A Novel Monoclonal Antibody Against DOG1 is a Sensitive and Specific Marker for Gastrointestinal Stromal Tumors
Bibliographic record
Abstract
Gastrointestinal stromal tumors (GIST) occur primarily in the wall of the intestine and are characterized by activating mutations in the receptor tyrosine kinases genes KIT or PDGFRA. The diagnosis of GIST relies heavily on the demonstration of KIT/CD117 protein expression by immunohistochemistry. However, KIT expression is absent in approximately 4% to 15% of GIST and this can complicate the diagnosis of GIST in patients who may benefit from treatment with receptor tyrosine kinase inhibitors. We previously identified DOG1/TMEM16A as a novel marker for GIST using a conventional rabbit antipeptide antiserum and an in situ hybridization probe. Here, we describe 2 new monoclonal antibodies against DOG1 (DOG1.1 and DOG1.3) and compare their staining profiles with KIT and CD34 antibodies on 447 cases of GIST. These included 306 cases with known mutational status for KIT and PDGFRA from a molecular consultation service. In addition, 935 other mesenchymal tumors and 432 nonsarcomatous tumors were studied. Both DOG1 antibodies showed high sensitivity and specificity for GIST, with DOG1.1 showing some advantages. This antibody yielded positive staining in 370 of 425 (87%) scorable GIST, whereas CD117 was positive in 317 of 428 (74%) GIST and CD34 in 254 of 430 (59%) GIST. In GIST with mutations in PDGFRA, 79% (23/29) showed DOG1.1 immunoreactivity while only 9% (3/32) and 27% (9/33) stained for CD117 and CD34, respectively. Only 1 of 326 (0.3%) leiomyosarcomas and 1 of 39 (2.5%) synovial sarcomas among the 935 soft tissue tumors examined showed positive immunostaining for DOG1.1. In addition, DOG1.1 immunoreactivity was seen in fewer cases of carcinoma, melanoma, and seminoma as compared with KIT.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".