Abstract B02: Genomic and transcriptomic profiling of urine in prostate cancer
Bibliographic record
Abstract
Abstract Prostate cancer (CaP) affects one in 8 men in the US and Canada, but many of these men could be spared aggressive treatment, often associated with significant morbidity, if diagnostic tools could more accurately assess the risk that the cancer will metastasize. Low pathologic/Gleason grade (Gleason Grade 3+3=6, equivalent to WHO Grade Group 1) is the main defining feature of nonlethal low-risk CaP, and active surveillance (AS) is the standard of care for such men. However, current diagnostic methods cannot accurately separate low and higher risk CaP based on core biopsies, presenting a major dilemma as repeated biopsies during AS carry significant risks. At present no diagnostic tools, either imaging-based or biomarker-based, can safely supplant repeated invasive biopsies. Genomic profiling of liquid biospecimens (“liquid biopsies”) is quickly becoming translated as part of contemporary clinical trials and is being translated for use in the diagnostic setting. Less invasive than traditional solid tissue biopsy approaches, liquid biopsy specimens have the potential to be used not only for early detection but also to monitor therapeutic response and progression. Leveraging the advantage of the anatomic relationship between the prostate and urinary tract, we obtained 11 post-digital rectal exam (DRE) urine samples from patients who presented at the Kingston Health Sciences Centre (Kingston, Ontario) to be evaluated for prostate cancer. Through a pan-Canadian research consortium called PRONTO, we developed a tissue-based classifier (PRONTO-T) differentiating between low- and high-risk cancer with an independently validated AUC of 0.85. This proof-of-concept study tests post-digital rectal examination (DRE) urine samples for genomic features associated with aggressive CaP, including features included in the PRONTO-T risk classifier. DNA and RNA were extracted from 11 post-DRE urine samples. Profiling with a customized NGS-based targeted-methylation panel by Thermo Fisher demonstrated robust signals from urine-derived DNA. Similarly, urine samples profiled with the pan-cancer Oncomine Comprehensive Assay (v3) detected potentially actionable mutations in MET and PIK3CA. RNA obtained from post-DRE urines met quality control standards for transcriptional profiling using the NanoString nCounter System and revealed patterns of gene expression changes consistent with prostate cancer biology. These encouraging preliminary results demonstrate that the profiling of urine can provide sufficient diagnostic and potentially prognostic information to significantly impact the clinical management of prostate cancer. Citation Format: Jane Bayani, Palak G. Patel, Dan Dion, Vanessa Bratti, Ania Ahmadian-Namin, Tamara Jamaspishvili, Jason Izard, D. Robert Siemens, David M. Berman, John M. S. Bartlett. Genomic and transcriptomic profiling of urine in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr B02.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".