Characterization of <i>ERG</i> , <i>AR</i> and <i>PTEN</i> Gene Status in Circulating Tumor Cells from Patients with Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
Hormone-driven expression of the ERG oncogene after fusion with TMPRSS2 occurs in 30% to 70% of therapy-naive prostate cancers. Its relevance in castration-resistant prostate cancer (CRPC) remains controversial as ERG is not expressed in some TMPRSS2-ERG androgen-independent xenograft models. However, unlike these models, CRPC patients have an increasing prostate-specific antigen, indicating active androgen receptor signaling. Here, we collected blood every month from 89 patients (54 chemotherapy-naive patients and 35 docetaxel-treated patients) treated in phase I/phase II clinical trials of an orally available, highly specific CYP17 inhibitor, abiraterone acetate, that ablates the synthesis of androgens and estrogens that drive TMPRSS2-ERG fusions. We isolated circulating tumor cells (CTC) by anti-epithelial cell adhesion molecule immunomagnetic selection followed by cytokeratin and CD45 immunofluorescence and 4',6-diamidino-2-phenylindole staining. We used multicolor fluorescence in situ hybridization to show that CRPC CTCs, metastases, and prostate tissue invariably had the same ERG gene status as therapy-naive tumors (n=31). We then used quantitative reverse transcription-PCR to show that ERG expression was maintained in CRPC. We also observed homogeneity in ERG gene rearrangement status in CTCs (n=48) in contrast to significant heterogeneity of AR copy number gain and PTEN loss, suggesting that rearrangement of ERG may be an earlier event in prostate carcinogenesis. We finally report a significant association between ERG rearrangements in therapy-naive tumors, CRPCs, and CTCs and magnitude of prostate-specific antigen decline (P=0.007) in CRPC patients treated with abiraterone acetate. These data confirm that CTCs are malignant in origin and indicate that hormone-regulated expression of ERG persists in CRPC.
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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".