PD40-04 A GENOMIC CLASSIFIER SHOWS IMPROVED PREDICTION OF ONCOLOGIC OUTCOMES IN AFRICAN-AMERICAN MEN TREATED WITH RADICAL PROSTATECTOMY
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Localized: Surgical Therapy I (PD40)1 Apr 2019PD40-04 A GENOMIC CLASSIFIER SHOWS IMPROVED PREDICTION OF ONCOLOGIC OUTCOMES IN AFRICAN-AMERICAN MEN TREATED WITH RADICAL PROSTATECTOMY Stephen Freedland*, Marguerite du Plessis, Jingbin Zhang, Lauren Howard, Amanda De Hoedt, and Elai Davicioni Stephen Freedland*Stephen Freedland* More articles by this author , Marguerite du PlessisMarguerite du Plessis More articles by this author , Jingbin ZhangJingbin Zhang More articles by this author , Lauren HowardLauren Howard More articles by this author , Amanda De HoedtAmanda De Hoedt More articles by this author , and Elai DavicioniElai Davicioni More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556415.00894.25AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Accurate risk stratification after radical prostatectomy (RP) is important to help select men at risk of recurrence who will benefit most postoperative radiation or multi-modal therapy. Increasingly genomic testing is being used in the clinic for this purpose. However, little is known about how these tests predict outcomes in African-American men (AAM), an underserved at risk population. Here we evaluate Decipher within a large Veteran Affairs cohort and compare its performance to the CAPRA-S clinical model for predicting outcomes in AAM and non-AAM RP patients. METHODS: Decipher genomic classifier (GC) scores were generated for 557 PCa patients, who underwent RP at the Veteran Affairs Medical Center Durham between 1989 and 2016. This was a clinically high-risk cohort which all underwent RP and were selected to have either T3a, positive margins, seminal vesicle invasion, or received post-op radiation. Cox univariable and multivariable proportional hazards models and survival c-index were used to compare the performance of Decipher and CAPRA-S for predicting risk of metastasis and PCa specific mortality (PCSM). RESULTS: Overall, 55% (n=306) of patients in the cohort were AAM. CAPRA-S classified 10.4% as low risk for recurrence while for GC it was 50.4%. With a median follow-up of 9 years, only 40 patients developed metastases and 18 patients died of PCa. In multivariable analyses, both GC (p=0.044 HR:1.30 95% CI:1.01-1.69) and CAPRA-S (p=0.037 HR:1.27 95% CI:1.01-1.58) were significant predictors for metastasis within non-AAM; however, only GC (p<0.001 HR:1.70 95% CI:1.31-2.20), was significant within AAM. GC but not CAPRA-S was a significant predictor of PCSM for both EAM (p=0.044 HR:1.54 95% CI:1.01-2.53) and AAM (p=0.002 HR:1.65 95% CI:1.19-2.42). The survival c-index of GC for predicting metastasis 8 years post RP was 0.84 (95% CI: 0.76-0.90) in AAM and 0.70 (95% CI:0.63-0.80) in non-AAM. For PCSM endpoint, it was 0.82 (0.61-0.93) in AAM and 0.73 (95% CI:0.63-0.84) in non-AAM. CONCLUSIONS: Our results among non-AAM confirm many prior studies showing that GC is a powerful predictor of metastasis and PCSM. Among AAM, not only was GC a very strong predictor of poor outcome, there was actually a suggestion that GC may perform better among AAM than EAM, though this requires further validation. Source of Funding: GenomeDx Biosciences Los Angeles, CA; Vancouver, Canada; Durham, NC; Durham, NC; San Diego, CA© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e739-e739 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Stephen Freedland* More articles by this author Marguerite du Plessis More articles by this author Jingbin Zhang More articles by this author Lauren Howard More articles by this author Amanda De Hoedt More articles by this author Elai Davicioni 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".