Abstract A047: Panorama of complex structural variants in primary localized prostate cancer
Bibliographic record
Abstract
Abstract Prostate cancer (PCa) is a hormone-driven disease characterized by an abundance of structural variations (SV). Deconvolving the SV patterns can both inform on prior mutational processes leading to cancer, and be leveraged to identify patients with more aggressive disease. Under the auspices of the Pan Prostate Cancer Group (PPCG) consortium, we used whole genome sequencing of 812 treatment naive PCa patients to study both simple and complex SVs. In total, we classified 15,708 simple SVs and 1,448 complex SV events. Complex SVs such as chromothripsis, chromoplexy and templated insertion were found in more than half of the cohort. We also detected tandem duplicator phenotype (TDP) in a subset of the patients associated with CDK12 aberrations. Breakpoint recurrence analysis of driver genes revealed disruption of e.g. PTEN and formation of TMPRSS2-ERG fusion genes frequently coincide with occurrences of specific complex SV types, suggesting specific mutational processes driving these alterations of these cancer genes. Based on the simple and complex SV classifications we extracted six SV signatures, including two TDP-like signatures, distinct deletion-specific SV signatures and a signature characteristic of AR binding sites. We found signatures associated with disease markers, including Gleason score, risk scores, as well as age. Together, these findings provide insights into mechanisms driving SV formation and driver gene alterations in PCa, with potential for identifying markers of aggressive disease. Citation Format: André Olsen, Francesco Favero, Yilong Li, Etsehiwot Girma, Breon Feran, Tony Papenfuss, Kristian Helin, Jüri Reimand, Joachim Weischenfeldt. Panorama of complex structural variants in primary localized prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A047.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".