Abstract B027: iSIGN panel identifies germ cell tumors via whole-proteome phage immunoprecipitation sequencing
Bibliographic record
Abstract
Abstract Purpose: Germ cell tumors (GCTs) pose significant diagnostic challenges due to the limited sensitivity and specificity of current tumor markers. Hereby we utilize phage immunoprecipitation sequencing (PhIP-Seq) technique to develop a unique immunosignature panel (iSIGN) with high sensitivity and specificity to improve the diagnosis and differentiation of GCTs. Patients and Methods: Serum samples from patients with confirmed gonadal or extragonadal GCTs and controls were collected from Mayo Clinic repositories. Controls included benign testicular lesions, autoimmune diseases, neurodegenerative disorders, and other cancers. Samples were randomized into development and validation cohorts. PhIP-Seq identified antigen-autoantibody interactions, and enrichment scores were calculated to develop a biomarker panel distinguishing GCT from non-GCT cases. A second biomarker panel was developed to predict seminoma from nonseminoma cases. Results: A total of 427 serum samples were analyzed, including 150 from GCT patients and 277 controls. For our primary model to distinguish GCT cases (GCT-iSIGN), 24 peptides from 16 antigens were identified based on significant differential autoantibody binding enrichment in GCT patients (p < 0.05), ≥2 peptides per antigen, and enrichment scores ≥ 2.5-fold. GCT-iSIGN was highly sensitive (93%) and specific (99%) for identifying GCT cases from controls with an area under curve (AUC) of 0.93 and 9% Unknown Case Rate (UCR). The secondary panel (Sem-iSIGN) included 17 peptides corresponding to 5 unique proteins with high specificity (93%), moderate sensitivity (65%) and AUC of 0.77 to distinguish seminoma from nonseminoma GCT cases. Conclusion: This study demonstrates the potential use of PhIP-Seq to identify unique immunosignature panels that can serve as reliable biomarkers for GCT. These panels address the limitations of traditional markers and other proteomic techniques. This immunosignature model represents a cost-effective, stable, and scalable approach to enhancing GCT diagnosis and management. Citation Format: M Bakri Hammami, Andrew Knight, Haidara Kherbek, Brian Costello, Silvana De Lorenzo, Bradley C. Leibovich, John C. Cheville, Yong Guo, Jessica Sagen, Janet E. Olson, Alicia Algeciras-Schimnich, Sean J. Pittock, John R. Mills, Surendra Dasari, Divyanshu Dubey. iSIGN panel identifies germ cell tumors via whole-proteome phage immunoprecipitation sequencing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr B027.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".