Abstract 4439: The urine miRNA transcriptome of prostate cancer is temporally stable and predicts disease aggressivity
Bibliographic record
Abstract
Abstract The clinical management of prostate cancer is the most common non-skin male malignancy in the world is hindered by the limitations of current diagnostic and prognostic tests, such as the low specificity of prostate-specific antigen (PSA) testing, low sensitivity of digital rectal examination (DRE) and complications of biopsies. To try to resolve these issues, we evaluated miRNA abundance in the urine of prostate cancer patients. Alterations in miRNA abundance levels have been reported as playing essential roles in the pathogenesis of cancer and is occurred in a tumor phenotype-specific manner (e.g. aggressive and non-aggressive). miRNAs are stable under diverse analytical conditions and can be detected in various types of body fluids including urine. These characteristics make them as promising non-invasive biomarkers. Here, we examined the intra- and inter-individual variance of urine miRNA abundance by investigating longitudinal changes over months to years in a cohort of patients with localized prostate cancer. We observed a large dynamic range of intra-individual variance in miRNA abundances and identified a set of miRNAs that is stable within individuals, and is biased toward specific biological functions including regulation of transmembrane channel activity. We combined this observation with machine-learning techniques to create a predictive model that can identify aggressive prostate cancer. This four miRNAs predictive model was validated in an independent prostate cancer cohort to non-invasively predict high-risk disease. Remarkably it showed comparable performance to the best existing tissue-based prognostic markers. These results demonstrated that non-invasive biomarkers can be developed to precede or supplement tissue-based tests by understanding the intra- and inter-tumoural heterogeneity of the urine miRNA transcriptome. Citation Format: Jouhyun Jeon. The urine miRNA transcriptome of prostate cancer is temporally stable and predicts disease aggressivity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4439. doi:10.1158/1538-7445.AM2017-4439
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".