T‐cell clonality testing for the diagnosis of T‐cell large granular lymphocytic leukemia: Are we identifying pathology or incidental clones?
Bibliographic record
Abstract
INTRODUCTION: T-cell clonality testing by T-cell receptor (TCR) gene rearrangement is key to the diagnosis of T-cell lymphoproliferative disorders such as T-cell large granular lymphocytic (T-LGL) leukemia. Benign clonal T-cell expansions, however, are commonly found in patients without identifiable disease, a condition referred to as T-cell clones of uncertain significance (T-CUS). In practice, T-cell clonality testing is performed for a range of reasons and results are often challenging to interpret given the overlap between benign and malignant clonal T-cell proliferations and uncertainties in the management of T-CUS. METHODS: We conducted a 5-year retrospective cohort study of 211 consecutive patients who underwent PCR-based T-cell clonality testing for suspected T-LGL leukemia at our institution to characterize the use of T-cell clonality testing and its impact on patient management. RESULTS: Overall, 46.4% (n = 98) of individuals tested had a clonal T-cell population identified. Patients with a monoclonal T-cell population were more likely to be older, have rheumatoid arthritis and have higher lymphocyte counts compared to patients with polyclonal populations. The majority of patients eventually diagnosed and treated for T-LGL leukemia had rheumatoid arthritis and lower neutrophil counts compared to untreated patients with monoclonal T-cell populations. A diagnosis of T-LGL leukemia was made in only a minority of patients (n = 48, 22.7%), and only a small proportion were treated (n = 17, 8.1%). CONCLUSION: Our study suggests that T-cell clonality testing most commonly identifies incidental T-cell clones with only a minority of patients receiving a diagnosis of T-LGL leukemia and fewer requiring active treatment. These finding indicate an opportunity to improve utilization of T-cell clonality testing in clinical practice to better target patients where the results of testing would impact clinical management.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".