Abstract 5860: Genomic architecture of prostate cancer at recurrence following radiotherapy
Bibliographic record
Abstract
Abstract Aim: Spatial intra-tumoural heterogeneity of prostate cancer is secondary to differential genomics and multi-clonality, even for tumours with the same Gleason grade. These unique features promote resistance to treatment. Here, we investigated if clonal selection or adaptation of new clones dominates in prostate cancer at the time of recurrence following high dose precision radiotherapy. Methods: We identified 11 patients with biopsy-proven multi-focal recurrent prostate cancer following definitive image-guided radiotherapy/brachytherapy. Copy number aberration (CNA) profiling was performed on 33 anatomically distinct tumour foci with 11 matched-normals in the radio-resistant cohort. To assess clonality, 4 cases had matched pre-radiotherapy tumours for copy number profiling. We evaluated for recurrent driver amplifications and deletions, and genomic instability as measured by percent genome aberration (PGA). We also compared these genomic indices against 373 comprehensively profiled sporadic prostate cancers from the Canadian Prostate Cancer Gene Network [Fraser, et al., Nature, 2016]. Results: Independent of Gleason grade, we observed large intra-patient (COV of 0.66-1.13) and inter-patient heterogeneity (p <0.001, one-way ANOVA) in the levels of genomic instability, as judged by PGA scores, among the radioresistant tumours. Interestingly, although total CNA counts did not differ between the radioresistant and sporadic (CPC-GENE) cohorts (median CNAs of 40, radioresistant vs 33, sporadic, p = 0.20], we observed a trend for increased genomic instability in the radioresistant cohort (median PGA of 8.8 vs 4.9, p = 0.059). This concurs with the findings on intra-tumoural spatial CNA analyses, which revealed the acquisition of CNAs that were both common and non-recurrent in the multi-focal radioresistant tumours, thus suggesting a common origin with subsequent divergent evolution. Importantly, we observed a mixture of CNAs, including known drivers of aggressive prostate cancer, namely NKX3-1, PTEN, TP53, CDKN1B, and CDH1, that was shared between pre-radiotherapy and radioresistant tumours, supporting a clonal selection process. We also discovered a novel deleted region on Chr3p, consisting of RAD18 and FANCD2, which was unique only in the radioresistant tumours. Conclusions: Our novel observations in a small cohort of radioresistant prostate cancers favour the model of selection of radioresistant clones, as opposed to new-onset tumours. These results support the current approach of discovering biomarkers a priori, and molecular therapeutic targets for these radioresistant clones, so as to improve the therapeutic ratio of precision radiotherapy. Citation Format: Melvin L.K. Chua, Erle Holgersen, Veronica Sabelnykova, Adriana Salcedo, Alice Meng, Michael Fraser, Theodorus van der Kwast, Paul C. Boutros, Robert G. Bristow. Genomic architecture of prostate cancer at recurrence following radiotherapy [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 5860. doi:10.1158/1538-7445.AM2017-5860
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".