The Tumor Immune Contexture of Prostate Cancer
Bibliographic record
Abstract
One in seven men in North America is expected to be diagnosed with prostate cancer (PCa) during their lifetime (1, 2). While a wide range of treatment options including surgery, radiation, androgen deprivation and chemotherapy have been in practice for the last few decades, there are limited treatment options for metastatic and treatment resistant disease. Immunotherapy targeting T-cell associated immune checkpoints such as CTLA-4, PD-L1, and PD-1 have not yet proven to be efficacious in PCa. Tumor mutational burden, mutations in DNA damage repair genes, immune cell composition and density in combination with their spatial organization, and expression of immune checkpoint proteins are some of the factors influencing the success of immune checkpoint inhibitor therapies. The paucity of these features in PCa potentially makes them unresponsive to contemporary immune checkpoint inhibition. In this review, we highlight the hallmark events in the PCa tumor immune microenvironment and provide insights into the current state of knowledge in this field with a focus on the role of tumor cell intrinsic events that potentially regulate immune related events and determine therapeutic outcomes. We surmise that the cumulative impact of factors such as the pre-treatment immune status, PTEN expression, DNA damage repair gene mutations, and the effects of conventionally used treatments on the anti-tumor immune response should be considered in immunotherapy trial design in PCa.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".