Multimodal single-cell analysis of cutaneous T-cell lymphoma reveals distinct subclonal tissue-dependent signatures
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
Cutaneous T-cell lymphoma (CTCL) is a heterogeneous group of mature T-cell neoplasms characterized by the accumulation of clonal malignant CD4+ T cells in the skin. The most common variant of CTCL, mycosis fungoides (MF ), is confined to the skin in early stages but can be accompanied by extracutaneous dissemination of malignant T cells to the blood and lymph nodes in advanced stages of disease. Sézary syndrome (SS), a leukemic form of disease, is characterized by significant blood involvement. Little is known about the transcriptional and genomic relationship between skin- and blood-residing malignant T cells in CTCL. To identify and interrogate malignant clones in matched skin and blood from patients with leukemic MF and SS, we combine T-cell receptor clonotyping with quantification of gene expression and cell surface markers at the single cell level. Our data reveal clonal evolution at a transcriptional and genetic level within the malignant populations of individual patients. We highlight highly consistent transcriptional signatures delineating skin- and blood-derived malignant T cells. Analysis of these 2 populations suggests that environmental cues, along with genetic aberrations, contribute to transcriptional profiles of malignant T cells. Our findings indicate that the skin microenvironment in CTCL promotes a transcriptional response supporting rapid malignant expansion, as opposed to the quiescent state observed in the blood, potentially influencing efficacy of therapies. These results provide insight into tissue-specific characteristics of cancerous cells and underscore the need to address the patients' individual malignant profiles at the time of therapy to eliminate all subclones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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