The molecular hallmarks and clinical consequences of tumor hypoxia in prostate cancer.
Bibliographic record
Abstract
81 Background: Localised prostate cancers are classified into risk-groups using clinical measurements like grade and stage to inform treatment decisions. However, these groupings are imprecise: ~30% of intermediate-risk patients suffer relapse of their disease despite precision image-guided radiotherapy or radical prostatectomy. One reason for this variability in response to treatment is the underlying cellular and molecular heterogeneity of tumours. Prostate tumour cells exist within a microenvironment characterized by gradients of oxygen levels and prostate tumours with low levels of oxygen (hypoxia) have poor clinical outcomes. Methods: Hypoxia was measured using multiple mRNA-based signatures. We examined 548 patients with localised prostate cancer and statistically assessed the association of hypoxia with copy-number alterations (CNAs), single-nucleotide variants (SNVs), genomic rearrangements, focal genomic events ( i.e. kataegis, chromothripsis), telomere length, clinical indices ( i.e. grade, stage) and subclonal architecture. Results: Elevated hypoxia was associated with allelic loss of PTEN, higher rates of chromothripsis and intraductal and cribriform carcinoma (IDC-CA). To translate these findings into a biomarker for prostate cancer precision medicine, we integrated tumour microenvironmental data with genomic and pathological information to stratify patients into distinct prognostic groups. Patients with localized prostate cancer that have polyclonal tumours with elevated hypoxia, allelic loss of PTEN and IDC-CA were at the highest risk of rapid biochemical failure (P = 3.48 x10-3, Logrank test) and metastasis (P = 4.61 x 10-3, Logrank test), even after controlling for T-category, Gleason score and pre-treatment PSA. Conclusions: These data suggest that the aggressiveness of prostate cancers is driven by the interplay of the tumour microenvironment, tumour evolutionary trajectories and its genomic mutational profile.
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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 | 0.001 | 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) | 0.001 | 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".