Single-cell heterogeneity in Sézary syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sézary syndrome (SS) is an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with a median life expectancy of less than 4 years. Although initial treatment responses are often good, the vast majority of patients with SS fail to respond to ongoing therapy. We hypothesize that malignant T cells are highly heterogeneous and harbor subpopulations of SS cells that are both sensitive and resistant to treatment. Here, we investigate the presence of single-cell heterogeneity and resistance to histone deacetylase inhibitors (HDACi) within primary malignant T cells from patients with SS. Using single-cell RNA sequencing and flow cytometry, we find that malignant T cells from all investigated patients with SS display a high degree of single-cell heterogeneity at both the mRNA and protein levels. We show that this heterogeneity divides the malignant cells into distinct subpopulations that can be isolated by their expression of different surface antigens. Finally, we show that treatment with HDACi (suberanilohydroxamic acid and romidepsin) selectively eliminates some subpopulations while leaving other subpopulations largely unaffected. In conclusion, we show that patients with SS display a high degree of single-cell heterogeneity within the malignant T-cell population, and that distinct subpopulations of malignant T cells carry HDACi resistance. Our data point to the importance of understanding the heterogeneous nature of malignant SS cells in each individual patient to design combinational and new therapies to counter drug resistance and treatment failure.
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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