Clinical significance of stratifying prostate cancer patients through specific circulating genes
Bibliographic record
Abstract
Patient stratification remains a challenge for optimal treatment of prostate cancer (PCa). This clinical heterogeneity implies intra-tumoural heterogeneity, with different prostate epithelial cell subtypes not all targeted by current treatments. We reported that such cell subtypes are traceable in liquid biopsies through representative transcripts. Expanding on this concept, we included 57 genes representing cell subtypes, drug targets and relevant to resistance as non-invasive biomarkers for stratification. This panel was tested by RT-qPCR (quantitative reverse transcription polymerase chain reaction) in blood of controls and different categories of PCa patients. Overall, circulating transcripts showed predictive value throughout the disease. Those with aggressive pathological features such as intra-ductal carcinoma at diagnosis showed more genes over-expressed. In metastatic patients, signatures of subtypes or resistance were associated with treatments, progression-free survival and overall survival. Altogether, testing markers of cell diversity, an intrinsic feature of tumours, and drug targets via liquid biopsies represents a valuable means to stratify patients and predict responses to current or new therapeutic modalities. Over-expressed drug target genes suggest potential benefit from targeted treatments, justifying new clinical trials to offer patient-tailored strategies to eventually impact on PCa mortality.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".