Abstract A027: Exploring autoantibody response to proteins encoded by prostate cancer driver genes
Bibliographic record
Abstract
Abstract Prostate cancer (PCa) is a complex disease driven by genomic alterations, and understanding the immune response to these alterations can provide valuable insights into the pathogenesis. This study aimed to explore the immunogenicity of proteins encoded by PCa driver genes and to further identify corresponding autoantibodies as potential biomarkers. Through a comprehensive screening process involving bioinformatics analysis based on published studies, a set of PCa driver genes was identified for further investigation. Three strategies were employed to identify candidate driver genes: (1) utilizing the IntOGen database (Martínez-Jiménez et.al, Nature Reviews Cancer 2020), nine cohorts comprising 1503 primary PCa patient samples were analyzed, resulting in the identification of 63 PCa driver genes, with the top 20 genes exhibiting high mutation rates and frequencies in these cohorts; (2) employing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, 16 driver genes that were enriched in prostate cancer pathways were identified from the initial 63 genes; (3) referring to a published article (Dietlein et.al, Nature Genetics 2020), the top 17 driver genes specific to prostate cancer across 28 tumor types were selected. A final set of 15 PCa driver genes, which appeared in at least two of the screening strategies, were included in subsequent analyses. To assess the presence of autoantibodies targeting proteins encoded by these 15 PCa driver genes, an enzyme-linked immunosorbent assay (ELISA) was performed. Autoantibodies to proteins encoded by the selected PCa driver genes were evaluated using serum samples from PCa patients and normal controls. The results revealed that seven out of the 15 autoantibodies showed significantly elevated levels in the sera of PCa patients compared to normal controls. Notably, the autoantibody targeting SPOP (speckle-type POZ protein) exhibited the highest frequency of 42.4%, followed by the autoantibody to PTEN (phosphatase and tensin homolog) at 37.4% in the PCa group. These findings suggest that proteins encoded by PCa driver genes may possess immunogenic properties, leading to the production of corresponding autoantibodies. The identification of specific autoantibodies associated with PCa driver genes could offer novel opportunities for the discovery of autoantibody-based biomarkers in prostate cancer. Further validation and characterization of these autoantibodies are warranted to evaluate their diagnostic or prognostic potential and elucidate their functional roles in PCa pathogenesis. Citation Format: Jian-Ying Zhang, Cuipeng Qiu, Xiao Wang, Bofei Wang. Exploring autoantibody response to proteins encoded by prostate cancer driver genes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A027.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".