Data from A Viral Vaccine Encoding Prostate-Specific Antigen Induces Antigen Spreading to a Common Set of Self-Proteins in Prostate Cancer Patients
Bibliographic record
Abstract
Abstract Purpose: We previously reported a randomized phase II clinical trial combining a poxvirus-based vaccine encoding prostate-specific antigen (PSA) with radiotherapy in patients with localized prostate cancer. Here, we investigate whether vaccination against PSA induced immune responses to additional tumor-associated antigens and how this influenced clinical outcome. Experimental Design: Pretreatment and posttreatment serum samples from patients treated with vaccine + external beam radiation therapy (EBRT) versus EBRT alone were evaluated by Western blot and serologic screening of a prostate cancer cDNA expression library (SEREX) to assess the development of treatment-associated autoantibody responses. Results: Western blotting revealed treatment-associated autoantibody responses in 15 of 33 (45.5%) patients treated with vaccine + EBRT versus 1 of 8 (12.5%) treated with EBRT alone. SEREX screening identified 18 antigens, which were assembled on an antigen array with 16 previously identified antigens. Antigen array screening revealed that 7 of 33 patients (21.2%) treated with vaccine + EBRT showed a vaccine-associated autoantibody response to four ubiquitously expressed self-antigens: DIRC2, NDUFS1, MRFAP1, and MATN2. These responses were not seen in patients treated with EBRT alone, or other control groups. Patients with autoantibody responses to this panel of antigens had a trend toward decreased biochemical-free survival. Conclusions: Vaccine + EBRT induced antigen spreading in a large proportion of patients. A subset of patients developed autoantibodies to a panel of four self-antigens and showed a trend toward inferior outcomes. Thus, cancer vaccines directed against tumor-specific antigens can trigger autoantibody responses to self-proteins, which may influence the efficacy of vaccination. Clin Cancer Res; 16(15); 4046–56. ©2010 AACR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".