Abstract 2966: The mutational landscape of localized gleason 6 and 7 prostate cancer
Bibliographic record
Abstract
Abstract Prostate cancer (CaP) remains the most common male malignancy worldwide, leading to over 300,000 deaths per year. In Western countries, most prostate tumours are diagnosed while they are confined to the prostate and have relatively indolent histology, as assessed by the Gleason Score (GS). CaP is a C-class tumour, characterized by large number of driver copy-number aberrations and genomic rearrangements. Therefore, while previous sequencing studies have focused largely on the coding regions of late-stage disease, herein we comprehensively characterized the copy-number profiles of 250 localized prostate cancers and analyzed the whole genomes of 124 matched tumour/normal pairs derived from patients with GS6 and GS7 prostate cancer. Using this – the largest whole-genome sequencing dataset of prostate cancer to date – we confirm the C-class character of the disease and identify strong genomic subtypes that stretch across multiple types of somatic alteration, including SNVs, CNAs and genomic rearrangements. We provide the first assessments of localized hyper-mutation phenomena (chromothripsis and kataegis) in prostate cancer, and identify specific genes driving higher levels of these hyper-mutations. We identify unexpected biases in the location and role of both non-coding SNVs and genomic rearrangements, including clear association with epigenetic processes, and with genome-wide profiling of methylation in 92 samples. Finally, we demonstrate a stark paucity of clinically-actionable mutations in localized GS6 and GS7 disease, even lacking those common in high-risk localized disease, indicating that novel therapeutic development against the recurrent targets identified here will be key to allowing less-aggressive, targeted treatment of early-stage disease. Citation Format: Michael E. Fraser, Veronica Y. Sabelnykova, Takafumi N. Yamaguchi, Alice Meng, Lawrence E. Heisler, Junyan Zhang, Julie Livingstone, Vincent Huang, Andre P. Masella, Fouad Yousif, Michael Xie, Nicholas J. Harding, Xihui Lin, Haiying Kong, Stephenie D. Prokopec, Alejandro Berlin, Dominique Trudel, Xuemei Luo, Timothy E. Beck, Richard de Borja, Alister D'Costa, Robert E. Denroche, Natalie S. Fox, Emilie Lalonde, Ada Wong, Taryne Chong, Michelle Sam, Jeremy Johns, Lee Timms, Nicholas Buchner, Michele Orain, Valerie Picard, Helene Hovington, Kenneth C. Chu, Christine P'ng, Bryan Lo, Francis Nguyen, Kathleen E. Houlahan, Christopher Cooper, Shaylan K. Govind, Clement Fung, Louis Lacombe, Colin C. Collins, Yves Fradet, Bernard Tetu, Theodorus van der Kwast, John McPherson, Thomas J. Hudson, Rob G. Bristow, Paul Boutros. The mutational landscape of localized gleason 6 and 7 prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2966. doi:10.1158/1538-7445.AM2015-2966
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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 | 0.000 | 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) | 0.004 | 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".