Prostate cancer cells converge to an inflammatory-like state upon metastatic dissemination
Bibliographic record
Abstract
Identifying drivers of cancer progression to guide treatment selection is hindered by our limited understanding of tumor heterogeneity and its impact on tumor evolution. Here, we delineate the phenotypic variability across ~300,000 cells collected from multiple tumor loci in primary prostate and matched locoregional metastases using single-cell chromatin accessibility and gene expression sequencing. We find inter-patient heterogeneity to be confined to malignant populations. Within individual tumor loci, we see phenotypic heterogeneity among malignant cell populations despite a shared clonal genotypic architecture. We also observe that malignant cell populations disseminating to locoregional lymph nodes mirror the clonal architecture and phenotypic heterogeneity across primary tumor loci, while shifting from canonical prostate-cancer states to non-canonical inflammatory-like states. Our findings suggest a bottleneck imposed during the dissemination process, funneling prostate cancer cells toward an inflammatory-like cell state. These insights into the interplay between phenotypic identity and clonal architecture refine our understanding of prostate cancer progression and suggest that convergence of cancer cells towards an inflammatory-like state underlies dissemination to lymph nodes, offering a critical framework for future studies into prostate cancer metastatic potential. Understanding tumor heterogeneity and its impact on prostate cancer progression remains elusive. Here, single nucleus snATAC and snRNA sequencing of a multi-loci sampled cohort of advanced prostate cancer patients identifies an inflammatory-like state underlying metastatic dissemination to the lymph nodes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".