Characterization of lymphoid infiltrates in prostatectomy specimens post neoadjuvant hormonal therapy using quantitative immunofluorescence and RNA FISH
Bibliographic record
Abstract
e16104 Background: Immunotherapy for advanced prostate cancer requires an understanding of the host response including lymphocyte subsets and cytokine expression. Using quantitative immunofluorescence (IF), image analysis and RNA FISH we examined a series of prostatectomy specimens from patients, ± neoadvjuvant hormonal therapy (NHT) and determined their baseline immune characteristics. Methods: CD4,8,25,45,68,69,86 and Foxp3 were evaluated by IF and IFNg with RNA FISH on prostatectomy specimens from 40 patients with T1c-T3 prostate cancer; 20 control and 20 post long-term NHT. Sections were analyzed with three multiplex IF assays, triplicate images acquired with spectral imaging, digitally masked and processed with IF image analysis software. IFNg RNA transcript localization was performed on all 40 patients using published protocols. Results: There was a predominance of CD8 in both control and NHT patients with a high but variable level of CD86 and 69 across the entire cohort, reflecting T cell activation. CD68 appeared reduced in NHT, but this change was not statistically significant. Number of Treg (CD4+/CD25+/Foxp3+) cells were similar between groups; however, in NHT an increased Treg population was associated with an elevated preoperative PSA compared with controls (p<0.05). IFNg transcript activity was low in the entire cohort and limited to tumor epithelial cells. Conclusions: The prominent CD8 expression and low level of IFNg in both control and NHT groups suggest an ongoing but not overly active immune response. Multiplex IF and in situ RNA FISH are useful tools for developing a disease phenotype and provide the necessary parameters for evaluating gene-protein expression especially in pharmacodynamic studies. [Table: see text]
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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 | 0.001 | 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) | 0.003 | 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".