Abstract 4542: Early detection of clinically significant prostate cancer at diagnosis: A prospective study using a novel panel of TMPRSS2:ETS fusion gene markers
Bibliographic record
Abstract
Abstract Background and objectives: The fusion of TMPRSS2 gene to an oncogenic ETS transcription factor gene (e.g. ERG) is both prevalent and unique to prostate cancer (PCa). We previously reported a panel of TMPRSS2:ERG fusion subtype markers for urine-based PCa detection with high specificity and sensitivity. Our current objectives are to investigate prospectively the sensitivity of a new panel of both common and low-prevalent TMPRSS2:ETS fusion gene markers for urine-based PCa detection, and to develop individualized molecular scores to predict the risk of cancer occurrence or the risk of aggressive cancer in PSA-screened patients at diagnostic biopsy. Participants and methods: A total of 92 subjects who were PSA screened and scheduled for diagnostic biopsy were enrolled from a prostate biopsy clinic at MUHC to form a pre-biopsy cohort. This cohort was designed for prospective molecular diagnosis of PCa using a panel of molecular markers in urine. Urine was collected after attentive digital rectal exam prior to biopsy and was coded for blind laboratory tests. RNA from urine sediments was analyzed using a panel of cancer-specific markers consisting of 6 TMPRSS2:ETS (i.e. ERG, ETV1, ETV4, ETV5) fusion genes/subtypes and 5 additional markers using established qPCR methods. Results: The pathology reported 39 biopsy-positive cases from 92 patients, a 42% biopsy-positive rate. In urine test, 10 unique combinations of fusion genes/subtypes or “fusion-types” were detected in 32 of 92 (34.8%) pre-biopsy samples. We identified a novel combination of fusion-types, termed Fx (III, V, ETS), that had a sensitivity of 51.3%, a specificity of 90.6% and an odds ratio of 10.1 in detecting PCa on biopsy. By incorporating the fusion-types Fx (III, V, ETS) with urine PCA3 and serum PSA, a regression model was developed to calculate individualized molecular scores for significantly improved prediction of biopsy outcomes and for stratification of pre-biopsy patients into distinct risk groups. As such, the sensitivity of PCa detection was 81% in a high risk group but only 16% in a low risk group. On the other hand, the overexpression profiles of the same set of informative fusion markers were shown to be significantly associated with high-grade cancers (Gleason > 6) and used to develop a novel regression model to predict the risk of aggressive cancer when coupled with PSA density. We demonstrated that the molecular scores for aggressiveness were highly correlated with Gleason scores (r = 0.64, p < 0.0001), the number of positive cores (r = 0.48, p < 0.01) and the % of cancer involvement (r = 0.59, p < 0.0001) in 39 PCa patients. Conclusions: We have identified multiple alternative fusion-types very specific to clinically significant prostate cancer in urine and developed highly effective regression models to predict the risk of cancer occurrence or the risk of aggressive cancer at diagnosis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4542. doi:1538-7445.AM2012-4542
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".