Castration induces autoantibody and T cell responses that correlate with inferior outcomes in an androgen‐dependent murine tumor model
Bibliographic record
Abstract
We recently reported that hormone therapy induces antigen-specific autoantibody responses in prostate cancer patients. However, the contribution of autoantibody responses to clinical outcomes is unknown. We used an animal model to test the hypothesis that hormone therapy-induced immune responses may be associated with delayed tumor recurrence. Male DD/S mice bearing established tumors from the androgen-dependent Shionogi carcinoma line were castrated to induce tumor regression. Tumor-specific autoantibody responses were measured by immunoblot, and the underlying antigen was identified by serological screening of a cDNA expression library. T cell responses were assessed by immunohistochemistry and IFN-gamma ELISPOT. Following castration, 97% of mice underwent complete tumor regression. Of these, 72% experienced tumor recurrence 18-79 days postcastration, whereas the remaining 28% remained tumor-free for the duration of the experiment. In 55% of mice, castration induced autoantibody responses to an antigen identified as poly(A) binding protein nuclear 1 (PABPN1). Castration also induced PABPN1-specific T cell responses, which were highly correlated to autoantibody responses, and this was accompanied by dense infiltration of tumors by CD3+ T cells 1-2 weeks after castration. Unexpectedly, mice that developed autoantibody and T cell responses to PABPN1 showed a higher rate and shorter latency of tumor recurrence. In mice with recurrent tumors, T cell responses to PABPN1 were still detectable; however, T cell infiltrates were restricted to the peripheral stroma of tumors. In conclusion, castration-induced immune responses are associated with inferior outcomes in the Shionogi carcinoma model, raising concerns about the influence of treatment-induced immune responses on clinical outcomes in humans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".