Standard Treatments Induce Antigen-Specific Immune Responses in Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Prostate tumors express antigens that are recognized by the immune system in a significant proportion of patients; however, little is known about the effect of standard treatments on tumor-specific immunity. Radiation therapy induces expression of inflammatory and immune-stimulatory molecules, and neoadjuvant hormone therapy causes prominent T-cell infiltration of prostate tumors. We therefore hypothesized that radiation therapy and hormone therapy may initiate tumor-specific immune responses. EXPERIMENTAL DESIGN: Pretreatment and posttreatment serum samples from 73 men with nonmetastatic prostate cancer and 50 cancer-free controls were evaluated by Western blotting and SEREX (serological identification of antigens by recombinant cDNA expression cloning) antigen arrays to examine whether autoantibody responses to tumor proteins arose during the course of standard treatment. RESULTS: Western blotting revealed the development of treatment-associated autoantibody responses in patients undergoing neoadjuvant hormone therapy (7 of 24, 29.2%), external beam radiation therapy (4 of 29, 13.8%), and brachytherapy (5 of 20, 25%), compared with 0 of 14 patients undergoing radical prostatectomy and 2 of 36 (5.6%) controls. Responses were seen within 4 to 9 months of initiation of treatment and were equally prevalent across different disease risk groups. Similarly, in the murine Shionogi tumor model, hormone therapy induced tumor-associated autoantibody responses in 5 of 10 animals. In four patients, SEREX immunoscreening of a prostate cancer cDNA expression library identified several antigens recognized by treatment-associated autoantibodies, including PARP1, ZNF707 + PTMA, CEP78, SDCCAG1, and ODF2. CONCLUSION: We show for the first time that standard treatments induce antigen-specific immune responses in prostate cancer patients. Thus, immunologic mechanisms may contribute to clinical outcomes after hormone and radiation therapy, an effect that could potentially be exploited as a practical, personalized form of immunotherapy.
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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 | 0.000 | 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) | 0.002 | 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".