Genomic architecture of radioresistant prostate cancer.
Bibliographic record
Abstract
26 Background: Spatial intra-tumoral heterogeneity of prostate cancer is secondary to genomic diversity and multi-clonality. These unique features potentially 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 radiotherapy. Methods: We identified 11 patients with biopsy-proven multifocal recurrent prostate cancer following radiotherapy. Copy number aberration (CNA) profiling was performed on 33 anatomically distinct tumor foci with 11 matched-normals. 4 cases had matched pre-radiotherapy tumors for CNA profiling to assess for clonality. We evaluated for recurrent gene amplifications and deletions, and determined genomic instability by percent genome aberration (PGA). We also compared these genomic indices against 373 sporadic prostate cancers from the Canadian Prostate Cancer Gene Network. Results: We observed large intra- and inter-patient variation (p <0.001, one-way ANOVA) in PGA scores among the radioresistant tumors. Interestingly, although total CNA counts did not differ between the radioresistant and sporadic cohorts (median = 40, radioresistant vs 33, sporadic, p = 0.20], there was a trend for increased genomic instability in the radioresistant cohort (median PGA = 8.8 vs 4.9, p = 0.059). Spatial resolution of gene-level CNAs revealed the acquisition of CNAs that were both common and non-recurrent in the multi-focal radioresistant tumors, thus suggesting a common clonal origin, with subsequent divergent evolution. Importantly, we observed a mixture of CNAs, including known prognostic genes in prostate cancer, namely NKX3-1, PTEN, TP53, CDKN1B, and CDH1,that was shared between pre-treatment and radioresistant tumors, favoring clonal selection. We also discovered a novel deleted region on Chr3p, consisting of RAD18 and FANCD2, which was uniquely present in the radioresistant tumors. Conclusions: Our novel observations in a small cohort of radioresistant prostate cancers favor the model of clonal selection, as opposed to new-onset tumors. These results support the discovery of biomarkers a priori, and targeted treatment of these radioresistant clones to improve the therapeutic ratio of precision radiotherapy.
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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 | 0.000 |
| 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".