Single-Cell RNAseq Analysis Reveals Robust, Anti-PD-1-Mediated Increase of Immune Infiltrate in Metastatic Castration-Sensitive Prostate Cancer
Bibliographic record
Abstract
SUMMARY Compared to other malignancies, the tumor microenvironment (TME) of primary and castration-resistant prostate cancer (CRPC) is relatively devoid of immune infiltrates. While androgen deprivation therapy (ADT) induces a complex immune infiltrate in localized prostate cancer, both in animal models and humans, the TME composition of metastatic, castration-sensitive prostate cancer (mCSPC) is relatively unknown and the effects of ADT and other treatments are poorly characterized in this context. To address this challenge, we analyzed metastatic sites from patients enrolled on a phase 2 clinical trial ( NCT03951831 ), in which men were treated with standard-of-care chemo-hormonal therapy with anti-PD-1 immunotherapy, at the single cell level. Longitudinal protein activity-based analysis of TME subpopulations identified immune subpopulations conserved across multiple metastatic sites, their dynamic, treatment-mediated evolution, and associated clinical response features. Our study revealed a therapy-resistant, transcriptionally distinct tumor subpopulation, which comprises an increasing number of cells in treatment-refractory patients, and identified several druggable targets in both tumor and immune cells as candidates to advance treatment and improve outcomes for patients with mCSPC.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".