Autoantibody landscape of advanced prostate cancer
Bibliographic record
Abstract
Abstract Although the importance of T-cell immune responses is well appreciated in cancer, autoantibody responses are less well-characterized. Nevertheless, autoantibody responses are of great interest, as they may be concordant with T-cell responses to cancer antigens or predictive of response to cancer immunotherapies. We performed serum epitope repertoire analysis (SERA) on a total of 1,229 serum samples obtained from a cohort of 72 men with metastatic castration-resistant prostate cancer (mCRPC) and 1,157 healthy control patients to characterize the autoantibody landscape of mCRPC. Using whole-genome sequencing results from paired solid-tumor metastasis biopsies and germline specimens, we identified tumor-specific epitopes in 29 mutant and 11 non-mutant proteins. Autoantibody enrichments for the top candidate autoantigen (NY-ESO-1) were validated using ELISA performed on the prostate cancer cohort and an independent cohort of 106 patients with melanoma. Our study recovers antigens of known importance and identifies novel tumor-specific epitopes of translational interest in advanced prostate cancer. Statement of significance Autoantibodies have been shown to inform treatment response and candidate drug targets in various cancers. We present the first large-scale profiling of autoantibodies in advanced prostate cancer, utilizing a new next-generation sequencing-based approach to antibody profiling to reveal novel cancer-specific antigens and epitopes. Disclosure of Potential Conflicts of Interest JJA reports receiving consulting income from Janssen Biotech and Merck and honoraria from Astellas for speaker’s fees. MR reports receiving commercial research support from Novartis, Johnson & Johnson, Merck, Astellas, and Medivation, and is a consultant/advisory board member for Constellation Pharmaceuticals, Amgen, Ambrx, Johnson & Johnson, and Bayer. A.R. has received honoraria from consulting with Amgen, Bristol-Myers Squibb, Chugai, Dynavax, Genentech, Merck, Nektar, Novartis, Roche and Sanofi, is or has been a member of the scientific advisory board and holds stock in Advaxis, Arcus Biosciences, Bioncotech Therapeutics, Compugen, CytomX, Five Prime, RAPT, ImaginAb, Isoplexis, Kite-Gilead, Lutris Pharma, Merus, PACT Pharma, Rgenix and Tango Therapeutics. FYF serves on the advisory board for Dendreon, EMD Serono, Janssen Oncology, Ferring, Sanofi, Blue Earth Diagnostics, Celgene, consults for Bayer, Medivation/Astellas, Genetech, and Nutcracker Therapeutics, has honoraria from Clovis Oncology, and is a founder and has an ownership stake in PFS Genomics. SGZ and FYF have patent applications with Decipher Biosciences. SGZ and FYF have a patent application licensed to PFS Genomics. SGZ and FYF have patent applications with Celgene. WAH, RW, KK, PSD, and JCS have ownership of stocks or shares at Serimmune, paid employment at Serimmune, board membership at Serimmune, and patent applications on behalf of Serimmune.
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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 | 0.001 |
| 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) | 0.001 | 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".