urine and might be useful as non-invasive urinary biomarkers for the early detection of BC. We aimed to assess whether a panel of previously identified differentially methylated genes can be used as diagnostic urine assay to predict the presence of BC and discriminate between low-grade (LG) and high-grade (HG) disease. METHODS: Urinary DNA was extracted from voided urine of 313 patients with LG or HG BC and BC-free controls in 4 different centers (Toronto (2) and Halifax, CA and Zurich, CH). Methylation status of urinary cell sediment DNA was evaluated using qPCR-based MethyLight assay for 5 different genes (TWIST1, RUNX3, GATA4, NID2, FOXE1). These genes were previously identified to be differentially methylated in LG and HG BC using two different genome-wide methylation-profiling platforms. Multivariable logistic regression prediction models were created. RESULTS: There were 211 bladder cancer patients (180 non-muscle invasive) and 102 controls. In univariate analyses, all methylation biomarkers were statistically significant predictors of cancer vs. no cancer (all p-values <0.01), and HG vs. LG-BC (all p-values<0.01). In multivariable analysis, NID2, TWIST1 and age were independent predictors of BC (all p<0.05). Multivariable models predicting BC overall and discriminating between high-grade and low-grade bladder cancer reached AUCs of 0.89 and 0.78, respectively. CONCLUSIONS: Our multi-centric study supports the promise of epigenetic urinary markers in non-invasively detecting bladder cancer and discriminating between grades. Validation in different clinical settings including patients with hematuria, bladder cancer surveillance or screening in high-risk populations is warranted to support its utility. Source of Funding: Canadian Urological Association / Astellas Research Grant Program Zurich, Switzerland; Toronto, Canada; Florence, Italy; Zurich, Switzerland; Toronto, Canada; Zurich, Switzerland; Toronto, Canada; Halifax, Canada; Liege, Belgium; Toronto, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e313-e314 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Thomas Hermanns* More articles by this author Andrea J. Savio More articles by this author Ekaterina Olkhov-Mitsel More articles by this author Andrea Mari More articles by this author Karim Saba More articles by this author Bimal Bhindi More articles by this author Bethany Gill More articles by this author Jenna Sykes More articles by this author Cynthia Kuk More articles by this author Cedric Poyet More articles by this author Peter J. Wild More articles by this author Aidan Noon More articles by this author Shaheena Bashir More articles by this author Tristan Juvet More articles by this author Ricardo A. Rendon More articles by this author David Waltregny More articles by this author Theodorus van der Kwast More articles by this author Antonio Finelli More articles by this author Girish S. Kulkarni More articles by this author Neil E. Fleshner More articles by this author Kirk Lo More articles by this author Bharati Bapat More articles by this author Alexandre R. Zlotta More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".