Clinical and genomic characterization of metastatic small cell/neuroendocrine prostate cancer (SCNC) and intermediate atypical prostate cancer (IAC): Results from the SU2C/PCF/AACRWest Coast Prostate Cancer Dream Team (WCDT).
Bibliographic record
Abstract
5019 Background: SCNC and a novel pathologic subtype, IAC, comprise a growing proportion of mCRPC patients resistant to androgen signaling inhibitors such as Abiraterone (Abi) or Enzalutamide (Enz). We sought to characterize these non-adenocarcinoma (adeno) subtypes in a prospective biopsy study. Methods: Eligible mCRPC pts underwent a metastasis biopsy (bx), and were followed for clinical outcomes. Tissue was both frozen and formalin fixed/paraffin embedded (FFPE). Independent review of FFPE specimens was undertaken by 3 pathologists. Frozen specimens underwent laser capture micro-dissection prior to RNA sequencing (seq). Machine learning was used to derive histology-specific expression signatures. Signature accuracy was evaluated with leave-pair-out cross-validation and application to an independent data set. Results: 226 of 300 planned mCRPC pts (74% resistant to Abi and/or Enz) have undergone bx (including 123 bone, 61 node, and 23 liver bx) with a 78% evaluable biopsy rate. Adeno was identified in 39%, non-adeno in 41% (SCNC in 12%, IAC in 29%), with other mixtures in the remaining 20%. Median overall survival (OS) from time of biopsy for pts with non-adeno histologies was 12.8 months (mos) [ IAC OS = 19.1 mos; SCNC OS = 12.8 mos] versus 25.8 mos in adeno pts (p=0.023). RNAseq data are available from 94 bx. Transcriptional signatures were developed which accurately distinguish adeno, SCNC and IAC. IAC appears to have a signature intermediate between adeno and SCNC. When applied to an independent data set (Beltran et al, Ca Disc 2011), these transcriptional signatures identified SCNC with 100% accuracy, and predict for IAC differentiation in a subset of non-SCNC tumors. Conclusions: A majority of CRPC metastases exhibit non-adenocarcinoma features, which are associated with a shortened survival. IAC and SCNC are genomically distinct, and newly derived transcriptional signatures based on these bx can be used to identify SCNC in an independent data set. Integration of whole exome with RNA-seq data are ongoing to identify pathways up-regulated in IAC and SCNC.
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 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 | 0.000 | 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".