Routine Clinically Detected Increased ROS1 Transcripts Are Related With ROS1 Expression by Immunohistochemistry and Associated With EGFR Mutations in Lung Adenocarcinoma
Bibliographic record
Abstract
Introduction Translocations of the ROS1 gene were found to drive tumorigenesis in 1% to 2% of lung adenocarcinoma . In clinical practice, ROS1 rearrangements are often screened by immunohistochemistry (IHC) before confirmation with either fluorescence in situ hybridization or molecular techniques. This screening test leads to a non-negligible number of cases that have equivocal or positive ROS1 IHC, without ROS1 translocation. Methods In this study, we have analyzed retrospectively 1021 cases of nonsquamous NSCLC having both ROS1 IHC and molecular analysis using next-generation sequencing. Results ROS1 IHC was negative in 938 cases (91.9%), equivocal in 65 cases (6.4%), and positive in 18 cases (1.7%). Among these 83 equivocal or positive cases, only two were ROS1 rearranged, leading to a low predictive positive value of the IHC test (2%). ROS1-positive IHC was correlated with an increased mRNA ROS1 transcripts. Moreover, we have found a mean statistically significant relationship between ROS1 expression and EGFR gene mutations, suggesting a crosstalk mechanism between these oncogenic driver molecules. Conclusion This study demonstrates that ROS1 IHC represents true ROS1 mRNA expression, and raises the question of a potential benefit of combined targeted therapy in EGFR -mutated NSCLC.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".