Framework for the Pathology Workup of Metastatic Castration-Resistant Prostate Cancer Biopsies
Bibliographic record
Abstract
Lineage plasticity and histologic transformation from prostate adenocarcinoma to neuroendocrine (NE) prostate cancer (NEPC) occur in up to 15% to 20% of patients with castration-resistant prostate cancer (CRPC) as a mechanism of treatment resistance and are associated with aggressive disease and poor prognosis. NEPC tumors typically display small cell carcinoma morphology with loss of androgen receptor (AR) expression and gain of NE lineage markers. However, there is a spectrum of phenotypes that are observed during the lineage plasticity process, and the clinical significance of mixed histologies or those that co-express AR and NE markers or lack all markers is not well defined. Translational research studies investigating NEPC have used variable definitions, making clinical trial design challenging. In this manuscript, we discuss the diagnostic workup of metastatic biopsies to help guide the reproducible classification of phenotypic CRPC subtypes. We recommend classifying CRPC tumors based on histomorphology (adenocarcinoma, small cell carcinoma, poorly differentiated carcinoma, other morphologic variant, or mixed morphology) and IHC markers with a priority for AR, NK3 homeobox 1, insulinoma-associated protein 1, synaptophysin, and cell proliferation based on Ki-67 positivity, with additional markers to be considered based on the clinical context. Ultimately, a unified workup of metastatic CRPC biopsies can improve clinical trial design and eventually practice.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".