Identification of SKOR2 IgG as a novel biomarker of paraneoplastic neurologic syndrome
Bibliographic record
Abstract
Introduction: The development of new autoantigen discovery techniques, like programmable phage immunoprecipitation sequencing (PhIP-Seq), has accelerated the discovery of neural-specific autoantibodies. Herein, we report the identification of a novel biomarker for paraneoplastic neurologic syndrome (PNS), Sloan-Kettering-Virus-Family-Transcriptional-Corepressor-2 (SKOR2)-IgG, utilizing PhIP-Seq. We have also performed a thorough clinical validation using normal, healthy, and disease/cancer control samples. Methods: Stored samples with unclassified staining at the junction of the Purkinje cell and the granule cell layers were analyzed by PhIP-Seq for putative autoantigen identification. The autoantigen was confirmed by recombinant antigen-expressing cell-based assay (CBA), Western blotting, and tissue immunofluorescence assay colocalization. Results: PhIP-Seq data revealed SKOR2 as the candidate autoantigen. The target antigen was confirmed by a recombinant SKOR-2-expressing, and cell lysate Western blot. Furthermore, IgG from both patient samples colocalized with a commercial SKOR2-specific IgG on cryosections of the mouse brain. Both SKOR2 IgG-positive patients had central nervous system involvement, one presenting with encephalitis and seizures (Patient 1) and the other with cognitive dysfunction, spastic ataxia, dysarthria, dysphagia, and pseudobulbar affect (Patient 2). They had a refractory progressive course and were diagnosed with adenocarcinoma (Patient 1: lung, Patient 2: gallbladder). Sera from adenocarcinoma patients without PNS (n=30) tested for SKOR2-IgG were negative. Discussion: SKOR2 IgG represents a novel biomarker for PNS associated with adenocarcinoma. Identification of additional SKOR2 IgG-positive cases will help categorize the associated neurological phenotype and the risk of underlying malignancy.
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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 | 0.000 |
| 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) | 0.001 | 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".