Clonal evolution and transcriptional plasticity shape metastatic dissemination routes in prostate cancer
Bibliographic record
Abstract
Prostate cancer is a highly heterogeneous disease, driven by genomic and transcriptional changes that impact disease progression and metastatic potential. The interplay between clonal evolution, transcriptional plasticity, and tumour microenvironment is, however, poorly understood. Here, we leverage and integrate single-nuclei RNA sequencing and whole-genome sequencing from 43 spatially distinct tumour samples from five patients with locally advanced prostate cancer to reconstruct clonal evolution trajectories and transcriptional changes driving metastasis at single-cell resolution. We find extensive clonal heterogeneity, including both monophyletic and polyphyletic metastatic dissemination, and ongoing clonal evolution in the primary tumour after metastatic spread. Metastatic seeding converges on disease trajectories involving both genomic and transcriptional changes, including androgen receptor independence and activation of estrogen-, WNT- and JAK-STAT- pathway activity, in spatially distinct areas. Our findings suggest an intricate interplay between clonal evolution and cellular plasticity driving metastatic seeding and point toward more integrative prognostic markers for improved patient management. The impact of tumour heterogeneity on metastatic potential in prostate cancer remains poorly understood. Here, the analysis of single nuclei RNA sequencing and whole-genome sequencing from samples from five patients suggests an interplay between clonal evolution and cellular plasticity driving metastatic seeding.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".