Gene Expression Profiling and Heterogeneity of Nonspecific Orbital Inflammation Affecting the Lacrimal Gland
Why this work is in the frame
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Bibliographic record
Abstract
Importance: Although a variety of well-characterized diseases, such as sarcoidosis and granulomatosis with polyangiitis, affect the lacrimal gland, many patients with dacryoadenitis are diagnosed as having nonspecific orbital inflammation (NSOI) on the basis of histology and systemic disease evaluation. The ability to further classify the disease in these patients should facilitate selection of effective therapies. Objective: To test the a priori hypothesis that gene expression profiles would complement clinical and histopathologic evaluations in identifying well-characterized diseases and in subdividing NSOI into clinically relevant groups. Design, Setting, and Participants: In this cohort study, gene expression levels in biopsy specimens of inflamed and control lacrimal glands were measured with microarrays. Stained sections of the same biopsy specimens were used for evaluation of histopathology. Tissue samples of patients were obtained from oculoplastic surgeons at 7 international centers representing 4 countries (United States, Saudi Arabia, Canada, and Taiwan). Gene expression analysis was done at Oregon Health & Science University. Participants were 48 patients, including 3 with granulomatosis with polyangiitis, 28 with NSOI, 7 with sarcoidosis, 4 with thyroid eye disease, and 6 healthy controls. The study dates were March 2012 to April 2017. Main Outcomes and Measures: The primary outcome was subdivision of biopsy specimens based on gene expression of a published list of approximately 40 differentially expressed transcripts in blood, lacrimal gland, and orbital adipose tissue from patients with sarcoidosis. Stained sections were evaluated for inflammation (none, mild, moderate, or marked), granulomas, nodules, or fibrosis by 2 independent ocular pathologists masked to the clinical diagnosis. Results: Among 48 patients (mean [SD] age, 41.6 [19.0] years; 32 [67%] female), the mclust algorithm segregated the biopsy specimens into 4 subsets, with the differences illustrated by a heat map and multidimensional scaling plots. Most of the sarcoidosis biopsy specimens were in subset 1, which had the highest granuloma score. Three NSOI biopsy specimens in subset 1 had no apparent granulomas. Thirty-two percent (9 of 28) of the NSOI biopsy specimens could not be distinguished from biopsy specimens of healthy controls in subset 4, while other examples of NSOI tended to group with gene expression resembling granulomatosis with polyangiitis or thyroid eye disease. The 4 subsets could also be partially differentiated by their fibrosis, granulomas, and inflammation pathology scores but not their lymphoid nodule scores. Conclusions and Relevance: Gene expression profiling discloses clear heterogeneity among patients with lacrimal inflammatory disease. Comparison of the expression profiles suggests that a subset of patients with nonspecific dacryoadenitis might have a limited form of sarcoidosis, while other patients with NSOI cannot be distinguished from healthy controls.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 it