Biallelic tumour suppressor loss and DNA repair defects in <i>de novo</i> small‐cell prostate carcinoma
Bibliographic record
Abstract
Small-cell prostate carcinoma (SCPC) is an aggressive malignancy that is managed similarly to small-cell lung cancer. SCPC can evolve from prostate adenocarcinoma in response to androgen deprivation therapy, but, in rare cases, is present at initial cancer diagnosis. The molecular aetiology of de novo SCPC is incompletely understood, owing to the scarcity of tumour tissue and the short life-expectancy of patients. Through a retrospective search of our regional oncology pharmacy database, we identified 18 patients diagnosed with de novo SCPC between 2004 and 2017. Ten patients had pure SCPC pathology, and the remainder had some admixed adenocarcinoma foci, but all were treated with first-line platinum-based chemotherapy. The median overall survival was 28 months. We performed targeted DNA sequencing, whole exome sequencing and mRNA profiling on formalin-fixed paraffin-embedded archival tumour tissue. We observed frequent biallelic deletion and/or mutation of the tumour suppressor genes TP53, RB1, and PTEN, similarly to what was found in treatment-related SCPC. Indeed, at the RNA level, pure de novo SCPC closely resembled treatment-related SCPC. However, five patients had biallelic loss of DNA repair genes, including BRCA1, BRCA2, ATM, and MSH2/6, potentially underlying the high genomic instability of this rare disease variant. Two patients with pure de novo SCPC harboured ETS gene rearrangements involving androgen-driven promoters, consistent with the evolution of de novo SCPC from an androgen-driven ancestor. Overall, our results reveal a highly aggressive molecular landscape that underlies this unusual pathological variant, and suggest opportunities for targeted therapy strategies in a disease with few treatment options. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".